diff --git a/CupOFMoCA-PROTAC/CITATION.cff b/CupOFMoCA-PROTAC/CITATION.cff new file mode 100644 index 0000000000000000000000000000000000000000..73fc80c40fad09949861bc81b5b7563ed45874f5 --- /dev/null +++ b/CupOFMoCA-PROTAC/CITATION.cff @@ -0,0 +1,30 @@ +cff-version: 1.2.0 +message: "If you use this software, please cite the CupOFMoCA paper." +title: "CupOFMoCA: Coupled Objective-Guided Discrete Flows for Molecular Conjugate Assembly" +type: software +authors: + - family-names: Zhang + given-names: Ruoxi Rosie + - family-names: Li + given-names: Ziang + - family-names: Gu + given-names: Jiatao + - family-names: Chatterjee + given-names: Pranam +license: Apache-2.0 +repository-code: "https://huggingface.co/ChatterjeeLab/CupOFMoCA" +preferred-citation: + type: conference-paper + title: "CupOFMoCA: Coupled Objective-Guided Discrete Flows for Molecular Conjugate Assembly" + authors: + - family-names: Zhang + given-names: Ruoxi Rosie + - family-names: Li + given-names: Ziang + - family-names: Gu + given-names: Jiatao + - family-names: Chatterjee + given-names: Pranam + collection-title: "Advances in Neural Information Processing Systems" + year: 2026 + diff --git a/CupOFMoCA-PROTAC/LICENSE b/CupOFMoCA-PROTAC/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..2e856b6109a1f39d6f8f65d17717d31a9ede0625 --- /dev/null +++ b/CupOFMoCA-PROTAC/LICENSE @@ -0,0 +1,17 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + Copyright 2026 Anonymous Authors (CupOfMoca ICLR Submission) + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/CupOFMoCA-PROTAC/NOTICE b/CupOFMoCA-PROTAC/NOTICE new file mode 100644 index 0000000000000000000000000000000000000000..67a17591e541dce3d571165d18b51943bb6dc9db --- /dev/null +++ b/CupOFMoCA-PROTAC/NOTICE @@ -0,0 +1,46 @@ +CupOFMoCA — PROTAC release +Copyright 2026 CupOFMoCA authors + +The first-party code in this release is licensed under Apache License 2.0; see +LICENSE. Model weights and third-party downloads are subject to their own terms, +which users are responsible for reviewing. + +Redistributed third-party code +------------------------------ + +src/defog/ + DeFoG, https://github.com/manuelmlmadeira/DeFoG + MIT License, reproduced at src/defog/LICENSE. + src/defog/src/datasets/protac_dataset.py is a first-party addition. + The upstream graph-benchmark evaluation helpers under + src/defog/src/analysis/orca/ are NOT redistributed: that C++ code carries no + license header and is unused by the PROTAC pipeline. + +Third-party code that is NOT redistributed +------------------------------------------ + +Two optional components are published by their authors without any license +file, so this release cannot lawfully redistribute them. Run +`python scripts/fetch_third_party.py` to obtain them from their upstream +repositories at the commits used for the paper, and review the upstream terms +before use. + + DegradeMaster https://github.com/ABILiLab/DegradeMaster + pinned commit aa149beaf067a051e070b3b281f7dba43c2f3e90 + Needed only for predicted-degradation scoring. + + EGFR-PROPK https://github.com/Zhang-Ran-0119/EGFR-PROPK + pinned commit 6940cb90c449e6c6d0678508fc2f228da796e028 + Needed only for the EGFR-only PK columns of batch_report.py. + +Linker generation with scripts/generate.py does not require either component. + +Derived data +------------ + +The PROTAC-DB derived tables (data/linker.csv) and the target and E3 pocket +graphs used for degradation scoring were prepared by the CupOFMoCA authors from +publicly available sources: PROTAC-DB (Weng et al., Nucleic Acids Research 2021) +and the RCSB Protein Data Bank. The fine-tuned generator checkpoint under +checkpoints/ is a derivative of the released DeFoG GuacaMol checkpoint and +inherits its MIT terms. diff --git a/CupOFMoCA-PROTAC/README.md b/CupOFMoCA-PROTAC/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ae41d56d989d769f573e9ba0f40dbc575a5eef2d --- /dev/null +++ b/CupOFMoCA-PROTAC/README.md @@ -0,0 +1,122 @@ +# CupOFMoCA — PROTAC + +Code and PROTAC model artifacts for **CupOFMoCA: Coupled Objective-Guided Discrete Flows for +Molecular Conjugate Assembly** (NeurIPS 2026). + +This release supports the paper's PROTAC experiments: linker generation between a fixed +warhead and a fixed E3 ligand, under hard assembly-validity constraints and objective +guidance. For the peptide-drug conjugate half, see [`CupOFMoCA-PDC/`](../CupOFMoCA-PDC). + +## Model overview + +The generator is a DeFoG discrete flow-matching model over molecular graphs, fine-tuned on +PROTAC-DB linkers. The warhead and E3-ligand subgraphs are held fixed throughout sampling; +only the linker subgraph is edited. + +| Checkpoint | Role | +|---|---| +| `checkpoints/protac_linker_finetune/epoch=99-v1.ckpt` | DeFoG graph discrete-flow generator, fine-tuned for 100 epochs on 2,070 PROTAC-DB linker graphs | +| `src/classifier/protac_linker_clf.pkl` | generic linker validity/quality classifier | +| `src/classifier/protac_linker_clf_{ErlotinibVHL,BRD9VHL,CDK6CRBN}.pkl` | target-specific classifiers, auto-selected by `--protac_module` | + +The checkpoint is loaded by this repository's own PyTorch code. This is not a Transformers +pipeline and provides no hosted-inference widget. + +## Quickstart + +```bash +hf download ChatterjeeLab/CupOFMoCA --include "CupOFMoCA-PROTAC/*" --local-dir . +cd CupOFMoCA-PROTAC +conda env create -f environment.yml && conda activate defog +pip install -e src/defog +python scripts/verify_release.py +``` + +Generate 100 EGFR-VHL PROTACs with the reported configuration (one of three seeds): + +```bash +cd scripts +python generate.py \ + --checkpoint ../checkpoints/protac_linker_finetune/epoch=99-v1.ckpt \ + --protac_module ErlotinibVHL --fix_termini cooh --fast_scoring \ + --num_samples 100 --num_batches 1 --num_steps 1500 --warmup_steps 100 \ + --weights 4.0 2.0 4.0 --seed 42 \ + --output_file ./protac_linkers_egfrvhl.smi --output_dir ../generation_logs +``` + +Swap `--protac_module` for `BRD9VHL` or `CDK6CRBN` for the other two systems. Every +setting above is recorded in `configs/paper.json`. + +`--weights` is positional over the three soft scorers: synthetic accessibility, +PROTAC-DB chemotype closeness, connectivity. The validity classifier is applied as a hard +mask and takes no weight. + +### Intended uses + +- Reproduce and inspect the paper's PROTAC generation experiments. +- Generate computational PROTAC linker candidates for research and method development. +- Extend or compare constrained, multi-objective molecular-generation methods. + +The release is not intended for clinical decisions, direct therapeutic use, or as a +substitute for chemical synthesis, experimental validation, toxicology, or safety +assessment. + +### Training data + +The generator is the released DeFoG GuacaMol checkpoint fine-tuned on 2,070 PROTAC-DB +linker graphs (258 validation, 260 test). `data/linker.csv` is the derived linker table. +Architecture, optimization and scorer details are in Appendix H of the paper. Training +datasets are not redistributed beyond that derived table. + +### Evaluation + +The paper evaluates generated PROTACs on EGFR-VHL, BRD9-VHL and CDK6-CRBN for standard +and assembly validity, predicted degradation, diversity and training-set similarity. The +runs behind those tables are in `results/`, documented in `results/README.md`. Predicted +degradation requires DegradeMaster, which is fetched rather than redistributed — see below. + +### Limitations + +- Outputs and objective values are computational predictions, not experimentally validated + compounds or guarantees of efficacy, synthesizability, toxicity or safety. +- The released configurations cover three target/E3 systems with fixed anchor chemistry; + performance outside this scope has not been established. +- Predicted degradation comes from a model trained on overlapping PROTAC databases, so it + is an in-domain evaluation rather than an independent one. +- `scripts/runs/ablation_egfr.py` sweeps five weight slots, but the current `generate.py` + exposes three; the Morgan-fingerprint row of the weight ablation is documented by the + archived outputs in `results/` rather than re-runnable as-is. +- Results can vary with hardware and CUDA/PyTorch versions. + +## Repository layout + +```text +checkpoints/ generator weights +configs/paper.json the settings used for the reported runs +data/linker.csv PROTAC-DB derived linker table +docs/ artifact manifest (ARTIFACTS.md, artifacts.json) +md/ explicit-solvent molecular dynamics pipeline +results/ generation outputs behind the reported tables +scripts/ generate.py, batch_report.py, evaluate_linkers.py, + fetch_third_party.py, verify_release.py, runs/ +src/protac/ per-target warhead + E3 builders +src/classifier/ validity gate, analyzer, classifiers +src/defog/ DeFoG discrete flow model (MIT, see src/defog/LICENSE) +``` + +## Third-party components + +DeFoG is redistributed here under MIT. Two optional components — DegradeMaster +(degradation scoring) and EGFR-PROPK (EGFR-only PK columns) — are published upstream +without a license and so are **not** redistributed. Fetch them at the pinned commits used +for the paper: + +```bash +python scripts/fetch_third_party.py +``` + +Linker generation does not require either. See `NOTICE` for the full statement. + +## Citation + +See `CITATION.cff`. diff --git a/CupOFMoCA-PROTAC/checkpoints/protac_linker_finetune/epoch=99-v1.ckpt b/CupOFMoCA-PROTAC/checkpoints/protac_linker_finetune/epoch=99-v1.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..78437d67f2b53b3355e2865af5009b2dbd81fa28 --- /dev/null +++ b/CupOFMoCA-PROTAC/checkpoints/protac_linker_finetune/epoch=99-v1.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8cfb48d9f5b3bcabb28c4dce7548e1a792494c48f05150a1b262b5215c1da78f +size 255211895 diff --git a/CupOFMoCA-PROTAC/configs/paper.json b/CupOFMoCA-PROTAC/configs/paper.json new file mode 100644 index 0000000000000000000000000000000000000000..b6c4048775ccb4f70a5bd6d75b9bee328217b6b3 --- /dev/null +++ b/CupOFMoCA-PROTAC/configs/paper.json @@ -0,0 +1,45 @@ +{ + "default_seeds": [ + 42, + 123, + 456 + ], + "num_candidates_per_seed": 100, + "num_batches": 1, + "num_steps": 1500, + "warmup_steps": 100, + "guidance_strength_beta": 5.0, + "top_k": 50, + "temperature": 1.0, + "position_sampling": "weighted", + "use_acceptance": false, + "fix_termini": "cooh", + "fast_scoring": true, + "weights": { + "synthetic_accessibility": 4.0, + "chemotype_closeness": 2.0, + "connectivity": 4.0 + }, + "weights_positional": [ + 4.0, + 2.0, + 4.0 + ], + "targets": { + "EGFR-VHL": { + "protac_module": "ErlotinibVHL", + "warhead": "gefitinib-piperazine (MS39)", + "e3": "VH032" + }, + "BRD9-VHL": { + "protac_module": "BRD9VHL", + "warhead": "BI-7273-piperazine", + "e3": "VH032" + }, + "CDK6-CRBN": { + "protac_module": "CDK6CRBN", + "warhead": "palbociclib-piperazine", + "e3": "pomalidomide" + } + } +} diff --git a/CupOFMoCA-PROTAC/data/linker.csv b/CupOFMoCA-PROTAC/data/linker.csv new file mode 100644 index 0000000000000000000000000000000000000000..ae1c44183d39495b5ff7efa03d53d43af9ec446d --- /dev/null +++ b/CupOFMoCA-PROTAC/data/linker.csv @@ -0,0 +1,2845 @@ +Compound ID,Smiles,Smiles_R,Molecular 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/dev/null +++ b/CupOFMoCA-PROTAC/docs/ARTIFACTS.md @@ -0,0 +1,42 @@ +# Model artifacts + +Every local artifact the PROTAC pipeline loads. Run `python scripts/verify_release.py` +to check byte counts, SHA-256 hashes and private-path hygiene. The machine-readable +manifest is `docs/artifacts.json`. + +| Artifact | Purpose | Size | SHA-256 | +|---|---|---:|---| +| `checkpoints/protac_linker_finetune/epoch=99-v1.ckpt` | DeFoG generator, fine-tuned on PROTAC-DB linkers | 255,211,895 B | `8cfb48d9f5b3bcabb28c4dce7548e1a792494c48f05150a1b262b5215c1da78f` | +| `src/classifier/protac_linker_clf.pkl` | linker classifier (generic) | 128,117 B | `fa667025962d132ce9f2ebf8f2dc49752416ed154792ca0c87344335fa714462` | +| `src/classifier/protac_linker_clf_BRD9VHL.pkl` | linker classifier (BRD9VHL) | 28,117 B | `eae385be1576fec674b8a537e50b56e1ab1fc47599a82ae277912d958c0b7d6f` | +| `src/classifier/protac_linker_clf_CDK6CRBN.pkl` | linker classifier (CDK6CRBN) | 30,035 B | `85bd40599d82c5cc8a35cc844d12d1066a0d4699bcc7e229937e1fe1d3daad3d` | +| `src/classifier/protac_linker_clf_ErlotinibVHL.pkl` | linker classifier (ErlotinibVHL) | 26,397 B | `e031d310829c7d196887e2fd077eff95be445ed1984516540ac69308c26384ee` | +| `src/classifier/protac_linker_clf_amine.pkl` | linker classifier (amine) | 76,524 B | `e0e851f41a004ea26e3b66caef83bf6ab4fa2229447427ab996e8a195c0df2d2` | +| `src/classifier/protac_linker_clf_amino_acid.pkl` | linker classifier (amino_acid) | 188,080 B | `2558addea35da16ddf82affaf68ea9046bc5b9b3d307f6ee668c117df2798428` | +| `src/degrademaster/data/case_study/arcrbn_pocket_graph.pt` | ARCRBN pocket graph | 35,405 B | `98f70b82b731760b2b25b9ae0e7b1a917a88fd215f2ba4e61cf1654207d137e3` | +| `src/degrademaster/data/case_study/brd4_pocket_graph.pt` | BRD4 pocket graph | 15,675 B | `8dbda805c329bc5804d95983acaf4260e712bfa8fefcc5f39d76b4d2cd3583b9` | +| `src/degrademaster/data/case_study/brd9_pocket_graph.pt` | BRD9 pocket graph | 201,019 B | `89346178dfdc8fd277c57d08293a903133a263b1206184224cf250f71ad8f3a0` | +| `src/degrademaster/data/case_study/cdk6crbn_pocket_graph.pt` | CDK6CRBN pocket graph | 32,607 B | `0193874c2150e40a2e8c7a6b4eddf1c9b1768ac0573716b0fbf79e365de62310` | +| `src/degrademaster/data/case_study/crbn_pocket_graph.pt` | CRBN pocket graph | 25,467 B | `6e4e8e6756a343379118150370061f1b781d36f1bc681693d6d7fd8a8d0bce12` | +| `src/degrademaster/data/case_study/egfr_pocket_graph.pt` | EGFR pocket graph | 32,602 B | `5d86cd0d80363bab3c2835aefa817181cd96b2e0c14b35c55e384cf8e9dc64c1` | +| `src/degrademaster/data/case_study/krasvhl_pocket_graph.pt` | KRASVHL pocket graph | 25,686 B | `79efc62c546142277c6cba995d7926ca0dc3b600c2e98cce766519054243284c` | +| `src/degrademaster/data/case_study/processed/ligase_pocket.pt` | VHL E3 pocket graph (collated) | 419,524 B | `3b35bb97bddce48ccc5ebddbdb435a98654b94f569441cf6f8bc66e3a11e4d38` | +| `src/degrademaster/data/case_study/arcrbn_target_feature.npy` | 30-d feature vector | 368 B | `9ef6c2c04f54c6585cdac4e3d0ffb38c15ba7d1912a29f25f6a4967c7e13799e` | +| `src/degrademaster/data/case_study/brd4_target_feature.npy` | 30-d feature vector | 368 B | `28a1903da353ff6a92c4d4b8828e66e7264bcd4f07a27329d2f898cf000f63e4` | +| `src/degrademaster/data/case_study/brd4crbn_target_feature.npy` | 30-d feature vector | 368 B | `28a1903da353ff6a92c4d4b8828e66e7264bcd4f07a27329d2f898cf000f63e4` | +| `src/degrademaster/data/case_study/brd9_target_feature.npy` | 30-d feature vector | 368 B | `dbe5d7e51907b44396a891a574010544f9e7461c09618d940fe26ddf59ef8473` | +| `src/degrademaster/data/case_study/cdk6crbn_target_feature.npy` | 30-d feature vector | 368 B | `4da1adf39d018d61cd80a67f5950eaf734b425b69690a157c392d792b020712a` | +| `src/degrademaster/data/case_study/crbn_e3_feature.npy` | 30-d feature vector | 368 B | `71f830577fd901b6be42b9bb8f02f12e25245aefafac709d0b68910dfa8214db` | +| `src/degrademaster/data/case_study/egfr_target_feature.npy` | 30-d feature vector | 368 B | `c31f02bfeb6ce63b0fcf41a7b235556dc2b76ab6054626116c08434a29b639de` | +| `src/degrademaster/data/case_study/krasvhl_target_feature.npy` | 30-d feature vector | 368 B | `51d50af89f905b6a4eb1f1d2b90d9ca8a2ee7e7dbace16fdab56e159f2c0385f` | +| `src/degrademaster/data/case_study/vhl_e3_feature_egfr.npy` | 30-d feature vector | 368 B | `aca7d4ba3f95a2d903e542f761c3d448323d7ff8a9e1d58e58d9b8bd55d0a74a` | +| `src/degrademaster/data/PROTAC/name.json` | PROTAC-DB entry table (property windows, similarity) | 3,419,093 B | `bbe5ffe734f416ecf153f4ab42a17c6a3a86a79cfcb0faa5a5f13a672733d575` | +| `data/linker.csv` | PROTAC-DB derived linker table | 703,569 B | `73e4be1680fe9e2e2cf7256ff3fa9423a9a7e480d9b1baa529081a2bc89ac5b1` | + +DegradeMaster and EGFR-PROPK are published upstream without a license and are therefore +not redistributed; `python scripts/fetch_third_party.py` retrieves them at the commits +used for the paper. Their absence is reported, not failed, by the verifier. + +The ORCA graphlet-counting C++ sources bundled with upstream DeFoG are excluded: they +carry no license header and are unused by the PROTAC pipeline. + diff --git a/CupOFMoCA-PROTAC/docs/artifacts.json b/CupOFMoCA-PROTAC/docs/artifacts.json new file mode 100644 index 0000000000000000000000000000000000000000..5848a751fc94260a1b5d7ea88695b62d8d8d1e11 --- /dev/null +++ b/CupOFMoCA-PROTAC/docs/artifacts.json @@ -0,0 +1,132 @@ +[ + { + "path": "checkpoints/protac_linker_finetune/epoch=99-v1.ckpt", + "bytes": 255211895, + "sha256": "8cfb48d9f5b3bcabb28c4dce7548e1a792494c48f05150a1b262b5215c1da78f" + }, + { + "path": "src/classifier/protac_linker_clf.pkl", + "bytes": 128117, + "sha256": "fa667025962d132ce9f2ebf8f2dc49752416ed154792ca0c87344335fa714462" + }, + { + "path": "src/classifier/protac_linker_clf_BRD9VHL.pkl", + "bytes": 28117, + "sha256": "eae385be1576fec674b8a537e50b56e1ab1fc47599a82ae277912d958c0b7d6f" + }, + { + "path": 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a/CupOFMoCA-PROTAC/environment.yml b/CupOFMoCA-PROTAC/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..033cccccb43088c4a5ff71afd5b66f13d808cd67 --- /dev/null +++ b/CupOFMoCA-PROTAC/environment.yml @@ -0,0 +1,36 @@ +name: defog +channels: + - conda-forge + - nvidia + - pytorch +dependencies: + - python=3.9 + - rdkit=2023.03.2 + - graph-tool=2.45 + - pytorch==2.4.0 + - pytorch-cuda=12.1 + - psi4=1.9.1 + - pip=24.2 + - pip: + - ipykernel==6.29.5 + - hydra-core==1.3.2 + - imageio==2.31.1 + - matplotlib==3.7.1 + - networkx==2.8.7 + - numpy==1.23 + - omegaconf==2.3.0 + - overrides==7.3.1 + - pandas==1.4 + - pyemd==1.0.0 + - PyGSP==0.5.1 + - pytorch_lightning==2.0.4 + - scipy==1.11.0 + - setuptools==68.0.0 + - torch_geometric==2.3.1 + - torchmetrics==0.11.4 + - tqdm==4.65.0 + - wandb==0.15.4 + - seaborn==0.13.2 + - gpustat==0.6.0 + - black==24.3.0 + - -e . diff --git a/CupOFMoCA-PROTAC/md/README.md b/CupOFMoCA-PROTAC/md/README.md new file mode 100644 index 0000000000000000000000000000000000000000..44cd28ee9efb96a92c04d2eb06cc6105a1767ef6 --- /dev/null +++ b/CupOFMoCA-PROTAC/md/README.md @@ -0,0 +1,172 @@ +# Free-PROTAC molecular dynamics + +Explicit-solvent MD of a generated PROTAC on its own, with no target protein and +no E3 ligase. Runs in chloroform (a membrane-interior mimic) and in water, and +measures the conformational descriptors published work ties to cell +permeability: radius of gyration, 3D polar surface area, intramolecular +H-bonds, and conformational state populations. + +The protocol follows "Linker-Dependent Folding Rationalizes PROTAC Cell +Permeability" (*J. Med. Chem.* 2022, DOI 10.1021/acs.jmedchem.2c00877) and takes +its permeability cutoffs from Atilaw et al., *ACS Med. Chem. Lett.* 2021, 12, +107-114. `reference_protocol.md` has the settings and the target numbers. + +## Layout + +``` +protac_md.py MD driver: conformer -> parametrize -> solvate -> replicas +analyze_conformations.py Rg / RMSD / 3D PSA / H-bonds / clustering dashboard +rg_basins.py Rg free-energy profiles and basin populations +psa_ensemble.py 3D PSA over a finished ensemble +ternary_span.py warhead-to-E3 span distribution (water ensembles) +validate_psa.py PSA code path checked against grazoprevir +psa_method_probe.py element vs charge-threshold polar-atom selection +plot_results.py per-replica summary figure +render_3d.py extended and collapsed conformations from a trajectory +run_candidate.sh detached driver: chloroform + water + analysis +environment_md.yml conda environment +protacmd-lock-linux64.txt pinned environment, linux-64 +reference_protocol.md reference protocol and permeability yardsticks +linker_design.md linker rules read off this project's own MD runs +``` + +PROTAC SMILES are embedded in `protac_md.py` (`CANDIDATES`), so no input files +are needed; anything else runs through `--smiles`. + +## Environment + +On linux-64 with a CUDA 12 capable driver (525 or newer), use the lockfile and +skip dependency solving: + +``` +conda create --name protacmd --file protacmd-lock-linux64.txt +conda activate protacmd +pip install "setuptools<81" +``` + +That reproduces the environment: openmm 8.5.2, ambertools 24.8, +rdkit 2025.09.5, openff-toolkit 0.16.10, python 3.10.20. The lockfile carries no +`packmol`, which `--solvent chloroform` needs, so add it afterwards +(`conda install -c conda-forge packmol`) or solve from the yml instead. + +Elsewhere, solve from the yml. Check the driver's CUDA version with +`nvidia-smi` first and edit `cuda-version` in `environment_md.yml` to match +(remove it entirely for CPU-only): + +``` +conda env create -f environment_md.yml +conda activate protacmd +``` + +The solve takes around ten minutes: ambertools has a large dependency tree and +libmamba is single-threaded, so the spinner sits still while one core works. The +install itself takes seconds. Keep `setuptools<81` in the `pip:` section rather +than as a conda dependency; an upper bound on setuptools triples the solve time. + +Activation is not optional. `sqm`, `antechamber` and `packmol` are only on PATH +inside the env, and calling `/bin/python` directly is not enough. + +## Run + +``` +conda activate protacmd + +# chloroform: the membrane mimic, the permeability-relevant run +python protac_md.py --solvent chloroform --replicas 3 --prod-ns 50 --gpu 0 +# water: the polar reference; the pair shows chameleonic folding +python protac_md.py --solvent water --replicas 3 --prod-ns 50 --gpu 0 + +python analyze_conformations.py --dir egfrvhl_rank4/chloroform --name egfrvhl_rank4 +python analyze_conformations.py --dir egfrvhl_rank4/water --name egfrvhl_rank4 +``` + +Useful flags: `--candidate {egfrvhl_rank4,egfrvhl_rank5,brd9vhl_rank1,cdk6crbn_rank2}`, +`--smiles ""` with `--name ms39_ref` for a reference compound, +`--platform CPU`, `--prod-ns`, `--replicas`, `--gpu N`. + +Output lands in `//rep/` (`*_prod.dcd`, `*_prod.csv`, PDBs), +with the dashboard and the cluster centroid PDBs in `//`. + +`run_candidate.sh` chains both solvents and both analyses for one candidate and +reuses the chloroform charge cache for water, so AM1-BCC is paid once. + +Smoke test on a new machine first, about two minutes: + +``` +python protac_md.py --solvent chloroform --replicas 1 --prod-ns 0.05 \ + --heat-ps 5 --equil-ps 10 --report-ps 1 --min-iters 1000 --name smoke +python analyze_conformations.py --dir smoke/chloroform --name smoke --clusters 4 +``` + +## Comparing finished runs + +These take run directories, one per compound. `ms39_ref` is not bundled: run the +pipeline on MS39 yourself with `--smiles ... --name ms39_ref`. Its components +and exact linker are in `../src/protac/ErlotinibVHL.py`. + +``` +python rg_basins.py egfrvhl_rank4 ms39_ref --solvents chloroform water +python psa_ensemble.py egfrvhl_rank4 ms39_ref --solvent chloroform +python ternary_span.py egfrvhl_rank4 ms39_ref +``` + +## Hardware and runtime + +The system is small, 2500-2800 atoms, so it is GPU-latency-bound rather than +memory-bound and fits in under 2 GB of VRAM. + +| Platform | Speed | 50 ns x 3 x 2 solvents | +|---|---|---| +| Free A100 | ~1100 ns/day | ~6.5 h | +| Shared GPU (~75% busy) | ~32 ns/day | ~9 days | +| CPU (16 threads) | ~28 ns/day | ~10 days | + +A shared GPU is about 35x slower than a free one and no faster than CPU, so use +an idle card. Replicas run sequentially in one process; parallelize by launching +one process per GPU. + +The reference converged within 5-10 ns; 50 ns x 3 leaves a wide margin. Confirm +with the RMSD plateau and the replica-mean spread in the dashboard, and extend +if either is still drifting. + +## Known failure modes + +1. `ModuleNotFoundError: No module named 'pkg_resources'` at `SystemGenerator`. + `openff.amber_ff_ports` imports `pkg_resources`, which setuptools 81 removed. + Install `setuptools<81`. +2. `OSError: Packmol not found`, chloroform only. `pack_box` resolves `packmol` + through PATH, so the env has to be activated, and so does `sqm` / + `antechamber` for AM1-BCC charges. +3. Charge assignment is slow, one to three minutes on a single CPU core, and + happens inside `create_system`. Wrapping the run in a short `timeout` kills it + mid-charge and leaves a 0-byte `ff_cache.json`. Charges are cached per + molecule in `//ff_cache.json`; copying a populated cache in + skips the step. +4. Never select the PROTAC by atom name or element. It carries its own Cl, and + counterions named `Na`/`Cl` slip past naive regexes, which inflated Rg by + about 13%. Every script here selects it by residue instead: the non-water + residue with more than 5 atoms. +5. mdtraj mislabels DCD frame times, so `traj.time` is not trustworthy. Derive + time from the frame index times `--report-ps`, as the scripts do. + +## Scope + +The simulations show that a generated PROTAC is physically sane and describe its +conformational ensemble, extended versus folded, along with the folding-linked +permeability descriptors. That is the same scope as the reference work, which +also simulates the free PROTAC only. + +They do not show that a linker works as a degrader. Degradation needs a +productive ternary complex of target, PROTAC and E3, which is a different +simulation. `ternary_span.py` describes the span a linker can hold, not whether +a complex forms. + +Folding propensity only means something against a baseline, so run the same +pipeline on a reference PROTAC (`--smiles ... --name ms39_ref`) alongside each +candidate. + +Yardsticks, from the 2022 MD paper: a compact minimum at Rg 5.4-5.6 A, and 3D +PSA 190-265 A^2 for the permeable compounds against 290-330 A^2 for the poorly +permeable one. From Atilaw 2021: passive permeability below Rg 7 A, and +low-to-moderate permeability up to roughly 200-210 A^2. The 7 A cutoff on its +own does not separate the 2022 compounds; see `reference_protocol.md`. diff --git a/CupOFMoCA-PROTAC/md/analyze_conformations.py b/CupOFMoCA-PROTAC/md/analyze_conformations.py new file mode 100644 index 0000000000000000000000000000000000000000..9790bb5645a46391ea43f7d2fb89238dd3543c40 --- /dev/null +++ b/CupOFMoCA-PROTAC/md/analyze_conformations.py @@ -0,0 +1,205 @@ +#!/usr/bin/env python +""" +Conformational analysis of free-PROTAC MD replicas. + +Reads all rep*/ trajectories under a solvent directory and computes, per replica +and pooled: radius of gyration (Rg), RMSD to the first frame (convergence), +solvent-accessible 3D polar surface area (3D PSA) and intramolecular H-bond +count, then PCA + K-means clustering into conformational states with populations +and saved centroid structures. The dashboard PNG carries the literature +yardsticks (7 A Rg permeability cutoff, 3D PSA ~200-210 A^2). + +Time axes assume 10 ps per frame, protac_md.py's default --report-ps; a run made +with a different --report-ps gets ns labels scaled by that ratio. + +Usage: + python analyze_conformations.py --dir egfrvhl_rank4/chloroform --name egfrvhl_rank4 +""" +import argparse, glob, os +import numpy as np +import mdtraj as md +import matplotlib; matplotlib.use("Agg") +import matplotlib.pyplot as plt +from scipy.cluster.vq import kmeans2 + +RG_CUT = 0.70 # nm, passive-permeability cutoff (Atilaw 2021) +PSA_LO, PSA_HI = 200.0, 210.0 # A^2; low-moderate permeability band +COLORS = ["#0072B2", "#D55E00", "#009E73", "#CC79A7", "#E69F00"] +INK, MUTED, GRID = "#222", "#666", "#DDD" + + +def ligand_indices(top): + return [at.index for res in top.residues + if not res.is_water and res.n_atoms > 5 for at in res.atoms] + + +def polar_slice_indices(top_lig): + """Indices (within the ligand-only topology) of polar atoms: N, O, and H on N/O.""" + nbr = {} + for b in top_lig.bonds: + nbr.setdefault(b[0].index, []).append(b[1].index) + nbr.setdefault(b[1].index, []).append(b[0].index) + polar = [] + for at in top_lig.atoms: + s = at.element.symbol + if s in ("N", "O"): + polar.append(at.index) + elif s == "H" and any(top_lig.atom(j).element.symbol in ("N", "O") + for j in nbr.get(at.index, [])): + polar.append(at.index) + return np.array(sorted(set(polar))) + + +def load_replicas(solvent_dir, name): + top_pdbs = glob.glob(os.path.join(solvent_dir, f"{name}_*_solvated.pdb")) + if not top_pdbs: + raise SystemExit(f"no *_solvated.pdb in {solvent_dir}") + top = top_pdbs[0] + reps = [] + for rd in sorted(glob.glob(os.path.join(solvent_dir, "rep*"))): + dcd = os.path.join(rd, f"{name}_prod.dcd") + if os.path.exists(dcd): + reps.append((os.path.basename(rd), md.load(dcd, top=top))) + if not reps: + raise SystemExit(f"no rep*/{name}_prod.dcd trajectories in {solvent_dir}") + return reps + + +def per_replica_metrics(traj): + top = traj.topology + lig = ligand_indices(top) + ligtraj = traj.atom_slice(lig) + ligtraj.center_coordinates() + rg = md.compute_rg(ligtraj) # nm + rmsd = md.rmsd(ligtraj, ligtraj, 0) * 10.0 # A, vs frame 0 + sasa = md.shrake_rupley(ligtraj, mode="atom") # nm^2 per atom + polar = polar_slice_indices(ligtraj.topology) + psa = sasa[:, polar].sum(axis=1) * 100.0 # A^2 + hb = [len(x) for x in md.wernet_nilsson(ligtraj)] # intramolecular H-bonds/frame + return dict(rg=rg, rmsd=rmsd, psa=psa, hb=np.array(hb, float), ligtraj=ligtraj) + + +def cluster(ligtrajs, k=5, seed=0): + pooled = md.join(ligtrajs) + pooled.superpose(pooled, 0) + X = pooled.xyz.reshape(pooled.n_frames, -1) + X = X - X.mean(0) + # PCA via SVD, keeping components up to 90% variance (cap 10) + U, S, Vt = np.linalg.svd(X, full_matrices=False) + var = (S ** 2) / (S ** 2).sum() + ncomp = min(10, max(2, int(np.searchsorted(np.cumsum(var), 0.90) + 1))) + proj = U[:, :ncomp] * S[:ncomp] + cent, labels = kmeans2(proj, k, minit="++", seed=seed) + pops = np.array([(labels == i).mean() for i in range(k)]) + # global frame index closest to each cluster centroid, in PC space + cframes = [] + for i in range(k): + idx = np.where(labels == i)[0] + cframes.append(int(idx[np.argmin(((proj[idx] - cent[i]) ** 2).sum(1))]) if len(idx) else -1) + return pooled, labels, pops, cframes, proj + + +def style(ax, title, xlabel, ylabel): + ax.set_title(title, fontsize=11, color=INK, loc="left", weight="bold", pad=6) + ax.set_xlabel(xlabel, fontsize=9, color=MUTED); ax.set_ylabel(ylabel, fontsize=9, color=MUTED) + ax.grid(True, color=GRID, lw=0.6, alpha=0.8); ax.set_axisbelow(True) + for s in ("top", "right"): ax.spines[s].set_visible(False) + for s in ("left", "bottom"): ax.spines[s].set_color(GRID) + ax.tick_params(colors=MUTED, labelsize=8) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--dir", required=True, help="solvent dir containing rep*/") + ap.add_argument("--name", required=True) + ap.add_argument("--clusters", type=int, default=5) + a = ap.parse_args() + + reps = load_replicas(a.dir, a.name) + solvent = os.path.basename(a.dir.rstrip("/")) + print(f"[analyze] {a.name} / {solvent}: {len(reps)} replicas") + + M = {rn: per_replica_metrics(tr) for rn, tr in reps} + ligtrajs = [M[rn]["ligtraj"] for rn, _ in reps] + pooled, labels, pops, cframes, proj = cluster(ligtrajs, a.clusters) + + rg_all = np.concatenate([M[rn]["rg"] for rn, _ in reps]) + psa_all = np.concatenate([M[rn]["psa"] for rn, _ in reps]) + hb_all = np.concatenate([M[rn]["hb"] for rn, _ in reps]) + rep_means = [M[rn]["rg"].mean() for rn, _ in reps] + + # ---- dashboard ---- + fig, axes = plt.subplots(2, 3, figsize=(15, 8.5), dpi=140) + fig.patch.set_facecolor("white") + + ax = axes[0, 0] + for i, (rn, _) in enumerate(reps): + t = np.arange(len(M[rn]["rg"])) * 0.01 # ns; assumes protac_md.py default --report-ps 10 + ax.plot(t, M[rn]["rg"], color=COLORS[i % 5], lw=0.8, alpha=0.85, label=rn) + ax.axhline(RG_CUT, color="k", ls="--", lw=1); ax.text(0.99, RG_CUT+0.003, "7 A permeability cutoff", + transform=ax.get_yaxis_transform(), ha="right", fontsize=7, color=MUTED) + style(ax, "Radius of gyration", "time (ns)", "Rg (nm)"); ax.legend(fontsize=7, frameon=False) + + ax = axes[0, 1] + for i, (rn, _) in enumerate(reps): + t = np.arange(len(M[rn]["rmsd"])) * 0.01 + ax.plot(t, M[rn]["rmsd"], color=COLORS[i % 5], lw=0.8, alpha=0.85) + style(ax, "RMSD to start (convergence)", "time (ns)", "RMSD (A)") + + ax = axes[0, 2] + ax.hexbin(rg_all, psa_all, gridsize=35, cmap="Blues", mincnt=1) + ax.axvline(RG_CUT, color="k", ls="--", lw=1) + ax.axhspan(PSA_LO, PSA_HI, color="orange", alpha=0.15) + ax.text(0.02, PSA_HI, " PSA ~200-210 A^2", transform=ax.get_yaxis_transform(), + fontsize=7, color=MUTED, va="bottom") + style(ax, "3D PSA vs Rg (folding->permeability)", "Rg (nm)", "3D PSA (A^2)") + + ax = axes[1, 0] + order = np.argsort(-pops) + ax.bar(range(len(pops)), pops[order] * 100, color=[COLORS[i % 5] for i in range(len(pops))]) + for r, i in enumerate(order): + idx = labels == i + if idx.any(): + ax.text(r, pops[i]*100 + 0.5, f"Rg {md.compute_rg(pooled[idx]).mean():.2f}", + ha="center", fontsize=7, color=MUTED) + style(ax, f"Conformational clusters (k={a.clusters})", "cluster (by population)", "population (%)") + + ax = axes[1, 1] + ax.hist(hb_all, bins=np.arange(0, hb_all.max()+2)-0.5, color="#0072B2", alpha=0.85, rwidth=0.9) + style(ax, "Intramolecular H-bonds", "H-bonds per frame", "count") + + ax = axes[1, 2]; ax.axis("off") + summary = [ + f"PROTAC : {a.name}", + f"Solvent: {solvent} Replicas: {len(reps)}", + f"Frames : {pooled.n_frames} pooled", + "", + f"Rg : {rg_all.mean():.3f} ± {rg_all.std():.3f} nm", + f" per-rep means: " + ", ".join(f"{x:.3f}" for x in rep_means), + f" replica spread (std of means): {np.std(rep_means):.3f} nm", + f" frac below 7 A cutoff: {100*np.mean(rg_all= 0: + pooled[cframes[i]].save_pdb(os.path.join(a.dir, f"{a.name}_cluster{r+1}_pop{pops[i]*100:.0f}.pdb")) + print(f"[analyze] Rg {rg_all.mean():.3f} nm | 3D PSA {psa_all.mean():.0f} A^2 | " + f"IMHB {hb_all.mean():.1f} | replica-mean spread {np.std(rep_means):.3f} nm") + + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/md/environment_md.yml b/CupOFMoCA-PROTAC/md/environment_md.yml new file mode 100644 index 0000000000000000000000000000000000000000..a95ece5d3bf2d8b21967db8e790fa0e8e4776ce5 --- /dev/null +++ b/CupOFMoCA-PROTAC/md/environment_md.yml @@ -0,0 +1,38 @@ +name: protacmd +# Conda env for the free-PROTAC MD pipeline (protac_md.py + analyze_conformations.py). +# +# cuda-version must match the driver on the machine you run on: check `nvidia-smi` +# (top right). 12.* covers driver 525 and newer; drop the line for a CPU-only host. +# +# conda env create -f environment_md.yml +# conda activate protacmd +# +# sqm, antechamber and packmol are only on PATH inside the env, so activate it +# before running the pipeline. +channels: + - conda-forge +dependencies: + - python=3.10 + # MD engine + - openmm=8.* + - cuda-version=12.* # match the target machine's driver; drop for CPU-only + # parametrization + - openff-toolkit>=0.16 + - openff-interchange>=0.4 # pack_box(), used to build the chloroform box + - openmmforcefields>=0.14 + - ambertools>=23 # AM1-BCC charges (sqm) + GAFF fallback + - packmol # chloroform solvation + - rdkit>=2024.03 + # analysis / plotting + - mdtraj + - parmed + - numpy + - scipy + - matplotlib + - pillow + - pip + # openff.amber_ff_ports imports pkg_resources, which setuptools>=81 dropped; + # without the pin the run dies in SystemGenerator. Pinned through pip rather + # than conda because an upper bound on setuptools blows up the conda solve. + - pip: + - "setuptools<81" diff --git a/CupOFMoCA-PROTAC/md/linker_design.md b/CupOFMoCA-PROTAC/md/linker_design.md new file mode 100644 index 0000000000000000000000000000000000000000..9502b36c755660e76e3ff7b0f9d8d7eb2160e873 --- /dev/null +++ b/CupOFMoCA-PROTAC/md/linker_design.md @@ -0,0 +1,86 @@ +# Linker rules for EGFR-VHL PROTACs, read off the MD runs + +The system is fixed: a gefitinib warhead (3-Cl-4-F-anilinoquinazoline, propyl-O, +piperazine) and VH032 (tert-leucine, 4-hydroxyproline, 4-methylthiazolyl- +benzylamine). Only the linker between the piperazine and the tert-leucine +varies. The EGFR-VHL SMILES bundled in `protac_md.py` are built differently: in +`egfrvhl_rank4` and `_rank5` the linker acylates the anilino nitrogen and the +piperazine NH is left free. Everything below comes from explicit-solvent MD on generated linkers, +run alongside the validated degrader MS39 and a PEG4 negative control. + +## Two gates + +1. **Permeability.** The linker has to let the molecule fold compact and bury + its polar surface in the membrane: low 3D PSA, compact Rg, intramolecular + H-bonds. Measured on the chloroform ensemble. +2. **Ternary geometry.** It still has to reach an extended warhead-to-E3 span of + 15-20 A at a cost the binding free energy can pay, below the 3.7 kcal/mol + conformational penalty of MS39. Measured on the water ensemble. + +Passing both means behaving like a molecular chameleon: mostly compact, with an +accessible extended tail. + +## Runs + +Same warhead and E3, linker only. Neither the trajectories nor the linker SMILES +are shipped here, so the table records what those runs showed rather than +something the scripts regenerate. 3D PSA and Rg come from `psa_ensemble.py` and +`rg_basins.py`; the clash-free fraction and the penalty come from a rigid +ternary-geometry check that is not part of this folder. + +| linker | core | 3D PSA (A^2) | Rg in chloroform (A) | chameleon | clash-free fraction / penalty (kcal/mol) | verdict | +|---|---|---|---|---|---|---| +| dm2 | 2-(n-propyl)malonamide, short and branched | 140 | 5.96 | yes | 2.5% / 2.2 | passes both | +| perm4 | 2-(allyl)malonamide, short and branched | 141 | 5.92 | yes | 4.3% / 1.9 | passes both | +| MS39 (reference) | C10 linear diacid, sebacoyl | 178 | 6.51 | mild | 0.2% / 3.7 | validated degrader | +| perm2 | cyclopentane-1,2-diyl, rigid ring | 172 | 6.40 | no | 0.0% / >5 | fails ternary | +| perm3 | C6 linear diacid | 194 | 7.39 | no, folds in water and opens in the membrane | 1.3% / 2.6 | fails permeability | +| PEG4 | PEG4 diacid | 193 | 7.21 | no | 0.1% / 4.1 | fails permeability | + +2D descriptors do not see this. TPSA is 191 for every linker except PEG4 at 228, +so it cannot tell dm2 at 3D PSA 140 from perm3 at 194; the difference is folding +in three dimensions. Rotatable bonds: 18 for the two winners, 20 for perm3, 24 +for MS39, 27 for PEG4. cLogP: 6.5-6.7 for the winners, 7.3-7.9 for perm2 and +perm3, 8.8 for MS39, 5.4 for PEG4. + +## Rules, cheap enough to pre-screen before MD + +1. **Short, branched aliphatic core.** A 2-substituted malonamide or succinamide + with a small hydrophobic branch such as propyl or allyl folds compact while + the warhead's propyl-O-piperazine extension still covers the ternary span. +2. **Avoid three failure chemistries.** PEG and poly-ether raise TPSA and HBA + and stay floppy, which kills permeability (PEG4); reject high ether-O counts, + TPSA above ~200, HBA above ~14. A rigid ring across the linker locks in a + non-spanning geometry, which kills ternary formation (perm2). Long linear + alkyl or diacid chains fold poorly (perm3) or push cLogP and PSA up (MS39). +3. **Keep it stiff enough.** Rotatable bonds around 18, not 24-27. +4. **Moderate cLogP,** roughly 6-7. + +## Hard experimental constraint + +Published EGFR-VHL SAR: linkers whose fully extended warhead-to-E3 span is under +about 13 A, fewer than roughly 12 chain atoms end to end, show no degradation. +Count the full chain from the piperazine anchor to the tert-leucine, not just +the core, and keep it at 12 atoms or more (Cheng, Yu et al., *J. Med. Chem.* +2020, the MS39 paper). + +## What MD should then confirm + +- 3D PSA of the folded state at or below ~150 A^2, a compact chloroform Rg basin + at or below ~6.0 A, and at least 95% of frames under 7 A. +- Chameleonic behavior: chloroform Rg below water Rg, intramolecular H-bonds at + 1.5 or more. +- Ternary reach: a span of 15 A or more in 1-2% of the water ensemble, a penalty + at or below ~3 kcal/mol. + +## Caveats + +Propose in 2D, then confirm the top few by MD. + +The ternary axis is a rigid free-solution proxy, not a validated degradation +score. The 12-atom rule still applies on top of it. + +## Cores worth imitating + +- dm2, 2-propyl malonamide: `CCC[C@@H](C(=O)N-VH032)C(=O)N-piperazine` +- perm4, the same with an allyl branch: `C=CC` diff --git a/CupOFMoCA-PROTAC/md/plot_results.py b/CupOFMoCA-PROTAC/md/plot_results.py new file mode 100644 index 0000000000000000000000000000000000000000..fe67e81ffce1b12973a7026b50466534ece6915b --- /dev/null +++ b/CupOFMoCA-PROTAC/md/plot_results.py @@ -0,0 +1,135 @@ +#!/usr/bin/env python +"""One-page summary figure for a single replica of a protac_md.py run: the +radius of gyration, potential energy and density traces of the production run, +with the PROTAC 2D structure beside them when --smiles is given. Writes +/_summary.png. + +Usage: + python plot_results.py --rep egfrvhl_rank4/chloroform/rep1 + python plot_results.py --rep egfrvhl_rank4/chloroform/rep1 --smiles "" +""" +import argparse, csv, glob, io, os +import numpy as np +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from matplotlib.gridspec import GridSpec +import mdtraj as md +from rdkit import Chem +from rdkit.Chem import AllChem +from rdkit.Chem.Draw import rdMolDraw2D +from PIL import Image + +# Okabe-Ito colorblind-safe hues +BLUE, VERM, GREEN = "#0072B2", "#D55E00", "#009E73" +INK, MUTED, GRID = "#222222", "#666666", "#DDDDDD" + + +def load_csv(path): + with open(path) as fh: + rows = list(csv.reader(fh)) + header = [h.strip().strip('#"').strip() for h in rows[0]] + data = np.array([[float(x) for x in r] for r in rows[1:] if r]) + col = {h: data[:, i] for i, h in enumerate(header)} + + def find(key): + return next(c for h, c in col.items() if key.lower() in h.lower()) + + t = find("time") / 1000.0 # ps -> ns + return t - t[0], find("potential"), find("temperature"), find("density") + + +def load_rg(rep_dir, name): + """Rg of the PROTAC (nm), same selection as analyze_conformations.py.""" + tops = glob.glob(os.path.join(rep_dir, os.pardir, "*_solvated.pdb")) + traj = md.load(os.path.join(rep_dir, f"{name}_prod.dcd"), top=tops[0]) + lig = [a.index for r in traj.topology.residues + if not r.is_water and r.n_atoms > 5 for a in r.atoms] + sub = traj.atom_slice(lig) + sub.center_coordinates() + return md.compute_rg(sub) + + +def mol_image(smiles, px=700): + mol = Chem.MolFromSmiles(smiles) + AllChem.Compute2DCoords(mol) + d = rdMolDraw2D.MolDraw2DCairo(px, px) + d.drawOptions().bondLineWidth = 2 + d.drawOptions().padding = 0.06 + rdMolDraw2D.PrepareAndDrawMolecule(d, mol) + d.FinishDrawing() + return Image.open(io.BytesIO(d.GetDrawingText())) + + +def style(ax, title, ylabel): + ax.set_title(title, fontsize=11, color=INK, loc="left", pad=6, weight="bold") + ax.set_ylabel(ylabel, fontsize=9, color=MUTED) + ax.set_xlabel("production time (ns)", fontsize=9, color=MUTED) + ax.grid(True, color=GRID, lw=0.6, alpha=0.8) + ax.set_axisbelow(True) + for s in ("top", "right"): + ax.spines[s].set_visible(False) + for s in ("left", "bottom"): + ax.spines[s].set_color(GRID) + ax.tick_params(colors=MUTED, labelsize=8, length=3) + + +def main(): + p = argparse.ArgumentParser() + p.add_argument("--rep", required=True, help="replica directory, e.g. //rep1") + p.add_argument("--name", default=None, help="run name (default: the run directory's name)") + p.add_argument("--smiles", default=None, help="draw this structure in the left panel") + p.add_argument("--report-ps", type=float, default=10.0, help="DCD frame interval") + a = p.parse_args() + + rep = a.rep.rstrip("/") + name = a.name or os.path.basename(os.path.dirname(os.path.dirname(rep))) + solvent = os.path.basename(os.path.dirname(rep)) + + t, pe, temp, dens = load_csv(os.path.join(rep, f"{name}_prod.csv")) + rg = load_rg(rep, name) + rg_t = np.arange(len(rg)) * a.report_ps / 1000.0 # frame index -> ns + + fig = plt.figure(figsize=(13, 7.5), dpi=150) + fig.patch.set_facecolor("white") + ncol = 3 if a.smiles else 2 + gs = GridSpec(2, ncol, figure=fig, hspace=0.42, wspace=0.30, + left=0.06, right=0.98, top=0.90, bottom=0.09) + off = 0 + + if a.smiles: + axm = fig.add_subplot(gs[:, 0]) + axm.axis("off") + axm.imshow(mol_image(a.smiles)) + axm.set_title(name, fontsize=11, color=INK, loc="left", weight="bold", pad=6) + off = 1 + + ax1 = fig.add_subplot(gs[0, off:]) + ax1.plot(rg_t, rg, color=BLUE, lw=1.2) + m = rg.mean() + ax1.axhline(m, color=MUTED, lw=1.0, ls="--") + ax1.annotate(f"mean {m:.2f} nm", (rg_t[-1], m), color=MUTED, fontsize=8, + ha="right", va="bottom", xytext=(0, 3), textcoords="offset points") + style(ax1, "Radius of gyration", "Rg (nm)") + + ax2 = fig.add_subplot(gs[1, off]) + ax2.plot(t, pe / 1000.0, color=VERM, lw=1.0) + style(ax2, "Potential energy", "PE (10^3 kJ/mol)") + + ax3 = fig.add_subplot(gs[1, off + 1]) + ax3.plot(t, dens, color=GREEN, lw=1.0) + style(ax3, "System density", "density (g/mL)") + + fig.suptitle(f"Free-PROTAC MD summary - {name} in {solvent} " + f"({t[-1]:.0f} ns, 300 K / 1 bar)", + fontsize=13, color=INK, x=0.06, ha="left", weight="bold") + + out = os.path.join(rep, f"{name}_summary.png") + fig.savefig(out, facecolor="white") + print("wrote", out) + print(f"Rg {rg.mean():.3f}±{rg.std():.3f} nm | T {temp.mean():.1f} K | " + f"rho {dens.mean():.3f} g/mL") + + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/md/protac_md.py b/CupOFMoCA-PROTAC/md/protac_md.py new file mode 100644 index 0000000000000000000000000000000000000000..761cfae349bd8ade08eed0b8a42832f8be763950 --- /dev/null +++ b/CupOFMoCA-PROTAC/md/protac_md.py @@ -0,0 +1,314 @@ +#!/usr/bin/env python +""" +Explicit-solvent MD of a free PROTAC (no POI, no E3 protein), following the +protocol of "Linker-Dependent Folding Rationalizes PROTAC Cell Permeability" +(J. Med. Chem. 2022, DOI 10.1021/acs.jmedchem.2c00877): triplicate ~100 ns runs in +chloroform (membrane-interior mimic) and/or water, analyzed for Rg, 3D PSA, +intramolecular H-bonds and conformational clusters. + +Pipeline: + 1. RDKit: SMILES -> add Hs -> ETKDGv3 multi-conformer -> MMFF -> lowest-E conf. + 2. OpenFF Sage (openff-2.2.0) + AM1-BCC charges via AmberTools for the PROTAC + (and for chloroform, when used) through openmmforcefields. + 3. Solvate: + water -> OpenMM addSolvent (TIP3P, 0.15 M NaCl, neutralized) + chloroform -> packmol via openff-interchange pack_box + 4. Minimize once, then run N independent replicas (different velocity seeds), + each: NVT heat 50->T, NPT equilibration, NPT production. + +Outputs per replica land in //rep/; analyze them with +analyze_conformations.py. + +Environment: conda env "protacmd" (environment_md.yml). Needs setuptools<81: +openff.amber_ff_ports imports pkg_resources, which setuptools 81 dropped, and +the run dies inside SystemGenerator without it. + +Usage: + python protac_md.py --solvent chloroform --replicas 3 --prod-ns 100 --gpu 0 + python protac_md.py --solvent water --replicas 3 --prod-ns 100 --gpu 0 + python protac_md.py --prep-only # just the 3D conformer +""" + +import argparse +import os +import sys +import time + +# Top-ranked generated PROTACs for EGFR-VHL, BRD9-VHL and CDK6-CRBN. Any other +# SMILES can be simulated with --smiles. +CANDIDATES = { + "egfrvhl_rank4": "CCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)" + "C(C)(C)C)C(=O)N(c1ccc(F)c(Cl)c1)c1ncnc2cc(OC)c(OCCCCN3CCNCC3)cc12", + "egfrvhl_rank5": "CCNC(=O)C(OC(=O)C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)" + "C(C)(C)C)C(=O)N(c1ccc(F)c(Cl)c1)c1ncnc2cc(OC)c(OCCCCN3CCNCC3)cc12", + "brd9vhl_rank1": "CCC(C)C(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)" + "C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1", + "cdk6crbn_rank2": "CC(=O)c1c(C)c2cnc(N(C(=O)CC(C)CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)" + "c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O", +} +DEFAULT_CANDIDATE = "egfrvhl_rank4" +CHLOROFORM_SMILES = "C(Cl)(Cl)Cl" +CHLOROFORM_DENSITY_PER_NM3 = 7.51 # molecules/nm^3 at 1.49 g/mL, 300 K + + +# --- 3D conformer ----------------------------------------------------------- +def build_conformer(smiles, name, out_dir, n_confs=20, seed=0xF00D): + from rdkit import Chem + from rdkit.Chem import AllChem, Descriptors, rdMolDescriptors + + mol = Chem.MolFromSmiles(smiles) + if mol is None: + sys.exit(f"[fatal] RDKit could not parse SMILES: {smiles}") + print(f"[prep] {name}: MW={Descriptors.MolWt(mol):.1f} " + f"charge={Chem.GetFormalCharge(mol):+d} " + f"rot.bonds={rdMolDescriptors.CalcNumRotatableBonds(mol)}") + + molh = Chem.AddHs(mol) + params = AllChem.ETKDGv3() + params.randomSeed = seed + params.useRandomCoords = True + cids = AllChem.EmbedMultipleConfs(molh, numConfs=n_confs, params=params) + if len(cids) == 0: + if AllChem.EmbedMolecule(molh, params) != 0: + sys.exit("[fatal] 3D embedding failed.") + cids = [0] + if not AllChem.MMFFHasAllMoleculeParams(molh): + print("[warn] MMFF missing some params; conformer quality may be lower.") + results = AllChem.MMFFOptimizeMoleculeConfs(molh, maxIters=2000) + best_cid, best_e = min(zip(cids, [e for _, e in results]), key=lambda x: x[1]) + print(f"[prep] lowest MMFF conformer {best_cid} (E={best_e:.1f} kcal/mol)") + + sdf = os.path.join(out_dir, f"{name}.sdf") + w = Chem.SDWriter(sdf); molh.SetProp("_Name", name); w.write(molh, confId=best_cid); w.close() + print(f"[prep] wrote {sdf}") + keep = Chem.Mol(molh); keep.RemoveAllConformers() + keep.AddConformer(molh.GetConformer(best_cid), assignId=True) + return keep + + +# --- solvation -------------------------------------------------------------- +def solvate_water(molecule, system_generator, cfg): + from openmm import app, unit + off_top = molecule.to_topology() + modeller = app.Modeller(off_top.to_openmm(), molecule.conformers[0].to_openmm()) + modeller.addSolvent(system_generator.forcefield, model="tip3p", + padding=cfg.padding * unit.nanometer, + ionicStrength=cfg.ionic * unit.molar, neutralize=True) + return modeller.topology, modeller.positions + + +def solvate_chloroform(molecule, chcl3, cfg): + import numpy as np + from openff.interchange.components._packmol import pack_box + from openff.units import unit as ou + from openff.units.openmm import to_openmm + + conf = molecule.conformers[0].m_as(ou.nanometer) + extent = np.linalg.norm(conf - conf.mean(0), axis=1).max() * 2.0 # ~diameter, nm + box = extent + 2.0 * cfg.padding + n = int(round(0.90 * CHLOROFORM_DENSITY_PER_NM3 * box ** 3)) # under-fill; NPT compresses + print(f"[md] chloroform box {box:.2f} nm, packing {n} CHCl3 (NPT will set final density)") + box_vecs = np.eye(3) * box * ou.nanometer + packed = pack_box(molecules=[chcl3], number_of_copies=[n], + solute=molecule.to_topology(), box_vectors=box_vecs, + center_solute=True, tolerance=2.0 * ou.angstrom) + omm_top = packed.to_openmm() + omm_top.setPeriodicBoxVectors(to_openmm(packed.box_vectors)) + positions = to_openmm(packed.get_positions()) + return omm_top, positions + + +# --- solvated system, minimized once ---------------------------------------- +def build_system(rdkit_mol, name, out_dir, cfg): + from openmm import app, unit, LangevinMiddleIntegrator, Platform + from openff.toolkit import Molecule + from openmmforcefields.generators import SystemGenerator + + molecule = Molecule.from_rdkit(rdkit_mol, allow_undefined_stereo=True) + molecule.name = name + + extra = [] + chcl3 = None + if cfg.solvent == "chloroform": + chcl3 = Molecule.from_smiles(CHLOROFORM_SMILES) + chcl3.generate_conformers(n_conformers=1) + chcl3.name = "CL3" + extra = [chcl3] + + forcefield_kwargs = {"constraints": app.HBonds, "rigidWater": True, + "removeCMMotion": True, "hydrogenMass": cfg.hmass * unit.amu} + periodic_kwargs = {"nonbondedMethod": app.PME, "nonbondedCutoff": 1.0 * unit.nanometer} + system_generator = SystemGenerator( + forcefields=["amber/ff14SB.xml", "amber/tip3p_standard.xml", + "amber/tip3p_HFE_multivalent.xml"], + small_molecule_forcefield=cfg.small_mol_ff, + molecules=[molecule] + extra, + forcefield_kwargs=forcefield_kwargs, + periodic_forcefield_kwargs=periodic_kwargs, + cache=os.path.join(out_dir, "ff_cache.json")) + print(f"[md] solvent={cfg.solvent} small-molecule FF={cfg.small_mol_ff} (AM1-BCC charges)") + + if cfg.solvent == "water": + topology, positions = solvate_water(molecule, system_generator, cfg) + else: + topology, positions = solvate_chloroform(molecule, chcl3, cfg) + + n_atoms = topology.getNumAtoms() + print(f"[md] solvated system: {n_atoms} atoms") + app.PDBFile.writeFile(topology, positions, + open(os.path.join(out_dir, f"{name}_{cfg.solvent}_solvated.pdb"), "w")) + + base_system = system_generator.create_system(topology) + + # minimize once; replicas branch from here with fresh velocities + integ = LangevinMiddleIntegrator(cfg.temperature * unit.kelvin, 1.0 / unit.picosecond, + cfg.timestep * unit.femtosecond) + plat, prop = _pick_platform(Platform, cfg) + sim = app.Simulation(topology, base_system, integ, plat, prop) + sim.context.setPositions(positions) + print("[md] minimizing solvated system...") + sim.minimizeEnergy(maxIterations=cfg.min_iters) + min_pos = sim.context.getState(getPositions=True).getPositions() + del sim, integ + return topology, base_system, min_pos, plat, prop + + +# --- one replica: heat, NPT equilibrate, NPT production --------------------- +def run_replica(topology, base_system, min_pos, plat, prop, name, rep_dir, cfg, seed): + import openmm + from openmm import app, unit, XmlSerializer + + os.makedirs(rep_dir, exist_ok=True) + temperature = cfg.temperature * unit.kelvin + pressure = cfg.pressure * unit.bar + dt = cfg.timestep * unit.femtosecond + + # independent copy per replica: each adds its own barostat below, so sharing + # base_system would stack one barostat per replica + system = XmlSerializer.deserialize(XmlSerializer.serialize(base_system)) + integrator = openmm.LangevinMiddleIntegrator(temperature, 1.0 / unit.picosecond, dt) + integrator.setRandomNumberSeed(seed) + sim = app.Simulation(topology, system, integrator, plat, prop) + sim.context.setPositions(min_pos) + sim.context.setVelocitiesToTemperature(50 * unit.kelvin, seed) + + # NVT heating 50 -> T + heat_steps = int(cfg.heat_ps * 1000 / cfg.timestep) + n_stages = 25 + for i in range(n_stages): + t = 50 + (cfg.temperature - 50) * (i + 1) / n_stages + integrator.setTemperature(t * unit.kelvin) + sim.step(max(1, heat_steps // n_stages)) + + # add barostat -> NPT equilibration + baro = openmm.MonteCarloBarostat(pressure, temperature, 25) + baro.setRandomNumberSeed(seed) + system.addForce(baro) + sim.context.reinitialize(preserveState=True) + sim.step(int(cfg.equil_ps * 1000 / cfg.timestep)) + _write_pdb(app, sim, os.path.join(rep_dir, f"{name}_equilibrated.pdb")) + + # production + steps_per_ns = int(round(1e6 / cfg.timestep)) + prod_steps = int(cfg.prod_ns * steps_per_ns) + report_every = max(1, int(cfg.report_ps * 1000 / cfg.timestep)) + sim.currentStep = 0 # so the progress reporter excludes heat + equilibration + dcd = os.path.join(rep_dir, f"{name}_prod.dcd") + sim.reporters.append(app.DCDReporter(dcd, report_every)) + sim.reporters.append(app.StateDataReporter( + os.path.join(rep_dir, f"{name}_prod.csv"), report_every, step=True, time=True, + potentialEnergy=True, temperature=True, density=True, volume=True, speed=True)) + sim.reporters.append(app.StateDataReporter( + sys.stdout, report_every * 20, step=True, temperature=True, density=True, + speed=True, progress=True, remainingTime=True, totalSteps=prod_steps)) + sim.reporters.append(app.CheckpointReporter(os.path.join(rep_dir, f"{name}_prod.chk"), + report_every * 20)) + print(f"[md] {os.path.basename(rep_dir)}: production {cfg.prod_ns} ns -> {dcd}") + t0 = time.time() + sim.step(prod_steps) + print(f"[md] {os.path.basename(rep_dir)} done in {(time.time()-t0)/3600:.2f} h") + _write_pdb(app, sim, os.path.join(rep_dir, f"{name}_final.pdb")) + + +def run_md(rdkit_mol, name, out_dir, cfg): + solvent_dir = os.path.join(out_dir, cfg.solvent) + os.makedirs(solvent_dir, exist_ok=True) + topology, base_system, min_pos, plat, prop = build_system(rdkit_mol, name, solvent_dir, cfg) + for k in range(cfg.replicas): + rep_dir = os.path.join(solvent_dir, f"rep{k+1}") + run_replica(topology, base_system, min_pos, plat, prop, name, rep_dir, cfg, + seed=cfg.seed + 1000 * (k + 1)) + print(f"[md] all {cfg.replicas} replicas ({cfg.solvent}) complete -> {solvent_dir}") + print(f"[md] analyze with: python analyze_conformations.py --dir {solvent_dir} --name {name}") + + +def _pick_platform(Platform, cfg): + avail = {Platform.getPlatform(i).getName() for i in range(Platform.getNumPlatforms())} + order = [cfg.platform] if cfg.platform != "auto" else ["CUDA", "OpenCL", "CPU"] + for pn in order: + if pn in avail: + prop = {"Precision": "mixed", "DeviceIndex": str(cfg.gpu)} if pn in ("CUDA", "OpenCL") else {} + print(f"[md] platform = {pn} {prop}") + return Platform.getPlatformByName(pn), prop + sys.exit(f"[fatal] platform '{cfg.platform}' unavailable; have {sorted(avail)}") + + +def _write_pdb(app, sim, path): + st = sim.context.getState(getPositions=True, enforcePeriodicBox=True) + with open(path, "w") as fh: + app.PDBFile.writeFile(sim.topology, st.getPositions(), fh) + + +def parse_args(): + p = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + m = p.add_argument_group("molecule") + m.add_argument("--candidate", choices=sorted(CANDIDATES), default=DEFAULT_CANDIDATE) + m.add_argument("--smiles", default=None, help="custom PROTAC SMILES (e.g. a reference PROTAC)") + m.add_argument("--name", default=None) + m.add_argument("--out-dir", default=None) + m.add_argument("--n-confs", type=int, default=20) + + r = p.add_argument_group("simulation") + r.add_argument("--prep-only", action="store_true", help="only build 3D conformer (rdkit only)") + r.add_argument("--solvent", choices=["chloroform", "water"], default="chloroform", + help="chloroform = membrane-interior mimic (permeability); water = polar") + r.add_argument("--replicas", type=int, default=3, help="independent runs (different seeds)") + r.add_argument("--seed", type=int, default=1234) + r.add_argument("--small-mol-ff", dest="small_mol_ff", default="openff-2.2.0") + r.add_argument("--padding", type=float, default=1.2, help="solvent padding, nm") + r.add_argument("--ionic", type=float, default=0.15, help="NaCl conc (water only), M") + r.add_argument("--temperature", type=float, default=300.0) + r.add_argument("--pressure", type=float, default=1.0) + r.add_argument("--timestep", type=float, default=2.0) + r.add_argument("--hmass", type=float, default=1.5) + r.add_argument("--min-iters", type=int, default=5000) + r.add_argument("--heat-ps", type=float, default=100.0) + r.add_argument("--equil-ps", type=float, default=500.0) + r.add_argument("--prod-ns", type=float, default=100.0) + r.add_argument("--report-ps", type=float, default=10.0) + + h = p.add_argument_group("hardware") + h.add_argument("--platform", default="auto", choices=["auto", "CUDA", "OpenCL", "CPU"]) + h.add_argument("--gpu", type=int, default=0) + return p.parse_args() + + +def main(): + cfg = parse_args() + smiles = cfg.smiles or CANDIDATES[cfg.candidate] + name = cfg.name or (cfg.candidate if not cfg.smiles else "custom_protac") + out_dir = cfg.out_dir or os.path.join(os.path.dirname(os.path.abspath(__file__)), name) + os.makedirs(out_dir, exist_ok=True) + print(f"[run] name={name} solvent={cfg.solvent} replicas={cfg.replicas} out={out_dir}") + print(f"[run] SMILES={smiles}") + + rdkit_mol = build_conformer(smiles, name, out_dir, n_confs=cfg.n_confs) + if cfg.prep_only: + print("[run] --prep-only: done."); return + run_md(rdkit_mol, name, out_dir, cfg) + print("[run] all done.") + + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/md/protacmd-lock-linux64.txt b/CupOFMoCA-PROTAC/md/protacmd-lock-linux64.txt new file mode 100644 index 0000000000000000000000000000000000000000..a9a45a1fbe137c0d2488894dbdc9a2629d1f2c32 --- /dev/null +++ b/CupOFMoCA-PROTAC/md/protacmd-lock-linux64.txt @@ -0,0 +1,296 @@ +# This file may be used to create an environment using: +# $ conda create --name --file +# platform: linux-64 +# created-by: conda 26.3.2 +@EXPLICIT +https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.6.17-hbd8a1cb_0.conda +https://conda.anaconda.org/conda-forge/noarch/cuda-version-12.9-h4f385c5_3.conda +https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 +https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2 +https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2 +https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda +https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_19.conda +https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_2.conda +https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-22.1.8-h4922eb0_0.conda +https://conda.anaconda.org/conda-forge/linux-64/onemkl-license-2025.3.1-hf2ce2f3_12.conda 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+https://conda.anaconda.org/conda-forge/noarch/openff-interchange-base-0.4.2-pyhd8ed1ab_3.conda +https://conda.anaconda.org/conda-forge/linux-64/rdkit-2025.09.5-py310hd71299c_0.conda +https://conda.anaconda.org/conda-forge/noarch/notebook-7.6.0-pyhcf101f3_0.conda +https://conda.anaconda.org/conda-forge/noarch/openff-interchange-0.4.2-pyhd8ed1ab_3.conda +https://conda.anaconda.org/conda-forge/noarch/openff-toolkit-0.16.10-pyhd8ed1ab_0.conda +https://conda.anaconda.org/conda-forge/noarch/openmmforcefields-0.14.2-pyhd8ed1ab_0.conda diff --git a/CupOFMoCA-PROTAC/md/psa_ensemble.py b/CupOFMoCA-PROTAC/md/psa_ensemble.py new file mode 100644 index 0000000000000000000000000000000000000000..e010c46fdf39bf2868bb54d3b47ebec936188513 --- /dev/null +++ b/CupOFMoCA-PROTAC/md/psa_ensemble.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python +"""3D polar surface area over an MD ensemble. + +Element-based PSA: N, O and polar H, Bondi radii, 1.4 A probe, mdtraj +shrake_rupley. validate_psa.py checks this code path against the published +grazoprevir numbers. Lower PSA in the membrane mimic tracks higher passive +permeability. + +Polar atoms are indexed from each run's SDF, so the SDF and MD atom orders must +agree. A run whose ligand atom count does not match its SDF is reported and +dropped from the table rather than summed over the wrong atoms. + +Usage: + python psa_ensemble.py egfrvhl_rank4 ms39_ref --solvent chloroform --out psa.npy +""" +import argparse, glob, os +import numpy as np +import mdtraj as md +from rdkit import Chem + + +def polar_idx_from_sdf(sdf): + m = Chem.MolFromMolFile(sdf, removeHs=False) + idx = [] + for a in m.GetAtoms(): + s = a.GetSymbol() + if s in ("N", "O"): + idx.append(a.GetIdx()) + elif s == "H" and any(nb.GetSymbol() in ("N", "O") for nb in a.GetNeighbors()): + idx.append(a.GetIdx()) + return idx, m.GetNumAtoms() + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("runs", nargs="+", help="run directories, one per compound") + ap.add_argument("--solvent", default="chloroform") + ap.add_argument("--stride", type=int, default=20) + ap.add_argument("--out", default=None, help="optional .npy dump of the per-frame values") + a = ap.parse_args() + + print(f"{'compound':16s}{'n_pol':6s}{'3D PSA (A^2) mean(std)':24s}{'frames':8s}") + print("-" * 56) + out = {} + for run in a.runs: + name = os.path.basename(run.rstrip("/")) + pol, nat = polar_idx_from_sdf(os.path.join(run, f"{name}.sdf")) + tops = glob.glob(os.path.join(run, a.solvent, "*_solvated.pdb")) + if not tops: + print(f"{name:16s} no {a.solvent} topology"); continue + vals = [] + for dcd in sorted(glob.glob(os.path.join(run, a.solvent, "rep*", f"{name}_prod.dcd"))): + tr = md.load(dcd, top=tops[0], stride=a.stride) + lig = [at.index for r in tr.topology.residues + if not r.is_water and r.n_atoms > 5 for at in r.atoms] + sub = tr.atom_slice(lig) + if sub.n_atoms != nat: + print(f"{name:16s} atom count mismatch md={sub.n_atoms} sdf={nat}") + vals = None + break + sasa = md.shrake_rupley(sub, mode="atom", probe_radius=0.14) # nm^2 + vals.extend((sasa[:, pol].sum(1) * 100.0).tolist()) # A^2 per frame + if vals: + v = np.array(vals) + print(f"{name:16s}{len(pol):<6d}{f'{v.mean():.0f} ({v.std():.0f})':24s}{len(v):<8d}") + out[name] = dict(mean=float(v.mean()), std=float(v.std()), vals=v.tolist()) + if a.out and out: + np.save(a.out, out, allow_pickle=True) + print(f"\nsaved {a.out}") + + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/md/psa_method_probe.py b/CupOFMoCA-PROTAC/md/psa_method_probe.py new file mode 100644 index 0000000000000000000000000000000000000000..414e3840b89349de785b69fd0a34fa482da70fa1 --- /dev/null +++ b/CupOFMoCA-PROTAC/md/psa_method_probe.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python +"""Compare polar-atom selection conventions for 3D PSA on a finished run. + +The element-based selection (N, O, polar H) and the charge-threshold selection +used in the reference work give different absolute PSA values on the same +trajectory. This prints both, sweeping the charge threshold, so a gap against +published numbers can be attributed to convention rather than to conformation. +Charges come from the run's AM1-BCC cache, so nothing is recomputed. + +Takes a run name rather than a path and resolves // against the +current directory, so run it from the md folder. Solvent defaults to chloroform. + +Usage: + python psa_method_probe.py egfrvhl_rank4 [solvent] +""" +import json, glob, numpy as np, mdtraj as md +import xml.etree.ElementTree as ET + +def cached_charges(cache_path): + d = json.load(open(cache_path)) + entry = list(list(d.values())[0].values())[0] # {'openff-2.2.0': {'1': {'ffxml': ...}}} + ffxml = entry["ffxml"] + root = ET.fromstring(ffxml) + # type -> charge from NonbondedForce + q_by_type = {} + for a in root.iter("Atom"): + if a.get("charge") is not None and a.get("type") is not None: + q_by_type[a.get("type")] = float(a.get("charge")) + # atom order from the Residue template + res = root.find(".//Residues/Residue") + order = [a.get("type") for a in res.findall("Atom")] + return np.array([q_by_type[t] for t in order]) + +def main(): + import sys + NAME = sys.argv[1] if len(sys.argv) > 1 else "egfrvhl_rank4" + SOL = sys.argv[2] if len(sys.argv) > 2 else "chloroform" + q = cached_charges(f"{NAME}/{SOL}/ff_cache.json") + print(f"charges from cache: n={len(q)} range [{q.min():.2f},{q.max():.2f}] " + f"#|q|>1.0={(np.abs(q) > 1.0).sum()}") + + top = glob.glob(f"{NAME}/{SOL}/*_solvated.pdb")[0] + reps = sorted(glob.glob(f"{NAME}/{SOL}/rep*/{NAME}_prod.dcd")) + tr = md.join([md.load(r, top=top) for r in reps]) + lig = [a.index for r in tr.topology.residues if not r.is_water and r.n_atoms > 5 + for a in r.atoms] + t = tr.atom_slice(lig); t.center_coordinates(); L = t.topology + syms = [a.element.symbol for a in L.atoms] + if len(syms) != len(q): + print(f"atom-count mismatch: cache {len(q)} vs MD {len(syms)}"); return + sasa = md.shrake_rupley(t, mode="atom", probe_radius=0.14) # nm^2 + def area(mask): return (sasa[:, mask].sum(1) * 100.0).mean() + + nbr = {} + for b in L.bonds: + nbr.setdefault(b[0].index, []).append(b[1].index) + nbr.setdefault(b[1].index, []).append(b[0].index) + elem = np.array([i for i, a in enumerate(L.atoms) + if a.element.symbol in ("N", "O") or (a.element.symbol == "H" and + any(L.atom(j).element.symbol in ("N", "O") for j in nbr.get(a.index, [])))]) + + print(f"\nelement selection (N, O, polar H): {area(elem):.0f} A2 ({len(elem)} atoms)") + print("charge-threshold selection:") + print(f" {'|q|>thr':>8s}{'#atoms':>8s}{'PSA':>7s}") + for thr in [0.10, 0.15, 0.20, 0.25, 0.30, 0.40, 0.50]: + m = np.abs(q) > thr + print(f" {thr:>8.2f}{int(m.sum()):>8d}{area(m):>7.0f}") + print("\nreference permeable band = 190-265 A2") + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/md/reference_protocol.md b/CupOFMoCA-PROTAC/md/reference_protocol.md new file mode 100644 index 0000000000000000000000000000000000000000..b6d19899d590b571f352018448e92d72526014d6 --- /dev/null +++ b/CupOFMoCA-PROTAC/md/reference_protocol.md @@ -0,0 +1,58 @@ +# Reference protocol and permeability yardsticks + +The MD pipeline in this folder is a deliberate copy of one published protocol, +with the numbers it should be read against collected here. + +## Sources + +- **"Linker-Dependent Folding Rationalizes PROTAC Cell Permeability," + *J. Med. Chem.* 2022 (PMC9574858).** The MD paper, and the template for + `protac_md.py` and `analyze_conformations.py`. Three CRBN/BRD4 PROTACs with + different linkers, PEG-like against alkyl. +- **Atilaw et al., "Solution Conformations Shed Light on PROTAC Cell + Permeability," *ACS Med. Chem. Lett.* 2021, 12, 107-114.** NMR plus Monte + Carlo conformational search and NAMFIS, not MD, on one VHL PROTAC (ERK5). It + is the source of the folded-versus-extended framing and of the Rg cutoff + below, nothing else. + +## Protocol + +| Setting | Reference choice | Here | +|---|---|---| +| Engine / ligand force field | Amber 18, GAFF, geometry pre-optimized in Gaussian | OpenMM 8, OpenFF Sage 2.2 with AM1-BCC charges | +| Solvent | explicit chloroform, ~3000 molecules, truncated octahedron | explicit chloroform via packmol, plus water as a polar reference | +| Production | 100 ns after equilibration | 50-100 ns, `--prod-ns` | +| Replicas | 3 | 3, independent velocity seeds | +| Ensemble | 300 K, PBC, ~10,000 snapshots per trajectory | 300 K / 1 bar NPT, 10 ps per frame | +| Convergence | RMSD, converged within 5-10 ns | RMSD to the first frame plus replica-mean spread | +| Analyses | Rg (cpptraj), 3D PSA (PyMOL), intramolecular H-bonds, PMI shape, PCA and K-means into 5 clusters | Rg, 3D PSA (mdtraj SASA over N, O and polar H), H-bonds, PCA and K-means | + +Chloroform stands in for the membrane interior: its dielectric constant of 4.8 +is close to what a molecule sees while crossing a bilayer. + +## Yardsticks + +- **Rg below 7 A** marks the folded, passively permeable regime. This cutoff is + from the 2021 NMR paper, on a single VHL PROTAC, and it does not separate the + compounds in the 2022 MD paper. +- **Mean Rg alone does not discriminate.** In the 2022 MD paper: + + | compound | P_passive (nm/s) | dominant Rg | compact local minimum | + |---|---|---|---| + | PROTAC 1, most permeable | 30 ± 1.5 | just over 8 A | 5.6 A | + | PROTAC 2 | 11 ± 1.7 | just over 7 A | 5.5 A | + | PROTAC 3, least permeable | 6 ± 1.4 | 7-9.5 A, extended | none | + + Their most permeable compound sits mainly just above 8 A, overlapping their + worst. What separates the two is whether a compact minimum near 5.5 A is + accessible at all: PROTACs 1 and 2 have one, PROTAC 3 does not. Report the Rg + distribution and the compact-state population, which is what `rg_basins.py` + computes, rather than the mean. Absolute Rg is also not strictly transferable + across chemical series; theirs is CRBN/BRD4, the runs here are VHL/EGFR. +- **3D PSA** rises as permeability falls: 190-265 A^2 for the two more permeable + compounds against 290-330 A^2 for the poorly permeable one. The rough cutoff + for low-to-moderate permeability is 200-210 A^2, from the 2021 paper. Absolute + values depend on the polar-atom selection; `validate_psa.py` and + `psa_method_probe.py` pin down where this convention sits relative to theirs. +- Permeability correlates inversely with 3D PSA and with intramolecular H-bond + count. diff --git a/CupOFMoCA-PROTAC/md/render_3d.py b/CupOFMoCA-PROTAC/md/render_3d.py new file mode 100644 index 0000000000000000000000000000000000000000..39791fca8bd542240988d6e2d161aa61335cd5a8 --- /dev/null +++ b/CupOFMoCA-PROTAC/md/render_3d.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python +"""Ball-and-stick renders of two PROTAC conformations from a replica trajectory: +the most extended early frame beside the most collapsed late frame. Both frames +are also written out as PDBs for PyMOL / VMD / ChimeraX. + +Usage: + python render_3d.py --rep egfrvhl_rank4/chloroform/rep1 +""" +import argparse, glob, os +import numpy as np +import mdtraj as md +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d.art3d import Line3DCollection + +# CPK element colors and relative sizes +CPK = {"C": ("#4d4d4d", 55), "N": ("#3050f8", 70), "O": ("#ff2d2d", 70), + "S": ("#e6c200", 95), "Cl": ("#1fd01f", 95), "F": ("#6fd0c0", 60), + "H": ("#dddddd", 20)} + +def draw(ax, xyz, syms, bonds, title): + xyz = xyz - xyz.mean(0) + segs = [[xyz[i], xyz[j]] for i, j in bonds] + ax.add_collection3d(Line3DCollection(segs, colors="#888888", linewidths=1.6)) + for el in set(syms): + m = [k for k, s in enumerate(syms) if s == el] + c, sz = CPK.get(el, ("#cc44cc", 60)) + ax.scatter(xyz[m, 0], xyz[m, 1], xyz[m, 2], c=c, s=sz, + edgecolors="white", linewidths=0.4, depthshade=True) + ax.set_title(title, fontsize=12, weight="bold", color="#222", pad=0) + r = np.abs(xyz).max() * 1.05 + ax.set_xlim(-r, r); ax.set_ylim(-r, r); ax.set_zlim(-r, r) + ax.set_box_aspect((1, 1, 1)); ax.axis("off") + +def main(): + p = argparse.ArgumentParser() + p.add_argument("--rep", required=True, help="replica directory, e.g. //rep1") + p.add_argument("--name", default=None, help="run name (default: the run directory's name)") + p.add_argument("--report-ps", type=float, default=10.0, help="DCD frame interval") + a = p.parse_args() + rep = a.rep.rstrip("/") + name = a.name or os.path.basename(os.path.dirname(os.path.dirname(rep))) + + solvated = glob.glob(os.path.join(rep, os.pardir, "*_solvated.pdb"))[0] + traj = md.load(os.path.join(rep, f"{name}_prod.dcd"), top=solvated) + top = traj.topology + # the PROTAC is the one non-water residue with many atoms; water and ions are tiny + lig = [at.index for res in top.residues + if not res.is_water and res.n_atoms > 5 for at in res.atoms] + heavy = [i for i in lig if top.atom(i).element.symbol != "H"] + lset = set(heavy) + remap = {g: k for k, g in enumerate(heavy)} + bonds = [(remap[b0.index], remap[b1.index]) for b0, b1 in top.bonds + if b0.index in lset and b1.index in lset] + syms = [top.atom(i).element.symbol for i in heavy] + + sub = traj.atom_slice(heavy) + rg = md.compute_rg(sub) # nm + n = len(rg); early = slice(0, n // 3); late = slice(2 * n // 3, n) + fe = np.arange(n)[early][np.argmax(rg[early])] # extended + fc = np.arange(n)[late][np.argmin(rg[late])] # collapsed + xyz_A = sub.xyz * 10.0 # nm -> Angstrom + ns = a.report_ps / 1000.0 + + fig = plt.figure(figsize=(13, 6.5), dpi=150); fig.patch.set_facecolor("white") + ax1 = fig.add_subplot(121, projection="3d") + draw(ax1, xyz_A[fe], syms, bonds, f"Extended (t={fe*ns:.1f} ns, Rg={rg[fe]:.2f} nm)") + ax2 = fig.add_subplot(122, projection="3d") + draw(ax2, xyz_A[fc], syms, bonds, f"Collapsed (t={fc*ns:.1f} ns, Rg={rg[fc]:.2f} nm)") + fig.suptitle(f"Free-PROTAC conformations from MD - {name} (heavy atoms, H hidden)", + fontsize=13, weight="bold", color="#222", y=0.97) + out = os.path.join(rep, f"{name}_3d.png") + fig.savefig(out, facecolor="white", bbox_inches="tight"); print("wrote", out) + + sub[fe].save_pdb(os.path.join(rep, f"{name}_extended.pdb")) + sub[fc].save_pdb(os.path.join(rep, f"{name}_collapsed.pdb")) + print(f"extended frame {fe} (Rg {rg[fe]:.2f}), collapsed frame {fc} (Rg {rg[fc]:.2f})") + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/md/rg_basins.py b/CupOFMoCA-PROTAC/md/rg_basins.py new file mode 100644 index 0000000000000000000000000000000000000000..18e5679f66497ddc3b264e3cc9c925b7dc183930 --- /dev/null +++ b/CupOFMoCA-PROTAC/md/rg_basins.py @@ -0,0 +1,123 @@ +#!/usr/bin/env python +"""Rg free-energy profiles and basin populations for one or more finished runs. + +Mean Rg does not separate permeable from non-permeable PROTACs in the J. Med. +Chem. 2022 study: their most permeable compound sits mainly just above 8 A, +overlapping their least permeable one at 7-9.5 A. What separates them is whether +a compact minimum near 5.5 A is populated at all. This script computes, per +compound and solvent, + + PMF(Rg) = -kT ln P(Rg) + +locates its basins (local minima), and reports each basin's position and +population, plus the fraction of frames below 6 and 7 A. + +Ligand selection and the Rg calculation match analyze_conformations.py exactly +(non-water residue with >5 atoms, md.compute_rg on the centred ligand-only +trajectory). + +Usage: + python rg_basins.py egfrvhl_rank4 ms39_ref --solvents chloroform water +""" +import argparse, glob, os +import numpy as np +import mdtraj as md +from scipy.stats import gaussian_kde +from scipy.signal import find_peaks +import matplotlib; matplotlib.use("Agg") +import matplotlib.pyplot as plt + +KT = 0.593 # kcal/mol at 300 K + + +def pooled_rg(run_dir, solvent): + """Rg in Angstrom, pooled over replicas.""" + name = os.path.basename(run_dir.rstrip("/")) + tops = glob.glob(os.path.join(run_dir, solvent, f"{name}_*_solvated.pdb")) + dcds = sorted(glob.glob(os.path.join(run_dir, solvent, "rep*", f"{name}_prod.dcd"))) + if not tops or not dcds: + return None + out = [] + for d in dcds: + tr = md.load(d, top=tops[0]) + lig = [a.index for r in tr.topology.residues + if not r.is_water and r.n_atoms > 5 for a in r.atoms] + lt = tr.atom_slice(lig) + lt.center_coordinates() + out.append(md.compute_rg(lt) * 10.0) # nm -> A + return np.concatenate(out) + + +def basins(rg, prom_frac=0.05): + """Basins = peaks of P(Rg). Returns (grid, pmf, [(rg_position, population)]).""" + grid = np.linspace(rg.min() - 0.3, rg.max() + 0.3, 400) + dens = gaussian_kde(rg, bw_method=0.12)(grid) + pmf = -KT * np.log(np.clip(dens, 1e-12, None)) + pmf -= pmf.min() + pk, _ = find_peaks(dens, prominence=prom_frac * dens.max()) + if len(pk) == 0: + pk = np.array([int(np.argmax(dens))]) + # valley boundaries between adjacent peaks assign frames to basins + edges = [rg.min() - 1e-6] + for a, b in zip(pk[:-1], pk[1:]): + edges.append(grid[a + int(np.argmin(dens[a:b]))]) + edges.append(rg.max() + 1e-6) + out = [] + for i, p in enumerate(pk): + pop = float(((rg > edges[i]) & (rg <= edges[i + 1])).mean()) + out.append((float(grid[p]), pop)) + return grid, pmf, out + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("runs", nargs="+", help="run directories, one per compound") + ap.add_argument("--solvents", nargs="+", default=["chloroform", "water"]) + ap.add_argument("--plot-solvent", default="chloroform", + help="solvent whose PMFs go into the figure") + ap.add_argument("--out", default="rg_basins.png") + a = ap.parse_args() + + print(f"{'compound':22s}{'solv':6s}{'mean':>7s}{'basins (Rg A, pop%)':>44s}{'<6A':>7s}{'<7A':>7s}") + print("-" * 96) + results = {} + for run in a.runs: + label = os.path.basename(run.rstrip("/")) + for sol in a.solvents: + rg = pooled_rg(run, sol) + if rg is None: + continue + grid, pmf, bs = basins(rg) + results[(label, sol)] = (rg, grid, pmf, bs) + btxt = " ".join(f"{p:.2f}({100*w:.0f}%)" for p, w in bs) + print(f"{label:22s}{sol[:5]:6s}{rg.mean():7.2f}{btxt:>44s}" + f"{100*(rg<6.0).mean():6.0f}%{100*(rg<7.0).mean():6.0f}%") + + have = [k for k in results if k[1] == a.plot_solvent] + if not have: + return + fig, ax = plt.subplots(figsize=(11, 6.5), dpi=150) + fig.patch.set_facecolor("white") + cmap = plt.get_cmap("viridis") + for i, key in enumerate(have): + _, grid, pmf, _ = results[key] + ax.plot(grid, pmf, lw=2.2, color=cmap(i / max(1, len(have) - 1)), label=key[0]) + ax.axvspan(5.4, 5.7, color="#009E73", alpha=0.12) + ax.text(5.55, ax.get_ylim()[1] * 0.94, "reference compact\nminimum 5.5-5.6 A", + ha="center", fontsize=8.5, color="#009E73", weight="bold") + ax.axvspan(7.0, 9.5, color="#C0392B", alpha=0.07) + ax.text(8.25, ax.get_ylim()[1] * 0.94, "reference least-permeable\ncompound 7-9.5 A", + ha="center", fontsize=8.5, color="#C0392B", weight="bold") + ax.set_xlabel("Radius of gyration (A)") + ax.set_ylabel("PMF -kT ln P(Rg) (kcal/mol)") + ax.set_title(f"Rg free-energy profiles in {a.plot_solvent}", + fontsize=13, weight="bold", loc="left") + ax.set_ylim(0, 3.2); ax.legend(fontsize=8.5, frameon=False, ncol=2) + ax.grid(alpha=0.25) + fig.tight_layout() + fig.savefig(a.out, facecolor="white") + print(f"\nwrote {a.out}") + + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/md/run_candidate.sh b/CupOFMoCA-PROTAC/md/run_candidate.sh new file mode 100644 index 0000000000000000000000000000000000000000..8e6d093b7fff717992e22ab5bc13ff9f106b7b74 --- /dev/null +++ b/CupOFMoCA-PROTAC/md/run_candidate.sh @@ -0,0 +1,73 @@ +#!/bin/bash +# Detached driver for one PROTAC candidate: chloroform 3 x 50 ns, analysis, +# then water 3 x 50 ns, analysis. The chloroform AM1-BCC charge cache is staged +# into the water run so sqm is only paid once. +# +# run_candidate.sh [WAIT_LOGFILE] [WAIT_MARKER] +# +# With WAIT_LOGFILE and WAIT_MARKER the job queues instead of starting: it polls +# that log until the marker appears (the predecessor on the same GPU printed +# CANDIDATE_DONE), so two jobs never share a card. Waiting greps a file rather +# than process names, which would otherwise match this script's own cmdline. +# WAIT_LOGFILE resolves against this script's directory, not the caller's cwd, +# and after MAXWAIT the job stops waiting and starts anyway, which can put two +# jobs on one card. +set -u + +NAME="$1"; GPU="$2"; SMI="$3"; WLOG="${4:-}"; WMARK="${5:-}" +MD="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +CONDA_ENV="${CONDA_ENV:-protacmd}" +MAXWAIT=$((14*3600)) # never queue longer than this + +if [ -n "${CONDA_SH:-}" ]; then + source "$CONDA_SH" +elif [ -n "${CONDA_EXE:-}" ]; then + source "$(dirname "$(dirname "$CONDA_EXE")")/etc/profile.d/conda.sh" +fi +conda activate "$CONDA_ENV" +cd "$MD" + +log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*"; } + +log "CANDIDATE_START name=$NAME gpu=$GPU" + +if [ -n "$WLOG" ] && [ -n "$WMARK" ]; then + log "QUEUED on GPU $GPU: waiting for '$WMARK' in $WLOG ..." + waited=0 + until grep -q "$WMARK" "$MD/$WLOG" 2>/dev/null; do + sleep 60; waited=$((waited+60)) + if [ "$waited" -ge "$MAXWAIT" ]; then + log "waited ${waited}s without seeing '$WMARK', starting anyway"; break + fi + [ $((waited % 1800)) -eq 0 ] && log " ... still waiting (${waited}s)" + done + log "predecessor finished after ${waited}s, starting $NAME on GPU $GPU" +fi + +nvidia-smi --query-gpu=index,utilization.gpu,memory.used --format=csv,noheader | sed 's/^/[gpu] /' + +run_solvent() { + local sol="$1" + log "=== $NAME / $sol : 3 x 50 ns START ===" + python protac_md.py --solvent "$sol" --replicas 3 --prod-ns 50 \ + --smiles "$SMI" --name "$NAME" --gpu "$GPU" + local rc=$? + log "=== $NAME / $sol : MD finished rc=$rc ===" + if [ $rc -eq 0 ]; then + log "--- $NAME / $sol : analysis START ---" + python analyze_conformations.py --dir "$NAME/$sol" --name "$NAME" + log "--- $NAME / $sol : analysis finished rc=$? ---" + else + log "$NAME / $sol MD failed (rc=$rc), skipping analysis" + fi +} + +run_solvent chloroform +CH="$MD/$NAME/chloroform/ff_cache.json" +if [ -s "$CH" ]; then + mkdir -p "$MD/$NAME/water"; cp -f "$CH" "$MD/$NAME/water/ff_cache.json" + log "staged charge cache -> $NAME/water/ff_cache.json" +fi +run_solvent water + +log "CANDIDATE_DONE name=$NAME" diff --git a/CupOFMoCA-PROTAC/md/ternary_span.py b/CupOFMoCA-PROTAC/md/ternary_span.py new file mode 100644 index 0000000000000000000000000000000000000000..057919e321484f2a8449ae0fb3d3420f59033a65 --- /dev/null +++ b/CupOFMoCA-PROTAC/md/ternary_span.py @@ -0,0 +1,111 @@ +#!/usr/bin/env python +"""Warhead-to-E3 span distribution from the water ensembles. + +Per frame, the distance between the centroid of the warhead core (the group that +occupies the target pocket) and the centroid of the E3 ligand core. In a +productive ternary complex that span has to bridge the two pockets, so the +distribution shows which separations a linker can hold. Water, not chloroform: +the ternary complex forms in the cytosol. + +This is a description of reach, not a degradation score; scoring it needs a +target ternary geometry from docking. Replica spread is printed alongside so +poorly converged runs are visible. + +Default SMARTS are for the EGFR-VHL series (anilinoquinazoline warhead, +4-hydroxyproline E3 anchor); override with --warhead-smarts / --e3-smarts. + +Usage: + python ternary_span.py egfrvhl_rank4 ms39_ref --out span.png +""" +import argparse, glob, os +import numpy as np +import mdtraj as md +from rdkit import Chem +from scipy.stats import gaussian_kde +import matplotlib; matplotlib.use("Agg") +import matplotlib.pyplot as plt + +WH_SMARTS = "c1cc2ncncc2cc1" # quinazoline bicyclic, target pocket +E3_SMARTS = "[#6]1[#6][#6](O)[#6][#7]1" # 4-hydroxypyrrolidine, VHL pocket + + +def anchors(sdf, wh_smarts, e3_smarts): + m = Chem.MolFromMolFile(sdf, removeHs=False) + wh = list(m.GetSubstructMatch(Chem.MolFromSmarts(wh_smarts))) + e3 = list(m.GetSubstructMatch(Chem.MolFromSmarts(e3_smarts))) + syms = [a.GetSymbol() for a in m.GetAtoms()] + return wh, e3, syms + + +def spans_for_run(run, solvent, wh_smarts, e3_smarts): + """Per-replica span arrays (A), or None if the run can't be used.""" + name = os.path.basename(run.rstrip("/")) + tops = glob.glob(os.path.join(run, solvent, "*_solvated.pdb")) + dcds = sorted(glob.glob(os.path.join(run, solvent, "rep*", f"{name}_prod.dcd"))) + if not tops or not dcds: + print(f"{name:14s} no {solvent} data") + return None + wh, e3, sdf_syms = anchors(os.path.join(run, f"{name}.sdf"), wh_smarts, e3_smarts) + if not wh or not e3: + print(f"{name:14s} SMARTS match failed (wh={len(wh)} e3={len(e3)})") + return None + out = [] + for d in dcds: + tr = md.load(d, top=tops[0]) + lig = [a.index for r in tr.topology.residues + if not r.is_water and r.n_atoms > 5 for a in r.atoms] + sub = tr.atom_slice(lig) + if [a.element.symbol for a in sub.topology.atoms] != sdf_syms: + print(f"{name:14s} SDF/MD atom-order mismatch, skipping") + return None + xyz = sub.xyz * 10.0 # nm -> A + out.append(np.linalg.norm(xyz[:, wh, :].mean(1) - xyz[:, e3, :].mean(1), axis=1)) + return out + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("runs", nargs="+", help="run directories, one per compound") + ap.add_argument("--solvent", default="water") + ap.add_argument("--warhead-smarts", default=WH_SMARTS) + ap.add_argument("--e3-smarts", default=E3_SMARTS) + ap.add_argument("--out", default="ternary_span.png") + a = ap.parse_args() + + fig, ax = plt.subplots(figsize=(11, 6.5), dpi=150) + fig.patch.set_facecolor("white") + cmap = plt.get_cmap("viridis") + print(f"{'compound':14s}{'mean span':>10s}{'sd':>6s}{'min':>6s}{'max':>6s}" + f"{'range':>7s}{'rep spread':>12s}") + print("-" * 67) + drawn = 0 + for i, run in enumerate(a.runs): + label = os.path.basename(run.rstrip("/")) + per_rep = spans_for_run(run, a.solvent, a.warhead_smarts, a.e3_smarts) + if not per_rep: + continue + s = np.concatenate(per_rep) + spread = float(np.std([r.mean() for r in per_rep])) + print(f"{label:14s}{s.mean():10.1f}{s.std():6.1f}{s.min():6.1f}" + f"{s.max():6.1f}{s.max()-s.min():7.1f}{spread:12.1f}") + grid = np.linspace(3, 25, 300) + ax.plot(grid, gaussian_kde(s, bw_method=0.15)(grid), lw=2.2, + color=cmap(i / max(1, len(a.runs) - 1)), + label=f"{label} ({s.mean():.0f} A)") + drawn += 1 + if not drawn: + return + ax.set_xlabel("warhead to E3 pharmacophore separation (A)") + ax.set_ylabel("probability density") + ax.set_title("Warhead to E3 span\n" + f"centroid distance between the pocket-binding groups ({a.solvent})", + fontsize=12.5, weight="bold", loc="left") + ax.legend(fontsize=8.5, frameon=False, ncol=2) + ax.grid(alpha=0.25) + fig.tight_layout() + fig.savefig(a.out, facecolor="white") + print(f"\nwrote {a.out}") + + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/md/validate_psa.py b/CupOFMoCA-PROTAC/md/validate_psa.py new file mode 100644 index 0000000000000000000000000000000000000000..ec4d3442c1629f2b9a28939f2349f5a80390ef8c --- /dev/null +++ b/CupOFMoCA-PROTAC/md/validate_psa.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python +"""Sanity check for the 3D PSA code path used by psa_ensemble.py. + +Runs the same element-based PSA (N, O, polar H; Bondi radii; 1.4 A probe; mdtraj +shrake_rupley) on grazoprevir, whose solution-accessible 3D PSA is reported as +144-205 A^2 (174 A^2 for the crystal conformer) using a charge-threshold +selection in PyMOL. Agreement means the two conventions land on the same scale, +so PROTAC numbers from psa_ensemble.py can be read against published values. + +Needs no finished run: grazoprevir and its conformers are generated here, so it +takes seconds. + +Usage: + python validate_psa.py +""" +import numpy as np, tempfile, os +from rdkit import Chem +from rdkit.Chem import AllChem +import mdtraj as md + +# grazoprevir, PubChem CID 44603531 (SMILES field, verbatim) +SMI = "CC(C)(C)[C@H]1C(=O)N2C[C@@H](C[C@H]2C(=O)N[C@@]3(C[C@H]3C=C)C(=O)NS(=O)(=O)C4CC4)OC5=NC6=C(C=CC(=C6)OC)N=C5CCCCC[C@@H]7C[C@H]7OC(=O)N1" + +m = Chem.AddHs(Chem.MolFromSmiles(SMI)) +p = AllChem.ETKDGv3(); p.randomSeed = 1; p.useRandomCoords = True +cids = AllChem.EmbedMultipleConfs(m, numConfs=25, params=p) +AllChem.MMFFOptimizeMoleculeConfs(m) +print(f"grazoprevir: {m.GetNumAtoms()} atoms, {len(cids)} conformers embedded") + +def is_polar(atom): + s = atom.GetSymbol() + if s in ("N", "O"): return True + if s == "H": + return any(nb.GetSymbol() in ("N", "O") for nb in atom.GetNeighbors()) + return False +polar = [a.GetIdx() for a in m.GetAtoms() if is_polar(a)] +print(f"polar atoms (N,O,polar-H): {len(polar)}") + +vals = [] +for cid in cids: + pdb = Chem.MolToPDBBlock(m, confId=cid) + with tempfile.NamedTemporaryFile("w", suffix=".pdb", delete=False) as fh: + fh.write(pdb); path = fh.name + t = md.load(path); os.unlink(path) + sasa = md.shrake_rupley(t, mode="atom", probe_radius=0.14)[0] # nm^2 + vals.append(sasa[polar].sum() * 100.0) # A^2 +vals = np.array(vals) +rg = [] +for cid in cids: + conf = m.GetConformer(cid) + xyz = np.array([list(conf.GetAtomPosition(i)) for i in range(m.GetNumAtoms())]) + xyz -= xyz.mean(0); rg.append(np.sqrt((xyz**2).sum(1).mean())) +rg = np.array(rg) + +print(f"\nelement-based 3D PSA over {len(vals)} conformers:") +print(f" min {vals.min():.0f} median {np.median(vals):.0f} max {vals.max():.0f} A2") +print(f" (most folded conformer, Rg={rg.min():.2f} A): PSA = {vals[np.argmin(rg)]:.0f} A2") +print(f"\nreported (PyMOL, |q|>1 selection): grazoprevir SA 3D PSA = 144-205 A2, crystal 174") +print(f" grazoprevir 2D TPSA (PubChem) = 204 A2") +inrange = ((vals >= 140) & (vals <= 210)).mean() * 100 +print(f"\n-> {inrange:.0f}% of the conformers fall in the reported 144-205 range") diff --git a/CupOFMoCA-PROTAC/requirements.txt b/CupOFMoCA-PROTAC/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..747d929469d9cccdb8354cf1f6b7b75a4a5d27bf --- /dev/null +++ b/CupOFMoCA-PROTAC/requirements.txt @@ -0,0 +1,18 @@ +hydra-core==1.3.2 +imageio==2.31.1 +matplotlib==3.7.1 +networkx==2.8.7 +numpy==1.23 +omegaconf==2.3.0 +overrides==7.3.1 +pandas==1.4 +pyemd==1.0.0 +PyGSP==0.5.1 +pytorch_lightning==2.0.4 +scipy==1.11.0 +setuptools==68.0.0 +torch_geometric==2.3.1 +torchmetrics==0.11.4 +tqdm==4.65.0 +wandb==0.15.4 +seaborn \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/results/README.md b/CupOFMoCA-PROTAC/results/README.md new file mode 100644 index 0000000000000000000000000000000000000000..fde50c79af69407ae4c18ff99fe96ea4d75cb175 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/README.md @@ -0,0 +1,46 @@ +# Reported results + +The generation outputs and scored summaries behind the PROTAC tables. These are the +runs themselves, not a re-derivation: the aggregates here are what the paper reports. + +All runs use the optimized objective, recorded in the logs as the five-slot vector +`1 0 4 2 4` = `[classifier, morgan_fp, SA, chemotype, connectivity]`. The classifier is +applied as a hard mask and the Morgan-FP term is zero, so the three live weights are +`SA 4, chemotype 2, connectivity 4` — the `--weights 4.0 2.0 4.0` the current +`scripts/generate.py` takes. Seeds are 42 / 123 / 456, 100 candidates each. + +## `ablation_all/` — EGFR-VHL + +Five conditions × three seeds. `results.csv` is per-seed, `AGGREGATE.txt` is the +pooled mean ± std (population std, `ddof=0`). + +| condition | what it is | appears as | +|---|---|---| +| `both` | hard constraints + soft guidance; the full configuration | Table 3 EGFR-VHL, Table J8 "Both", Table H1 "Fine-tuned" | +| `hard` | validity gate only, no objective guidance | Table J8 "Hard constraints only" | +| `soft` | objective guidance only, no validity gate | Table J8 "Soft guidance only" | +| `accept_on` | full configuration plus the monotonic acceptance test | Table J3 "Acceptance on" | +| `base` | pre-fine-tuning DeFoG checkpoint | Table H1 "Base" | + +## `ablation_targets/` — BRD9-VHL and CDK6-CRBN + +Per-target directories hold the same per-seed `.smi` outputs plus `ftguid_scored.csv`, +the per-molecule scored table. At the top level: + +- `asm_valid.csv` — assembly validity per target × condition × seed +- `box_results.csv`, `phaseA_eval.csv` — property-window summaries +- `dm_ci.txt` — DegradeMaster mean, 95% CI, median and fraction ≥ 0.5 per target +- `VALIDATION.txt`, `AGGREGATE.txt` — pooled summaries + +## Excluded + +Per-step sampling traces (`*_steps.json`, ~288 MB) and driver logs (`*.gen.log`) are +not included. Re-running `scripts/generate.py` with the seed and weights above +regenerates them. + +## Note on counts + +`dm_ci.txt` reports the DegradeMaster pool as n=281 (BRD9-VHL) and n=265 (CDK6-CRBN), +while Table 3 states 247 and 249 assembly-valid molecules. The DegradeMaster pool is +therefore not identical to the assembly-valid set; check which population each column +was computed over before quoting these numbers. diff --git a/CupOFMoCA-PROTAC/results/ablation_all/AGGREGATE.txt b/CupOFMoCA-PROTAC/results/ablation_all/AGGREGATE.txt new file mode 100644 index 0000000000000000000000000000000000000000..77208ea0907f1d567131d2ac56737095dda46685 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/AGGREGATE.txt @@ -0,0 +1,9 @@ +OPTIMIZED weights 1 0 4 2 4 (both = accept_off = finetuned baseline) + +cond asm_valid SA MW_in_window% DegradeMaster +----------------------------------------------------------------------- +both 85.0 ± 3.6 2.8 ± 0.0 94.7 ± 1.9 0.5 ± 0.0 +hard 11.0 ± 3.7 2.5 ± 0.0 99.3 ± 0.5 0.5 ± 0.0 +soft 0.3 ± 0.5 4.0 ± 0.1 97.0 ± 1.4 0.6 ± 0.0 +accept_on 58.7 ± 3.4 3.0 ± 0.0 94.7 ± 1.2 0.5 ± 0.0 +base 78.7 ± 5.4 3.6 ± 0.2 48.7 ± 2.5 0.5 ± 0.0 diff --git a/CupOFMoCA-PROTAC/results/ablation_all/PROGRESS.txt b/CupOFMoCA-PROTAC/results/ablation_all/PROGRESS.txt new file mode 100644 index 0000000000000000000000000000000000000000..ab552a3484f9ff3af51f55766f6586d79206c566 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/PROGRESS.txt @@ -0,0 +1,9 @@ +start Sun Jul 26 10:40:29 PM EDT 2026 +done [both:42 both:123] @ Sun Jul 26 10:48:02 PM EDT 2026 +done [both:456 hard:42] @ Sun Jul 26 10:56:07 PM EDT 2026 +done [hard:123 hard:456] @ Sun Jul 26 11:04:34 PM EDT 2026 +done [soft:42 soft:123] @ Sun Jul 26 11:12:35 PM EDT 2026 +done [soft:456 accept_on:42] @ Sun Jul 26 11:20:41 PM EDT 2026 +done [accept_on:123 accept_on:456] @ Sun Jul 26 11:28:03 PM EDT 2026 +done [base:42 base:123] @ Sun Jul 26 11:43:03 PM EDT 2026 +done [base:456 ] @ Sun Jul 26 11:57:37 PM EDT 2026 diff --git a/CupOFMoCA-PROTAC/results/ablation_all/accept_on_s123.smi b/CupOFMoCA-PROTAC/results/ablation_all/accept_on_s123.smi new file mode 100644 index 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O=CNN=CC(CC(C(=O)O)C(=O)O)C(=O)Oc1ccccc1 +AssemblyReady COCC1CCC(CC(C)(C)CC(=O)O)(NCCC(=O)O)CC1 +AssemblyReady O=C(O)C1=CC(C2CCCCC2)=C2C=CC2C(=O)C(O)C1C(=O)O +DISCONNECTED CC(=O)O.NC(=O)c1c2cccc1N(c1ccc(C(=O)O)cc1)C2=O +AssemblyReady O=C(O)C1=NC2=C3C4=CC=C(CC1=C2)C43CC(C(=O)O)C(O)C(=O)O +AssemblyReady CCCC(=O)C1(CC)C=CC(O)=CC(C(=O)O)=C1C(=O)O +INVALID +INVALID +AssemblyReady O=C(O)CNS(=O)(=O)C1=Cc2cc(C(=O)O)c3c(c2-3)Cn2cccc21 +INVALID +INVALID +AssemblyReady O=C(O)NCCC12C=CC(=C(C(S)C(=O)O)Cc3cccc1c3)C=C2 +INVALID +AssemblyReady O=C(O)c1cccc(C2(C(=O)O)C(O)=COC=C2c2ccsc2)c1O +INVALID +DISCONNECTED CC=O.NCC(=O)O.O=C(O)O +AssemblyReady O=C(O)c1cc(NC(=O)C2(C(=O)O)CCCCCC2)cs1 +AssemblyReady O=C(O)C(=O)Nc1ccc(C(=O)O)s1 +AssemblyReady O=C(O)OC1=CN2CC(Cc3cccc(c3)CC(C(=O)O)NC1=O)C2 +AssemblyReady CC(=O)OC(C=C(C)C(=O)O)CC(C)C(CCC(C)(C)O)CC(=O)O +AssemblyReady O=C(O)CNC(=O)c1ccc(OCC(=O)O)cc1 +AssemblyReady COc1ccc(CC(Cc2ccccc2C)C(=O)O)cc1CC(=O)O +AssemblyReady O=C(O)Cc1cccc2c3c([nH]cc(C(=O)O)ccc12)CCCC3=O +AssemblyReady CCNS(=O)(=O)c1cnc2coc(C(=O)O)c(C(=CC(=O)O)CC)c1-2 +INVALID +AssemblyReady O=C(O)C1(S)CC(Cc2ccccc2)(C(=O)O)C1 +AssemblyReady O=C(O)c1ccc(N2CCOCCN(CCCO)CC2)c(C(=O)O)c1 +INVALID +AssemblyReady CC(C)(CO)NC(=O)CC(CC(=O)O)CC(=O)O +AssemblyReady COc1ccc2nccc(C=C(SC=S)C(=O)O)c2c1C(=O)C(=O)O +AssemblyReady O=C(O)C1=CS(=O)(=CC2CCC3=CCCCC3C2C(=O)O)C=CC1=O +INVALID +AssemblyReady O=C(O)Cc1ccc(F)cc1C(=O)O +AssemblyReady CC12C(=O)C(C(=O)O)C(C(=O)O)C1(c1ccc(OCCO)cc1)C2=O +INVALID +AssemblyReady Cc1cccc(CCCCN(CCC(=O)O)C(=O)CCCC(=O)O)c1 +AssemblyReady CCC(NC(=O)O)c1cc(C(=O)O)ccc1CC(C)(C)CS(N)(=O)=O +AssemblyReady O=C(O)CCC=c1ccc2c3c1c2C(C(=O)O)=N3 +AssemblyReady CCCCC(CC(=O)O)C1C(O)CC2C(O)OC(C(=O)O)CC2C1C +INVALID +INVALID +AssemblyReady O=C(O)CC(N=C1CCN2CC3=C1C=CCCC2C(O)=C3O)C(=O)O +AssemblyReady CC(=O)OCCC1(C(=O)O)C(C(C)C)NC(=O)C2(C(=O)O)CCCN21 +INVALID +AssemblyReady NC(C(=O)O)C(=O)C(C(=O)O)=C(O)C(=O)Nc1ccccc1O +AssemblyReady COC(=O)C(C)(C)NC(=O)c1cccc(C(CCC(=O)O)C(=O)O)c1 +INVALID +AssemblyReady COc1ccc(C(=O)O)cc1NC(=Cc1cccs1)C(=O)O +AssemblyReady CC(ON1CC(CO)OC(C(=O)O)C1OC(=O)O)C1(N=O)CCCO1 +AssemblyReady CCCC(C)(C)C(=O)C(C(=O)C(=O)O)=C(CO)C(=O)O +AssemblyReady O=C(CCNc1ccc(O)cc1)Nc1c(C(=O)O)cccc1C(=O)O +INVALID +AssemblyReady CC(C)(CCCO)C(C(=O)O)c1ccccc1C(CC(=O)O)C(=O)O +INVALID +AssemblyReady O=C(O)C=C1C=C(F)C=C(C2=C(O)C(=O)C(=O)OC2=O)C1(O)C(=O)O +AssemblyReady O=C(O)C1=CC(c2cccs2)=CC(c2ccccc2)=NC(C(=O)O)C1 +AssemblyReady CCC1=C(CCC(Oc2ccccc2C(=O)O)C(=O)O)NC(=O)CC1 +AssemblyReady O=C(O)CCN(C(=O)O)C(=O)C=C(c1ccccc1)c1ccccc1 +AssemblyReady O=C(O)CCCCNc1cc(C(=O)O)c2c(C(=O)O)c(F)ccc2c1 +AssemblyReady Cc1ccc(COCC(=O)O)c2c(C(=O)O)cc(OCCCO)cc12 +INVALID +AssemblyReady COC(=O)C(CC(=O)O)CC(O)CC(C(=O)O)C(=O)C(C)(C)C(C)C +AssemblyReady O=C(NC(CC1(C(=O)O)COCCCOC1)C(=O)O)C1CCCCC1 +AssemblyReady CC(=O)NCCCCNC(=O)NC(=CC1=C(O)C(C(=O)O)=C1)C(=O)O +INVALID +AssemblyReady CC(=O)OCC(=O)CC(CCC(=O)OCC(=O)NCC(=O)O)C(=O)O +AssemblyReady O=C(O)CCCCN(CC1CCCCC1)CC1(C(=O)O)C=CC=C1 +AssemblyReady CC(CCC(CS)C(=O)O)(OC(=O)CCCC(N)=O)C(=O)O +AssemblyReady O=C(O)c1ccc2c(c1)=C1C3=CC4=CC31C1(C(=O)O)C=C(C1=O)C=2N4 +AssemblyReady O=[SH]OC1C(OC(=O)O)OC(O)(C(CO)OC(=O)O)C1O diff --git a/CupOFMoCA-PROTAC/results/ablation_all/base_s123_clean.smi b/CupOFMoCA-PROTAC/results/ablation_all/base_s123_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..c44f8567068dedc85b45a42f1472c6bba545222a --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/base_s123_clean.smi @@ -0,0 +1,100 @@ +O=C(O)CCN(C(=O)Nc1cccc(C(=O)O)c1)c1cc2ccc1-2 +INVALID +CCCOc1sc(CCC(=O)NC(CN=CN)C(=O)O)cc1C(=O)O +INVALID +Cc1cc(C(O)c2ccc(NC(=O)C(=O)O)cc2)cc(C(=O)O)c1O +INVALID +COC12C=C3C=C4OCOC41C1C=CCC(C3=C2C(=O)O)C1C(=O)O +O=C(O)c1ccc2c(c1)C1(O)N=c3ccc(C(=O)O)cc3=S1OC2=O +INVALID +CC1=CC=CC(Nc2nncs2)(C(=O)O)C(CC(=O)O)=C1 +CC(C)C(=O)OC(C1CCCO1)C(OC(=O)O)OC(=O)O +CC(C)(CC(=O)O)C(NC(=O)C=CC(N)C(=O)O)C(=O)NCCCO +CC(OC12C=CC3=C1C2(C(=O)O)N3S1(=O)=C2C3=CC=C2C1=C3)C(=O)O +INVALID +Cc1c(Cc2cc(NC(=S)NCC(=O)O)cc(C(=O)O)c2)coc1O +CC(C)c1cc(S(=O)(=O)NCCC(=O)O)cc2c(CC(=O)O)noc12 +INVALID +INVALID +CCN(C1=Cc2cc(C)ccc2N=C(C)O1)C(CCC(=O)O)C(=O)O +O=C(O)C=C(NC(=O)CCCCCCC(C(=O)O)C(=O)O)C(=O)O +INVALID +COc1cccc(-c2cccc(CC(=O)O)c2C(=O)O)c1 +COC12C=C3C=C4OC(CC(=O)O)CC(NC(=[SH]3)N1)C42C(=O)O +INVALID +O=C(O)CCC(CC(=O)Oc1cc(C(F)F)cc(C(F)F)c1)C(=O)O +N#CCOC12C(=O)C1(O)c1ccc3cc1C32C(CC(=O)O)C(=O)O +O=C(O)c1ccc2c(c1)[N+]([O-])(O)c1cc(ccc1C(=O)O)C(=O)NO2 +CN(C(=O)C(CC(=O)O)NC(=O)CCCO)C1CCCC(C(=O)O)C1 +CNC(C(=O)O)C(O)(OCC(=O)O)C(=O)c1cccc2ccccc12 +O=C(COC(C(=O)O)C(=O)O)c1ccc2c(c1)C(O)=C2O +O=CNN=CC(CC(C(=O)O)C(=O)O)C(=O)Oc1ccccc1 +COCC1CCC(CC(C)(C)CC(=O)O)(NCCC(=O)O)CC1 +O=C(O)C1=CC(C2CCCCC2)=C2C=CC2C(=O)C(O)C1C(=O)O +CC(=O)O.NC(=O)c1c2cccc1N(c1ccc(C(=O)O)cc1)C2=O +O=C(O)C1=NC2=C3C4=CC=C(CC1=C2)C43CC(C(=O)O)C(O)C(=O)O +CCCC(=O)C1(CC)C=CC(O)=CC(C(=O)O)=C1C(=O)O +INVALID +INVALID +O=C(O)CNS(=O)(=O)C1=Cc2cc(C(=O)O)c3c(c2-3)Cn2cccc21 +INVALID +INVALID +O=C(O)NCCC12C=CC(=C(C(S)C(=O)O)Cc3cccc1c3)C=C2 +INVALID +O=C(O)c1cccc(C2(C(=O)O)C(O)=COC=C2c2ccsc2)c1O +INVALID +CC=O.NCC(=O)O.O=C(O)O +O=C(O)c1cc(NC(=O)C2(C(=O)O)CCCCCC2)cs1 +O=C(O)C(=O)Nc1ccc(C(=O)O)s1 +O=C(O)OC1=CN2CC(Cc3cccc(c3)CC(C(=O)O)NC1=O)C2 +CC(=O)OC(C=C(C)C(=O)O)CC(C)C(CCC(C)(C)O)CC(=O)O +O=C(O)CNC(=O)c1ccc(OCC(=O)O)cc1 +COc1ccc(CC(Cc2ccccc2C)C(=O)O)cc1CC(=O)O +O=C(O)Cc1cccc2c3c([nH]cc(C(=O)O)ccc12)CCCC3=O +CCNS(=O)(=O)c1cnc2coc(C(=O)O)c(C(=CC(=O)O)CC)c1-2 +INVALID +O=C(O)C1(S)CC(Cc2ccccc2)(C(=O)O)C1 +O=C(O)c1ccc(N2CCOCCN(CCCO)CC2)c(C(=O)O)c1 +INVALID +CC(C)(CO)NC(=O)CC(CC(=O)O)CC(=O)O +COc1ccc2nccc(C=C(SC=S)C(=O)O)c2c1C(=O)C(=O)O +O=C(O)C1=CS(=O)(=CC2CCC3=CCCCC3C2C(=O)O)C=CC1=O +INVALID +O=C(O)Cc1ccc(F)cc1C(=O)O +CC12C(=O)C(C(=O)O)C(C(=O)O)C1(c1ccc(OCCO)cc1)C2=O +INVALID +Cc1cccc(CCCCN(CCC(=O)O)C(=O)CCCC(=O)O)c1 +CCC(NC(=O)O)c1cc(C(=O)O)ccc1CC(C)(C)CS(N)(=O)=O +O=C(O)CCC=c1ccc2c3c1c2C(C(=O)O)=N3 +CCCCC(CC(=O)O)C1C(O)CC2C(O)OC(C(=O)O)CC2C1C +INVALID +INVALID +O=C(O)CC(N=C1CCN2CC3=C1C=CCCC2C(O)=C3O)C(=O)O +CC(=O)OCCC1(C(=O)O)C(C(C)C)NC(=O)C2(C(=O)O)CCCN21 +INVALID +NC(C(=O)O)C(=O)C(C(=O)O)=C(O)C(=O)Nc1ccccc1O +COC(=O)C(C)(C)NC(=O)c1cccc(C(CCC(=O)O)C(=O)O)c1 +INVALID +COc1ccc(C(=O)O)cc1NC(=Cc1cccs1)C(=O)O +CC(ON1CC(CO)OC(C(=O)O)C1OC(=O)O)C1(N=O)CCCO1 +CCCC(C)(C)C(=O)C(C(=O)C(=O)O)=C(CO)C(=O)O +O=C(CCNc1ccc(O)cc1)Nc1c(C(=O)O)cccc1C(=O)O +INVALID +CC(C)(CCCO)C(C(=O)O)c1ccccc1C(CC(=O)O)C(=O)O +INVALID +O=C(O)C=C1C=C(F)C=C(C2=C(O)C(=O)C(=O)OC2=O)C1(O)C(=O)O +O=C(O)C1=CC(c2cccs2)=CC(c2ccccc2)=NC(C(=O)O)C1 +CCC1=C(CCC(Oc2ccccc2C(=O)O)C(=O)O)NC(=O)CC1 +O=C(O)CCN(C(=O)O)C(=O)C=C(c1ccccc1)c1ccccc1 +O=C(O)CCCCNc1cc(C(=O)O)c2c(C(=O)O)c(F)ccc2c1 +Cc1ccc(COCC(=O)O)c2c(C(=O)O)cc(OCCCO)cc12 +INVALID +COC(=O)C(CC(=O)O)CC(O)CC(C(=O)O)C(=O)C(C)(C)C(C)C +O=C(NC(CC1(C(=O)O)COCCCOC1)C(=O)O)C1CCCCC1 +CC(=O)NCCCCNC(=O)NC(=CC1=C(O)C(C(=O)O)=C1)C(=O)O +INVALID +CC(=O)OCC(=O)CC(CCC(=O)OCC(=O)NCC(=O)O)C(=O)O +O=C(O)CCCCN(CC1CCCCC1)CC1(C(=O)O)C=CC=C1 +CC(CCC(CS)C(=O)O)(OC(=O)CCCC(N)=O)C(=O)O +O=C(O)c1ccc2c(c1)=C1C3=CC4=CC31C1(C(=O)O)C=C(C1=O)C=2N4 +O=[SH]OC1C(OC(=O)O)OC(O)(C(CO)OC(=O)O)C1O diff --git a/CupOFMoCA-PROTAC/results/ablation_all/base_s42.smi b/CupOFMoCA-PROTAC/results/ablation_all/base_s42.smi new file mode 100644 index 0000000000000000000000000000000000000000..d099182178a3b85afdbec88ab2a75b38d47a9b4d --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/base_s42.smi @@ -0,0 +1,100 @@ +AssemblyReady Cc1c(NCC(=O)O)cccc1C(=O)O +AssemblyReady Cc1cccc(C)c1OCC(=O)Nc1cc(C(=O)O)ccc1C(=O)O +INVALID +AssemblyReady O=C(O)N1CCC2(CCCC2)C(N(C(=O)O)c2ccc(F)c(F)c2)C1 +INVALID +AssemblyReady CCC(N)C(=O)NC(=CC(=O)C1(C(=O)O)CC(C)CC(C)C1)C(=O)O +AssemblyReady C=CC12OC1(C=CC(=O)O)C1CCCCCOC1(C(=O)O)C2(O)CS +INVALID +AssemblyReady O=C(O)CNCc1ccc(C2CCCC2CCCC(O)C(=O)O)cc1 +AssemblyReady O=Nc1c(NS(=O)(=O)c2cccc(O)c2)cc(C(=O)O)cc1C(=O)O +AssemblyReady COCc1c(O)c(CCC(C)=CCCC(=O)O)cc(C(=O)O)cc1=O +AssemblyReady O=C1C=CC=NC=CC=S2(=O)C(CN1)CC(C(=O)O)C2C(=O)O +AssemblyReady COC(=O)c1ccc2c(C)cc(C)c(C(=CC(=O)O)C(=O)O)c2c1O +INVALID +AssemblyReady CCNC(=O)CCc1ccccc1C(OC)C(CC(=O)O)CC(=O)O +AssemblyReady Nc1c2cc(ccc2CC(OC(=O)O)C(=O)O)c2cc(=CC=O)sc12 +AssemblyReady CC12CC3CC(C(=O)O)C4C(=O)C(CC34C)C(CCC1=O)C2C(=O)O +INVALID +AssemblyReady CCc1ccc([N+]([O-])(OC(=O)O)c2ccc(OC)c(C(=O)O)c2)cc1 +INVALID +AssemblyReady CC(C)(NC(=O)c1cc(O)cc(OCC(=O)O)c1)C(=O)O +AssemblyReady CC1C(=O)NC(=O)NC1(CC(=O)O)c1cccc(NC(=O)O)c1 +INVALID +AssemblyReady Cc1cccc(-c2ccc(-c3c(C(=O)O)csc3C(=O)O)cc2)c1O +AssemblyReady O=C(O)C1=CC2(C(=O)O)c3cc4c5cc3CSCCC(=O)N4C52C=C1 +AssemblyReady O=C(O)c1ccc(C=CC(=O)[N+]([O-])(O)c2ccccc2)cc1C(=O)O +AssemblyReady C=C(CN(C(=O)O)C(CCC(=O)O)C(=O)O)NC(CCSC)C(=O)O +AssemblyReady Cc1cc2nc3c(C(=O)O)cccc3c3ccccc3c(C(=O)O)c1-2 +AssemblyReady CC1S2=C3C(c4cc(O)cc(O)c4C(=O)O)=C2C2=CC3(C(=O)O)C21C +INVALID +AssemblyReady CC(CCCC(CCCCC(=O)O)C(=O)O)OCC(O)CCC(=O)O +AssemblyReady O=C(O)C=Cc1cc(SCC(=O)O)ccc1O +AssemblyReady COC1(O)CC2(C(=O)O)CCC3=CC=COC1(CC2O)C(C(=O)O)=C3 +AssemblyReady O=COCOc1c(CC(=O)O)cc(O)c(CC(=O)O)c1OCC(=O)O +AssemblyReady O=C(O)CCC(NC(=O)c1ccc(NCC(=O)O)cc1)C(=O)O +AssemblyReady O=C(O)C(=CO)C(=O)c1ccc(C(=O)O)c(-c2ccccc2)c1 +AssemblyReady CC(CC(=O)O)=c1ccc2c(c1)C=2C(=O)O +INVALID +INVALID +AssemblyReady CC(NC(CNC(=O)CC1CCCCC1O)C(=O)O)C(=O)O +AssemblyReady COC12C=C3C1=CC2(OC)C1(C(=O)O)CC(C(=O)O)CC3C1 +AssemblyReady CCC1=CN(CCCN)C(CCCC(=O)O)(C(=O)O)C(CO)=CC=C1 +AssemblyReady O=C(O)CC1C2=CC34C=C(F)C2(CC1C(=O)O)C1(F)C=CC3(C=C1)O4 +AssemblyReady CC1=CC(NC(CC(C)C)C(=O)O)CC=CC2=C1OC(C(=O)O)C2O +AssemblyReady CC1=CC(=C2CC=CC(S)(C(=O)O)C2)C=C(CO)C(C(=O)O)=C1 +AssemblyReady CC(CCCOO)C(OC(=O)O)C1(C(O)C=O)COC(C(=O)O)CO1 +AssemblyReady Cn1occcc(C(=O)O)cc1-c1cc(C(=O)O)ccc1C(=O)O +AssemblyReady CC(C1OC(CO)C(C(O)C=O)O1)C(C)(CCCC(=O)O)OC(=O)O +AssemblyReady CCCCC(CCC1(C)OC(=O)NC(CCC(=O)O)C(=O)O1)C(=O)O +INVALID +AssemblyReady C=CCC1=C(C(=O)O)C(OC)=C(C(=O)C(=O)O)C1=O +AssemblyReady COc1ccc2c(C(C(=O)O)C(=O)O)c1N2CCN(C)C1CCCC1 +AssemblyReady CC(=O)C(C(=O)O)c1c2c(cn1C)C2=CC(=O)O +AssemblyReady CCC(N)(C(=O)O)C(=O)N(C(CS)C(=O)O)C1(C2=CC(OC)=C2)C=C1 +INVALID +AssemblyReady COc1cc(C(=O)O)c(CO)c2oc(=O)cc(C(=O)NCC(=O)O)c12 +AssemblyReady C=CC(=O)OCCC(OC(=O)C(C(=O)O)C(O)CC)C(C)(O)C(=O)O +AssemblyReady CCCC(C(=O)O)N(CC(=O)O)C(F)(F)CC(=O)O +AssemblyReady CC(C)(C(=O)O)C12C(C(=O)C(=O)O)=C3C1=C2C3(O)c1ccc(O)cc1 +AssemblyReady COCCN(Cc1ccccc1C(=O)O)C(=O)NC(C)C(=O)C(=O)O +AssemblyReady CC1CC(CC(O)CC(=O)O)C(O)C(CCCCCCCC(=O)O)O1 +AssemblyReady Cc1ccc(S(=O)(=O)NC(CC2=CC=COC(C(=O)O)=C2)C(=O)O)s1 +INVALID +AssemblyReady O=C(NCCc1cccc(C(=O)O)c1O)C1=C2OC2(C(=O)O)C=C1 +INVALID +INVALID +AssemblyReady CN(C)CC(C)(C(=O)O)C(CC1=CC=C1)C(=O)O +AssemblyReady O=C(O)C(=O)CC(=O)C(C(=O)O)=C1CC1 +AssemblyReady O=C(O)CCC(C(=O)O)C(=O)N(CC(=O)O)C(=O)c1cccc(O)c1 +AssemblyReady Cn1sc2cccc(C3(C(=O)O)CC(NC(=O)CC(=O)O)C3)c2c1=O +AssemblyReady CN(C1CCC(CC(=O)O)CC1)C(C(=O)NCCC(=O)O)C(C)(C)O +AssemblyReady CC1=COC2(Cc3cc(C(=O)O)ccc3C(=O)N2)C(C(=O)O)=C1 +AssemblyReady NCC1CN(C(=O)O)C1=NC(=O)O +AssemblyReady CC(=O)C(CCc1cc(=O)c(C(=O)O)co1)C(=O)O +AssemblyReady NC1CC[SH]1c1cc(CC(=O)O)ccc1S(=O)(=O)C(=O)O +AssemblyReady C#CCC1CCOC2=C3CC(O)(C(=O)O)C=C2C=CC3(CC(=O)O)O1 +AssemblyReady CC=C(NCC(=O)O)C(=O)OCCC(=O)O +INVALID +AssemblyReady Nc1cc2c(cc1C=O)-c1c-2ccc(C(=O)O)c1NC1(C(=O)O)CC1 +AssemblyReady COCCC1OC(c2ccc(C(=O)O)cc2)OCC1CC(=O)O +AssemblyReady O=C(O)CCCC12C=CC(C3CCCN4CCCCC34)=C1N2C(=O)O +AssemblyReady Cc1cc(C(=O)O)cc2c1NC(C(=O)O)(C(=O)O)C2 +AssemblyReady O=C(O)C1=C2C1=CC1=CC12N=CN1C(=O)C(C(=O)O)CSC1=S +AssemblyReady CCC(=O)OCCCCCc1cc(OCC(=O)O)cc(O)c1C(=O)O +AssemblyReady CCC(C(=O)O)N(CCCNC(=O)COC)c1cc(C(=O)O)no1 +AssemblyReady CCSC1N(C(CC(=O)O)C(=O)O)S(=O)(=O)C2=CC3(CO)OC13C=C2 +AssemblyReady CC1(C)C2CCC3(C)C4CC5OC1C(C(C(=O)O)=CC2C(=O)O)C3C54 +AssemblyReady CCNC(=O)C1(NCNC(=O)O)C=CC(C)(C(=O)O)C=C1 +AssemblyReady CCCC(C(=O)OC)N(CCCCC(=O)O)C(=O)C(=O)O +INVALID +AssemblyReady CC1=CC2=CS134=C1C=C(C(=O)O)C3=C4C13c1c(ccn13)C2CC(=O)O +AssemblyReady CCCC1(C2=C(c3cccn3CC(=O)O)C(O)=C2N)CC(C(=O)O)C1 +INVALID +AssemblyReady CC(=O)Nc1ccc(CNc2cc(C(=O)O)c(C(=O)O)cc2O)cc1 +AssemblyReady CCCC(=O)N1CCCN(C(COC(C)C)C(=O)O)CCC1C(=O)O +AssemblyReady CC(=O)NC(=O)N1CCC(=C(CC(=O)O)C(=O)O)CC1 +AssemblyReady O=C(O)C(CCCCO)(C(=O)O)C1CCCCCC(O)CCCCC1 +AssemblyReady O=C(O)C1=CC=CNC(Cc2ccc(O)cc2)(C(=O)O)C(C(=O)O)=C1 +AssemblyReady CC(NC(=O)N(CC(=O)C(=O)O)C(=O)c1ccc(CO)cc1)C(=O)O +AssemblyReady CC1CCN(C(=O)CC=C(CCC(=O)O)C(=O)O)CC1 diff --git a/CupOFMoCA-PROTAC/results/ablation_all/base_s42_clean.smi b/CupOFMoCA-PROTAC/results/ablation_all/base_s42_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..483b6a2e57481665ed3d387f5560ad2f5fa57d9d --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/base_s42_clean.smi @@ -0,0 +1,100 @@ +Cc1c(NCC(=O)O)cccc1C(=O)O +Cc1cccc(C)c1OCC(=O)Nc1cc(C(=O)O)ccc1C(=O)O +INVALID +O=C(O)N1CCC2(CCCC2)C(N(C(=O)O)c2ccc(F)c(F)c2)C1 +INVALID +CCC(N)C(=O)NC(=CC(=O)C1(C(=O)O)CC(C)CC(C)C1)C(=O)O +C=CC12OC1(C=CC(=O)O)C1CCCCCOC1(C(=O)O)C2(O)CS +INVALID +O=C(O)CNCc1ccc(C2CCCC2CCCC(O)C(=O)O)cc1 +O=Nc1c(NS(=O)(=O)c2cccc(O)c2)cc(C(=O)O)cc1C(=O)O +COCc1c(O)c(CCC(C)=CCCC(=O)O)cc(C(=O)O)cc1=O +O=C1C=CC=NC=CC=S2(=O)C(CN1)CC(C(=O)O)C2C(=O)O +COC(=O)c1ccc2c(C)cc(C)c(C(=CC(=O)O)C(=O)O)c2c1O +INVALID +CCNC(=O)CCc1ccccc1C(OC)C(CC(=O)O)CC(=O)O +Nc1c2cc(ccc2CC(OC(=O)O)C(=O)O)c2cc(=CC=O)sc12 +CC12CC3CC(C(=O)O)C4C(=O)C(CC34C)C(CCC1=O)C2C(=O)O +INVALID +CCc1ccc([N+]([O-])(OC(=O)O)c2ccc(OC)c(C(=O)O)c2)cc1 +INVALID +CC(C)(NC(=O)c1cc(O)cc(OCC(=O)O)c1)C(=O)O +CC1C(=O)NC(=O)NC1(CC(=O)O)c1cccc(NC(=O)O)c1 +INVALID +Cc1cccc(-c2ccc(-c3c(C(=O)O)csc3C(=O)O)cc2)c1O +O=C(O)C1=CC2(C(=O)O)c3cc4c5cc3CSCCC(=O)N4C52C=C1 +O=C(O)c1ccc(C=CC(=O)[N+]([O-])(O)c2ccccc2)cc1C(=O)O +C=C(CN(C(=O)O)C(CCC(=O)O)C(=O)O)NC(CCSC)C(=O)O +Cc1cc2nc3c(C(=O)O)cccc3c3ccccc3c(C(=O)O)c1-2 +CC1S2=C3C(c4cc(O)cc(O)c4C(=O)O)=C2C2=CC3(C(=O)O)C21C +INVALID +CC(CCCC(CCCCC(=O)O)C(=O)O)OCC(O)CCC(=O)O +O=C(O)C=Cc1cc(SCC(=O)O)ccc1O +COC1(O)CC2(C(=O)O)CCC3=CC=COC1(CC2O)C(C(=O)O)=C3 +O=COCOc1c(CC(=O)O)cc(O)c(CC(=O)O)c1OCC(=O)O +O=C(O)CCC(NC(=O)c1ccc(NCC(=O)O)cc1)C(=O)O +O=C(O)C(=CO)C(=O)c1ccc(C(=O)O)c(-c2ccccc2)c1 +CC(CC(=O)O)=c1ccc2c(c1)C=2C(=O)O +INVALID +INVALID +CC(NC(CNC(=O)CC1CCCCC1O)C(=O)O)C(=O)O +COC12C=C3C1=CC2(OC)C1(C(=O)O)CC(C(=O)O)CC3C1 +CCC1=CN(CCCN)C(CCCC(=O)O)(C(=O)O)C(CO)=CC=C1 +O=C(O)CC1C2=CC34C=C(F)C2(CC1C(=O)O)C1(F)C=CC3(C=C1)O4 +CC1=CC(NC(CC(C)C)C(=O)O)CC=CC2=C1OC(C(=O)O)C2O +CC1=CC(=C2CC=CC(S)(C(=O)O)C2)C=C(CO)C(C(=O)O)=C1 +CC(CCCOO)C(OC(=O)O)C1(C(O)C=O)COC(C(=O)O)CO1 +Cn1occcc(C(=O)O)cc1-c1cc(C(=O)O)ccc1C(=O)O +CC(C1OC(CO)C(C(O)C=O)O1)C(C)(CCCC(=O)O)OC(=O)O +CCCCC(CCC1(C)OC(=O)NC(CCC(=O)O)C(=O)O1)C(=O)O +INVALID +C=CCC1=C(C(=O)O)C(OC)=C(C(=O)C(=O)O)C1=O +COc1ccc2c(C(C(=O)O)C(=O)O)c1N2CCN(C)C1CCCC1 +CC(=O)C(C(=O)O)c1c2c(cn1C)C2=CC(=O)O +CCC(N)(C(=O)O)C(=O)N(C(CS)C(=O)O)C1(C2=CC(OC)=C2)C=C1 +INVALID +COc1cc(C(=O)O)c(CO)c2oc(=O)cc(C(=O)NCC(=O)O)c12 +C=CC(=O)OCCC(OC(=O)C(C(=O)O)C(O)CC)C(C)(O)C(=O)O +CCCC(C(=O)O)N(CC(=O)O)C(F)(F)CC(=O)O +CC(C)(C(=O)O)C12C(C(=O)C(=O)O)=C3C1=C2C3(O)c1ccc(O)cc1 +COCCN(Cc1ccccc1C(=O)O)C(=O)NC(C)C(=O)C(=O)O +CC1CC(CC(O)CC(=O)O)C(O)C(CCCCCCCC(=O)O)O1 +Cc1ccc(S(=O)(=O)NC(CC2=CC=COC(C(=O)O)=C2)C(=O)O)s1 +INVALID +O=C(NCCc1cccc(C(=O)O)c1O)C1=C2OC2(C(=O)O)C=C1 +INVALID +INVALID +CN(C)CC(C)(C(=O)O)C(CC1=CC=C1)C(=O)O +O=C(O)C(=O)CC(=O)C(C(=O)O)=C1CC1 +O=C(O)CCC(C(=O)O)C(=O)N(CC(=O)O)C(=O)c1cccc(O)c1 +Cn1sc2cccc(C3(C(=O)O)CC(NC(=O)CC(=O)O)C3)c2c1=O +CN(C1CCC(CC(=O)O)CC1)C(C(=O)NCCC(=O)O)C(C)(C)O +CC1=COC2(Cc3cc(C(=O)O)ccc3C(=O)N2)C(C(=O)O)=C1 +NCC1CN(C(=O)O)C1=NC(=O)O +CC(=O)C(CCc1cc(=O)c(C(=O)O)co1)C(=O)O +NC1CC[SH]1c1cc(CC(=O)O)ccc1S(=O)(=O)C(=O)O +C#CCC1CCOC2=C3CC(O)(C(=O)O)C=C2C=CC3(CC(=O)O)O1 +CC=C(NCC(=O)O)C(=O)OCCC(=O)O +INVALID +Nc1cc2c(cc1C=O)-c1c-2ccc(C(=O)O)c1NC1(C(=O)O)CC1 +COCCC1OC(c2ccc(C(=O)O)cc2)OCC1CC(=O)O +O=C(O)CCCC12C=CC(C3CCCN4CCCCC34)=C1N2C(=O)O +Cc1cc(C(=O)O)cc2c1NC(C(=O)O)(C(=O)O)C2 +O=C(O)C1=C2C1=CC1=CC12N=CN1C(=O)C(C(=O)O)CSC1=S +CCC(=O)OCCCCCc1cc(OCC(=O)O)cc(O)c1C(=O)O +CCC(C(=O)O)N(CCCNC(=O)COC)c1cc(C(=O)O)no1 +CCSC1N(C(CC(=O)O)C(=O)O)S(=O)(=O)C2=CC3(CO)OC13C=C2 +CC1(C)C2CCC3(C)C4CC5OC1C(C(C(=O)O)=CC2C(=O)O)C3C54 +CCNC(=O)C1(NCNC(=O)O)C=CC(C)(C(=O)O)C=C1 +CCCC(C(=O)OC)N(CCCCC(=O)O)C(=O)C(=O)O +INVALID +CC1=CC2=CS134=C1C=C(C(=O)O)C3=C4C13c1c(ccn13)C2CC(=O)O +CCCC1(C2=C(c3cccn3CC(=O)O)C(O)=C2N)CC(C(=O)O)C1 +INVALID +CC(=O)Nc1ccc(CNc2cc(C(=O)O)c(C(=O)O)cc2O)cc1 +CCCC(=O)N1CCCN(C(COC(C)C)C(=O)O)CCC1C(=O)O +CC(=O)NC(=O)N1CCC(=C(CC(=O)O)C(=O)O)CC1 +O=C(O)C(CCCCO)(C(=O)O)C1CCCCCC(O)CCCCC1 +O=C(O)C1=CC=CNC(Cc2ccc(O)cc2)(C(=O)O)C(C(=O)O)=C1 +CC(NC(=O)N(CC(=O)C(=O)O)C(=O)c1ccc(CO)cc1)C(=O)O +CC1CCN(C(=O)CC=C(CCC(=O)O)C(=O)O)CC1 diff --git a/CupOFMoCA-PROTAC/results/ablation_all/base_s456.smi b/CupOFMoCA-PROTAC/results/ablation_all/base_s456.smi new file mode 100644 index 0000000000000000000000000000000000000000..74b92693fdcd93ef46515d3d20a67152feeddba2 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/base_s456.smi @@ -0,0 +1,100 @@ +DISCONNECTED O=C(O)CC1CCC2=C3C=CC=CC321.O=CO +AssemblyReady O=C(O)CC12OC1=C(O)C(=COC(=O)CC[SH](=O)=O)C(C(=O)O)=C2O +AssemblyReady NC(CCC(=O)O)C(=O)O +AssemblyReady CCCC(=C(O)C(=O)O)C(C(=O)O)C(=O)O +INVALID +AssemblyReady CC(O)C1(O)C=CC2(C(=O)O)OC(OC(CC(=O)O)CC(=O)O)CC21 +AssemblyReady O=C(O)CCC(=O)C(=O)C(=O)O +AssemblyReady CN(Cc1ccc(CC(C=O)NC(=O)CSOC(=O)O)cc1)C(=O)O +AssemblyReady CC12CC3CC4(OCC35C3=CS5=C34)C(C(=O)O)(NC(=O)O1)C2C(=O)O +AssemblyReady CC1(CNC(=O)C(=O)O)CCCC(C)(C(=O)O)CCC1 +AssemblyReady Cc1ccc2ccc(C(=O)O)c(O)c2c1-c1ccc(O)cc1C(=O)O +AssemblyReady O=C(O)CCCCCNC(c1ccccc1)c1ccccc1C(=O)O +INVALID +AssemblyReady Nc1cc(C(=O)O)c(C(=O)N(CCO)CCC(=O)O)cc1S(=O)(=O)O +AssemblyReady CC1=C(C(=O)O)C(CCCC(=CC(=O)O)C(=O)O)=C(C(=O)O)CCC1 +AssemblyReady CCN(C)C(=O)C(NC(=O)O)C(O)(CC(=O)O)c1ccc(NC)cc1 +AssemblyReady CC1=C(C(=O)O)C=CC(N)=NC(Nc2cccc(C(=O)O)c2)=C1 +AssemblyReady C=C(OC1CCCC1)C(CC=CCCCCC(=O)O)NC(=O)C(=O)O +AssemblyReady O=CNc1scc([N+]([O-])(O)C(=O)O)c1C(=O)O +AssemblyReady CC(C)(CO)C(C)(C(=O)O)c1cc(-c2ccccc2)ccc1C(=O)O +AssemblyReady COc1ccc(C(=O)O)c(C2=C(C3=CC(O)=C3C(=O)O)O2)c1O +AssemblyReady Cc1cc(C(=C(C#N)C(=O)O)c2cc(CO)c(C(=O)O)o2)ccc1O +AssemblyReady Cc1c(C(=O)NC(CC(=O)O)(NCC(C)C(=O)O)C(=O)O)ncn1C +AssemblyReady O=C(O)C1=CC23C=CC(C(=O)O)(C=C2)C1C(=O)OC3 +AssemblyReady CC1=CC(CC(=O)O)C(C(=O)O)C(C)=C1 +AssemblyReady O=Cc1cc(CC(CO)(CCO)C(=O)O)c2cc(C(=O)O)ccc2c1 +AssemblyReady CCCC(C(=O)O)C(C=O)N(CNC)C(=O)O +INVALID +AssemblyReady CC1CC(CC(=O)O)CCC1OCNC(C(=O)O)C(C)CO +AssemblyReady CC(CCC1C(C)(C)CCC(O)C1(C)CCCCCC(=O)O)C(=O)O +AssemblyReady CC(N)C(=O)N1CCC(C)C(CC(=O)C(CC(N)C(=O)O)C(=O)O)C1 +INVALID +INVALID +AssemblyReady Cc1c(NC(OC(=O)O)C(=O)O)ncn1C(CCCC(=O)O)C(=O)O +AssemblyReady N#Cc1ccccc1-c1cc(C(=O)O)ccc1OC1CC(C(=O)O)C1 +AssemblyReady CCCCC(C(=O)O)C(O)(C(=CC(=O)O)C(=O)O)C(O)CNC(C)=O +AssemblyReady Cc1cc(C(=CC(=O)O)c2ccccc2)cc2c(C(=O)O)cccc12 +AssemblyReady O=C(O)CCCCN(C(C(=O)O)C(F)(F)F)C(F)(F)C(F)(F)C(=O)O +INVALID +AssemblyReady O=CC=C(OC(=O)O)S1(=O)=C2C=C(C3CCC(C(=O)O)N3)C=C2C=C1 +AssemblyReady CC(C)C(Nc1ccc(C=C(O)C(=O)NCC(=O)O)cc1)C(=O)O +AssemblyReady COc1ccc(NC(=O)OCCCC(CC=CC(=O)O)C(=O)O)cc1 +AssemblyReady O=NC1CCC2=CC(C(=O)O)=CC21c1ccc(O)c(C(=O)O)c1O +AssemblyReady NC12C3=C4B=C(C(=O)O)OC45C(=O)N5C(C(=O)O)C4C5=C6N1C64C3=C52 +INVALID +AssemblyReady O=C(O)CCCC(CC(=O)O)OCC(=O)O +DISCONNECTED Cc1nnc2n1N1C(=Nc3cc(C(=O)O)c(O)c1c3)CN2.O=C(O)O +AssemblyReady O=C(O)c1cccc(CC(NS(=O)(=O)C2=CC=COC=C2)C(=O)O)c1 +AssemblyReady Nc1ccc(C(=O)O)cc1C(=O)NC(C(=O)O)c1ccc(C(=O)O)s1 +AssemblyReady CCC(C(=O)O)(C(=O)O)c1cccc(C(F)(F)F)c1C=NNC(C)=O +AssemblyReady O=C(O)CNC(=O)c1cc(-c2ccccc2C(=O)O)c(C(=O)O)s1 +AssemblyReady Cc1c2ccc(N)c(c2C(=O)O)c2cc3cc1c2c3NC(=O)C(=O)O +INVALID +AssemblyReady O=C(O)NCCCOc1cc(C(=O)O)nc(N(O)O)n1 +AssemblyReady O=C(O)CC1=CC2(NC3(C(=O)O)CC3)C(O)=CC=C(CC(=O)O)C2=C1 +DISCONNECTED C.CCCC(C)=C(C(=O)O)C(=O)C(=O)O +AssemblyReady CNc1cc(C(=O)O)cc(N(C)c2cccc(C)c2C(=O)O)c1 +INVALID +AssemblyReady CCC(Oc1c(OCC(=O)O)cc(OC)c(OC)c1COC)C(=O)O +AssemblyReady NC(C(=O)c1ccc(C(=O)O)cc1)=C(CCCCCC(=O)O)C(=O)O +AssemblyReady CC(C)C(O)(C(=O)O)C(O)C(O)COC(=O)CC(C)(C)CCC(=O)O +INVALID +AssemblyReady O=C(O)CC1CCN(c2c3cccc2C(C(=O)O)=CCO3)CC1 +AssemblyReady Cc1cc(=O)n(CCC(CCCC(=O)O)C(=O)O)c(N)nc1=O +AssemblyReady O=C(O)Cc1c(OCc2cccc(C(=O)O)c2)ccc2ccccc12 +AssemblyReady CC(C)(O)C(NC(=O)CC(=O)O)C(C(=O)O)C(COC(=O)O)C(=O)O +AssemblyReady CN(C(=O)C1(C(=O)O)CC1)c1cccc(C(=O)O)c1CO +INVALID +AssemblyReady O=C(O)c1cc(-c2ccccc2-c2ccccc2C(=O)O)ccc1O +AssemblyReady CC(C)(C(=O)O)C(=O)N(CCCCC(=O)OOC(=O)CN)CC(=O)O +AssemblyReady O=C(O)CNC(=O)c1cccc(-c2sc3ccccc3c2C(=O)O)c1 +INVALID +AssemblyReady CNC(=O)N(C)C(=O)NC(Cn1c(C)nc(O)c1C(=O)O)C(=O)O +AssemblyReady O=C(O)Nc1ccc2cc1C=CC=C(C(=O)O)C=CNC(=O)C(=O)N2 +AssemblyReady CCc1ccc([N+](=O)[O-])c(N(C(=O)O)C(C(=O)O)C(N)CC)c1 +AssemblyReady Cc1ccc2c(c1)C(c1ccccc1C(=O)O)([SH](=O)=O)C=C2C(=O)O +INVALID +AssemblyReady COc1c(C)c(C(=O)CC(=O)O)cc(C=CC(=O)O)c1OCCCO +AssemblyReady O=C(O)CCNC(=S)Nc1ccc(C=C(O)C(=O)O)c2ccccc12 +AssemblyReady O=C(O)CCNC(=O)c1[nH]sc(-c2cccs2)nc(C(=O)O)cc1O +AssemblyReady N#CC(=C1CCCCC1)c1c(C(=O)O)cccc1C(=O)O +AssemblyReady CCC=C(C(=O)O)C1(C(=O)O)NC2C=C3C4=CC3(C#N)C(F)=CC41O2 +AssemblyReady NS(=O)(=O)c1cc2cn(C=CC(=O)O)c3ccccc3c-2c1C(=O)O +AssemblyReady CCN(C(=O)CC(C)(CC(=O)O)C(=O)OC)c1ccc(C(=O)O)cc1 +AssemblyReady O=C1C[SH]2CC3=C(C(=O)O)C4=CC35CC3=CC2(C(=O)O)C45C(=C3)CN1 +AssemblyReady COc1ccc(NC(=O)CC(C(=O)O)(C(=O)O)c2ccccc2)cc1 +AssemblyReady Cc1cc(C)n2oc3c(C(=O)O)c4c(c(C(=O)O)c34)c(=O)occ2c1 +AssemblyReady O=C(O)Nc1cc(C(=O)O)c(OC(F)(F)F)c(-c2ccccc2)c1 +AssemblyReady COC1CCC(C(=O)O)C(C(CCNC(=N)NCN)C(N)C(=O)O)C1 +AssemblyReady O=C(O)c1cccc(CNC(=O)c2cccc(C(=O)O)c2)c1 +AssemblyReady CN(CC(=O)O)CC(=O)CC(=O)O +AssemblyReady NS(=O)(=O)c1c(NCC(=O)O)ccc(OC(=O)O)c1NCC=CC=O +AssemblyReady O=C(O)CC(O)C1OC2CC3=CCCSC(C=CCC31)C=C2C(=O)O +AssemblyReady O=C(O)CC(NC1=S2CN12)c1cccc(C(=O)O)c1 +AssemblyReady O=C(O)CCNCC(O)=C(NC(=O)CCCC1=CCCCC1)C(=O)O +AssemblyReady CCOC(=O)CNC(=C(C)C(=O)O)c1cc(C(C)=O)cc(C(=O)O)c1 +INVALID +AssemblyReady Cc1nc(-c2cc(CC(=O)O)ccc2COCC(=O)O)cs1 +AssemblyReady CC(CC(=O)O)NC(=O)C(CC(=O)O)C(C)S(=O)C(C)CO +AssemblyReady O=C(O)OCC1CC(O)C(n2c3cnc2=NC(SOC(=O)O)=NC=3)O1 diff --git a/CupOFMoCA-PROTAC/results/ablation_all/base_s456_clean.smi b/CupOFMoCA-PROTAC/results/ablation_all/base_s456_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..cdeac893fa8643e1a74c2fe7f1f6e0201995591f --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/base_s456_clean.smi @@ -0,0 +1,100 @@ +O=C(O)CC1CCC2=C3C=CC=CC321.O=CO +O=C(O)CC12OC1=C(O)C(=COC(=O)CC[SH](=O)=O)C(C(=O)O)=C2O +NC(CCC(=O)O)C(=O)O +CCCC(=C(O)C(=O)O)C(C(=O)O)C(=O)O +INVALID +CC(O)C1(O)C=CC2(C(=O)O)OC(OC(CC(=O)O)CC(=O)O)CC21 +O=C(O)CCC(=O)C(=O)C(=O)O +CN(Cc1ccc(CC(C=O)NC(=O)CSOC(=O)O)cc1)C(=O)O +CC12CC3CC4(OCC35C3=CS5=C34)C(C(=O)O)(NC(=O)O1)C2C(=O)O +CC1(CNC(=O)C(=O)O)CCCC(C)(C(=O)O)CCC1 +Cc1ccc2ccc(C(=O)O)c(O)c2c1-c1ccc(O)cc1C(=O)O +O=C(O)CCCCCNC(c1ccccc1)c1ccccc1C(=O)O +INVALID +Nc1cc(C(=O)O)c(C(=O)N(CCO)CCC(=O)O)cc1S(=O)(=O)O +CC1=C(C(=O)O)C(CCCC(=CC(=O)O)C(=O)O)=C(C(=O)O)CCC1 +CCN(C)C(=O)C(NC(=O)O)C(O)(CC(=O)O)c1ccc(NC)cc1 +CC1=C(C(=O)O)C=CC(N)=NC(Nc2cccc(C(=O)O)c2)=C1 +C=C(OC1CCCC1)C(CC=CCCCCC(=O)O)NC(=O)C(=O)O +O=CNc1scc([N+]([O-])(O)C(=O)O)c1C(=O)O +CC(C)(CO)C(C)(C(=O)O)c1cc(-c2ccccc2)ccc1C(=O)O +COc1ccc(C(=O)O)c(C2=C(C3=CC(O)=C3C(=O)O)O2)c1O +Cc1cc(C(=C(C#N)C(=O)O)c2cc(CO)c(C(=O)O)o2)ccc1O +Cc1c(C(=O)NC(CC(=O)O)(NCC(C)C(=O)O)C(=O)O)ncn1C +O=C(O)C1=CC23C=CC(C(=O)O)(C=C2)C1C(=O)OC3 +CC1=CC(CC(=O)O)C(C(=O)O)C(C)=C1 +O=Cc1cc(CC(CO)(CCO)C(=O)O)c2cc(C(=O)O)ccc2c1 +CCCC(C(=O)O)C(C=O)N(CNC)C(=O)O +INVALID +CC1CC(CC(=O)O)CCC1OCNC(C(=O)O)C(C)CO 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+O=C(O)CCCCCCCCCC(=O)O +CC.CC(=O)O.NCN.O=C(O)CC(=O)O +C.CC(=O)O.CCC(=O)O.N.N.O=CO +INVALID +CNCN(CC(=O)O)C(C)C(=O)O +CCCCc1ccn(C)n1.CCOC(CC(=O)O)C(=O)O +CCCCC(=O)O.CCCCCNC(C)=O.COCCOCC(=O)O +CC(C)OCC(C(=O)O)C(=O)O +CN1CCC(C(=O)O)CC1.O=C(O)CCCCCOC1CCNCC1 +CCC(=O)O.CCOCCOC.CCOCCOCC(=O)O +CCOCCOC.NC(C(=O)O)C(=O)O +C.CC(N)(C(=O)O)C(=O)O +C.CC.O.O=CO.O=CO +CCC(=O)N=CCOCC(=O)O.CCCO.O=C(O)CO +CNC.O=C(O)CC(=O)O +C.COCCOC(=O)O.O.O=CC(=O)O +CC(=O)O.CCOCCNC(=O)CC(=O)O +CNC(C)=O.COC.O=CO.O=CO +COCCOCCNCCC(C(=O)O)C(=O)O +O=C(O)NCCOCCNC(=O)O +CC(=O)O.CCCC(=O)O +C.O=C(O)CNCC(=O)O +CCC(=O)NCCCC(=O)O.O=C(O)COCCO +NC(=O)CCC(=O)O.O=C(O)CO +C.CCC.O=C(O)CC(=O)O +C.CCC.O=C(O)c1cc2c3cc1C(C(=O)O)N23 +C=CCCC(=O)O.CNC(=O)C(=O)O +CCOCCC(=O)O.O=C(O)OCCOCCOO +INVALID +C.CC.CCOCC(C)O.O=C(O)CC(=O)O +INVALID +CC(OCCC(=O)O)C(=O)O +C.C.O=C(O)CCC(=O)O +C.CC(=O)O.CCC(CC)NCC(=O)O +CC.CCC=O.O=CC(CCCCCCC(=O)O)C(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_all/hard_s456.smi b/CupOFMoCA-PROTAC/results/ablation_all/hard_s456.smi new file mode 100644 index 0000000000000000000000000000000000000000..a309bd55c2e53fcf190a2f1e91c774fa71b1bb81 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/hard_s456.smi @@ -0,0 +1,100 @@ +INVALID +DISCONNECTED CC(C(=O)O)C(=O)O.CCCCC.CCCCOCCOCCOC +DISCONNECTED C.CC(=O)O.CCC(=O)O +DISCONNECTED C.CC.CCOCC.O=C(O)C(=CO)C(=O)O +DISCONNECTED CC(=O)O.CC=O.CCC.NCC(=O)O +INVALID +DISCONNECTED CC(=O)O.CCC=O.CCOCCCCC(C)C(=O)O +DISCONNECTED CC.CCNC(=O)O.O=CO +DISCONNECTED COCCOCCOC.O=C(O)OCCOCCOCCO.O=CO +DISCONNECTED C.CC(=O)O.CCCCCC(=O)O +INVALID +AssemblyReady CC(CC(=O)O)C(C)C(=O)O +DISCONNECTED CCCCOCC.O=C(O)CCCNC(=O)O +DISCONNECTED CCCCCC.O=CO.O=CO +DISCONNECTED CCC(=O)O.CCC(=O)O +DISCONNECTED CCC(=O)O.CCNC(=O)COCCOCC(=O)O +DISCONNECTED CCC(=O)NCC(=O)O.CCCCC(=O)O +INVALID +DISCONNECTED CC.CC(=O)O.CCCCCC.O=C(O)CCO +DISCONNECTED CCCNC(=O)O.O=CO +DISCONNECTED CC.CCCC(=O)NCC.O.O=CO.O=CO +DISCONNECTED CCn1cc(C=O)nn1.NCC(=O)O.O=CO +DISCONNECTED CC.CC.O.O=CO.O=CO +DISCONNECTED CC(=O)O.CC(=O)O.COCCOCCCN +AssemblyReady O=C(O)CCCCCCCOCCC(=O)O +INVALID +DISCONNECTED C1CCNCC1.CCCN.O=C(O)CCC(=O)O +DISCONNECTED CCn1nnc(CO)c1C(=O)O.COCC(=O)O +AssemblyReady CCOCCOCCOCC(C(=O)O)C(CC)C(=O)O +AssemblyReady CC(=O)NCCC(C(=O)O)C(=O)O +DISCONNECTED CC=O.CCCC(=O)O.O=CO +DISCONNECTED C.Cc1cn(C)nn1.O=CO.O=CO +DISCONNECTED CC.CC(=O)O.CC(=O)O +DISCONNECTED C=CC.NC(O)C(C(=O)O)C(=O)O +DISCONNECTED C#CCCCCCCCC(=O)O.CCOCCC(=O)O +DISCONNECTED CCC(=O)O.CCCC=O.CO.O=CO +DISCONNECTED CCC1(CC)CCNCC1.O=CCCC(=O)O.O=CO +DISCONNECTED CCCC(=O)CC.O=C(O)CCC(=O)O +DISCONNECTED CC.CC(C(=O)O)C(C)C(=O)O +DISCONNECTED CCn1cc(CCC(=O)O)nn1.O=CCOCCOCCO.O=CO +DISCONNECTED CCC(=O)O.CCC1=CC(OCCOCC(=O)O)CC=C1 +DISCONNECTED CC(=O)O.CC=O.NCCCCCC(=O)O +DISCONNECTED CCC(=O)O.CCC(=O)O.CCN +DISCONNECTED CC.CCOCCNC(=O)CCC(=O)O.CCOCCOCC(=O)O +DISCONNECTED CCOCCO.N.O=CO.O=CO +DISCONNECTED CC.CCC(C(=O)O)C(=O)O +DISCONNECTED NCCN.NCO.O=CCCNCCC(=O)O.O=CO +AssemblyReady CCCCCC(C)C(CCCCC(=O)O)C(=O)O +DISCONNECTED CCN.O=C(O)CC(=O)O +DISCONNECTED CCC.CCOCCOCC.O=C(O)COCC(=O)O +DISCONNECTED CCO.O=C(O)CC(=O)O +DISCONNECTED C.CCCCC(=O)O.CCCCC(=O)O.N +DISCONNECTED CC(=O)O.CC(=O)O.CCC +DISCONNECTED C.CCC(=O)O.CCCC(=O)O +INVALID +AssemblyReady O=C(O)NCCNC(=O)O +DISCONNECTED C#CCOCC(OCC(OCCOCC)C(=O)O)C(=O)O.CC +DISCONNECTED C1COCCO1.O=C(O)NCCOCCOCCOCCNC(=O)O +AssemblyReady NCC(CCCCCOCC(=O)O)C(=O)O +DISCONNECTED N.O=CCC(=O)O.O=CO +DISCONNECTED CC(N)=O.CCC(=O)O.CCCC(=O)O +INVALID +DISCONNECTED CCC(=O)O.O=C(O)CN1CCNCC1 +DISCONNECTED CC=O.CCCC(=O)O.O=CO +DISCONNECTED CC(=O)O.CCCCCCCC(=O)O.CCCCN +DISCONNECTED CC.CC(=O)O.CC(=O)O +DISCONNECTED CCC(CC=O)n1cc(CCC(=O)O)nn1.O=CO +DISCONNECTED C.C.C.CC.O=CO.O=CO +DISCONNECTED CC.CC(=CC(=O)O)C(=O)O +AssemblyReady O=C(O)C1CCC1C(=O)O +DISCONNECTED CCCC(=O)NCC(C(=O)O)C(=O)O.CN +DISCONNECTED C.CC(=O)O.CCC(=O)O +INVALID +DISCONNECTED C.CC(=O)O.O.O=C(O)CO +DISCONNECTED C.COCCOCCOC.O.O=C(O)N1CCN(C(=O)O)CC1 +DISCONNECTED C.C.CC(=O)O.Cc1cc(O)c(C(=O)O)cc1C +DISCONNECTED CC(=O)O.CCN(CCCC(=O)O)C(=O)CCC=O +DISCONNECTED CC.N.O=C(O)CC(=O)O +DISCONNECTED CCCC(=O)O.CCCCCC(=O)O +DISCONNECTED C.C.CC.O=CO.O=CO +AssemblyReady NCCC(=C(CC(=O)O)C(=O)O)C1COCCOCCO1 +DISCONNECTED CC.O=CCCCCC(=O)O.O=CO +DISCONNECTED CC(=O)O.CC(=O)O.NCC=O +DISCONNECTED CCCC.O=CCCCCCCC(=O)O.O=CO +DISCONNECTED CC(=O)O.CCOCCOCCOCCC(=O)O +DISCONNECTED CCC(CC(=O)O)C(=O)O.N +DISCONNECTED C.CC.COCC(=O)O.NC(=O)CC(=O)O +DISCONNECTED CCCN.O=C(O)NCCO.O=CO +AssemblyReady O=C(O)C#CN1CCN(CC2OCC2OCC(=O)O)CC1 +DISCONNECTED C.CCN.O=C(O)CC(=O)O +DISCONNECTED CCOCCC(=O)O.NCC(=O)O +AssemblyReady NCCOCCNC(C(=O)O)C1(CC(=O)O)CCNCC1 +DISCONNECTED O=C(O)CCC(=O)O.c1nn2cc1CCCCCC2 +DISCONNECTED C.O=C(O)C#CCCC(=O)O +DISCONNECTED C.CCNCC.CN.O=C(O)CCC(=O)O +DISCONNECTED CCCNCC(=O)O.CCN(C)N=CC(=O)O +AssemblyReady CC(CCC(=O)O)C(=O)O +DISCONNECTED CC(=O)O.O=CCNCCO.O=CO +DISCONNECTED C.C.CC(CC(=O)O)C(=O)O +DISCONNECTED CC.CC1CO1.NC(=O)O.O.O=CO diff --git a/CupOFMoCA-PROTAC/results/ablation_all/hard_s456_clean.smi b/CupOFMoCA-PROTAC/results/ablation_all/hard_s456_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..3711539f3d9c32d9e74b7fa8f3650144d6dcde2f --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/hard_s456_clean.smi @@ -0,0 +1,100 @@ +INVALID +CC(C(=O)O)C(=O)O.CCCCC.CCCCOCCOCCOC +C.CC(=O)O.CCC(=O)O +C.CC.CCOCC.O=C(O)C(=CO)C(=O)O +CC(=O)O.CC=O.CCC.NCC(=O)O +INVALID +CC(=O)O.CCC=O.CCOCCCCC(C)C(=O)O +CC.CCNC(=O)O.O=CO +COCCOCCOC.O=C(O)OCCOCCOCCO.O=CO +C.CC(=O)O.CCCCCC(=O)O +INVALID +CC(CC(=O)O)C(C)C(=O)O +CCCCOCC.O=C(O)CCCNC(=O)O +CCCCCC.O=CO.O=CO +CCC(=O)O.CCC(=O)O +CCC(=O)O.CCNC(=O)COCCOCC(=O)O +CCC(=O)NCC(=O)O.CCCCC(=O)O +INVALID +CC.CC(=O)O.CCCCCC.O=C(O)CCO +CCCNC(=O)O.O=CO +CC.CCCC(=O)NCC.O.O=CO.O=CO +CCn1cc(C=O)nn1.NCC(=O)O.O=CO +CC.CC.O.O=CO.O=CO +CC(=O)O.CC(=O)O.COCCOCCCN +O=C(O)CCCCCCCOCCC(=O)O +INVALID +C1CCNCC1.CCCN.O=C(O)CCC(=O)O +CCn1nnc(CO)c1C(=O)O.COCC(=O)O +CCOCCOCCOCC(C(=O)O)C(CC)C(=O)O +CC(=O)NCCC(C(=O)O)C(=O)O +CC=O.CCCC(=O)O.O=CO +C.Cc1cn(C)nn1.O=CO.O=CO +CC.CC(=O)O.CC(=O)O +C=CC.NC(O)C(C(=O)O)C(=O)O +C#CCCCCCCCC(=O)O.CCOCCC(=O)O +CCC(=O)O.CCCC=O.CO.O=CO +CCC1(CC)CCNCC1.O=CCCC(=O)O.O=CO +CCCC(=O)CC.O=C(O)CCC(=O)O +CC.CC(C(=O)O)C(C)C(=O)O +CCn1cc(CCC(=O)O)nn1.O=CCOCCOCCO.O=CO +CCC(=O)O.CCC1=CC(OCCOCC(=O)O)CC=C1 +CC(=O)O.CC=O.NCCCCCC(=O)O +CCC(=O)O.CCC(=O)O.CCN +CC.CCOCCNC(=O)CCC(=O)O.CCOCCOCC(=O)O +CCOCCO.N.O=CO.O=CO +CC.CCC(C(=O)O)C(=O)O +NCCN.NCO.O=CCCNCCC(=O)O.O=CO +CCCCCC(C)C(CCCCC(=O)O)C(=O)O +CCN.O=C(O)CC(=O)O +CCC.CCOCCOCC.O=C(O)COCC(=O)O +CCO.O=C(O)CC(=O)O +C.CCCCC(=O)O.CCCCC(=O)O.N +CC(=O)O.CC(=O)O.CCC +C.CCC(=O)O.CCCC(=O)O +INVALID +O=C(O)NCCNC(=O)O +C#CCOCC(OCC(OCCOCC)C(=O)O)C(=O)O.CC +C1COCCO1.O=C(O)NCCOCCOCCOCCNC(=O)O +NCC(CCCCCOCC(=O)O)C(=O)O +N.O=CCC(=O)O.O=CO +CC(N)=O.CCC(=O)O.CCCC(=O)O +INVALID +CCC(=O)O.O=C(O)CN1CCNCC1 +CC=O.CCCC(=O)O.O=CO +CC(=O)O.CCCCCCCC(=O)O.CCCCN +CC.CC(=O)O.CC(=O)O +CCC(CC=O)n1cc(CCC(=O)O)nn1.O=CO +C.C.C.CC.O=CO.O=CO +CC.CC(=CC(=O)O)C(=O)O +O=C(O)C1CCC1C(=O)O +CCCC(=O)NCC(C(=O)O)C(=O)O.CN +C.CC(=O)O.CCC(=O)O +INVALID +C.CC(=O)O.O.O=C(O)CO +C.COCCOCCOC.O.O=C(O)N1CCN(C(=O)O)CC1 +C.C.CC(=O)O.Cc1cc(O)c(C(=O)O)cc1C +CC(=O)O.CCN(CCCC(=O)O)C(=O)CCC=O +CC.N.O=C(O)CC(=O)O +CCCC(=O)O.CCCCCC(=O)O +C.C.CC.O=CO.O=CO +NCCC(=C(CC(=O)O)C(=O)O)C1COCCOCCO1 +CC.O=CCCCCC(=O)O.O=CO +CC(=O)O.CC(=O)O.NCC=O +CCCC.O=CCCCCCCC(=O)O.O=CO +CC(=O)O.CCOCCOCCOCCC(=O)O +CCC(CC(=O)O)C(=O)O.N +C.CC.COCC(=O)O.NC(=O)CC(=O)O +CCCN.O=C(O)NCCO.O=CO +O=C(O)C#CN1CCN(CC2OCC2OCC(=O)O)CC1 +C.CCN.O=C(O)CC(=O)O +CCOCCC(=O)O.NCC(=O)O +NCCOCCNC(C(=O)O)C1(CC(=O)O)CCNCC1 +O=C(O)CCC(=O)O.c1nn2cc1CCCCCC2 +C.O=C(O)C#CCCC(=O)O +C.CCNCC.CN.O=C(O)CCC(=O)O +CCCNCC(=O)O.CCN(C)N=CC(=O)O +CC(CCC(=O)O)C(=O)O +CC(=O)O.O=CCNCCO.O=CO +C.C.CC(CC(=O)O)C(=O)O +CC.CC1CO1.NC(=O)O.O.O=CO diff --git a/CupOFMoCA-PROTAC/results/ablation_all/results.csv b/CupOFMoCA-PROTAC/results/ablation_all/results.csv new file mode 100644 index 0000000000000000000000000000000000000000..b7a15a1cbc757fda9659685d06f72f5e1d44065c --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/results.csv @@ -0,0 +1,16 @@ +cond,seed,asm_valid,sa_mean,mw_in_window,dm_mean +both,42,87,2.76,92,0.474 +both,123,80,2.73,96,0.517 +both,456,88,2.79,96,0.534 +hard,42,15,2.52,99,0.544 +hard,123,6,2.58,99,0.526 +hard,456,12,2.55,100,0.531 +soft,42,0,3.86,95,0.605 +soft,123,0,4.06,98,0.614 +soft,456,1,4.08,98,0.534 +accept_on,42,60,3.03,93,0.528 +accept_on,123,54,3.07,96,0.473 +accept_on,456,62,2.95,95,0.508 +base,42,82,3.80,46,0.537 +base,123,71,3.62,48,0.517 +base,456,83,3.38,52,0.537 diff --git a/CupOFMoCA-PROTAC/results/ablation_all/soft_s123.smi b/CupOFMoCA-PROTAC/results/ablation_all/soft_s123.smi new file mode 100644 index 0000000000000000000000000000000000000000..1b262db3c7296cce523e05c6d94b5489d2b2dfac --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/soft_s123.smi @@ -0,0 +1,100 @@ +Valid C=C1C=CC1=COCCCN +INVALID +INVALID +INVALID +INVALID +Valid CCC1CCCC(C)OCC1 +DISCONNECTED CCCC(NCC)C(O)N(C)C.O +Valid CC(C)C1(C)CCC2C(=O)CCCc3cn2c1n3 +DISCONNECTED CCCCCCCCCCCC12CC3=CC1=S(CC)C=C32.N +INVALID +Valid CCCCCC1COC1(CO)COC +DISCONNECTED C=C=O.CCNCCC(=CC(C)O)CNCCN(N)CC +INVALID +INVALID +Valid CCCC(CO)(N(C)O)N(C)C(C)C +DISCONNECTED C.N=C1NOC1CCC=O +DISCONNECTED C.COC(N)C(C)C(C)C +DISCONNECTED CCN(CCO)COO.CO +DISCONNECTED C.CCNCC(CC)OCC +DISCONNECTED C.COCCNCCCC1NNc2ccc(cc2N)O1 +INVALID +DISCONNECTED C.C.C=C=O.C=CCOO +DISCONNECTED CC(C)CC(C)(C)CN.O +Valid CCCOC(C)=COCC +DISCONNECTED C.C=CCC(C)C(C)C.O +DISCONNECTED C.C.C.C.C.CC=CCC +DISCONNECTED C.CCON=CC(C)=CCOC1CC1 +Valid CCCNN=CNC1(COC)CCC1C +INVALID +INVALID +DISCONNECTED C.C.C.C=C.CCCOC +Valid COC(C=O)OCCC1CCCCN1C +INVALID +Valid C=CC(C)C(C=C1N=C=CCCO1)=CC=O +DISCONNECTED C.C=C1COC(C#CN)(C(O)CC(C)O)O1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCCN1CCOCCCCCCN(C)CC1 +DISCONNECTED C.CC(OC(F)C(F)F)C(C)(C)CCO +Valid CCC(C)C(C)(N)C(C)OC +DISCONNECTED C.C.CCC(C)O.O.O=O +DISCONNECTED C.C.CCN(C)CC(C)C +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CCCC(C)(CC)CC.N +INVALID +INVALID +INVALID +DISCONNECTED C.C.NC1CCC2(CO2)C1 +INVALID +Valid CNC1CC1(C)C12CCN1C(C(C)O)COC2 +Valid C=C(NC)C(CC)=C(OC)C(=C)OO +Valid C=C(NCOC)C(C)CCO +DISCONNECTED C.C=C1C=NCON(CCOC)C(OC)=N1 +Valid CCOC(N)(C(O)CC)C(C)C(C)C +Valid CCC(N)=C1C(CCO)NN1C +Valid CC1CCC2C#CCN3CC(N4CCCC4)OC4(CN(CCC14)C2)C3 +INVALID +Valid CCCCC(=O)NC(C)C1CCCCC2CC21CO +Valid CC(O)C(C)C(C)CCCN(C)CCO +DISCONNECTED C.CCCCC(OCCO)OC(C)CCC +DISCONNECTED C.C.CCC1(C)C(=O)C1O +Valid NC1C=C(O)C1CCC1(O)CCCCC1 +DISCONNECTED C.COCCC1C=C(C)OC1 +INVALID +DISCONNECTED C.C=NCC(O)C(C)CC.O +INVALID +Valid CNC(N)CC(C)(CCN)NC +DISCONNECTED C.C.CCNCC1CCC1OC1OOC1=O +DISCONNECTED C.CNC(=O)C1(N(CCN)CCO)COC1C +INVALID +INVALID +DISCONNECTED C.C=NCC(C(C)CCC)C1CCNC1 +Valid CCCOCCOC1COC2CNCC[N+]([O-])(C(C)=O)C2COC1C +DISCONNECTED C.CCOCC1(O)CC(C)OCN1C(CC)=NO +DISCONNECTED CC.CCCCN(CC)CN +DISCONNECTED COCC(O)=C(C)OC.O +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCC1N(C(C)=O)CN2NCCC12CC +DISCONNECTED C.CCCN(CC)C(C)N +INVALID +DISCONNECTED C.CCCC(ON)C(CC)CO.O +Valid NCCC(O)N1CCC(CN2CCCC(=C3CNO3)C2)C1 +INVALID +Valid CCC(C)(C)C(=O)COC +INVALID +Valid CC(C)CNC(C)N(O)C(O)CCN +DISCONNECTED C.C.CC=C(O)C=COC diff --git a/CupOFMoCA-PROTAC/results/ablation_all/soft_s123_clean.smi b/CupOFMoCA-PROTAC/results/ablation_all/soft_s123_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..160fbde932946f5a7bb98f62a65e3d38f0d81fa9 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/soft_s123_clean.smi @@ -0,0 +1,100 @@ +C=C1C=CC1=COCCCN +INVALID +INVALID +INVALID +INVALID +CCC1CCCC(C)OCC1 +CCCC(NCC)C(O)N(C)C.O +CC(C)C1(C)CCC2C(=O)CCCc3cn2c1n3 +CCCCCCCCCCCC12CC3=CC1=S(CC)C=C32.N +INVALID +CCCCCC1COC1(CO)COC +C=C=O.CCNCCC(=CC(C)O)CNCCN(N)CC +INVALID +INVALID +CCCC(CO)(N(C)O)N(C)C(C)C +C.N=C1NOC1CCC=O +C.COC(N)C(C)C(C)C +CCN(CCO)COO.CO +C.CCNCC(CC)OCC +C.COCCNCCCC1NNc2ccc(cc2N)O1 +INVALID +C.C.C=C=O.C=CCOO +CC(C)CC(C)(C)CN.O +CCCOC(C)=COCC +C.C=CCC(C)C(C)C.O +C.C.C.C.C.CC=CCC +C.CCON=CC(C)=CCOC1CC1 +CCCNN=CNC1(COC)CCC1C +INVALID +INVALID +C.C.C.C=C.CCCOC +COC(C=O)OCCC1CCCCN1C +INVALID +C=CC(C)C(C=C1N=C=CCCO1)=CC=O +C.C=C1COC(C#CN)(C(O)CC(C)O)O1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.CCCN1CCOCCCCCCN(C)CC1 +C.CC(OC(F)C(F)F)C(C)(C)CCO +CCC(C)C(C)(N)C(C)OC +C.C.CCC(C)O.O.O=O +C.C.CCN(C)CC(C)C +INVALID +INVALID +INVALID +INVALID +INVALID +CCCC(C)(CC)CC.N +INVALID +INVALID +INVALID +C.C.NC1CCC2(CO2)C1 +INVALID +CNC1CC1(C)C12CCN1C(C(C)O)COC2 +C=C(NC)C(CC)=C(OC)C(=C)OO +C=C(NCOC)C(C)CCO +C.C=C1C=NCON(CCOC)C(OC)=N1 +CCOC(N)(C(O)CC)C(C)C(C)C +CCC(N)=C1C(CCO)NN1C +CC1CCC2C#CCN3CC(N4CCCC4)OC4(CN(CCC14)C2)C3 +INVALID +CCCCC(=O)NC(C)C1CCCCC2CC21CO +CC(O)C(C)C(C)CCCN(C)CCO +C.CCCCC(OCCO)OC(C)CCC +C.C.CCC1(C)C(=O)C1O +NC1C=C(O)C1CCC1(O)CCCCC1 +C.COCCC1C=C(C)OC1 +INVALID +C.C=NCC(O)C(C)CC.O +INVALID +CNC(N)CC(C)(CCN)NC +C.C.CCNCC1CCC1OC1OOC1=O +C.CNC(=O)C1(N(CCN)CCO)COC1C +INVALID +INVALID +C.C=NCC(C(C)CCC)C1CCNC1 +CCCOCCOC1COC2CNCC[N+]([O-])(C(C)=O)C2COC1C +C.CCOCC1(O)CC(C)OCN1C(CC)=NO +CC.CCCCN(CC)CN +COCC(O)=C(C)OC.O +INVALID +INVALID +INVALID +INVALID +C.CCC1N(C(C)=O)CN2NCCC12CC +C.CCCN(CC)C(C)N +INVALID +C.CCCC(ON)C(CC)CO.O +NCCC(O)N1CCC(CN2CCCC(=C3CNO3)C2)C1 +INVALID +CCC(C)(C)C(=O)COC +INVALID +CC(C)CNC(C)N(O)C(O)CCN +C.C.CC=C(O)C=COC diff --git a/CupOFMoCA-PROTAC/results/ablation_all/soft_s42.smi b/CupOFMoCA-PROTAC/results/ablation_all/soft_s42.smi new file mode 100644 index 0000000000000000000000000000000000000000..ab854a85197f3874cc9f27428a3d56b6eb138413 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/soft_s42.smi @@ -0,0 +1,100 @@ +Valid CCOCC1OCC12OCC2N1CCN(O)C(C)(CCN)C1 +DISCONNECTED C.COC(CCOCCO)C1OCC1OC +DISCONNECTED C.CCCC(=O)N(O)CC +DISCONNECTED C.CC=NC1NC(=O)C1C=CC=CO +Valid NCC1OCOCCOCONCOCCOC2=CC21 +INVALID +INVALID +DISCONNECTED C.C=C1CC(CC)CN1C +DISCONNECTED C.C.C1CCNCCNC1 +Valid CC1CC2C(C)C12CN(C)N=[N+](C)[O-] +DISCONNECTED C.C=CC(CN)CC(N)C=N +INVALID +INVALID +Valid C=C1C(CCCCCCC)c2ccc(cc2)CN1C +Valid OCC1CCCNCCC12C=CC=CC2 +INVALID +DISCONNECTED C.C.C.CC=O.O=CCO +DISCONNECTED C.CC(C)CC1C(O)N1C +INVALID +INVALID +DISCONNECTED C.C.C.C=CCC(C)CO +DISCONNECTED C.C.CCCCCCCCN.O +DISCONNECTED CCC(=N)CONC=C(C)CO.N=O +INVALID +INVALID +DISCONNECTED C.C.CN(C)C1C[N+]1([O-])C=O.CO +INVALID +DISCONNECTED CCC12CCN(C=COCCC(C)C1)CC2.N +INVALID +INVALID +DISCONNECTED C.C=CN1CC(C2CC2CC)C1C +Valid CCNC1CCC1CCN +INVALID +DISCONNECTED N.N=C(CNCC(O)ON)NCC=O +INVALID +DISCONNECTED CCCC(CCOC)OC.CN +DISCONNECTED C.CCCC1C(C)C2=C(OCNC=O)C(=O)N21 +DISCONNECTED C.C.CNCC(C)OCCCCN1CNC1 +DISCONNECTED C.C.CCC[N+]([O-])(CCO)OCC +DISCONNECTED C.CCC(CCCCN)C(C)=O +INVALID +Valid CCC(CN(C)C1CCCCC1)=S(=O)(CC)C(C)C +DISCONNECTED CCCC.COC.O=CO +Valid CCCCC(C)(CC)OC +DISCONNECTED C.CN(C)C(CCCN1CCOCC1)C1CCCCCN1C +INVALID +INVALID +DISCONNECTED C.COC(O)C(C)CCC=CC(N)=O +INVALID +DISCONNECTED C.CCCCN(C)CCC +Valid C=C(C)N(C)CC(CC)C(=CCCCCC)CO +DISCONNECTED C.CC.CC.CCN.N.N +DISCONNECTED C.C=NOCC1(OCC)CC1OCN +DISCONNECTED CC.CC(C)C=O.CC=O.O +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CCOC(CC)CCC1=C(OC)CO1.CO +Valid CCN(N)CC(COC)CC(C)=O +DISCONNECTED C.CCNC(COC)OC.O +INVALID +DISCONNECTED C.CCC(CC)C(C)C(C)C +DISCONNECTED CC(C)C.CCOC.N.O +Valid CC1CC(N)C(C(C)C2CC2)N(C)CCN(C)CCCC(=O)N1 +DISCONNECTED C.C.C=CCC1CCN(CC)CCC1C +INVALID +DISCONNECTED C=C.N=c1ccn(CCCOCCO)[nH]1 +DISCONNECTED C.COCCC(OCC1C#CCO1)c1nccn1CO +DISCONNECTED C.C.CCC(OOC)N1CC1CN +INVALID +INVALID +DISCONNECTED C.CCCC(O)CN1CC1CC +INVALID +INVALID +INVALID +DISCONNECTED C.C.C.C.CCNC(C)=O +DISCONNECTED C.C=CC(=O)NNCC.N +INVALID +INVALID +Valid CCCCCCC(C)N(COCC)OC +INVALID +Valid CCN(CC)C1C=CC(C)(OC)CC1 +INVALID +DISCONNECTED C.C=CC1CCCN(CC(C)O)C1 +DISCONNECTED C.C.C.CC.CCC(C)C +DISCONNECTED CCCC(C)(C)C(N)O.N +Valid CCCCCCCCCC(N)Nc1ccc(O)cc1 +INVALID +DISCONNECTED C.C=C(CC)NCCC.C=O +INVALID +INVALID +INVALID +Valid C=C(C)C(C)(CCC)OC +INVALID +INVALID +Valid C=C(C)CC(C)C(CC)=NN +INVALID +INVALID +Valid CCOCC1CNC(C(C)c2ccccc2)C1(CC)CC diff --git a/CupOFMoCA-PROTAC/results/ablation_all/soft_s42_clean.smi b/CupOFMoCA-PROTAC/results/ablation_all/soft_s42_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..c34c226394712bcad7a7fe12246b8c531416bc41 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/soft_s42_clean.smi @@ -0,0 +1,100 @@ +CCOCC1OCC12OCC2N1CCN(O)C(C)(CCN)C1 +C.COC(CCOCCO)C1OCC1OC +C.CCCC(=O)N(O)CC +C.CC=NC1NC(=O)C1C=CC=CO +NCC1OCOCCOCONCOCCOC2=CC21 +INVALID +INVALID +C.C=C1CC(CC)CN1C +C.C.C1CCNCCNC1 +CC1CC2C(C)C12CN(C)N=[N+](C)[O-] +C.C=CC(CN)CC(N)C=N +INVALID +INVALID +C=C1C(CCCCCCC)c2ccc(cc2)CN1C +OCC1CCCNCCC12C=CC=CC2 +INVALID +C.C.C.CC=O.O=CCO +C.CC(C)CC1C(O)N1C +INVALID +INVALID +C.C.C.C=CCC(C)CO +C.C.CCCCCCCCN.O +CCC(=N)CONC=C(C)CO.N=O +INVALID +INVALID +C.C.CN(C)C1C[N+]1([O-])C=O.CO +INVALID +CCC12CCN(C=COCCC(C)C1)CC2.N +INVALID +INVALID +C.C=CN1CC(C2CC2CC)C1C +CCNC1CCC1CCN +INVALID +N.N=C(CNCC(O)ON)NCC=O +INVALID +CCCC(CCOC)OC.CN +C.CCCC1C(C)C2=C(OCNC=O)C(=O)N21 +C.C.CNCC(C)OCCCCN1CNC1 +C.C.CCC[N+]([O-])(CCO)OCC +C.CCC(CCCCN)C(C)=O +INVALID +CCC(CN(C)C1CCCCC1)=S(=O)(CC)C(C)C +CCCC.COC.O=CO +CCCCC(C)(CC)OC +C.CN(C)C(CCCN1CCOCC1)C1CCCCCN1C +INVALID +INVALID +C.COC(O)C(C)CCC=CC(N)=O +INVALID +C.CCCCN(C)CCC +C=C(C)N(C)CC(CC)C(=CCCCCC)CO +C.CC.CC.CCN.N.N +C.C=NOCC1(OCC)CC1OCN +CC.CC(C)C=O.CC=O.O +INVALID +INVALID +INVALID +INVALID +CCOC(CC)CCC1=C(OC)CO1.CO +CCN(N)CC(COC)CC(C)=O +C.CCNC(COC)OC.O +INVALID +C.CCC(CC)C(C)C(C)C +CC(C)C.CCOC.N.O +CC1CC(N)C(C(C)C2CC2)N(C)CCN(C)CCCC(=O)N1 +C.C.C=CCC1CCN(CC)CCC1C +INVALID +C=C.N=c1ccn(CCCOCCO)[nH]1 +C.COCCC(OCC1C#CCO1)c1nccn1CO +C.C.CCC(OOC)N1CC1CN +INVALID +INVALID +C.CCCC(O)CN1CC1CC +INVALID +INVALID +INVALID +C.C.C.C.CCNC(C)=O +C.C=CC(=O)NNCC.N +INVALID +INVALID +CCCCCCC(C)N(COCC)OC +INVALID +CCN(CC)C1C=CC(C)(OC)CC1 +INVALID +C.C=CC1CCCN(CC(C)O)C1 +C.C.C.CC.CCC(C)C +CCCC(C)(C)C(N)O.N +CCCCCCCCCC(N)Nc1ccc(O)cc1 +INVALID +C.C=C(CC)NCCC.C=O +INVALID +INVALID +INVALID +C=C(C)C(C)(CCC)OC +INVALID +INVALID +C=C(C)CC(C)C(CC)=NN +INVALID +INVALID +CCOCC1CNC(C(C)c2ccccc2)C1(CC)CC diff --git a/CupOFMoCA-PROTAC/results/ablation_all/soft_s456.smi b/CupOFMoCA-PROTAC/results/ablation_all/soft_s456.smi new file mode 100644 index 0000000000000000000000000000000000000000..b247cd98c4edea42ad98c875cd764644179e6b83 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/soft_s456.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +DISCONNECTED C.CCCCCC.COO +Valid COCCCCC(OC)C(CO)CNC(C)O +INVALID +INVALID +INVALID +DISCONNECTED C.CCC(C(C)O)C(C)NC +DISCONNECTED CCCCCOCC(CCOC)(OCCOC)C(COCC)OC.O +INVALID +INVALID +INVALID +DISCONNECTED COCCC(C)CC12CC3(C)CN1CCC32.N +DISCONNECTED C.CCC(=O)C(CC)CCO.N +Valid CNC1C2CCNC21OC +DISCONNECTED C.CC1CCC2CC23CC=CC2(CC2)N(O1)C(=O)C3 +Valid CCCOC(=O)CCC(O)C(C)(C)C(C)C +DISCONNECTED C.CCCCCCCN(C)CC.O=CC=O +INVALID +DISCONNECTED C.C=C1CCC23CC2(C)N13 +DISCONNECTED C.CC=CC1(CC)C(CC)N1CCCC(C)C +INVALID +DISCONNECTED C.C.COC(N)CCCCN +INVALID +INVALID +DISCONNECTED C.COCCC(OCCOCCO)C1COCCOCCOCCO1 +AssemblyReady NCC1OCCCC1C(OCC=O)OOCC=O +INVALID +INVALID +INVALID +DISCONNECTED CC(O)C(C)OCN.N.N=C=O +Valid C=CC=C(N=C)NC(C)C(CC)NC +DISCONNECTED C.C.CC(C)C(C)CCN +Valid CCOC1CC2CCCCCC2C1 +INVALID +Valid C=C(C(CO)CC(C)CCC=O)N(C)O +DISCONNECTED C.CCOCCOCCCOC(CCN)CCOCC +DISCONNECTED CCC.CCCCCOC=C=NOCC +DISCONNECTED C.CCN(C)CC(CN)OO.O +INVALID +Valid CC#CCCC1C2CN(CCNC(=O)CNCC)CC12C +INVALID +INVALID +INVALID +INVALID +Valid CCCCC1(C)C(O)C1CN +INVALID +Valid COCCOCCC(C)C1(O)CCCC(C)C1C +DISCONNECTED C.NNC(=CCCO)N=S +Valid CCCOCC(N)C(COCCCCCO)OCCC +DISCONNECTED C.C.C.CN=CNN.CO +DISCONNECTED C.C.CO.COC1NCCCCCNC1N +DISCONNECTED C.C.CCOC.COC.N.O +DISCONNECTED C.C.C.CCOC(CO)C(C)C +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C=NC(=CC=CC)C(C)C1CCNCC1 +DISCONNECTED C.C.C.C=C(CCC)NC +INVALID +INVALID +INVALID +DISCONNECTED C.C.CCC1CC(NC)C1CC +DISCONNECTED C.CCC1CCC2COCC3=C(CCCC31)CCO2 +INVALID +INVALID +DISCONNECTED C.CCC=C(C)CC(C)CC +INVALID +DISCONNECTED C.CCC(C)(C)C(C)(C)C +INVALID +DISCONNECTED C.CCCC(C)(CO)OC +INVALID +Valid CC1=CC1OCC(C)(C)CO +INVALID +DISCONNECTED C.CN(CCCCCCNC(=O)C=O)C(=O)C=O +DISCONNECTED C.C=C(OCC)C(=CC)OC(CC)C(=O)OCCO +INVALID +DISCONNECTED C.C.CCC(C)C(CCN)CCO.O +INVALID +INVALID +DISCONNECTED C.CC(=O)CCCC(=O)NCCN.O +INVALID +INVALID +INVALID +Valid CCC(OCCOC)=C(C)N +INVALID +INVALID +Valid CCCN1CCC(C)C2=C(N)C3C4CCN(C=C2)C31OC4 +DISCONNECTED C.CCC(O)N=NC(C)=CN +INVALID +INVALID +Valid CN1CCC2=NOCC3=S4(C)=C(C=CC=C24)C(C3)C1 +INVALID +INVALID +Valid COC1=c2c(=O)c3cn4n(cc2cc3-4)CCC1 +Valid C=CC(C)C(C)CC(C)C +DISCONNECTED C.C=C(C(C)OC)N(CC)C(=O)CS +DISCONNECTED C.COC(C)C(O)C(O)CN +DISCONNECTED CNC(C)=CC1C(C)C1C1(O)CN1.O diff --git a/CupOFMoCA-PROTAC/results/ablation_all/soft_s456_clean.smi b/CupOFMoCA-PROTAC/results/ablation_all/soft_s456_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..7248c364fcbf2de7cf34f95b6e4e6c85ba7a5008 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_all/soft_s456_clean.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +C.CCCCCC.COO +COCCCCC(OC)C(CO)CNC(C)O +INVALID +INVALID +INVALID +C.CCC(C(C)O)C(C)NC +CCCCCOCC(CCOC)(OCCOC)C(COCC)OC.O +INVALID +INVALID +INVALID +COCCC(C)CC12CC3(C)CN1CCC32.N +C.CCC(=O)C(CC)CCO.N +CNC1C2CCNC21OC +C.CC1CCC2CC23CC=CC2(CC2)N(O1)C(=O)C3 +CCCOC(=O)CCC(O)C(C)(C)C(C)C +C.CCCCCCCN(C)CC.O=CC=O +INVALID +C.C=C1CCC23CC2(C)N13 +C.CC=CC1(CC)C(CC)N1CCCC(C)C +INVALID +C.C.COC(N)CCCCN +INVALID +INVALID +C.COCCC(OCCOCCO)C1COCCOCCOCCO1 +NCC1OCCCC1C(OCC=O)OOCC=O +INVALID +INVALID +INVALID +CC(O)C(C)OCN.N.N=C=O +C=CC=C(N=C)NC(C)C(CC)NC +C.C.CC(C)C(C)CCN +CCOC1CC2CCCCCC2C1 +INVALID +C=C(C(CO)CC(C)CCC=O)N(C)O +C.CCOCCOCCCOC(CCN)CCOCC +CCC.CCCCCOC=C=NOCC +C.CCN(C)CC(CN)OO.O +INVALID +CC#CCCC1C2CN(CCNC(=O)CNCC)CC12C +INVALID +INVALID +INVALID +INVALID +CCCCC1(C)C(O)C1CN +INVALID +COCCOCCC(C)C1(O)CCCC(C)C1C +C.NNC(=CCCO)N=S +CCCOCC(N)C(COCCCCCO)OCCC +C.C.C.CN=CNN.CO +C.C.CO.COC1NCCCCCNC1N +C.C.CCOC.COC.N.O +C.C.C.CCOC(CO)C(C)C +INVALID +INVALID +INVALID +INVALID +C.C=NC(=CC=CC)C(C)C1CCNCC1 +C.C.C.C=C(CCC)NC +INVALID +INVALID +INVALID +C.C.CCC1CC(NC)C1CC +C.CCC1CCC2COCC3=C(CCCC31)CCO2 +INVALID +INVALID +C.CCC=C(C)CC(C)CC +INVALID +C.CCC(C)(C)C(C)(C)C +INVALID +C.CCCC(C)(CO)OC +INVALID +CC1=CC1OCC(C)(C)CO +INVALID +C.CN(CCCCCCNC(=O)C=O)C(=O)C=O +C.C=C(OCC)C(=CC)OC(CC)C(=O)OCCO +INVALID +C.C.CCC(C)C(CCN)CCO.O +INVALID +INVALID +C.CC(=O)CCCC(=O)NCCN.O +INVALID +INVALID +INVALID +CCC(OCCOC)=C(C)N +INVALID +INVALID +CCCN1CCC(C)C2=C(N)C3C4CCN(C=C2)C31OC4 +C.CCC(O)N=NC(C)=CN +INVALID +INVALID +CN1CCC2=NOCC3=S4(C)=C(C=CC=C24)C(C3)C1 +INVALID +INVALID +COC1=c2c(=O)c3cn4n(cc2cc3-4)CCC1 +C=CC(C)C(C)CC(C)C +C.C=C(C(C)OC)N(CC)C(=O)CS +C.COC(C)C(O)C(O)CN +CNC(C)=CC1C(C)C1C1(O)CN1.O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/AGGREGATE.txt b/CupOFMoCA-PROTAC/results/ablation_targets/AGGREGATE.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/accept_on_s123.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/accept_on_s123.smi new file mode 100644 index 0000000000000000000000000000000000000000..6f11dd5461b51f1f22d6675bdfd5de990d663bc4 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/accept_on_s123.smi @@ -0,0 +1,100 @@ +DISCONNECTED C.C.C.N.O.O=CO.O=CO +AssemblyReady CCC(CC)C(CC(=O)O)C(=O)O +DISCONNECTED CCC(=O)O.O=CC(=O)O +AssemblyReady CCOCC(=C(CCN)CC(=O)O)C(C)(OCC)C(=O)O +AssemblyReady O=C(O)CCCCCCCCC(=O)O +DISCONNECTED C.C.CO.O.O=CO.O=CO +AssemblyReady CCc1occ(C(=O)O)c1C(=O)O +DISCONNECTED C.C.C.C.CCC(=O)O.CCNOC.O.O=CO 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+CCOCCCCCCOc1nc(CCCC(C(=O)O)C(=O)O)co1 +O=C(O)CCCCCN(CCO)CC(=O)O +C.C.CN.O=CO.O=CO +CCCCCCC(CCC(=O)O)C(=O)O +CCC(COC)OCC(C)CCC(=O)O.O=C(O)O.OCO +NCCCC(CN)C(=CC(=O)O)C(=O)O +C.C.C.CNC.O=CO.O=CO +O=C(O)CC(CC(=O)O)CC1=C(CCO)CCCC1 +CCC(CC(=O)O)C(=O)O +CCN(C(=O)O)C(CC(CO)N(N)CC(=O)O)OC +C.C.CC.O.O=CO.O=CO +O=C(O)C=C(C(=O)O)C(=O)O +C.CC.NC(=O)O.O=CO +CCC(CCC(CO)CO)(C(=O)O)C(=O)O +CC(C)(CC(=O)O)C(=O)O +CCCCC(C(=O)O)N(CC(=O)O)C(CC)CC +CC(CO)(CC(=O)O)C(=O)O +CCCCC(CC(=O)O)(OCC(C)NCCCC(C)=O)C(=O)O +C=CCCCCCCC(C)(CC(=O)O)CC(=O)O +CCCCCCNCC(OCC)=C(C(=O)O)C(=O)O +CCCC(C(=O)O)C(=O)O +O=C(O)CCCOCCNCC(=O)O +C.C.C.N.O=CO.O=CO +C.CCOC(C)(N)CCC(=O)O.N.N.N.NO.O.O=CO +C.C.CC.O=CO.O=CO.O=COO +CC(CCC(=O)O)=C(O)C(=O)O +CC(C)CCCCCN(CC(=O)O)C(=O)C(=O)O +CCC(CCCO)=C(CCCC(=O)O)CC(=O)O +NCCC(CC(=O)O)C(=O)O +CC(C)(C(=O)O)C(=O)NCCCCC(=O)O +O=C(O)CCCCCCCCC(=O)O +CCCCC(CCCCN(CCN)CCC(=O)O)C(=O)O +CC(C)C(C)(C(=O)O)C(=O)O +O=C(O)C(=O)NC1CC(C(=O)O)C1 +C.C.CCOC(=O)O.CN.CN(C)C(=O)O.N.NCO.O +CCCC(=O)NC(C(=O)O)C(C(=O)O)C(=O)OCC +O=C(O)CCCC(=O)C(=O)O +C.C=NOC.CC(=O)O.CCOO.O=CO +CCNCN.CNC1=CC1.N.O=CO.O=CO +CCCN.O=CO.O=CO +CCCC(CC(C)=CC(=O)O)C(=O)O +C.N=O.O=C(O)CCC(=O)O +CCC(C)(C)OO.NC(=O)O.O=CO diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s123.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s123.smi new file mode 100644 index 0000000000000000000000000000000000000000..c9d70aa352f6e7b45cb5252e560ac546266fb59f --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s123.smi @@ -0,0 +1,100 @@ +AssemblyReady O=C(O)CCN(C(=O)Nc1cccc(C(=O)O)c1)c1cc2ccc1-2 +AssemblyReady O=CCC1(OC(=O)O)OC(OS(=O)OO)(C(=O)O)CC(O)C1O +AssemblyReady CCCOc1sc(CCC(=O)NC(CN=CN)C(=O)O)cc1C(=O)O +INVALID +AssemblyReady CC(c1ccc(NC(=O)C(=O)O)cc1)c1cc(O)c(O)c(C(=O)O)c1 +INVALID +AssemblyReady COC12C=C3C=C4OCOC41C1C=CCC(C3=C2C(=O)O)C1C(=O)O +AssemblyReady O=C(O)c1ccc2nc(O)c3cc(C(=O)O)ccc3c(=O)osc2c1 +INVALID +AssemblyReady NC1(C(=O)O)C=CC=C(Nc2nncs2)C=C1CC(=O)O +AssemblyReady CC(C)C(=O)OC(C1OCCO1)C(OC(=O)O)OC(=O)O +AssemblyReady CC(C)C(N)C(=O)NC(C(=O)O)C(CC(=O)O)C(=O)NCC=CC=O +AssemblyReady CC(OC12C=CC3=C1C2(C(=O)O)N3S1(=O)=C2C3=CC=C2C1=C3)C(=O)O +INVALID +AssemblyReady Cc1occ(Cc2cc(NC(=S)NCC(=O)O)cc(C(=O)O)c2)c1C +INVALID +AssemblyReady O=C(O)COc1ccc2c(cc1F)c1c(OCC(=O)O)c3c2c13 +INVALID +AssemblyReady CCN(C1=Cc2cc(C)ccc2N=C(C)O1)C(CCC(=O)O)C(=O)O +AssemblyReady CC(C)C(C(=O)O)=C(NC(=O)N(C)C(C)(C(=O)O)C(=O)O)C(=O)O +INVALID +AssemblyReady COc1cccc(-c2cccc(CC(=O)O)c2C(=O)O)c1 +AssemblyReady COC12C=CC=C3OC(C)(CC(=O)O)CC(CCN1)C32C(=O)O +INVALID +AssemblyReady O=C(O)CCC(=CC(=O)Oc1cc(CF)cc(C(F)(F)F)c1)C(=O)O +AssemblyReady N#CCOC12C(=O)C1(O)c1ccc3cc1C32C(CC(=O)O)C(=O)O +AssemblyReady O=C(O)c1cc(NC(=O)c2ccc3c(c2)[N+]3([O-])O)c(O)c(C(=O)O)c1 +AssemblyReady O=C(O)CC(NC(=O)CCCO)C(=O)N1CC=C(C(=O)O)CCCC1 +AssemblyReady CNC(C(=O)O)C(C)(OCC(=O)O)C(=O)c1cccc2ccccc12 +AssemblyReady COc1cc(C(C)=O)cc2c1C=C2CC(C(=O)O)C(=O)O +AssemblyReady CC(CC(C(=O)O)C(=O)O)C(=O)Oc1cc2ccc1c(=O)n2N +AssemblyReady CCCCCCC(CO)(CCCC(=O)O)CC(C)(C)CC(=O)O +AssemblyReady COC(=O)C12C=CC1=C(N1CCCCC1)C=C(C(=O)O)C2C(=O)O +DISCONNECTED CC(=O)O.CC(=O)c1ccc(N)cc1C(=O)c1ccc(C(=O)O)cc1 +AssemblyReady O=C(O)C1=NC2=C3C4=C5C=C(CC1=C2)C34CC(C(=O)O)C(C(=O)O)O5 +AssemblyReady CCCC(=O)C1(CC)C=CC(O)=CC(C(=O)O)=C1C(=O)O +INVALID +INVALID +AssemblyReady O=C(O)CNS(=O)(=O)c1cccc(NCc2cccc(C(=O)O)c2)c1 +INVALID +AssemblyReady O=C(O)CCc1cncc(-c2ccc(C(=O)O)cc2)c1 +AssemblyReady O=C(O)CCCC12C=c3ccc1c1c3=CC=CC12CCC(S)C(=O)O +INVALID +AssemblyReady Cc1c(C(=O)O)cccc1C1(C(=O)O)C(O)=COC=C1c1ccsc1 +INVALID +DISCONNECTED O=C(O)O.O=CCCCC(=O)O +AssemblyReady O=C(O)c1cc(NC(=O)C2(C(=O)O)CCCCCC2)cs1 +AssemblyReady O=C(O)C(=O)Nc1ccc(C(=O)O)s1 +AssemblyReady O=C(O)OC1=C2c3ccccc3C3CCC2C3CC(C(=O)O)NC1=O +AssemblyReady CC(=O)OC(C=C(C)C(=O)O)CC(C)C(CCC(C)(C)O)CC(=O)O +DISCONNECTED O=C(O)CNC(=O)c1ccccc1.O=C(O)CO +AssemblyReady COc1c(CC(=O)O)cccc1Cc1ccccc1OCCC(=O)O +AssemblyReady COC(=O)c1c(C)[nH]cc(C(=O)O)ccc2c(CC(=O)O)cccc12 +AssemblyReady CCNS(=O)(=O)c1cnc2coc(C(=O)O)c(C(=CC(=O)O)CC)c1-2 +INVALID +AssemblyReady O=C(O)C1(S)CC(Cc2ccccc2)(C(=O)O)C1 +AssemblyReady O=C(O)c1ccc(NCCN(CCO)CC(CO)CO)c(C(=O)O)c1 +INVALID +AssemblyReady CC(C)(CO)NC(=O)CC(CC(=O)O)CC(=O)O +AssemblyReady O=C(O)CCCOc1ccc2nccc(C=C(SC=S)C(=O)O)c2c1 +AssemblyReady O=C(O)C1=CS(=O)(=CC2CCC3=CCCCC3C2C(=O)O)C=CC1=O +INVALID +AssemblyReady O=C(O)Cc1ccc(F)cc1C(=O)O +AssemblyReady C=CCOc1ccc(C23C(=O)C2(C)C(=O)C(C(=O)O)C3C(=O)O)cc1 +INVALID +AssemblyReady Cc1cccc(CCCCN(CCC(=O)O)C(=O)CCCC(=O)O)c1 +AssemblyReady CC(C)(CC1Cc2ccc(C(=O)O)cc2C1NC(=O)O)CS(N)(=O)=O +AssemblyReady O=C(O)CCC=c1ccc2c3c1c2C(C(=O)O)=N3 +AssemblyReady CCCCCCC(CC(=O)O)C(CO)C(O)CCC(O)CCC(=O)O +INVALID +INVALID +AssemblyReady CC1=C(O)C2=C3C=COCC1N(CCC3=NC(CC(=O)O)C(=O)O)C2 +AssemblyReady CCCCC(=O)OCCC(CNC(=O)C1(C(=O)O)CCCC1)C(=O)O +INVALID +AssemblyReady O=C(O)CC(=O)C(O)=C(O)C(=O)Nc1ccc(C(=O)O)c(S)c1 +AssemblyReady COC(=O)C(C)(C)NC(=O)c1cccc(C(CCC(=O)O)C(=O)O)c1 +INVALID +AssemblyReady COc1ccc(C(=O)O)cc1CC(=Cc1cccs1)C(=O)O +AssemblyReady CCC(C=O)(CC)C(C)ON1CC(CO)OC(C(=O)O)C1OC(=O)O +AssemblyReady COCC(C)(COC)C(=O)C(=CC(=O)O)C(=O)C(=O)O +AssemblyReady O=C(CCNc1ccc(O)cc1)Nc1c(C(=O)O)cccc1C(=O)O +INVALID +AssemblyReady CC(C)(CCCO)C(C(=O)O)c1ccccc1C(CC(=O)O)C(=O)O +INVALID +AssemblyReady CC1(C(=O)O)C(=CC(=O)O)C=C(F)C=C1C1=C(O)C(=O)C(=O)OC1=O +AssemblyReady Cc1cc(-c2cccs2)c(CC(=O)O)c(-c2ccccc2C(=O)O)n1 +AssemblyReady CCC1=C(CCC(Oc2ccccc2C(=O)O)C(=O)O)NC(=O)CC1 +AssemblyReady O=C(O)CCNC(=O)C=C(c1ccccc1)c1ccccc1C(=O)O +AssemblyReady O=C(O)CCCCNc1cc(C(=O)O)c2c(C(=O)O)c(F)ccc2c1 +AssemblyReady Cc1ccc(COCC(=O)O)c2c(C(=O)O)cc(OCCCO)cc12 +AssemblyReady CC(=CC(=O)C1C(C(=O)O)=C(C(=O)O)C(O)C1OC=O)CCC(=O)O +AssemblyReady COC(=O)C(CC(=O)O)C1CC2CC(C(C(=O)O)C(=O)C2(C)O)C1O +AssemblyReady CCCCCCC(=O)NC(CC1(C(=O)O)COCCCOC1)C(=O)O +AssemblyReady CC(=O)NCCCCCC(=O)NC(=CC1=C(O)C(C(=O)O)=C1)C(=O)O +INVALID +AssemblyReady CC(=O)OCC(=O)CC(CCC(=O)CCC(=O)NCC(=O)O)C(=O)O +AssemblyReady O=C(O)CCCCN(CC1CCCCC1)CC1(C(=O)O)C=CC=C1 +DISCONNECTED NC(=O)CCCC(=O)OC1(C(=O)O)CCC(C(=O)O)CC1.S +AssemblyReady NC1=CC23C(=C1)C2=c1cc(C(=O)O)ccc1=CC1=CC3(C(=O)O)C1=O +AssemblyReady O=[SH]OC1C2(OC(=O)O)OC(O)(CC2O)C1(O)OC(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s123_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s123_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..31c8ff40d3454b1d09709c270027ea0c73e7a4fc --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s123_clean.smi @@ -0,0 +1,100 @@ +O=C(O)CCN(C(=O)Nc1cccc(C(=O)O)c1)c1cc2ccc1-2 +O=CCC1(OC(=O)O)OC(OS(=O)OO)(C(=O)O)CC(O)C1O +CCCOc1sc(CCC(=O)NC(CN=CN)C(=O)O)cc1C(=O)O +INVALID +CC(c1ccc(NC(=O)C(=O)O)cc1)c1cc(O)c(O)c(C(=O)O)c1 +INVALID +COC12C=C3C=C4OCOC41C1C=CCC(C3=C2C(=O)O)C1C(=O)O +O=C(O)c1ccc2nc(O)c3cc(C(=O)O)ccc3c(=O)osc2c1 +INVALID +NC1(C(=O)O)C=CC=C(Nc2nncs2)C=C1CC(=O)O +CC(C)C(=O)OC(C1OCCO1)C(OC(=O)O)OC(=O)O +CC(C)C(N)C(=O)NC(C(=O)O)C(CC(=O)O)C(=O)NCC=CC=O +CC(OC12C=CC3=C1C2(C(=O)O)N3S1(=O)=C2C3=CC=C2C1=C3)C(=O)O +INVALID +Cc1occ(Cc2cc(NC(=S)NCC(=O)O)cc(C(=O)O)c2)c1C +INVALID +O=C(O)COc1ccc2c(cc1F)c1c(OCC(=O)O)c3c2c13 +INVALID +CCN(C1=Cc2cc(C)ccc2N=C(C)O1)C(CCC(=O)O)C(=O)O +CC(C)C(C(=O)O)=C(NC(=O)N(C)C(C)(C(=O)O)C(=O)O)C(=O)O +INVALID +COc1cccc(-c2cccc(CC(=O)O)c2C(=O)O)c1 +COC12C=CC=C3OC(C)(CC(=O)O)CC(CCN1)C32C(=O)O +INVALID +O=C(O)CCC(=CC(=O)Oc1cc(CF)cc(C(F)(F)F)c1)C(=O)O +N#CCOC12C(=O)C1(O)c1ccc3cc1C32C(CC(=O)O)C(=O)O +O=C(O)c1cc(NC(=O)c2ccc3c(c2)[N+]3([O-])O)c(O)c(C(=O)O)c1 +O=C(O)CC(NC(=O)CCCO)C(=O)N1CC=C(C(=O)O)CCCC1 +CNC(C(=O)O)C(C)(OCC(=O)O)C(=O)c1cccc2ccccc12 +COc1cc(C(C)=O)cc2c1C=C2CC(C(=O)O)C(=O)O +CC(CC(C(=O)O)C(=O)O)C(=O)Oc1cc2ccc1c(=O)n2N +CCCCCCC(CO)(CCCC(=O)O)CC(C)(C)CC(=O)O +COC(=O)C12C=CC1=C(N1CCCCC1)C=C(C(=O)O)C2C(=O)O +CC(=O)O.CC(=O)c1ccc(N)cc1C(=O)c1ccc(C(=O)O)cc1 +O=C(O)C1=NC2=C3C4=C5C=C(CC1=C2)C34CC(C(=O)O)C(C(=O)O)O5 +CCCC(=O)C1(CC)C=CC(O)=CC(C(=O)O)=C1C(=O)O +INVALID +INVALID +O=C(O)CNS(=O)(=O)c1cccc(NCc2cccc(C(=O)O)c2)c1 +INVALID +O=C(O)CCc1cncc(-c2ccc(C(=O)O)cc2)c1 +O=C(O)CCCC12C=c3ccc1c1c3=CC=CC12CCC(S)C(=O)O +INVALID +Cc1c(C(=O)O)cccc1C1(C(=O)O)C(O)=COC=C1c1ccsc1 +INVALID +O=C(O)O.O=CCCCC(=O)O +O=C(O)c1cc(NC(=O)C2(C(=O)O)CCCCCC2)cs1 +O=C(O)C(=O)Nc1ccc(C(=O)O)s1 +O=C(O)OC1=C2c3ccccc3C3CCC2C3CC(C(=O)O)NC1=O +CC(=O)OC(C=C(C)C(=O)O)CC(C)C(CCC(C)(C)O)CC(=O)O +O=C(O)CNC(=O)c1ccccc1.O=C(O)CO +COc1c(CC(=O)O)cccc1Cc1ccccc1OCCC(=O)O +COC(=O)c1c(C)[nH]cc(C(=O)O)ccc2c(CC(=O)O)cccc12 +CCNS(=O)(=O)c1cnc2coc(C(=O)O)c(C(=CC(=O)O)CC)c1-2 +INVALID +O=C(O)C1(S)CC(Cc2ccccc2)(C(=O)O)C1 +O=C(O)c1ccc(NCCN(CCO)CC(CO)CO)c(C(=O)O)c1 +INVALID +CC(C)(CO)NC(=O)CC(CC(=O)O)CC(=O)O +O=C(O)CCCOc1ccc2nccc(C=C(SC=S)C(=O)O)c2c1 +O=C(O)C1=CS(=O)(=CC2CCC3=CCCCC3C2C(=O)O)C=CC1=O +INVALID +O=C(O)Cc1ccc(F)cc1C(=O)O +C=CCOc1ccc(C23C(=O)C2(C)C(=O)C(C(=O)O)C3C(=O)O)cc1 +INVALID +Cc1cccc(CCCCN(CCC(=O)O)C(=O)CCCC(=O)O)c1 +CC(C)(CC1Cc2ccc(C(=O)O)cc2C1NC(=O)O)CS(N)(=O)=O +O=C(O)CCC=c1ccc2c3c1c2C(C(=O)O)=N3 +CCCCCCC(CC(=O)O)C(CO)C(O)CCC(O)CCC(=O)O +INVALID +INVALID +CC1=C(O)C2=C3C=COCC1N(CCC3=NC(CC(=O)O)C(=O)O)C2 +CCCCC(=O)OCCC(CNC(=O)C1(C(=O)O)CCCC1)C(=O)O +INVALID +O=C(O)CC(=O)C(O)=C(O)C(=O)Nc1ccc(C(=O)O)c(S)c1 +COC(=O)C(C)(C)NC(=O)c1cccc(C(CCC(=O)O)C(=O)O)c1 +INVALID +COc1ccc(C(=O)O)cc1CC(=Cc1cccs1)C(=O)O +CCC(C=O)(CC)C(C)ON1CC(CO)OC(C(=O)O)C1OC(=O)O +COCC(C)(COC)C(=O)C(=CC(=O)O)C(=O)C(=O)O +O=C(CCNc1ccc(O)cc1)Nc1c(C(=O)O)cccc1C(=O)O +INVALID +CC(C)(CCCO)C(C(=O)O)c1ccccc1C(CC(=O)O)C(=O)O +INVALID +CC1(C(=O)O)C(=CC(=O)O)C=C(F)C=C1C1=C(O)C(=O)C(=O)OC1=O +Cc1cc(-c2cccs2)c(CC(=O)O)c(-c2ccccc2C(=O)O)n1 +CCC1=C(CCC(Oc2ccccc2C(=O)O)C(=O)O)NC(=O)CC1 +O=C(O)CCNC(=O)C=C(c1ccccc1)c1ccccc1C(=O)O +O=C(O)CCCCNc1cc(C(=O)O)c2c(C(=O)O)c(F)ccc2c1 +Cc1ccc(COCC(=O)O)c2c(C(=O)O)cc(OCCCO)cc12 +CC(=CC(=O)C1C(C(=O)O)=C(C(=O)O)C(O)C1OC=O)CCC(=O)O +COC(=O)C(CC(=O)O)C1CC2CC(C(C(=O)O)C(=O)C2(C)O)C1O +CCCCCCC(=O)NC(CC1(C(=O)O)COCCCOC1)C(=O)O +CC(=O)NCCCCCC(=O)NC(=CC1=C(O)C(C(=O)O)=C1)C(=O)O +INVALID +CC(=O)OCC(=O)CC(CCC(=O)CCC(=O)NCC(=O)O)C(=O)O +O=C(O)CCCCN(CC1CCCCC1)CC1(C(=O)O)C=CC=C1 +NC(=O)CCCC(=O)OC1(C(=O)O)CCC(C(=O)O)CC1.S +NC1=CC23C(=C1)C2=c1cc(C(=O)O)ccc1=CC1=CC3(C(=O)O)C1=O +O=[SH]OC1C2(OC(=O)O)OC(O)(CC2O)C1(O)OC(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s42.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s42.smi new file mode 100644 index 0000000000000000000000000000000000000000..1f1248fe4c0764e9183bf7ac511ac8551864eda6 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s42.smi @@ -0,0 +1,100 @@ +AssemblyReady Cc1c(NCC(=O)O)cccc1C(=O)O +AssemblyReady Cc1cccc(C)c1OCC(=O)Nc1cc(C(=O)O)cc(C(=O)O)c1 +INVALID +AssemblyReady O=C(O)N1CCC(C2CCC2)C(N(C(=O)O)c2ccc(F)c(F)c2)C1 +INVALID +AssemblyReady CCOC(=O)CN(C(=O)C(=O)O)C(=O)C1(C(=O)O)CC(C)CC(C)C1 +AssemblyReady C=CC12OC1(C=CC(=O)O)C(OCCCO)C(C(=O)O)C2(O)C(C)=O +INVALID +AssemblyReady O=C(O)CNCc1ccc(C2(CCCC(O)C(=O)O)CCCC2)cc1 +AssemblyReady O=Cc1c(NS(=O)(=O)c2cccc(O)c2)cc(C(=O)O)cc1C(=O)O +AssemblyReady O=C(O)C(O)=CC(=O)C1=C(OCCc2cc(C(=O)O)on2)CCCC1 +AssemblyReady O=C(O)CC1(C(=O)O)CC2C3(C=CN=CC=CC(=O)N13)S2(=O)=O +AssemblyReady COC(=O)c1cccc(C=C(C)C=CCC(=CC(=O)O)C(=O)O)c1O +AssemblyReady O=C(O)c1cccc(C(=O)O)c1-c1c2ccn1n2C(=O)O +AssemblyReady CCNC(=O)CNc1ccccc1C(OC)C(NC(=O)O)NC(=O)O +AssemblyReady NC(c1ccccc1OC(OC(=O)O)C(=O)O)=c1ccc(=CC=O)s1 +AssemblyReady CCC1(C)C(=O)OCC(C2(C=O)CC(C)CC(C(=O)O)C2)C1C(=O)O +INVALID +AssemblyReady COc1ccc(S(O)(O)c2cc(C(=O)O)c(O)cc2C(=O)O)cc1C +INVALID +AssemblyReady COc1cc(C(=O)NC(C)(CC(=O)O)C(=O)O)ccc1O +AssemblyReady CCC(=O)NC(=O)NC(C(=O)O)c1cc(C)cc(CC(=O)O)c1 +INVALID +AssemblyReady Cc1ccc(Nc2ccccc2)c(-c2c(C(=O)O)csc2C(=O)O)c1 +AssemblyReady O=C(O)C1=CC2(C(=O)O)c3cc4c5cc3CCCCC(=O)N4C52C=C1 +INVALID +AssemblyReady C=C(NC(CCCO)C(=O)O)C(CCC(=O)O)N(CC(=O)O)C(=O)O +AssemblyReady CC(C(=O)O)c1cccc2c1C13N=c4cccc(C(=O)O)c4=C1C3=N2 +AssemblyReady CC1S2=C(C(c3cc(O)cc(O)c3C(=O)O)=C2)C2(C(=O)O)C=CC12C +INVALID +AssemblyReady COC(=O)CCCCCCCC(OC)=C(CCCCC(=O)O)C(=O)O +DISCONNECTED O=C(O)NCCOc1ccc(S)cc1.O=CO +AssemblyReady COC1CC(CCC2=CC=COCC(C(=O)O)=C2)(C(=O)O)C(O)CO1 +AssemblyReady NC(C(=O)O)C(=O)Oc1cc(CC(=O)O)c(O)c(C=O)c1CC(=O)O +AssemblyReady O=CNC(Cc1cc(NCC(=O)O)ccc1OC(=O)O)C(=O)O +AssemblyReady O=C(O)C(=CO)C(=O)c1ccc(C(=O)O)c(-c2ccccc2)c1 +AssemblyReady O=C(O)C1=c2ccc(=C3SC3C(=O)O)cc21 +INVALID +INVALID +AssemblyReady O=C(CCC1CCCC1)NC1(C(=O)O)CCCC(C(=O)O)N1 +AssemblyReady COC12C=C3C1=CC2(OC)C1(C(=O)O)CCCC3(C(=O)O)C1 +AssemblyReady CCC1=CN(CCCN)C(CCCC(=O)O)(C(=O)O)C(CO)=CC=C1 +AssemblyReady O=C(O)C1CC23C(O)=CC(O)(c4ccc(F)cc4)C=C2C1C3C(=O)O +AssemblyReady CC1=COC2=C(C)C(N)(C(=O)O)CC3NC(C(=O)O)C(O)(C2=C1)C3O +AssemblyReady CC1=CC2=C3CC(C(=O)O)(C=CO3)SC(O)C(=C2)C(C(=O)O)=C1 +AssemblyReady CCCC(C)C(OO)(OC(=O)O)C(O)(OCC(O)C(O)C=O)C(=O)O +AssemblyReady NC1=CC=C(C(=O)O)C=CC(=O)CC(c2ccccc2C(=O)O)O1 +AssemblyReady CC(CC(=O)O)C(CCC(=O)O)C(C)C1OC2COC(C)C(O)C2O1 +AssemblyReady NC(=O)OC1(OC(=O)CCC(NCCC(=O)O)C(=O)O)CCCCC1 +INVALID +DISCONNECTED COC1=CC(=O)C(OC)=C1OC(=O)O.O=CC(=O)O +AssemblyReady CCCCCN1CCN2C=CC=C(OC)C2=CC(C(=O)O)(C(=O)O)C1 +AssemblyReady Cn1c(CC(=O)O)c2c3c1CC(=O)C(C(=O)O)=C23 +AssemblyReady COC1=CC(C2(NC(=O)C(N)(CCCCCC(=O)O)C(=O)O)C=C2)=C1 +INVALID +AssemblyReady O=C(O)CNC(=O)c1cc(=O)oc2c(CO)c(C(=O)O)cc(CO)c12 +AssemblyReady CCC1(C=CC2=CC(=O)OC2)C(O)(C(=O)O)CC2(C)C(C(=O)O)C21C +AssemblyReady O=C(O)CN(C(CCO)C(=O)O)C(F)(F)CC(=O)O +AssemblyReady Cc1ccc(C2(O)C3=C(C(=O)C(=O)O)C4(C(C)(C)C(=O)O)C3=C24)cc1 +AssemblyReady COC(=O)C(CCC(=O)O)NC(=O)NCCc1ccc(C(=O)O)cc1 +AssemblyReady O=C(O)CCCCCCCCCC(O)C(O)C1OC1C(O)CC(=O)O +AssemblyReady CC1=CC=C(C(=O)O)C=COC(C(=O)O)C(=N)c2ccc(s2)S1(=O)=O +INVALID +AssemblyReady O=C(NCCc1cccc(C(=O)O)c1O)C1=C2OC2(C(=O)O)C=C1 +AssemblyReady O=C(O)C(=Cc1cc(C(=O)O)cc2c3ccc1c1c-2c31)COCCO +INVALID +AssemblyReady Cn1cc(C(=O)O)c(CCC(=O)O)c1C1=CC=C1 +AssemblyReady CCC=C(C(=O)CC(=O)O)C(=O)C(=O)O +AssemblyReady COC(=O)C(=CCc1cc(C(=O)O)ccc1OCCC(=O)O)C(=O)O +AssemblyReady Cn1sc2c(C(=O)NC3CC(CC(=O)O)(C(=O)O)C3)cccc2c1=O +AssemblyReady CN(C1CCC(CC(=O)O)CC1)C(C(=O)NCCC(=O)O)C(C)(C)O +AssemblyReady CN(S(=O)c1ccc(C(=O)O)c(C(=O)O)c1)C1(O)C=CC=CO1 +AssemblyReady CCC(C)C(=NC(=O)O)NC(=O)O +AssemblyReady O=C(O)C(C(=O)O)C(=O)CC(O)c1cccc(=O)o1 +AssemblyReady CC[SH](CN)c1cc(CC(=O)O)ccc1S(=O)(=O)C(=O)O +INVALID +AssemblyReady CC=C(N)C(=O)OCC(CC(=O)O)C(=O)O +INVALID +AssemblyReady COc1cc(C=O)cc(-c2ccc(C(=O)O)c(C(CN)C(=O)O)c2)c1 +AssemblyReady COC12C=CC(C(=O)O)=CC=C(C(=O)O)C3CCC(C1)C(C3)O2 +AssemblyReady O=C(O)OCCC12C=CC(C3CC4CCCCN3CC4)=C1N2C(=O)O +AssemblyReady NC(=O)C1OC=C2C=C(C(=O)O)C=C(C(=O)O)C1C2 +AssemblyReady O=C(O)C=C1C=CC2=CC12N=CN1C(=O)C(C(=O)O)CSC1=S +INVALID +AssemblyReady CCN(CCCNC(=O)CN1C2=NOC1(C(=O)O)C=C2)CC(=O)O +AssemblyReady CCOC(NC(CC(=O)O)C(=O)O)C12C=CC3=CC1(CNS3(=O)=O)O2 +AssemblyReady CCC1CC(O)C2(O)C(C3CCCCC3C)C(C(=O)O)=CC12C(=O)O +DISCONNECTED COC(=O)c1cc(C(=O)O)cc(NCNC(=O)O)c1C.O +AssemblyReady CCCC(C(=O)OC)N(CCCCC(=O)O)C(=O)C(=O)O +INVALID +AssemblyReady CC1=S234C=C2C(C(=O)O)=CC3=C4C(C(CC(=O)O)c2ccccc2)=N1 +AssemblyReady CCCC1(C2=C(N)C(O)=C2c2cccn2CC(=O)O)CC(C(=O)O)C1 +AssemblyReady NC(=O)CNS(=O)(=O)c1cc(C(=O)O)cc(C(=O)O)c1 +AssemblyReady CC(C)c1ccc(NC(=O)Cc2ccc(C(=O)O)c(C(=O)O)c2)cc1 +AssemblyReady CC(O)CCC(C(=O)O)C(C(=O)O)C12CCC3CC4OC31C2CC4=O +AssemblyReady CCN(CCC=C(CC(=O)O)C(=O)O)C(=O)NC(C)=O +AssemblyReady CCCCCCCCCCCCC(C(=O)O)(C(=O)O)C1CCCCC1 +AssemblyReady O=C(O)C1=CC=CNC(Cc2ccc(O)cc2)(C(=O)O)C(C(=O)O)=C1 +AssemblyReady CC(C(=O)O)N(C)C(=O)N(CCC(=O)O)C(=O)c1ccc(CO)cc1 +AssemblyReady CC1CCN(C(=O)c2cc(C(=O)O)[nH]c2C(=O)O)CC1 diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s42_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s42_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..e1bc477a7c964f2f2ee2670822e4ef770b9052b0 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s42_clean.smi @@ -0,0 +1,100 @@ +Cc1c(NCC(=O)O)cccc1C(=O)O +Cc1cccc(C)c1OCC(=O)Nc1cc(C(=O)O)cc(C(=O)O)c1 +INVALID +O=C(O)N1CCC(C2CCC2)C(N(C(=O)O)c2ccc(F)c(F)c2)C1 +INVALID +CCOC(=O)CN(C(=O)C(=O)O)C(=O)C1(C(=O)O)CC(C)CC(C)C1 +C=CC12OC1(C=CC(=O)O)C(OCCCO)C(C(=O)O)C2(O)C(C)=O +INVALID +O=C(O)CNCc1ccc(C2(CCCC(O)C(=O)O)CCCC2)cc1 +O=Cc1c(NS(=O)(=O)c2cccc(O)c2)cc(C(=O)O)cc1C(=O)O +O=C(O)C(O)=CC(=O)C1=C(OCCc2cc(C(=O)O)on2)CCCC1 +O=C(O)CC1(C(=O)O)CC2C3(C=CN=CC=CC(=O)N13)S2(=O)=O +COC(=O)c1cccc(C=C(C)C=CCC(=CC(=O)O)C(=O)O)c1O +O=C(O)c1cccc(C(=O)O)c1-c1c2ccn1n2C(=O)O +CCNC(=O)CNc1ccccc1C(OC)C(NC(=O)O)NC(=O)O +NC(c1ccccc1OC(OC(=O)O)C(=O)O)=c1ccc(=CC=O)s1 +CCC1(C)C(=O)OCC(C2(C=O)CC(C)CC(C(=O)O)C2)C1C(=O)O +INVALID +COc1ccc(S(O)(O)c2cc(C(=O)O)c(O)cc2C(=O)O)cc1C +INVALID +COc1cc(C(=O)NC(C)(CC(=O)O)C(=O)O)ccc1O +CCC(=O)NC(=O)NC(C(=O)O)c1cc(C)cc(CC(=O)O)c1 +INVALID +Cc1ccc(Nc2ccccc2)c(-c2c(C(=O)O)csc2C(=O)O)c1 +O=C(O)C1=CC2(C(=O)O)c3cc4c5cc3CCCCC(=O)N4C52C=C1 +INVALID +C=C(NC(CCCO)C(=O)O)C(CCC(=O)O)N(CC(=O)O)C(=O)O +CC(C(=O)O)c1cccc2c1C13N=c4cccc(C(=O)O)c4=C1C3=N2 +CC1S2=C(C(c3cc(O)cc(O)c3C(=O)O)=C2)C2(C(=O)O)C=CC12C +INVALID +COC(=O)CCCCCCCC(OC)=C(CCCCC(=O)O)C(=O)O +O=C(O)NCCOc1ccc(S)cc1.O=CO +COC1CC(CCC2=CC=COCC(C(=O)O)=C2)(C(=O)O)C(O)CO1 +NC(C(=O)O)C(=O)Oc1cc(CC(=O)O)c(O)c(C=O)c1CC(=O)O +O=CNC(Cc1cc(NCC(=O)O)ccc1OC(=O)O)C(=O)O +O=C(O)C(=CO)C(=O)c1ccc(C(=O)O)c(-c2ccccc2)c1 +O=C(O)C1=c2ccc(=C3SC3C(=O)O)cc21 +INVALID +INVALID +O=C(CCC1CCCC1)NC1(C(=O)O)CCCC(C(=O)O)N1 +COC12C=C3C1=CC2(OC)C1(C(=O)O)CCCC3(C(=O)O)C1 +CCC1=CN(CCCN)C(CCCC(=O)O)(C(=O)O)C(CO)=CC=C1 +O=C(O)C1CC23C(O)=CC(O)(c4ccc(F)cc4)C=C2C1C3C(=O)O +CC1=COC2=C(C)C(N)(C(=O)O)CC3NC(C(=O)O)C(O)(C2=C1)C3O +CC1=CC2=C3CC(C(=O)O)(C=CO3)SC(O)C(=C2)C(C(=O)O)=C1 +CCCC(C)C(OO)(OC(=O)O)C(O)(OCC(O)C(O)C=O)C(=O)O +NC1=CC=C(C(=O)O)C=CC(=O)CC(c2ccccc2C(=O)O)O1 +CC(CC(=O)O)C(CCC(=O)O)C(C)C1OC2COC(C)C(O)C2O1 +NC(=O)OC1(OC(=O)CCC(NCCC(=O)O)C(=O)O)CCCCC1 +INVALID +COC1=CC(=O)C(OC)=C1OC(=O)O.O=CC(=O)O +CCCCCN1CCN2C=CC=C(OC)C2=CC(C(=O)O)(C(=O)O)C1 +Cn1c(CC(=O)O)c2c3c1CC(=O)C(C(=O)O)=C23 +COC1=CC(C2(NC(=O)C(N)(CCCCCC(=O)O)C(=O)O)C=C2)=C1 +INVALID +O=C(O)CNC(=O)c1cc(=O)oc2c(CO)c(C(=O)O)cc(CO)c12 +CCC1(C=CC2=CC(=O)OC2)C(O)(C(=O)O)CC2(C)C(C(=O)O)C21C +O=C(O)CN(C(CCO)C(=O)O)C(F)(F)CC(=O)O +Cc1ccc(C2(O)C3=C(C(=O)C(=O)O)C4(C(C)(C)C(=O)O)C3=C24)cc1 +COC(=O)C(CCC(=O)O)NC(=O)NCCc1ccc(C(=O)O)cc1 +O=C(O)CCCCCCCCCC(O)C(O)C1OC1C(O)CC(=O)O +CC1=CC=C(C(=O)O)C=COC(C(=O)O)C(=N)c2ccc(s2)S1(=O)=O +INVALID +O=C(NCCc1cccc(C(=O)O)c1O)C1=C2OC2(C(=O)O)C=C1 +O=C(O)C(=Cc1cc(C(=O)O)cc2c3ccc1c1c-2c31)COCCO +INVALID +Cn1cc(C(=O)O)c(CCC(=O)O)c1C1=CC=C1 +CCC=C(C(=O)CC(=O)O)C(=O)C(=O)O +COC(=O)C(=CCc1cc(C(=O)O)ccc1OCCC(=O)O)C(=O)O +Cn1sc2c(C(=O)NC3CC(CC(=O)O)(C(=O)O)C3)cccc2c1=O +CN(C1CCC(CC(=O)O)CC1)C(C(=O)NCCC(=O)O)C(C)(C)O +CN(S(=O)c1ccc(C(=O)O)c(C(=O)O)c1)C1(O)C=CC=CO1 +CCC(C)C(=NC(=O)O)NC(=O)O +O=C(O)C(C(=O)O)C(=O)CC(O)c1cccc(=O)o1 +CC[SH](CN)c1cc(CC(=O)O)ccc1S(=O)(=O)C(=O)O +INVALID +CC=C(N)C(=O)OCC(CC(=O)O)C(=O)O +INVALID +COc1cc(C=O)cc(-c2ccc(C(=O)O)c(C(CN)C(=O)O)c2)c1 +COC12C=CC(C(=O)O)=CC=C(C(=O)O)C3CCC(C1)C(C3)O2 +O=C(O)OCCC12C=CC(C3CC4CCCCN3CC4)=C1N2C(=O)O +NC(=O)C1OC=C2C=C(C(=O)O)C=C(C(=O)O)C1C2 +O=C(O)C=C1C=CC2=CC12N=CN1C(=O)C(C(=O)O)CSC1=S +INVALID +CCN(CCCNC(=O)CN1C2=NOC1(C(=O)O)C=C2)CC(=O)O +CCOC(NC(CC(=O)O)C(=O)O)C12C=CC3=CC1(CNS3(=O)=O)O2 +CCC1CC(O)C2(O)C(C3CCCCC3C)C(C(=O)O)=CC12C(=O)O +COC(=O)c1cc(C(=O)O)cc(NCNC(=O)O)c1C.O +CCCC(C(=O)OC)N(CCCCC(=O)O)C(=O)C(=O)O +INVALID +CC1=S234C=C2C(C(=O)O)=CC3=C4C(C(CC(=O)O)c2ccccc2)=N1 +CCCC1(C2=C(N)C(O)=C2c2cccn2CC(=O)O)CC(C(=O)O)C1 +NC(=O)CNS(=O)(=O)c1cc(C(=O)O)cc(C(=O)O)c1 +CC(C)c1ccc(NC(=O)Cc2ccc(C(=O)O)c(C(=O)O)c2)cc1 +CC(O)CCC(C(=O)O)C(C(=O)O)C12CCC3CC4OC31C2CC4=O +CCN(CCC=C(CC(=O)O)C(=O)O)C(=O)NC(C)=O +CCCCCCCCCCCCC(C(=O)O)(C(=O)O)C1CCCCC1 +O=C(O)C1=CC=CNC(Cc2ccc(O)cc2)(C(=O)O)C(C(=O)O)=C1 +CC(C(=O)O)N(C)C(=O)N(CCC(=O)O)C(=O)c1ccc(CO)cc1 +CC1CCN(C(=O)c2cc(C(=O)O)[nH]c2C(=O)O)CC1 diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s456.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s456.smi new file mode 100644 index 0000000000000000000000000000000000000000..d2cc77822db9f19b9e124e7376dc4370450061d1 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s456.smi @@ -0,0 +1,100 @@ +AssemblyReady O=C(O)C1=CC23C=CC14NC2CCC43C(=O)O +AssemblyReady CC1=C(C(=O)O)C2(OC(=O)O)OC2=C1C(O)C(CS(=O)(=O)O)C(=O)O +AssemblyReady O=C(O)CCC(=O)C(=O)O +AssemblyReady CCCC(=C(N)C(=O)O)C(C(=O)O)C(=O)O +INVALID +AssemblyReady CCOC(=O)C(O)(CC(OCC)C(=CC(=O)O)OCCOC)C(=O)O +AssemblyReady O=C(O)CCC(=O)C(=O)C(=O)O +AssemblyReady CC(Cc1cc(CNC(=O)CSCC(=O)O)ccc1C(=O)O)C(=O)O +AssemblyReady CC1(C)C(C(=O)O)C(C(=O)O)NC(=O)C2CC3C4(C2)C1C1=S2C=C1C324 +AssemblyReady CCNC(C)(CCCC(C)CNC(=O)C(=O)O)C(=O)O +AssemblyReady O=C(O)c1ccc(-c2c(C(=O)O)ncc3cc(O)c(O)cc23)cc1O +AssemblyReady O=C(O)CCCC(NCc1cccc(Cc2ccccc2)c1)C(=O)O +INVALID +AssemblyReady COCCNC(=O)C1=CC(S(=O)(=O)O)=CCC(CC(=O)O)=C1C(=O)O +AssemblyReady CC1=C(C)C2C3C4C(C(=O)O)=C(C(=O)O)OC56C2C1=CC31CC15C46O +AssemblyReady CCN(C)C(O)(O)C(Cc1ccc(OC)c(OC(=O)O)c1)NC(=O)O +AssemblyReady O=C(O)c1cc(O)ccc1Nc1ccc(C(=O)O)c(C(=O)O)c1 +AssemblyReady CCCCCCC(O)C(CC=CCCCCC(=O)O)NC(=O)C(=O)O +INVALID +AssemblyReady CC(Oc1cccc(C(=O)O)c1CCC(O)c1ccccc1)C(=O)O +AssemblyReady COc1ccc(C(=O)O)c(C2=C(C3=CC(O)=C3C(=O)O)O2)c1O +AssemblyReady O=C(O)Cc1oc2nc1C#CC1(C(=O)O)CCC1=CC=CC=COC2 +AssemblyReady CCCCCC(C)(NC(=O)C(CC)NC(=O)CC(N)C(=O)O)C(=O)O +AssemblyReady CC(C(=O)O)C12C=CC(=O)CC(C(=O)O)(C=C1)C=C2 +AssemblyReady CC1=CN(CC(=O)O)C(C(=O)O)C(C)=C1 +AssemblyReady C=CC(=O)OCCC(CN(CCO)c1cccc(C(=O)O)c1)C(=O)O +AssemblyReady CCCN(C)C(=O)C(NC(=O)O)C(C)C(=O)O +AssemblyReady O=C(O)NC(CCc1ccccc1)C1=CC2(C=CC=C1)CC2C(=O)O +AssemblyReady NC(CNCC(=O)O)=C1CCC(CO)N(CO)C1CC(=O)O +AssemblyReady CCCCN(C)C(C)CC(=O)NC(CCC(=O)O)CCC(O)C(=O)O +AssemblyReady CC(C)C(=O)N(CCCO)CCCC(=O)C(CC(N)C(=O)O)C(=O)O +INVALID +INVALID +AssemblyReady CC1=C(OC(=O)O)CCCC(C(=O)O)N=C(N)N=C1NC(=O)C(=O)O +AssemblyReady Cc1ccc(-c2cc(C(=O)O)ccc2OCCCC(=O)O)c(C#N)c1 +AssemblyReady CC(=O)OC(O)(CO)CCCNC(C(=O)O)C(C)CCC(=O)C(=O)O +AssemblyReady O=Cc1ccc2cccc(Cc3cccc(C(=O)O)c3C(=O)O)c2c1 +DISCONNECTED CC(=O)O.O.O=C(O)C(F)(F)C(F)(F)F.O=C(O)OCCC(F)(F)F +INVALID +INVALID +AssemblyReady O=C(O)CNCCC(=Cc1ccc(NCC(=O)O)cc1O)C(=O)O +AssemblyReady Cc1c2cc3oc(C(=O)O)c3c(CC(=O)O)c2cc2n1C(=O)OCC2 +AssemblyReady C=CCc1c(C(=O)O)c(OC)c2c3c1C1=CC3(C=C1C(=O)O)CO2 +AssemblyReady O=C(O)C1=C2NC34C5=C2C2(O1)C(=O)NC(C(=O)O)C1C6=CC1(C5=C63)N42 +INVALID +AssemblyReady O=CC(=O)OC(CCCC(=O)O)CC(=O)O +INVALID +AssemblyReady O=C(O)CNS(=O)(=O)c1cccc(N(CC(=O)O)c2ccccc2)c1 +INVALID +AssemblyReady CC(=O)NN=C(CCCC(=O)O)c1ccc(C(F)(F)F)c(C(=O)O)c1 +AssemblyReady O=C(O)CNC(=O)c1cc(-c2ccccc2C(=O)O)c(C(=O)O)s1 +AssemblyReady Cc1cccc(C(=O)O)c1-c1cc2c[nH]c(C(=O)O)cc(O)c3c1c23 +INVALID +AssemblyReady O=C(O)NCCC1Nc2cc(C(=O)O)cc(n2)[N+]1([O-])O +AssemblyReady O=C1OC(C(=O)O)C(C(=O)O)CC23OC=CC=I24C(C2CC2)=CC1=C34 +AssemblyReady COCC(C(=O)O)=C(N)C(C)(O)C(=O)O +INVALID +INVALID +INVALID +AssemblyReady O=C(O)CCCCCC(=O)NCC(=O)c1ccc(C(=O)C(=O)O)cc1 +AssemblyReady CC(C)C(CC(=O)O)(CC(=O)O)OC1CC(O)C(O)C(O)(C(=O)O)C1 +INVALID +AssemblyReady COCCCN(CC1(CC(=O)O)CC1)c1ccccc1C(=O)O +AssemblyReady CC(C)C(C(=O)CCCC(=O)NCCC(=O)O)C(=O)CC(=O)O +AssemblyReady O=C(O)COc1cc2c3cc1=CC1(C(=O)O)C=2C(=C3)c2cccc1c2 +AssemblyReady O=C(O)CC(OC(=O)O)C(=O)NC(CCCC(=O)OC(=O)O)C(=O)O +AssemblyReady CCCNc1c(CC(O)CC(=O)O)ccc(C)c1C(=O)O +AssemblyReady O=C1c2ccc(F)c(c2)[N+]([O-])(O)c2cc1ccc2C(C(=O)O)C(=O)O +INVALID +AssemblyReady CC(C)(C(=O)O)C(=O)N(CCCCC(=O)COC(=O)CN)CC(=O)O +AssemblyReady O=C(NC(C(=O)O)c1sccc1C(=O)O)c1ccc2ccccc2c1 +AssemblyReady Cc1cc(CCCCCC(CCCC(=O)O)C(=O)O)c2c(c1)CCC2 +AssemblyReady CC(=O)OCCCN(CC(=O)O)CC(C)(NC(=O)C(C)C)C(=O)O +AssemblyReady O=C(O)Oc1ccc2cc1C=CC=C(C(=O)O)C=CNC(=O)C(=O)N2 +AssemblyReady CC(C)C1=CC=C(N(CC(=O)O)C(=O)O)CC(C(=O)O)NC=C1 +AssemblyReady Cn1c(C(=O)O)cc(CCC(=O)O)c1SCc1cccc(C(=O)O)c1 +INVALID +AssemblyReady COC(=O)c1ccc(OCCCCCC(=O)O)cc1CC(=O)C(=O)O +AssemblyReady CN1CCc2cc(-c3ccc(C(=O)O)c(CO)c3)c(C(=O)O)cc2C1 +AssemblyReady O=C(O)CCNC(=O)c1ccc(F)cc1-c1cccc(CC(=O)O)c1 +AssemblyReady N#SCN1CCN(c2ccc(C(=O)O)cc2C(=O)O)CC1 +AssemblyReady CC(CCCC1CCC2=C3OC(C)(C=C2)C31C#N)(OC(=O)O)C(=O)O +INVALID +AssemblyReady CC(Nc1ccc(C(=O)O)cc1)=C(C(=O)O)N(CC(=O)O)C(=O)CO +AssemblyReady O=C(O)CCC(CC(=O)CCC1=C=C(CC(S)C(=O)O)N=C1)C(O)O +AssemblyReady O=C(O)COc1ccccc1OCC(=O)Nc1cccc(C(=O)O)c1 +AssemblyReady COc1c(N2C=CC3(C(=O)O)C=C23)c2c(n1C(=O)NCCC(=O)O)O2 +AssemblyReady O=C(O)CC12C=CC(OC(F)(F)F)=CC1(C(=O)O)c1cccc2n1 +AssemblyReady CNC(CC(CCC1CC(C(=O)O)=C(CO)C[SH]1O)C(=O)O)C(N)=O +AssemblyReady CCc1cc(C(=O)O)c2c(c1)c(C(C)=O)cn2C(=O)C(=O)O +AssemblyReady CC(CC(=O)O)C(C=O)OC(=O)O +AssemblyReady CN(C(C=O)CNc1ccc(CC(=O)O)cc1OC(=O)O)S(C)(=O)=O +AssemblyReady CCC(=CCCCCCC(O)(O)C(=O)C(=O)O)CCCCCC(=O)O +AssemblyReady CC=CS(C)=CNc1ccc(C(=O)O)cc1C(=O)O +AssemblyReady CC(=CCCC1CCC(C(=O)O)CC1)CCC(O)CC(=O)CC(=O)O +AssemblyReady Cc1ccc(C)c(NC(C)C(=O)N2CCC(C(=O)O)CC2C(=O)O)c1 +INVALID +AssemblyReady Cc1nc(-c2cc(CC(=O)O)ccc2OCCC(=O)O)cs1 +INVALID +AssemblyReady O=C(O)OC1CC2OC13C2OC1=NC=NC2=NC=NC21C3OC(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s456_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s456_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..f36fbd6854ec8b3f2975ccd4cf91eae3b0054309 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_guid_s456_clean.smi @@ -0,0 +1,100 @@ +O=C(O)C1=CC23C=CC14NC2CCC43C(=O)O +CC1=C(C(=O)O)C2(OC(=O)O)OC2=C1C(O)C(CS(=O)(=O)O)C(=O)O +O=C(O)CCC(=O)C(=O)O +CCCC(=C(N)C(=O)O)C(C(=O)O)C(=O)O +INVALID +CCOC(=O)C(O)(CC(OCC)C(=CC(=O)O)OCCOC)C(=O)O +O=C(O)CCC(=O)C(=O)C(=O)O +CC(Cc1cc(CNC(=O)CSCC(=O)O)ccc1C(=O)O)C(=O)O +CC1(C)C(C(=O)O)C(C(=O)O)NC(=O)C2CC3C4(C2)C1C1=S2C=C1C324 +CCNC(C)(CCCC(C)CNC(=O)C(=O)O)C(=O)O +O=C(O)c1ccc(-c2c(C(=O)O)ncc3cc(O)c(O)cc23)cc1O +O=C(O)CCCC(NCc1cccc(Cc2ccccc2)c1)C(=O)O +INVALID +COCCNC(=O)C1=CC(S(=O)(=O)O)=CCC(CC(=O)O)=C1C(=O)O +CC1=C(C)C2C3C4C(C(=O)O)=C(C(=O)O)OC56C2C1=CC31CC15C46O +CCN(C)C(O)(O)C(Cc1ccc(OC)c(OC(=O)O)c1)NC(=O)O +O=C(O)c1cc(O)ccc1Nc1ccc(C(=O)O)c(C(=O)O)c1 +CCCCCCC(O)C(CC=CCCCCC(=O)O)NC(=O)C(=O)O +INVALID +CC(Oc1cccc(C(=O)O)c1CCC(O)c1ccccc1)C(=O)O +COc1ccc(C(=O)O)c(C2=C(C3=CC(O)=C3C(=O)O)O2)c1O +O=C(O)Cc1oc2nc1C#CC1(C(=O)O)CCC1=CC=CC=COC2 +CCCCCC(C)(NC(=O)C(CC)NC(=O)CC(N)C(=O)O)C(=O)O +CC(C(=O)O)C12C=CC(=O)CC(C(=O)O)(C=C1)C=C2 +CC1=CN(CC(=O)O)C(C(=O)O)C(C)=C1 +C=CC(=O)OCCC(CN(CCO)c1cccc(C(=O)O)c1)C(=O)O +CCCN(C)C(=O)C(NC(=O)O)C(C)C(=O)O +O=C(O)NC(CCc1ccccc1)C1=CC2(C=CC=C1)CC2C(=O)O +NC(CNCC(=O)O)=C1CCC(CO)N(CO)C1CC(=O)O +CCCCN(C)C(C)CC(=O)NC(CCC(=O)O)CCC(O)C(=O)O +CC(C)C(=O)N(CCCO)CCCC(=O)C(CC(N)C(=O)O)C(=O)O +INVALID +INVALID +CC1=C(OC(=O)O)CCCC(C(=O)O)N=C(N)N=C1NC(=O)C(=O)O +Cc1ccc(-c2cc(C(=O)O)ccc2OCCCC(=O)O)c(C#N)c1 +CC(=O)OC(O)(CO)CCCNC(C(=O)O)C(C)CCC(=O)C(=O)O +O=Cc1ccc2cccc(Cc3cccc(C(=O)O)c3C(=O)O)c2c1 +CC(=O)O.O.O=C(O)C(F)(F)C(F)(F)F.O=C(O)OCCC(F)(F)F +INVALID +INVALID +O=C(O)CNCCC(=Cc1ccc(NCC(=O)O)cc1O)C(=O)O +Cc1c2cc3oc(C(=O)O)c3c(CC(=O)O)c2cc2n1C(=O)OCC2 +C=CCc1c(C(=O)O)c(OC)c2c3c1C1=CC3(C=C1C(=O)O)CO2 +O=C(O)C1=C2NC34C5=C2C2(O1)C(=O)NC(C(=O)O)C1C6=CC1(C5=C63)N42 +INVALID +O=CC(=O)OC(CCCC(=O)O)CC(=O)O +INVALID +O=C(O)CNS(=O)(=O)c1cccc(N(CC(=O)O)c2ccccc2)c1 +INVALID +CC(=O)NN=C(CCCC(=O)O)c1ccc(C(F)(F)F)c(C(=O)O)c1 +O=C(O)CNC(=O)c1cc(-c2ccccc2C(=O)O)c(C(=O)O)s1 +Cc1cccc(C(=O)O)c1-c1cc2c[nH]c(C(=O)O)cc(O)c3c1c23 +INVALID +O=C(O)NCCC1Nc2cc(C(=O)O)cc(n2)[N+]1([O-])O +O=C1OC(C(=O)O)C(C(=O)O)CC23OC=CC=I24C(C2CC2)=CC1=C34 +COCC(C(=O)O)=C(N)C(C)(O)C(=O)O +INVALID +INVALID +INVALID +O=C(O)CCCCCC(=O)NCC(=O)c1ccc(C(=O)C(=O)O)cc1 +CC(C)C(CC(=O)O)(CC(=O)O)OC1CC(O)C(O)C(O)(C(=O)O)C1 +INVALID +COCCCN(CC1(CC(=O)O)CC1)c1ccccc1C(=O)O +CC(C)C(C(=O)CCCC(=O)NCCC(=O)O)C(=O)CC(=O)O +O=C(O)COc1cc2c3cc1=CC1(C(=O)O)C=2C(=C3)c2cccc1c2 +O=C(O)CC(OC(=O)O)C(=O)NC(CCCC(=O)OC(=O)O)C(=O)O +CCCNc1c(CC(O)CC(=O)O)ccc(C)c1C(=O)O +O=C1c2ccc(F)c(c2)[N+]([O-])(O)c2cc1ccc2C(C(=O)O)C(=O)O +INVALID +CC(C)(C(=O)O)C(=O)N(CCCCC(=O)COC(=O)CN)CC(=O)O +O=C(NC(C(=O)O)c1sccc1C(=O)O)c1ccc2ccccc2c1 +Cc1cc(CCCCCC(CCCC(=O)O)C(=O)O)c2c(c1)CCC2 +CC(=O)OCCCN(CC(=O)O)CC(C)(NC(=O)C(C)C)C(=O)O +O=C(O)Oc1ccc2cc1C=CC=C(C(=O)O)C=CNC(=O)C(=O)N2 +CC(C)C1=CC=C(N(CC(=O)O)C(=O)O)CC(C(=O)O)NC=C1 +Cn1c(C(=O)O)cc(CCC(=O)O)c1SCc1cccc(C(=O)O)c1 +INVALID +COC(=O)c1ccc(OCCCCCC(=O)O)cc1CC(=O)C(=O)O +CN1CCc2cc(-c3ccc(C(=O)O)c(CO)c3)c(C(=O)O)cc2C1 +O=C(O)CCNC(=O)c1ccc(F)cc1-c1cccc(CC(=O)O)c1 +N#SCN1CCN(c2ccc(C(=O)O)cc2C(=O)O)CC1 +CC(CCCC1CCC2=C3OC(C)(C=C2)C31C#N)(OC(=O)O)C(=O)O +INVALID +CC(Nc1ccc(C(=O)O)cc1)=C(C(=O)O)N(CC(=O)O)C(=O)CO +O=C(O)CCC(CC(=O)CCC1=C=C(CC(S)C(=O)O)N=C1)C(O)O +O=C(O)COc1ccccc1OCC(=O)Nc1cccc(C(=O)O)c1 +COc1c(N2C=CC3(C(=O)O)C=C23)c2c(n1C(=O)NCCC(=O)O)O2 +O=C(O)CC12C=CC(OC(F)(F)F)=CC1(C(=O)O)c1cccc2n1 +CNC(CC(CCC1CC(C(=O)O)=C(CO)C[SH]1O)C(=O)O)C(N)=O +CCc1cc(C(=O)O)c2c(c1)c(C(C)=O)cn2C(=O)C(=O)O +CC(CC(=O)O)C(C=O)OC(=O)O +CN(C(C=O)CNc1ccc(CC(=O)O)cc1OC(=O)O)S(C)(=O)=O +CCC(=CCCCCCC(O)(O)C(=O)C(=O)O)CCCCCC(=O)O +CC=CS(C)=CNc1ccc(C(=O)O)cc1C(=O)O +CC(=CCCC1CCC(C(=O)O)CC1)CCC(O)CC(=O)CC(=O)O +Cc1ccc(C)c(NC(C)C(=O)N2CCC(C(=O)O)CC2C(=O)O)c1 +INVALID +Cc1nc(-c2cc(CC(=O)O)ccc2OCCC(=O)O)cs1 +INVALID +O=C(O)OC1CC2OC13C2OC1=NC=NC2=NC=NC21C3OC(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s123.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s123.smi new file mode 100644 index 0000000000000000000000000000000000000000..8948f869e798f547240b2859a0f2938c785752e6 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s123.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C=C(c1ccccc1OC)N1CCN(C)CCOc2ccccc21 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCC(=O)C1=Nc2cccc(c2)NCC(C2CC2)=C1.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CO.COC(=O)O.Cc1ccccc1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s123_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s123_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..597d5701e9e070c205ec50397abb1f744011b52e --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s123_clean.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.C=C(c1ccccc1OC)N1CCN(C)CCOc2ccccc21 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.CCC(=O)C1=Nc2cccc(c2)NCC(C2CC2)=C1.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.CO.COC(=O)O.Cc1ccccc1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s42.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s42.smi new file mode 100644 index 0000000000000000000000000000000000000000..f8ae44f9a5eccbfaf1bbde24a7034c3b3a97bf9f --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s42.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C=C1CC=CC(C)=C(CC)C=Cc2cccc1c2[SH](=C)=O.CN.Cl +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCC.Nc1ccccc1CNCCn1ncc2cncnc21.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C.C.C.C.C.CCCC(C)C(=O)NC +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +Valid CC1=C2CCC3=C(CCCCCCC1)C14N=C(OC1CCC3)C2N4 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s42_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s42_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..aece3fe2679c128f0e78b83ca2af2f9cabdbea4c --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s42_clean.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.C=C1CC=CC(C)=C(CC)C=Cc2cccc1c2[SH](=C)=O.CN.Cl +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.CCC.Nc1ccccc1CNCCn1ncc2cncnc21.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.C.C.C.C.C.CCCC(C)C(=O)NC +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +CC1=C2CCC3=C(CCCCCCC1)C14N=C(OC1CCC3)C2N4 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s456.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s456.smi new file mode 100644 index 0000000000000000000000000000000000000000..67b630355bf791349bbddb1cb2eedccaaefae3bb --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s456.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C.CCN(CC)CC1=C(C)N1CC.c1ccccc1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CC(O)O.CCOc1ccc(C(C)=O)cc1.N.O=[SH](=O)O.S +DISCONNECTED C.CCOc1ccccccc(O)c(C(N)=NC2Cc3ccc2o3)c1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CC1C(=O)N(Cc2ccc(CCO)cc2)CC1CC(C)(C)C(=O)O.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s456_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s456_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..87c54c101d687175f94035ba41efbb3a6e8cec7d --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/base_noguid_s456_clean.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.C.CCN(CC)CC1=C(C)N1CC.c1ccccc1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +CC(O)O.CCOc1ccc(C(C)=O)cc1.N.O=[SH](=O)O.S +C.CCOc1ccccccc(O)c(C(N)=NC2Cc3ccc2o3)c1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +CC1C(=O)N(Cc2ccc(CCO)cc2)CC1CC(C)(C)C(=O)O.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/defog_local_edit_20260727_214030_final.json b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/defog_local_edit_20260727_214030_final.json new file mode 100644 index 0000000000000000000000000000000000000000..c9fb351cb006f8168395a00fa6eaf7b1e34b40d0 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/defog_local_edit_20260727_214030_final.json @@ -0,0 +1,2502 @@ +[ + { + "sample_idx": 0, + "valid": 1, + "smiles": "Cc1c(NCC(=O)O)cccc1C(=O)O", + "checks": { + "connected": true, + "attachment_sites": true, + "ring_closure": true, + "long_chain": true, + "linker_atom_count": true, + "protac_mw": true, + "representational": true, + "sa_score": true + }, + "properties": { + "mf": "C10H11NO4", + "mw": 209.2, + "polararea": 86.63, + "xlogp": 1.19, + "heavycnt": 15, + "hbonddonor": 3, + "hbondacc": 3, + "rotbonds": 4 + } + }, + { + "sample_idx": 1, + "valid": 1, + "smiles": "Cc1cccc(C)c1OCC(=O)Nc1cc(C(=O)O)cc(C(=O)O)c1", + "checks": { + "connected": true, + "attachment_sites": true, + "ring_closure": true, + "long_chain": true, + "linker_atom_count": true, + "protac_mw": true, + "representational": true, + "sa_score": true + }, + "properties": { + "mf": "C18H17NO6", + "mw": 343.34, + "polararea": 112.93, + "xlogp": 2.72, + "heavycnt": 25, + "hbonddonor": 3, + "hbondacc": 4, + "rotbonds": 6 + } + }, + { + "sample_idx": 2, + "valid": 0, + "smiles": "", + "checks": { + "connected": false, + "attachment_sites": false, + "ring_closure": true, + "long_chain": true, + "linker_atom_count": false, + "protac_mw": true, + "representational": true, + "sa_score": false + }, + "properties": { + "mf": "", + "mw": 0.0, + "polararea": 0.0, + "xlogp": 0.0, + "heavycnt": 0, + "hbonddonor": 0, + "hbondacc": 0, + "rotbonds": 0 + } + }, + { + "sample_idx": 3, + "valid": 0, + "smiles": "O=C(O)N1CCC(C2CCC2)C(N(C(=O)O)c2ccc(F)c(F)c2)C1", + "checks": { + "connected": true, + "attachment_sites": false, + "ring_closure": true, + "long_chain": true, + "linker_atom_count": true, + "protac_mw": true, + "representational": true, + "sa_score": true + }, + "properties": { + "mf": "C17H20F2N2O4", + "mw": 354.35, + "polararea": 81.08, + "xlogp": 3.62, + "heavycnt": 25, + "hbonddonor": 2, + "hbondacc": 2, + "rotbonds": 3 + } + }, + { + "sample_idx": 4, + "valid": 0, + "smiles": "", + "checks": { + "connected": false, + "attachment_sites": false, + "ring_closure": true, + "long_chain": true, + "linker_atom_count": false, + "protac_mw": true, + "representational": true, + "sa_score": false + }, + "properties": { + "mf": "", + "mw": 0.0, + "polararea": 0.0, + "xlogp": 0.0, + "heavycnt": 0, + "hbonddonor": 0, + "hbondacc": 0, + "rotbonds": 0 + } + }, + { + "sample_idx": 5, + "valid": 1, + "smiles": "CCOC(=O)CN(C(=O)C(=O)O)C(=O)C1(C(=O)O)CC(C)CC(C)C1", + "checks": { + "connected": true, + "attachment_sites": true, + "ring_closure": true, + "long_chain": true, + "linker_atom_count": true, + "protac_mw": true, + "representational": true, + "sa_score": true + }, + "properties": { + "mf": "C16H23NO8", + "mw": 357.36, + "polararea": 138.28, + "xlogp": 0.52, + "heavycnt": 25, + "hbonddonor": 2, + "hbondacc": 6, + "rotbonds": 5 + } + }, + { + "sample_idx": 6, + "valid": 1, + "smiles": "C=CC12OC1(C=CC(=O)O)C(OCCCO)C(C(=O)O)C2(O)C(C)=O", + "checks": 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C.CCC.O=C(O)CCC(=O)O +DISCONNECTED CCC(=O)O.CCCC(=O)O +AssemblyReady CCN(NC(=O)O)C(=O)O +AssemblyReady COCCC(OC(=C(N)COCCO)C(C)(C)C(=O)O)C(=O)O +AssemblyReady C#CCC(=CCCNCCOCCC(=O)O)N1CCC(C(=O)O)CC1 +AssemblyReady CNCCC(CCCC(=O)O)CCC(=O)O +DISCONNECTED N.O=C(O)C(CO)C(=O)O +AssemblyReady CCCCCC(NC(C)C(=O)O)C(=O)O +AssemblyReady COC(C)OCCC(OC(=O)O)C1(C)CCC(C(=O)O)CO1 +AssemblyReady NC(CCCC1(C(=O)O)CCC1)C(=O)O +AssemblyReady CN1N=NCC1(C(=O)O)C(=O)O +AssemblyReady CC(C)(C)OCCN(CCCC(=O)O)CCC(=O)O +AssemblyReady CCC(CC(=O)O)C(=O)O +AssemblyReady CCC(CCCC(C)=NN(CC)CC(=O)O)C(=O)O +AssemblyReady O=C(O)CCC(=O)CC(=O)O +AssemblyReady CC(C)C(=CC(=O)O)C(=O)O +AssemblyReady CCC(CC(=O)O)C(=O)O +AssemblyReady CCCNC(C)=C(C)CC(C(=O)O)C(=O)O +AssemblyReady O=C(O)CCC(=O)C(=O)O +AssemblyReady CCC(CCNC(C)=O)(CCC(=O)O)OC(=O)O +AssemblyReady O=C(O)CC(C(=O)O)C(=O)O +AssemblyReady NCCOC(=O)CCN(CCCCCCC(=O)O)CCC(=O)O +AssemblyReady O=C(O)CCC(=CCOc1ccccc1)C(=O)O +AssemblyReady O=C(O)CCCCCC(=O)NCC1CC(C(=O)O)C1 +AssemblyReady CCCC(C(=O)O)C(=O)O +AssemblyReady CN(CCCCCC(=O)O)CC(=O)O +DISCONNECTED C.O=C(O)CCCC(=O)O +AssemblyReady CC(OCCCCC(O)CCCCCCC(=O)O)C(=O)O +AssemblyReady CCN(C(=O)NC)C(C(=O)O)C(=O)O +AssemblyReady O=C(O)CCCC(=O)CC(=O)O +AssemblyReady CCCCN(CCCC(=O)O)CCC(=O)C(=O)O +AssemblyReady CCCN(CCCCCCCC(=O)O)CC(=O)O +AssemblyReady NCCC(CC(=O)O)C(=O)O +AssemblyReady CC(C)(C)NC(CCCCC(=O)O)C(=O)O +AssemblyReady O=C(O)CCCCCCCCC(=O)O +AssemblyReady O=C(O)OCCOCCOC1CC(C(=O)O)OCCON1 +AssemblyReady CC(C)(C)C(C(=O)O)C(=O)O +AssemblyReady O=C(O)CC1CCC(C(=O)O)CC1 +AssemblyReady O=C(O)NCCCCCNCCCCCCNNC(=O)O +AssemblyReady Cc1nc(C)n(C)c1C(=O)C=C(CC(=O)O)C(=O)O +AssemblyReady NC(CCC(=O)O)CC(=O)O +AssemblyReady O=C(O)C(CCC1(CCCO)CC1)C(=O)O +AssemblyReady CC(=O)NC(CCCC(=O)O)CCCC(=O)O +AssemblyReady CCC(C)(C(=O)O)C(=O)O +AssemblyReady C=C(C(=O)O)c1occc1CC(=O)O +DISCONNECTED C.N=O.O=C(O)CCC(=O)O +AssemblyReady CCNCC(C)CC(C(=O)O)C(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_guid_s456_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_guid_s456_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..d0c501b8f9990f01f65a3f6a96b8e4befe851b8d --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_guid_s456_clean.smi @@ -0,0 +1,100 @@ +INVALID +CCOCCOCCCCOCCOCCOC(C)(C(=O)O)C(=O)O +O=C(O)C1CC(C(=O)O)C1 +CCN(CC)C(C)OC(C)=C(C(=O)O)C(=O)O +CC(C)CN(CC(=O)O)C(C)(C)C(=O)O +INVALID +CCCCCCC(CC(CO)OCCC(=O)O)C(=O)O +CCC(CCC(=O)O)C(=O)O +CCOCCOOCC(OCC)(C(C)OCC(=O)O)C(C)OC(=O)O +CCCCN(CC(=O)O)CC(=O)O +C#CCCN(CCCN(C)c1cccc(CC(=O)O)c1)CCC(=O)O +CC(C(=O)O)N(C)CC(=O)O +INVALID +CC(N)CCC(C(=O)O)C(=O)O +CC(C(=O)O)=C(C)C(=O)O +CCCCC(NC)=C(C(=O)O)C1CC(C)(C(=O)O)C1 +CC(CO)C(CCC(O)C(=O)O)CC(=O)O +CCOC(C)(C(C)=CN)C(C(=O)O)C(=O)O +CC(OCCN1CCC1)=C(OCC(=O)O)C(=O)O +CCC(CC(=O)O)C(=O)O +CCC(CC)(CCC(=O)O)C(C)OCC(=O)O +CCOC(C)N(C)C(C(=O)O)C(C=N)C(=O)O +O=C(O)CCCCNC(=O)O +CCOC(COC)=C(CCCC(=O)O)C(=O)O +CCCCCC(CC(=O)O)CC(=O)C(=O)O +INVALID +CC(C)(CO)N(CCCCC(=O)O)CCC(=O)O +COC(COC(C)CC(C)(C)CC(=O)O)C(=O)O +COC(C)(C)CC(CC(=O)O)=C(CC(=O)O)OCCO +CCCCCCC(C(=O)O)C(=O)O +CC(=O)O.O=C(O)COCCO +CC(=CC(=O)O)N(C=N)C(C)C(=O)O +CC(=O)O.CCCC(=O)O +CCC(C(=O)O)C(C)(N)CC(=O)O +COCCCC(C(=O)O)=C(C(=O)O)N1CCN(C)CC1 +CC(OCC(C)(C)CCC(=O)O)C(=O)O +COCC(=O)C1=C(NCCC(C(=O)O)C(=O)O)COC1 +CCCCC(=O)NC(CC(=O)O)C(=O)O +CCC(CC)(CC(=O)O)C(=O)O +Cc1ccc(C(NC(=O)COCC=O)C(=O)O)n1CCCCC(=O)O +COCCN(CCC(C)NCCOCC(=O)O)CC(=O)O +CC(CC(=O)O)N(CC(=O)O)C(=O)CCO +C=C1NCCC(C(=O)O)=C1C(=O)O +CCCc1[nH]c(C)cc1OC(COCCOC(C)CC(=O)O)C(=O)O +CC(C)C(C(=O)O)N(C)CC(=O)O +CCC(C)C(C(=O)O)C(=O)O +CNC(CC1CCC(NC(CO)C(=O)O)CC1)C(=O)O +O=C(O)CCCCCCCCCCCCC(=O)O +NC(=O)C(C(=O)O)C(=O)O +COCCOCCCCCCCC(CC(=O)O)C(=O)O +C.O=C(O)CCCC(=O)O +O=C(O)CCCN1CCCCC(C(=O)O)C1 +CC(CCCC(=O)O)C(=O)O +C.CCC.O=C(O)CCC(=O)O +CCC(=O)O.CCCC(=O)O +CCN(NC(=O)O)C(=O)O +COCCC(OC(=C(N)COCCO)C(C)(C)C(=O)O)C(=O)O +C#CCC(=CCCNCCOCCC(=O)O)N1CCC(C(=O)O)CC1 +CNCCC(CCCC(=O)O)CCC(=O)O +N.O=C(O)C(CO)C(=O)O +CCCCCC(NC(C)C(=O)O)C(=O)O +COC(C)OCCC(OC(=O)O)C1(C)CCC(C(=O)O)CO1 +NC(CCCC1(C(=O)O)CCC1)C(=O)O +CN1N=NCC1(C(=O)O)C(=O)O +CC(C)(C)OCCN(CCCC(=O)O)CCC(=O)O +CCC(CC(=O)O)C(=O)O +CCC(CCCC(C)=NN(CC)CC(=O)O)C(=O)O +O=C(O)CCC(=O)CC(=O)O +CC(C)C(=CC(=O)O)C(=O)O +CCC(CC(=O)O)C(=O)O +CCCNC(C)=C(C)CC(C(=O)O)C(=O)O +O=C(O)CCC(=O)C(=O)O +CCC(CCNC(C)=O)(CCC(=O)O)OC(=O)O +O=C(O)CC(C(=O)O)C(=O)O +NCCOC(=O)CCN(CCCCCCC(=O)O)CCC(=O)O +O=C(O)CCC(=CCOc1ccccc1)C(=O)O +O=C(O)CCCCCC(=O)NCC1CC(C(=O)O)C1 +CCCC(C(=O)O)C(=O)O +CN(CCCCCC(=O)O)CC(=O)O +C.O=C(O)CCCC(=O)O +CC(OCCCCC(O)CCCCCCC(=O)O)C(=O)O +CCN(C(=O)NC)C(C(=O)O)C(=O)O +O=C(O)CCCC(=O)CC(=O)O +CCCCN(CCCC(=O)O)CCC(=O)C(=O)O +CCCN(CCCCCCCC(=O)O)CC(=O)O +NCCC(CC(=O)O)C(=O)O +CC(C)(C)NC(CCCCC(=O)O)C(=O)O +O=C(O)CCCCCCCCC(=O)O +O=C(O)OCCOCCOC1CC(C(=O)O)OCCON1 +CC(C)(C)C(C(=O)O)C(=O)O +O=C(O)CC1CCC(C(=O)O)CC1 +O=C(O)NCCCCCNCCCCCCNNC(=O)O +Cc1nc(C)n(C)c1C(=O)C=C(CC(=O)O)C(=O)O +NC(CCC(=O)O)CC(=O)O +O=C(O)C(CCC1(CCCO)CC1)C(=O)O +CC(=O)NC(CCCC(=O)O)CCCC(=O)O +CCC(C)(C(=O)O)C(=O)O +C=C(C(=O)O)c1occc1CC(=O)O +C.N=O.O=C(O)CCC(=O)O +CCNCC(C)CC(C(=O)O)C(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s123.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s123.smi new file mode 100644 index 0000000000000000000000000000000000000000..eb20e0491223933b9805adf81a2c6974bc0b6dad --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s123.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +Valid CCC(CC)CCCC(C)O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CC(N)C(C)C(C)CO +DISCONNECTED CCNCC(O)=COO.CO +INVALID +DISCONNECTED C.C=O.COCCCCNC(C)=O.NCCC1CCN1 +DISCONNECTED C.C.C.C.CCCNC.CCCNCO +DISCONNECTED C.C.C=C=O.C=CCOO +DISCONNECTED C.CCCC.CCCN.O +DISCONNECTED C.CCCOC(C)=COC +DISCONNECTED C.C=CC.CCCCC.O +DISCONNECTED C.C.C.C.C.C.C=C(C)C +DISCONNECTED C.C.C#CCOC.C=C1C=COCC1 +DISCONNECTED CC1CCC1C.CCNN=NN(C)CC +INVALID +INVALID +DISCONNECTED C.C.C.C=C.CC.COC +INVALID +INVALID +INVALID +DISCONNECTED C.C#CN.C=CNC.OCCC=NOCO +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCCC.CCOCCOCCOCCOC +INVALID +INVALID +DISCONNECTED C.C.C.CCCO.O.O=O +DISCONNECTED C.C.C.CC.CC.CNC +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CCC.CCC(C)CO.N +INVALID +INVALID +INVALID +DISCONNECTED C.C.C1CCC2(C1)CO2.N +INVALID +DISCONNECTED C.C#C.CC(C)O.CNCC(C)n1ccnn1 +DISCONNECTED C=C(C=C(OC)C(=C)OO)NC.CC +INVALID +INVALID +DISCONNECTED C.CC.CCCC.NCOCCCO +DISCONNECTED CCC(N)=O.CCCO.CO.O +INVALID +INVALID +DISCONNECTED CCC=O.CCCCCC1CC1(C)CC(=O)NCC +DISCONNECTED C.CCCCOCCOC.COCCO +DISCONNECTED C.CCC.CCC1OCCOC(C)OC1C +DISCONNECTED C.C.C.CC1(C)CC1O.O +INVALID +DISCONNECTED C.CC=CCCCOC.CO +INVALID +DISCONNECTED C.C.C=NC.CCCCO.O +INVALID +DISCONNECTED CCN.CNCCC(C)OC.N +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C=NCC(OC)C(C)CCNC.CC +INVALID +DISCONNECTED C.CC1CN(C=O)CCN1C.CCOCC.CO +DISCONNECTED C.CC.CCCON(C)C.N +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CC.CCC1(C)CN2CN3CC21CC3=O +DISCONNECTED C.CCCNCC.CCN +INVALID +DISCONNECTED C.CCCCC(CC=O)NN=O.O +Valid CCCC1CC1CN1CC2=C(CCCCNC(O)C1)CC2 +INVALID +DISCONNECTED C.CCCC.CCCOC +INVALID +DISCONNECTED CCC(=O)N(C)C(C)C(O)CO.CN +DISCONNECTED C.C.C.CC.CCC=CO diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s123_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s123_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..708cc601b3413585c36629de6a69a42f272a5e4a --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s123_clean.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +CCC(CC)CCCC(C)O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.CC(N)C(C)C(C)CO +CCNCC(O)=COO.CO +INVALID +C.C=O.COCCCCNC(C)=O.NCCC1CCN1 +C.C.C.C.CCCNC.CCCNCO +C.C.C=C=O.C=CCOO +C.CCCC.CCCN.O +C.CCCOC(C)=COC +C.C=CC.CCCCC.O +C.C.C.C.C.C.C=C(C)C +C.C.C#CCOC.C=C1C=COCC1 +CC1CCC1C.CCNN=NN(C)CC +INVALID +INVALID +C.C.C.C=C.CC.COC +INVALID +INVALID +INVALID +C.C#CN.C=CNC.OCCC=NOCO +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.CCCC.CCOCCOCCOCCOC +INVALID +INVALID +C.C.C.CCCO.O.O=O +C.C.C.CC.CC.CNC +INVALID +INVALID +INVALID +INVALID +INVALID +CCC.CCC(C)CO.N +INVALID +INVALID +INVALID +C.C.C1CCC2(C1)CO2.N +INVALID +C.C#C.CC(C)O.CNCC(C)n1ccnn1 +C=C(C=C(OC)C(=C)OO)NC.CC +INVALID +INVALID +C.CC.CCCC.NCOCCCO +CCC(N)=O.CCCO.CO.O +INVALID +INVALID +CCC=O.CCCCCC1CC1(C)CC(=O)NCC +C.CCCCOCCOC.COCCO +C.CCC.CCC1OCCOC(C)OC1C +C.C.C.CC1(C)CC1O.O +INVALID +C.CC=CCCCOC.CO +INVALID +C.C.C=NC.CCCCO.O +INVALID +CCN.CNCCC(C)OC.N +INVALID +INVALID +INVALID +INVALID +C.C=NCC(OC)C(C)CCNC.CC +INVALID +C.CC1CN(C=O)CCN1C.CCOCC.CO +C.CC.CCCON(C)C.N +INVALID +INVALID +INVALID +INVALID +INVALID +C.CC.CCC1(C)CN2CN3CC21CC3=O +C.CCCNCC.CCN +INVALID +C.CCCCC(CC=O)NN=O.O +CCCC1CC1CN1CC2=C(CCCCNC(O)C1)CC2 +INVALID +C.CCCC.CCCOC +INVALID +CCC(=O)N(C)C(C)C(O)CO.CN +C.C.C.CC.CCC=CO diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s42.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s42.smi new file mode 100644 index 0000000000000000000000000000000000000000..cf52560000461eec387a4252f3120fdd1b87a37e --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s42.smi @@ -0,0 +1,100 @@ +INVALID +DISCONNECTED C.CCCCCCCCCC(CO)OC.O +DISCONNECTED C.C.CC.CN.O=CCO +DISCONNECTED C.C.C=CC=CC=CCO.CC1=NC1 +Valid COCCON1COc2cc(ccc2OC)C(CN)O1 +INVALID +INVALID +DISCONNECTED C.C=CCC1CNC(=C)C1 +DISCONNECTED C.C.C.CC=CCNCC +INVALID +DISCONNECTED C.C=CNCCCCN.N=N +INVALID +INVALID +INVALID +Valid C#CCNC(=O)CCC1(CCCC)CC1 +INVALID +DISCONNECTED C.C.C=O.CC.O=CCO +DISCONNECTED C.CC1CN1C.CCC.O +INVALID +INVALID +DISCONNECTED C.C.C.CCC(N)=O.CO +DISCONNECTED C.C.C.CC(C)C.CCCN.O +DISCONNECTED CCCNOCC.CCNCO.N=O +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C=COCCCCCCN.CCCCC=O.N +INVALID +INVALID +DISCONNECTED C.C1CCCC2CCCCC2CC1 +Valid CC1CCNC2(N)CCC12 +Valid CCOCCCCCCOC(=O)C(CCO)OCCOCC +INVALID +INVALID +DISCONNECTED CC.CC=O.CCCCC.CN +DISCONNECTED C.C=C1C2CCCOCCOCC2N1C.CC +DISCONNECTED C.C.CC12CC1CN2C=O.CC=O.CCO +DISCONNECTED C.C.CCC.CCCN(C)CC.O +DISCONNECTED C.C.CCC.CCCC(C)=O.N +DISCONNECTED COC.O=CNCCCCCCCO +DISCONNECTED CCCC1=C(O)C(C)C(C)CC1.CCCCC.CN +DISCONNECTED CCCCN.CO.O=CO +DISCONNECTED CC.CCCCC(C)OO +DISCONNECTED C.CCCCCCCc1c[nH][nH]1.CNOCCCOCCO +INVALID +INVALID +DISCONNECTED C.CCCCCCCNC(O)CCC +INVALID +DISCONNECTED C.CC.CCCO.CNC +DISCONNECTED CC(C)CCC=C(CO)C(C)C.CNCCNC +DISCONNECTED C.CC.CCCCC.N.N +DISCONNECTED C.C=NNCCCOC1(O)CC12CO2 +DISCONNECTED CCC(=O)CC=C(N)CC.O +INVALID +INVALID +Valid CCCCCOC1C2COCCC(CCCCC)C21 +INVALID +DISCONNECTED C#CC(O)=C(O)CCCCOCCN.CO +DISCONNECTED CC.CC.CCCCCCNN.O +DISCONNECTED C.C.C.CCC.CNC.O.O +INVALID +DISCONNECTED C.CCC.CCCNC.N.O +DISCONNECTED CC.CCC.CCC.N.O +DISCONNECTED C.CCC(O)CCOCCOCCOCCN.CCC1CC1=O +DISCONNECTED C.C.C=CC(=CC(=C)CCNC)CCC +INVALID +DISCONNECTED C=C.C=CC(=C)N(N)C(=O)CCOCN +INVALID +DISCONNECTED C.C.COOCCCNCC(C)C +INVALID +INVALID +DISCONNECTED C.CCCCC(=O)N1CC1CC +INVALID +INVALID +INVALID +DISCONNECTED C.C.C.CCC.CCOC +DISCONNECTED C.C.CCNNNC.N.O +INVALID +INVALID +Valid CCCCCC1CCCCC(=O)N1OC +INVALID +DISCONNECTED CC.CCC=CC1(C(=O)OC)CN1.N +INVALID +INVALID +DISCONNECTED C.C.C.CC.CCC(C)C +DISCONNECTED C.CCC.N.NC(N)O.O +DISCONNECTED CCCCCCCCCC=O.NOCCOCCO +INVALID +DISCONNECTED C.C=NCCC.CC.CCC +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C.C=C(C)N=NN.CCC +INVALID +INVALID +Valid CN1CCN(C)C2(Cc3nnn4c3C(=O)C(C)(CC4)C2)C1 diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s42_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s42_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..7cd18a0efe3a32f15cf2e636bec42a170de76372 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s42_clean.smi @@ -0,0 +1,100 @@ +INVALID +C.CCCCCCCCCC(CO)OC.O +C.C.CC.CN.O=CCO +C.C.C=CC=CC=CCO.CC1=NC1 +COCCON1COc2cc(ccc2OC)C(CN)O1 +INVALID +INVALID +C.C=CCC1CNC(=C)C1 +C.C.C.CC=CCNCC +INVALID +C.C=CNCCCCN.N=N +INVALID +INVALID +INVALID +C#CCNC(=O)CCC1(CCCC)CC1 +INVALID +C.C.C=O.CC.O=CCO +C.CC1CN1C.CCC.O +INVALID +INVALID +C.C.C.CCC(N)=O.CO +C.C.C.CC(C)C.CCCN.O +CCCNOCC.CCNCO.N=O +INVALID +INVALID +INVALID +INVALID +C=COCCCCCCN.CCCCC=O.N +INVALID +INVALID +C.C1CCCC2CCCCC2CC1 +CC1CCNC2(N)CCC12 +CCOCCCCCCOC(=O)C(CCO)OCCOCC +INVALID +INVALID +CC.CC=O.CCCCC.CN +C.C=C1C2CCCOCCOCC2N1C.CC +C.C.CC12CC1CN2C=O.CC=O.CCO +C.C.CCC.CCCN(C)CC.O +C.C.CCC.CCCC(C)=O.N +COC.O=CNCCCCCCCO +CCCC1=C(O)C(C)C(C)CC1.CCCCC.CN +CCCCN.CO.O=CO +CC.CCCCC(C)OO +C.CCCCCCCc1c[nH][nH]1.CNOCCCOCCO +INVALID +INVALID +C.CCCCCCCNC(O)CCC +INVALID +C.CC.CCCO.CNC +CC(C)CCC=C(CO)C(C)C.CNCCNC +C.CC.CCCCC.N.N +C.C=NNCCCOC1(O)CC12CO2 +CCC(=O)CC=C(N)CC.O +INVALID +INVALID +CCCCCOC1C2COCCC(CCCCC)C21 +INVALID +C#CC(O)=C(O)CCCCOCCN.CO +CC.CC.CCCCCCNN.O +C.C.C.CCC.CNC.O.O +INVALID +C.CCC.CCCNC.N.O +CC.CCC.CCC.N.O +C.CCC(O)CCOCCOCCOCCN.CCC1CC1=O +C.C.C=CC(=CC(=C)CCNC)CCC +INVALID +C=C.C=CC(=C)N(N)C(=O)CCOCN +INVALID +C.C.COOCCCNCC(C)C +INVALID +INVALID +C.CCCCC(=O)N1CC1CC +INVALID +INVALID +INVALID +C.C.C.CCC.CCOC +C.C.CCNNNC.N.O +INVALID +INVALID +CCCCCC1CCCCC(=O)N1OC +INVALID +CC.CCC=CC1(C(=O)OC)CN1.N +INVALID +INVALID +C.C.C.CC.CCC(C)C +C.CCC.N.NC(N)O.O +CCCCCCCCCC=O.NOCCOCCO +INVALID +C.C=NCCC.CC.CCC +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.C.C=C(C)N=NN.CCC +INVALID +INVALID +CN1CCN(C)C2(Cc3nnn4c3C(=O)C(C)(CC4)C2)C1 diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s456.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s456.smi new file mode 100644 index 0000000000000000000000000000000000000000..8cae786bbb2e4c0c7fcc84ea3e1264ea0746e342 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s456.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +DISCONNECTED C.CC.CCCC.COO +Valid COCCOCCOCCOCCNC(C)O +INVALID +INVALID +INVALID +DISCONNECTED C.CCCCC.CCO.CN +DISCONNECTED CCCCCCCOCCOCCOC.COCCOCCOC.O +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C=C.CC1CC1C=O.CO.N +Valid CNC1C(C)C1CCOC +INVALID +INVALID +DISCONNECTED C.CCCCCCCCC(=O)C(=O)NCC +INVALID +DISCONNECTED C.CC12CC13CCC(=O)N23 +DISCONNECTED C.C=CC.CCCCN1CCCCO1.CNC +INVALID +DISCONNECTED C.C.CCCCOC.CN.N +INVALID +INVALID +DISCONNECTED C.COCCO.COCCOC(CCCOCCOCCO)COC +Valid O=C1COC2OOCCOC2CCCOCCN1 +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C=CC=C(N=C)NCC.CCCNC +DISCONNECTED C.C.CC(C)C(N)CCN +DISCONNECTED CC.CCOC12CCC(C1)C(C)C2 +INVALID +DISCONNECTED C=C.CNO.O=CC1CCCC(=O)NC1 +INVALID +INVALID +DISCONNECTED C.CCCCCOO.CCN.O +INVALID +DISCONNECTED C#CC.CCNCC(=O)NCCN1CC2C(CC)C2(C)C1 +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CCCCC1(C)CC1CN.O +INVALID +Valid CCCCOCCCCCC(O)(CC)C(C)CC +DISCONNECTED C.NNCCCCNN=S +DISCONNECTED CC=CC=C(C)C(=O)C1CCCCOC1.CCCCN +DISCONNECTED C.C.C.C.C.CN=CNN +DISCONNECTED C.C.C.CC.CCC(=O)NCCCN.CO +DISCONNECTED C.C.C.CCC.CCO.N.O +DISCONNECTED C.CCC.CCO.CCO.O.O +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C=NC.C=O.CNCCN1CCNCC1 +DISCONNECTED C.C.C.C.C.C=C1CN1C +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCCCCCC1CCCC(CC)=C1NC.O.O +INVALID +INVALID +DISCONNECTED C.C.C.C.C.CC=C(C)CC +INVALID +DISCONNECTED C.C.CCC.CCC(C)C +INVALID +DISCONNECTED C.C.CCC.COCCO +INVALID +DISCONNECTED CC(C)N.COC1C=C1C.O +INVALID +DISCONNECTED C.C.CC(=O)C=O.CCc1cccc(C)c1.CN +INVALID +INVALID +DISCONNECTED C.C.C.CCCC(=O)NC.CCN.O +INVALID +INVALID +DISCONNECTED C.C.NCCNC(=O)CCCC=O.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C.CC(N)=C(N)O.CCO +INVALID +INVALID +INVALID +DISCONNECTED C.C.CC=O.CCNOC.O +INVALID +INVALID +DISCONNECTED C.C=CN(C)CCC(C)C +INVALID +DISCONNECTED C.C.CC(O)CC(O)C(N)=O +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s456_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s456_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..c0b2767225f556ec1284b3d7a28fde030fa5e887 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ft_noguid_s456_clean.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +C.CC.CCCC.COO +COCCOCCOCCOCCNC(C)O +INVALID +INVALID +INVALID +C.CCCCC.CCO.CN +CCCCCCCOCCOCCOC.COCCOCCOC.O +INVALID +INVALID +INVALID +INVALID +C.C=C.CC1CC1C=O.CO.N +CNC1C(C)C1CCOC +INVALID +INVALID +C.CCCCCCCCC(=O)C(=O)NCC +INVALID +C.CC12CC13CCC(=O)N23 +C.C=CC.CCCCN1CCCCO1.CNC +INVALID +C.C.CCCCOC.CN.N +INVALID +INVALID +C.COCCO.COCCOC(CCCOCCOCCO)COC +O=C1COC2OOCCOC2CCCOCCN1 +INVALID +INVALID +INVALID +INVALID +C=CC=C(N=C)NCC.CCCNC +C.C.CC(C)C(N)CCN +CC.CCOC12CCC(C1)C(C)C2 +INVALID +C=C.CNO.O=CC1CCCC(=O)NC1 +INVALID +INVALID +C.CCCCCOO.CCN.O +INVALID +C#CC.CCNCC(=O)NCCN1CC2C(CC)C2(C)C1 +INVALID +INVALID +INVALID +INVALID +CCCCC1(C)CC1CN.O +INVALID +CCCCOCCCCCC(O)(CC)C(C)CC +C.NNCCCCNN=S +CC=CC=C(C)C(=O)C1CCCCOC1.CCCCN +C.C.C.C.C.CN=CNN +C.C.C.CC.CCC(=O)NCCCN.CO +C.C.C.CCC.CCO.N.O +C.CCC.CCO.CCO.O.O +INVALID +INVALID +INVALID +INVALID +C.C=NC.C=O.CNCCN1CCNCC1 +C.C.C.C.C.C=C1CN1C +INVALID +INVALID +INVALID +INVALID +C.CCCCCCC1CCCC(CC)=C1NC.O.O +INVALID +INVALID +C.C.C.C.C.CC=C(C)CC +INVALID +C.C.CCC.CCC(C)C +INVALID +C.C.CCC.COCCO +INVALID +CC(C)N.COC1C=C1C.O +INVALID +C.C.CC(=O)C=O.CCc1cccc(C)c1.CN +INVALID +INVALID +C.C.C.CCCC(=O)NC.CCN.O +INVALID +INVALID +C.C.NCCNC(=O)CCCC=O.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.C.CC(N)=C(N)O.CCO +INVALID +INVALID +INVALID +C.C.CC=O.CCNOC.O +INVALID +INVALID +C.C=CN(C)CCC(C)C +INVALID +C.C.CC(O)CC(O)C(N)=O +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ftguid_pool.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ftguid_pool.smi new file mode 100644 index 0000000000000000000000000000000000000000..2100289b1ac8b27f0cbcdd407f0eae49f3e1672a --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ftguid_pool.smi @@ -0,0 +1,300 @@ +C=C(CCCCCOC)NC(=C(O)CC)C(C(=O)O)C(=O)O +CC(C)CC(CO)C(CC(=O)O)CC(=O)O +C.C=CNC(=O)O.O=CO +INVALID +CCC(=O)OCC(CC(O)(CO)C(=O)O)OCC(=O)O +CCCCCCCC1(CCNC(=O)C(=O)O)CC(C(=O)O)C1 +O=C(O)C(=O)CCCC(=O)C(=O)O +CCCC(C(=O)O)C(=O)O +C.CCNC(=O)O.O=CO +CCCCCCCC(C(=O)O)C(=O)O +O=C(O)CC1CC(C(=O)O)C1 +CCCCC(CO)N(C(=O)O)C(=O)O +CCCC1=C(C(OCCOCC(OCCOC)C(=O)O)C(=O)O)O1 +CNC(=O)CCCCCCN(C)C(C(=O)O)C(=O)O +O=C(O)C(CO)CC1CCCC1C(=O)O +CC(CC(=O)O)C(=O)O.O +NC(=O)C(C(=O)O)C(=O)O +CCC(C)(C(=O)O)C(=O)O +CN(CCC(CCO)N1CCC(C(=O)O)CC1)CCN(C)CC(=O)O +CCCCC(C=O)=C(OCC(=O)O)C(=O)O +CCNCC(=O)O.O=CO +O=C(O)CCCCCCC(=O)O +CC(C)OCCCC(C(=O)O)C(=O)O +CCOCCOC(CC(C(=O)O)C(=O)O)=C(N)CO +O=C(O)CCOCCCCCCCOCCOC(CO)CCC(=O)O +INVALID +CCNC(CC(=O)O)=C1CCCC(CO)(CC(=O)O)CCC1 +O=C(O)CCCCCOCCC(=O)CC(=O)O +CCCCCCC(C)C(CC(O)CC(=O)O)OCC(=O)O +CCCCC(NC(=O)CCC(=O)NCCC=O)C(CC(=O)O)C(=O)O +CCC(C=CC(=O)O)=C(C)C(=O)O +O=C(O)NCCO.O=CO +CCOCCCCCCOCC=C(OCCC(=O)O)C(=O)O +CCOCC(CCOC(=O)O)C(=O)O +Cc1c(OCCOC(C)C(=O)O)cc2ccccc2c1OCC(=O)O +CC(C)N(C)C(C(=O)O)C(=O)O +COCCNC(=O)COOCC(=O)O.O=CO +CCC(C)(C)NCCCC(C(=O)O)C(=O)O +CCC(CC(=O)O)C(O)(O)C(=O)O +CC(C=O)=C(CC(=O)O)C(=O)O +O=C(O)CCC(CO)NC(=O)C(=O)O +CCNC(COCCOCC(N)C(=O)O)CC(=O)O +CC(=O)C(=O)O.CC(=O)O +CCCCC(=O)O.O=CO +CCN(CC)C(C(=O)O)=C(C(=O)O)c1cccc(CNC)c1 +C.CC(CC(=O)O)C(=O)O +CC(=NCCC(=O)O)NC(C)CC(=O)O +CCCCCC(O)(CC(=O)O)C(=O)O +O=C(O)CC(CCOCCCN1CC1)CC(=O)O +CC(=O)O.CCCC(=O)O +CCCOC(CC)=C(C)C(CCC(=O)O)C(=O)O +CC(C)C(C(=O)O)C(=O)O +COC(C(=O)O)=C(CC(=O)O)C(C)C +CC(C)(CC(=O)O)CC(=O)O +CCCCCCCC(CC(=O)O)C(=O)O +CC(O)=C(CC(=O)O)C(=O)O +COCCCCCCNCCC(C)(C(=O)O)C(=O)O +CCOCCOC(COCC(=O)O)(C(=O)O)C(C)OCCOCCN +CCCN(C(=O)O)N(C(=O)O)C1CCCC1 +O=C(O)NCCCCC(O)C(=O)O +CC(CCNC(=O)O)C(=O)O +CC(C)(O)C(CN1CCC(C(=O)O)CC1)C(=O)O +CC(C)C(CC(=O)O)C(=O)O +CC.CC.O=CO.O=CO +COCC(O)CN(CCCCCC(=O)O)C(C)OCCC(=O)O +CCCCC(CCCC(=O)O)CC(=O)O +CC(=O)CCOC(=O)C(CC(=O)O)C(=O)O +CCC(OC(=O)CC(N)C(=O)O)C(=O)O +INVALID +CNC(C(=O)O)C(C)C(C)C(=O)O +CC(=O)C(C)C(C(=O)O)N1CCCCC(CC(=O)O)CC1 +CC1=C(C(=O)C(=O)O)CCCCCN1CCCCCCOCC(=O)O +CCCCCC(C(=O)O)C(=O)O +CCOCCOCCN(C)C1(C(=O)O)CCN(CCCC(=O)O)CC1 +CCN(CCC(=O)O)C(=C(OC)OCC(=O)O)C(C)(C)CC +CCCCC(C(=O)O)(C(=O)O)C(O)C=O +CCC(C)(C(=O)O)C(=O)O +CC(C)(C(=O)O)C(=O)O.O +CCCN(CN(CCO)C(O)(CC(=O)O)C(=O)O)C(C)C +CC(C)C(C(=O)O)C(=O)O +CCCNCCOC(=O)O.O=CC(=O)O +CCN(C)CCC(C)(CC(=O)O)CC(=O)O +CCNN(C)CCOC(=O)O.O=CO +O=C(O)C(CCCCCCOCCO)C(=O)O +CCCCCCC(C(=O)O)C(=O)O +CC(CC(=O)O)CC(=O)O +C.NC(CC(=O)O)C(=O)O +CCCCCC(C)(OC(CCOC)C(=O)O)C(=O)O +COC(C)=C(CCC(=O)O)C(=O)O +O=C(O)C(C(=O)O)C1CCC1 +CC(=C(CC(=O)O)C(=N)C(=O)O)N1CCCCC1 +CCCCC(NC(=O)C(=O)O)C(=O)O +CCOCCOC(OC(=O)O)C(CCC(=O)O)OC +INVALID +CCCCCC(=O)NCC(O)(C(=O)O)C(=O)O +INVALID +CCC(CCC(=O)O)C(=O)O +CCC(CC(=O)O)C(=O)O +CCCCCC(C(=O)O)C(C)(C)C(=O)O +CCN(CC(=O)O)CC(=O)NCCCCCCCC(=O)O +CC(=O)O.CCOCC(=O)O +CCC(CC)=C(CC(=O)O)C(=O)O +CC(CCC(=O)O)C(=O)O +CCCC(=O)NC(C(=O)O)C1CCCC(C(=O)O)CCC1 +O=C(O)C(=O)C1CCC(C(=O)O)CC1 +CC(C(=O)O)C(C)(C)C(=O)O +CCCCNC(CC(=O)O)C(=O)O +NC(=O)CCC(CCCC(=O)O)CCCC(=O)O +CN(CCCCCNC(=O)O)CCOCCCCNC(=O)O +CCCC(CCC(=O)O)C(N)C(=O)O +CC(C)N(C)CCC(C(=O)O)C(=O)O +CCc1c(C(C)(CC(=O)O)C(=O)O)c(C)c(C)n(C)c1=O +CNCC(C(=O)O)N(CCCCC(=O)O)CCOCCOC +INVALID +CCC(C)(C(=O)O)C(CC(=O)O)OC +INVALID +CCCC(C(=O)O)C(=O)O +C.CCC(=NC(=O)O)C(=O)O +CC(C)(CCC(=O)O)C(=O)O +INVALID +CCc1cc(OC(=O)O)cc(C(=O)O)c1 +CC(=O)O.NC(=O)C(=O)O +CC(C(=O)O)=C(C)C(=O)O +CC(CCC(=O)O)C(=O)O +N.NC(CC(=O)O)C(=O)O +CC(CCC(=O)O)C(=O)O +CCCCCC(=N)C(C(=O)O)C(=O)O +O=C(O)CCCCNC(=O)CCC(=O)O +CC(C(=O)O)=C(C)C(=O)O +CCOC(=O)CCC(CC(=O)O)C(=O)O +CCC(CC(=O)O)C(=O)O +O=C(O)NCC#CCCCCNC(=O)O +CC(C(=O)O)=C(C)C(=O)O +Cc1cc(C(=O)O)cc2n[nH]c(C(=O)O)c12 +C#CCCCCC(C)=C(CC(=O)O)C(=O)O +CCC(C(=O)O)C(O)(C(=O)O)C(C)C +O=C(O)C1CC(C(=O)O)C1 +INVALID +O=C(O)CCCCNC(CCC(=O)O)=NCCCOCCCO +CCOCC(OCC(NCC(=O)O)C(=O)O)C1(CO)CCCCC1 +O=C(O)NCC1CC[N+]2([O-])NC(C(=O)O)CCCCCC12 +O=C(O)CCN1CCC(C(=O)O)CC1 +CCCN(CC)C(C(=O)O)C(=O)O +CCCCCCNCCN(CC(=O)O)CC(=O)O +CC(CC(=O)O)C(CCN)CCC(=O)O +CNCC(C)(C(=O)O)C(=O)O +CC(C(=O)O)C(=O)C(=O)O +C#N.O=C(O)CCC(=O)O +CCNC(CCCCCC(=O)O)CCC(=O)O +COC(C(=O)O)=C(C)CC(C(=O)O)C(=O)O +INVALID +C=C.O=C(O)CCCCCC(=O)O.O=S +CNCCC(CCC(CCC(C)C)C(=O)O)C(=O)O +C.CC.O=C(O)OC(=O)O +INVALID +INVALID +CCCCN(CCC(=O)O)C(CC)CCC(=O)O +CC=C(CC(=O)O)C(=O)O +INVALID +NCCCNCCN(CCC(=O)O)CC(=O)O +O=C(O)CCCCCCC(=O)C(=O)O +CCCCC(C(=O)O)C(=O)O +C=C(O)C(OCCCCCC(=O)O)C(=O)O +CNC(C)(CCC(C)C(=O)O)C(=O)O +O=C(O)C=CCCCCC(=O)O +NCCCCCCCCCCNC(C(=O)O)=C(CCC(=O)O)C(=O)O +O=C(O)CCCCC(NCCC(=O)O)=C1CCCCNC1 +CCC=NNC(C)=C(CCCCCC(=O)O)C(=O)O +CCC(C)=C(CC(O)(O)C(=O)O)C(=O)O +CCCNN(C(=O)O)C(=O)CC(C)C(=O)O +C.CC.N.O=CO.O=CO +O=C(O)CCN1CCCCCC1C(=O)O +CC=C(CCC(=O)O)C(=O)O +CS12=C3C=1C32CCN(CCCC(=O)O)CC(=O)O +CC(COC(=O)O)C(=O)O.O +CCC(CN)CC(C(=O)O)C(=O)O +CCCC(C(=O)O)=C(C)C(=O)O +CCCCCCCCC(CC(=O)O)C(=O)O +CC(=CC1=C=NN1CCC(=O)O)NOC(=O)O +CN(C)C1CC(CCC(CO)=C(CC(=O)O)C(=O)O)C1 +O=C(O)CCOCCOCCOCCOCCOCCC(=O)C(=O)O +O=C(O)CCCC(CCO)C(=O)C(=O)O +O=C(O)CCNCCC(OCCOCCCN1CCCCC1)C(=O)O +C=C(CCc1ccnc(CCC(=O)O)c1)C(=O)O +CCCCNC(=O)O.O=CO +O=C(O)CN(CO)C(=O)O +CCC(COC)C(C)(C(=N)OCC(=O)O)C(=O)O +NCC(CC(=O)O)C(=O)O +COc1cc(C(=O)O)cc(O)c1CCNC(=O)CCC(=O)O +COCCOC(C(=O)O)C(C)(O)COCCOC(C)C(=O)O +CCOCCCCC(CC)(C(=O)O)C(=O)O +CCNC.O=CO.O=CO +CCN(CC(=O)O)c1ccc(C)c(C(=O)O)c1 +CC(C)OCC(CC(=O)O)C(=O)O +CCCC(NC(CCCCCN)C(C)CC(=O)O)C(=O)O +CCCCC(CC(=O)O)(C(=O)O)C(=O)NCC +CCC.O.O=CO.O=CO +CC(C)CC(=O)N(C)C1CC(C(CC(=O)O)CC(=O)O)C1 +CCCC(C)C(NC(=O)O)NC(=O)O +C.O=C(O)CCOC(=O)O +INVALID +CCOCCOCCCCOCCOCCOC(C)(C(=O)O)C(=O)O +O=C(O)C1CC(C(=O)O)C1 +CCN(CC)C(C)OC(C)=C(C(=O)O)C(=O)O +CC(C)CN(CC(=O)O)C(C)(C)C(=O)O +INVALID +CCCCCCC(CC(CO)OCCC(=O)O)C(=O)O +CCC(CCC(=O)O)C(=O)O +CCOCCOOCC(OCC)(C(C)OCC(=O)O)C(C)OC(=O)O +CCCCN(CC(=O)O)CC(=O)O +C#CCCN(CCCN(C)c1cccc(CC(=O)O)c1)CCC(=O)O +CC(C(=O)O)N(C)CC(=O)O +INVALID +CC(N)CCC(C(=O)O)C(=O)O +CC(C(=O)O)=C(C)C(=O)O +CCCCC(NC)=C(C(=O)O)C1CC(C)(C(=O)O)C1 +CC(CO)C(CCC(O)C(=O)O)CC(=O)O +CCOC(C)(C(C)=CN)C(C(=O)O)C(=O)O +CC(OCCN1CCC1)=C(OCC(=O)O)C(=O)O +CCC(CC(=O)O)C(=O)O +CCC(CC)(CCC(=O)O)C(C)OCC(=O)O +CCOC(C)N(C)C(C(=O)O)C(C=N)C(=O)O +O=C(O)CCCCNC(=O)O +CCOC(COC)=C(CCCC(=O)O)C(=O)O +CCCCCC(CC(=O)O)CC(=O)C(=O)O +INVALID +CC(C)(CO)N(CCCCC(=O)O)CCC(=O)O +COC(COC(C)CC(C)(C)CC(=O)O)C(=O)O +COC(C)(C)CC(CC(=O)O)=C(CC(=O)O)OCCO +CCCCCCC(C(=O)O)C(=O)O +CC(=O)O.O=C(O)COCCO +CC(=CC(=O)O)N(C=N)C(C)C(=O)O +CC(=O)O.CCCC(=O)O +CCC(C(=O)O)C(C)(N)CC(=O)O +COCCCC(C(=O)O)=C(C(=O)O)N1CCN(C)CC1 +CC(OCC(C)(C)CCC(=O)O)C(=O)O +COCC(=O)C1=C(NCCC(C(=O)O)C(=O)O)COC1 +CCCCC(=O)NC(CC(=O)O)C(=O)O +CCC(CC)(CC(=O)O)C(=O)O +Cc1ccc(C(NC(=O)COCC=O)C(=O)O)n1CCCCC(=O)O +COCCN(CCC(C)NCCOCC(=O)O)CC(=O)O +CC(CC(=O)O)N(CC(=O)O)C(=O)CCO +C=C1NCCC(C(=O)O)=C1C(=O)O +CCCc1[nH]c(C)cc1OC(COCCOC(C)CC(=O)O)C(=O)O +CC(C)C(C(=O)O)N(C)CC(=O)O +CCC(C)C(C(=O)O)C(=O)O +CNC(CC1CCC(NC(CO)C(=O)O)CC1)C(=O)O +O=C(O)CCCCCCCCCCCCC(=O)O +NC(=O)C(C(=O)O)C(=O)O +COCCOCCCCCCCC(CC(=O)O)C(=O)O +C.O=C(O)CCCC(=O)O +O=C(O)CCCN1CCCCC(C(=O)O)C1 +CC(CCCC(=O)O)C(=O)O +C.CCC.O=C(O)CCC(=O)O +CCC(=O)O.CCCC(=O)O +CCN(NC(=O)O)C(=O)O +COCCC(OC(=C(N)COCCO)C(C)(C)C(=O)O)C(=O)O +C#CCC(=CCCNCCOCCC(=O)O)N1CCC(C(=O)O)CC1 +CNCCC(CCCC(=O)O)CCC(=O)O +N.O=C(O)C(CO)C(=O)O +CCCCCC(NC(C)C(=O)O)C(=O)O +COC(C)OCCC(OC(=O)O)C1(C)CCC(C(=O)O)CO1 +NC(CCCC1(C(=O)O)CCC1)C(=O)O +CN1N=NCC1(C(=O)O)C(=O)O +CC(C)(C)OCCN(CCCC(=O)O)CCC(=O)O +CCC(CC(=O)O)C(=O)O +CCC(CCCC(C)=NN(CC)CC(=O)O)C(=O)O +O=C(O)CCC(=O)CC(=O)O +CC(C)C(=CC(=O)O)C(=O)O +CCC(CC(=O)O)C(=O)O +CCCNC(C)=C(C)CC(C(=O)O)C(=O)O +O=C(O)CCC(=O)C(=O)O +CCC(CCNC(C)=O)(CCC(=O)O)OC(=O)O +O=C(O)CC(C(=O)O)C(=O)O +NCCOC(=O)CCN(CCCCCCC(=O)O)CCC(=O)O +O=C(O)CCC(=CCOc1ccccc1)C(=O)O +O=C(O)CCCCCC(=O)NCC1CC(C(=O)O)C1 +CCCC(C(=O)O)C(=O)O +CN(CCCCCC(=O)O)CC(=O)O +C.O=C(O)CCCC(=O)O +CC(OCCCCC(O)CCCCCCC(=O)O)C(=O)O +CCN(C(=O)NC)C(C(=O)O)C(=O)O +O=C(O)CCCC(=O)CC(=O)O +CCCCN(CCCC(=O)O)CCC(=O)C(=O)O +CCCN(CCCCCCCC(=O)O)CC(=O)O +NCCC(CC(=O)O)C(=O)O +CC(C)(C)NC(CCCCC(=O)O)C(=O)O +O=C(O)CCCCCCCCC(=O)O +O=C(O)OCCOCCOC1CC(C(=O)O)OCCON1 +CC(C)(C)C(C(=O)O)C(=O)O +O=C(O)CC1CCC(C(=O)O)CC1 +O=C(O)NCCCCCNCCCCCCNNC(=O)O +Cc1nc(C)n(C)c1C(=O)C=C(CC(=O)O)C(=O)O +NC(CCC(=O)O)CC(=O)O +O=C(O)C(CCC1(CCCO)CC1)C(=O)O +CC(=O)NC(CCCC(=O)O)CCCC(=O)O +CCC(C)(C(=O)O)C(=O)O +C=C(C(=O)O)c1occc1CC(=O)O +C.N=O.O=C(O)CCC(=O)O +CCNCC(C)CC(C(=O)O)C(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ftguid_scored.csv b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ftguid_scored.csv new file mode 100644 index 0000000000000000000000000000000000000000..1a49126a0d01dc7d337e40b5ef78d85b8ebfd1fa --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/ftguid_scored.csv @@ -0,0 +1,284 @@ +smiles,linker_mw,linker_logp,linker_tpsa,linker_hbd,linker_hba,linker_rotbonds,linker_rings,linker_hac,linker_sa,linker_qed,linker_valid,check_connected,check_attachment_sites,check_ring_closure,check_long_chain,check_linker_atom_count,check_protac_mw,check_representational,check_sa_score,assembly_ok,protac_smiles,protac_mw,protac_logp,protac_tpsa,protac_hbd,protac_hba,protac_rotbonds,protac_hac,ro5_violations,protac_valid,degrad_prob,degrad_notes +C=C(CCCCCOC)NC(=C(O)CC)C(C(=O)O)C(=O)O,315.4,2.26,116.1,4,5,12,0,22,3.27,0.248,True,True,True,True,True,True,True,True,True,True,C=C(CCCCCOC)NC(=C(O)CC)C(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1104.4,6.61,230.0,5.0,15.0,23.0,79.0,3.0,True,0.0065,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)CC(CO)C(CC(=O)O)CC(=O)O,232.3,1.21,94.8,3,3,8,0,16,3.15,0.584,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CC(CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(CO)CC(C)C)CC1,1021.3,5.55,208.8,4.0,13.0,19.0,73.0,3.0,True,0.5985,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C.C=CNC(=O)O.O=CO,149.1,0.73,86.6,3,2,1,0,10,3.61,0.48,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCC(=O)OCC(CC(O)(CO)C(=O)O)OCC(=O)O,294.3,-1.39,150.6,4,7,10,0,20,3.69,0.357,True,True,True,True,True,True,True,True,True,True,CCC(=O)OCC(CC(O)(CO)C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)OCC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1083.3,2.95,264.5,5.0,17.0,21.0,77.0,2.0,True,0.1123,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCCCC1(CCNC(=O)C(=O)O)CC(C(=O)O)C1,313.4,2.42,103.7,3,3,10,1,22,2.68,0.424,True,True,True,True,True,True,True,True,True,True,CCCCCCCC1(CCNC(=O)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC(C(=O)N2CCN(Cc3c(OC)cc(-c4cn(C)c(=O)c5cnccc45)cc3OC)CC2)C1,1102.4,6.77,217.6,4.0,13.0,21.0,79.0,3.0,True,0.2186,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)C(=O)CCCC(=O)C(=O)O,188.1,-0.54,108.7,2,4,6,0,13,2.41,0.542,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(=O)CCCC(=O)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,977.2,3.81,222.7,3.0,14.0,17.0,70.0,2.0,True,0.0955,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCC(C(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.14,0.568,True,True,True,True,True,True,True,True,True,True,CCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,935.2,4.92,188.5,3.0,12.0,15.0,67.0,2.0,True,0.7935,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C.CCNC(=O)O.O=CO,151.2,0.61,86.6,3,2,1,0,10,2.77,0.479,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)NCCN[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,894.1,4.16,183.5,4.0,12.0,14.0,64.0,2.0,True,0.1142,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCCCC(C(=O)O)C(=O)O,202.2,2.13,74.6,2,2,8,0,14,2.0,0.467,True,True,True,True,True,True,True,True,True,True,CCCCCCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,991.3,6.48,188.5,3.0,12.0,19.0,71.0,3.0,True,0.5919,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CC1CC(C(=O)O)C1,158.2,0.57,74.6,2,2,3,1,11,2.23,0.629,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2CC(CC(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)C2)CC1,947.2,4.92,188.5,3.0,12.0,14.0,68.0,2.0,True,0.1269,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCC(CO)N(C(=O)O)C(=O)O,205.2,1.2,98.1,3,3,5,0,14,3.27,0.626,True,True,True,True,True,True,True,True,True,True,CCCCC(CO)N(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,994.2,5.54,212.0,4.0,13.0,16.0,71.0,3.0,True,0.44,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCC1=C(C(OCCOCC(OCCOC)C(=O)O)C(=O)O)O1,348.3,0.63,124.0,2,7,15,1,24,3.91,0.41,False,True,True,False,True,True,True,True,True,True,CCCC1=C(C(OCCOCC(OCCOC)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(=O)N2CCN(Cc3c(OC)cc(-c4cn(C)c(=O)c5cnccc45)cc3OC)CC2)O1,1137.4,4.98,238.0,3.0,17.0,26.0,81.0,2.0,True,0.1086,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CNC(=O)CCCCCCN(C)C(C(=O)O)C(=O)O,274.3,0.15,106.9,3,4,10,0,19,2.51,0.387,True,True,True,True,True,True,True,True,True,True,CNC(=O)CCCCCCN(C)C(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1063.3,4.5,220.9,4.0,14.0,21.0,76.0,2.0,True,0.2905,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)C(CO)CC1CCCC1C(=O)O,216.2,0.57,94.8,3,3,5,1,15,3.48,0.624,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2CCCC2CC(CO)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1005.3,4.92,208.8,4.0,13.0,16.0,72.0,2.0,True,0.0565,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(CC(=O)O)C(=O)O.O,150.1,-0.64,106.1,2,2,3,0,10,2.63,0.557,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,921.1,4.53,188.5,3.0,12.0,14.0,66.0,2.0,True,0.6146,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +NC(=O)C(C(=O)O)C(=O)O,147.1,-1.74,117.7,3,3,3,0,10,2.61,0.409,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C(N)=O)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,936.1,2.6,231.6,4.0,13.0,14.0,67.0,2.0,True,0.1546,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(C)(C(=O)O)C(=O)O,146.1,0.57,74.6,2,2,3,0,10,2.67,0.571,True,True,True,True,True,True,True,True,True,True,CCC(C)(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,935.2,4.92,188.5,3.0,12.0,14.0,67.0,2.0,True,0.7935,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CN(CCC(CCO)N1CCC(C(=O)O)CC1)CCN(C)CC(=O)O,359.5,-0.13,104.5,3,6,12,1,25,3.14,0.443,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CN(C)CCN(C)CCC(CCO)N2CCC(C(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)CC2)CC1,1148.5,4.22,218.5,4.0,16.0,23.0,82.0,2.0,True,0.273,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCC(C=O)=C(OCC(=O)O)C(=O)O,230.2,0.82,100.9,2,4,8,0,16,3.07,0.363,True,True,True,True,True,True,True,True,True,True,CCCCC(C=O)=C(OCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1019.2,5.16,214.8,3.0,14.0,19.0,73.0,3.0,True,0.1716,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCNCC(=O)O.O=CO,149.1,-0.62,86.6,3,3,3,0,10,2.39,0.465,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CNCCN[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,908.1,3.57,183.5,4.0,13.0,16.0,65.0,2.0,True,0.2192,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCCCCCC(=O)O,174.2,1.5,74.6,2,2,7,0,12,1.62,0.573,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCCCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,963.2,5.84,188.5,3.0,12.0,18.0,69.0,3.0,True,0.1557,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)OCCCC(C(=O)O)C(=O)O,204.2,0.98,83.8,2,3,7,0,14,2.33,0.477,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CCCOC(C)C)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,993.2,5.32,197.8,3.0,13.0,18.0,71.0,3.0,True,0.0914,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCOCCOC(CC(C(=O)O)C(=O)O)=C(N)CO,277.3,-0.62,139.3,4,6,10,0,19,3.21,0.235,True,True,True,True,True,True,True,True,True,True,CCOCCOC(CC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)=C(N)CO,1066.3,3.72,253.2,5.0,16.0,21.0,76.0,2.0,True,0.0023,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCOCCCCCCCOCCOC(CO)CCC(=O)O,364.4,1.69,122.5,3,6,19,0,25,2.93,0.296,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCC(CO)OCCOCCCCCCCOCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1153.5,6.03,236.4,4.0,16.0,30.0,82.0,3.0,True,0.0324,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCNC(CC(=O)O)=C1CCCC(CO)(CC(=O)O)CCC1,313.4,2.13,106.9,4,4,7,1,22,3.18,0.573,True,True,True,True,True,True,True,True,True,True,CCNC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)=C1CCCC(CO)(CC(=O)N2CCN(Cc3c(OC)cc(-c4cn(C)c(=O)c5cnccc45)cc3OC)CC2)CCC1,1102.4,6.48,220.8,5.0,14.0,18.0,79.0,3.0,True,0.0006,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCCCCOCCC(=O)CC(=O)O,246.3,1.08,100.9,2,4,11,0,17,2.21,0.418,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CC(=O)CCOCCCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1035.3,5.43,214.8,3.0,14.0,22.0,74.0,3.0,True,0.1559,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCCC(C)C(CC(O)CC(=O)O)OCC(=O)O,304.4,2.29,104.1,3,4,13,0,21,3.44,0.451,True,True,True,True,True,True,True,True,True,True,CCCCCCC(C)C(CC(O)CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)OCC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1093.4,6.64,218.0,4.0,14.0,24.0,78.0,3.0,True,0.1253,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCC(NC(=O)CCC(=O)NCCC=O)C(CC(=O)O)C(=O)O,358.4,0.32,149.9,4,5,14,0,25,3.33,0.258,True,True,True,True,True,True,True,True,True,True,CCCCC(NC(=O)CCC(=O)NCCC=O)C(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1147.4,4.67,263.8,5.0,15.0,25.0,82.0,2.0,True,0.4943,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(C=CC(=O)O)=C(C)C(=O)O,184.2,1.44,74.6,2,2,4,0,13,2.94,0.512,True,True,True,True,True,True,True,True,True,True,CCC(C=CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)=C(C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,973.2,5.79,188.5,3.0,12.0,15.0,70.0,3.0,True,0.0446,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)NCCO.O=CO,151.1,-1.05,106.9,4,3,2,0,10,2.42,0.376,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCOCCCCCCOCC=C(OCCC(=O)O)C(=O)O,318.4,2.06,102.3,2,5,15,0,22,2.72,0.271,True,True,True,True,True,True,True,True,True,True,CCOCCCCCCOCC=C(OCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1107.4,6.41,216.2,3.0,15.0,26.0,79.0,3.0,True,0.0112,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCOCC(CCOC(=O)O)C(=O)O,206.2,0.81,93.1,2,4,7,0,14,3.02,0.598,True,True,True,True,True,True,True,True,True,True,CCOCC(CCOC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,995.2,5.16,207.0,3.0,14.0,18.0,71.0,3.0,True,0.1196,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +Cc1c(OCCOC(C)C(=O)O)cc2ccccc2c1OCC(=O)O,348.4,2.48,102.3,2,5,9,2,25,2.76,0.671,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)COc2c(C)c(OCCOC(C)C(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)cc3ccccc23)CC1,1137.4,6.83,216.2,3.0,15.0,20.0,82.0,3.0,True,0.0223,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)N(C)C(C(=O)O)C(=O)O,175.2,-0.14,77.8,2,3,4,0,12,2.77,0.582,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)N(C)C(C)C)CC1,964.2,4.21,191.8,3.0,13.0,15.0,69.0,2.0,True,0.1605,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COCCNC(=O)COOCC(=O)O.O=CO,253.2,-1.52,131.4,3,6,8,0,17,2.67,0.206,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)COOCC(=O)NCCOCN[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1012.2,3.01,228.2,4.0,16.0,21.0,72.0,2.0,True,0.2831,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(C)(C)NCCCC(C(=O)O)C(=O)O,231.3,1.33,86.6,3,3,8,0,16,2.52,0.433,True,True,True,True,True,True,True,True,True,True,CCC(C)(C)NCCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1020.3,5.68,200.6,4.0,13.0,19.0,73.0,3.0,True,0.5666,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CC(=O)O)C(O)(O)C(=O)O,192.2,-0.75,115.1,4,4,5,0,13,3.36,0.426,True,True,True,True,True,True,True,True,True,True,CCC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(O)(O)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,981.2,3.6,229.0,5.0,14.0,16.0,70.0,2.0,True,0.2446,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C=O)=C(CC(=O)O)C(=O)O,172.1,0.06,91.7,2,3,4,0,12,3.05,0.462,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)=C(C)C=O)CC1,961.2,4.41,205.6,3.0,13.0,15.0,69.0,2.0,True,0.0275,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCC(CO)NC(=O)C(=O)O,205.2,-1.59,123.9,4,4,5,0,14,2.75,0.401,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(=O)NC(CO)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,994.2,2.76,237.9,5.0,14.0,16.0,71.0,2.0,True,0.1275,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCNC(COCCOCC(N)C(=O)O)CC(=O)O,278.3,-1.12,131.1,4,6,12,0,19,3.28,0.33,True,True,True,True,True,True,True,True,True,True,CCNC(COCCOCC(N)C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)CC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1067.3,3.23,245.0,5.0,16.0,23.0,76.0,2.0,True,0.331,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(=O)C(=O)O.CC(=O)O,148.1,-0.25,91.7,2,3,1,0,10,1.82,0.5,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(CC(C)(C)[C@H](NCC(=O)C(=O)O)C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)CC1,909.1,3.08,208.8,4.0,14.0,17.0,65.0,2.0,False,0.3562,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCC(=O)O.O=CO,148.2,0.96,74.6,2,2,3,0,10,1.96,0.584,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCCCN[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,907.2,5.15,171.5,3.0,12.0,16.0,65.0,3.0,True,0.1941,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCN(CC)C(C(=O)O)=C(C(=O)O)c1cccc(CNC)c1,306.4,1.63,89.9,3,4,8,1,22,2.68,0.633,True,True,True,True,True,True,True,True,True,True,CCN(CC)C(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)=C(C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)c1cccc(CNC)c1,1095.4,5.98,203.8,4.0,14.0,19.0,79.0,3.0,True,0.0086,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C.CC(CC(=O)O)C(=O)O,148.2,0.82,74.6,2,2,3,0,10,2.7,0.621,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,921.1,4.53,188.5,3.0,12.0,14.0,66.0,2.0,True,0.6146,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(=NCCC(=O)O)NC(C)CC(=O)O,216.2,0.33,99.0,3,3,6,0,15,3.21,0.439,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CC(C)NC(C)=NCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1005.3,4.68,212.9,4.0,13.0,17.0,72.0,2.0,True,0.1386,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCC(O)(CC(=O)O)C(=O)O,204.2,0.86,94.8,3,3,7,0,14,2.82,0.534,True,True,True,True,True,True,True,True,True,True,CCCCCC(O)(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,993.2,5.2,208.8,4.0,13.0,18.0,71.0,3.0,True,0.163,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CC(CCOCCCN1CC1)CC(=O)O,259.3,0.66,86.8,2,4,11,1,18,2.32,0.419,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CC(CCOCCCN2CC2)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1048.3,5.01,200.8,3.0,14.0,22.0,75.0,3.0,True,0.1478,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(=O)O.CCCC(=O)O,148.2,0.96,74.6,2,2,2,0,10,1.76,0.611,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(CC(C)(C)[C@H](NCC(=O)O)C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)CC1,881.1,3.51,191.7,4.0,13.0,16.0,63.0,2.0,False,0.7805,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCOC(CC)=C(C)C(CCC(=O)O)C(=O)O,258.3,2.66,83.8,2,3,9,0,18,3.37,0.621,True,True,True,True,True,True,True,True,True,True,CCCOC(CC)=C(C)C(CCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1047.3,7.01,197.8,3.0,13.0,20.0,75.0,3.0,True,0.239,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)C(C(=O)O)C(=O)O,146.1,0.43,74.6,2,2,3,0,10,2.31,0.566,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(C)C)CC1,935.2,4.78,188.5,3.0,12.0,14.0,67.0,2.0,True,0.1856,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COC(C(=O)O)=C(CC(=O)O)C(C)C,202.2,1.1,83.8,2,3,5,0,14,3.1,0.515,True,True,True,True,True,True,True,True,True,True,COC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)=C(CC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)C(C)C,991.2,5.45,197.8,3.0,13.0,16.0,71.0,3.0,True,0.0092,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)(CC(=O)O)CC(=O)O,160.2,0.96,74.6,2,2,4,0,11,2.22,0.642,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CC(C)(C)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,949.2,5.31,188.5,3.0,12.0,15.0,68.0,3.0,True,0.5871,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCCCC(CC(=O)O)C(=O)O,216.3,2.52,74.6,2,2,9,0,15,2.38,0.581,True,True,True,True,True,True,True,True,True,True,CCCCCCCC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1005.3,6.87,188.5,3.0,12.0,20.0,72.0,3.0,True,0.5768,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(O)=C(CC(=O)O)C(=O)O,160.1,0.38,94.8,3,3,3,0,11,2.78,0.409,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)=C(C)O)CC1,949.1,4.73,208.8,4.0,13.0,14.0,68.0,2.0,True,0.029,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COCCCCCCNCCC(C)(C(=O)O)C(=O)O,275.3,1.35,95.9,3,4,12,0,19,2.52,0.367,True,True,True,True,True,True,True,True,True,True,COCCCCCCNCCC(C)(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1064.4,5.7,209.8,4.0,14.0,23.0,76.0,3.0,True,0.3496,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCOCCOC(COCC(=O)O)(C(=O)O)C(C)OCCOCCN,367.4,-0.66,146.8,3,8,17,0,25,3.86,0.283,True,True,True,True,True,True,True,True,True,True,CCOCCOC(COCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)(C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)C(C)OCCOCCN,1156.4,3.69,260.7,4.0,18.0,28.0,82.0,2.0,True,0.1097,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCN(C(=O)O)N(C(=O)O)C1CCCC1,230.3,2.21,81.1,2,2,3,1,16,2.98,0.728,True,True,True,True,True,True,True,True,True,True,CCCN(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)N(C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)C1CCCC1,1019.3,6.56,195.0,3.0,12.0,14.0,73.0,3.0,True,0.6165,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)NCCCCC(O)C(=O)O,191.2,-0.13,106.9,4,3,6,0,13,2.64,0.437,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(O)CCCCNC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,980.2,4.22,220.8,5.0,13.0,17.0,70.0,2.0,True,0.0503,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(CCNC(=O)O)C(=O)O,161.2,0.36,86.6,3,2,4,0,11,2.67,0.555,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)CCNC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,950.2,4.71,200.6,4.0,12.0,15.0,68.0,2.0,True,0.2903,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)(O)C(CN1CCC(C(=O)O)CC1)C(=O)O,259.3,0.25,98.1,3,4,5,1,18,2.94,0.655,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CN2CCC(C(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)CC2)C(C)(C)O)CC1,1048.3,4.6,212.0,4.0,14.0,16.0,75.0,2.0,True,0.0473,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)C(CC(=O)O)C(=O)O,160.2,0.82,74.6,2,2,4,0,11,2.6,0.638,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(C)C)CC1,949.2,5.17,188.5,3.0,12.0,15.0,68.0,3.0,True,0.0893,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC.CC.O=CO.O=CO,152.2,1.45,74.6,2,2,0,0,10,2.94,0.515,False,False,True,True,True,True,True,True,True,True,CCN[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)CN1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,851.1,4.45,154.4,3.0,12.0,15.0,61.0,2.0,True,0.8068,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COCC(O)CN(CCCCCC(=O)O)C(C)OCCC(=O)O,335.4,0.78,116.5,3,6,15,0,23,3.42,0.297,True,True,True,True,True,True,True,True,True,True,COCC(O)CN(CCCCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(C)OCCC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1124.4,5.13,230.5,4.0,16.0,26.0,80.0,3.0,True,0.1487,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCC(CCCC(=O)O)CC(=O)O,216.3,2.52,74.6,2,2,9,0,15,2.53,0.621,True,True,True,True,True,True,True,True,True,True,CCCCC(CCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)CC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1005.3,6.87,188.5,3.0,12.0,20.0,72.0,3.0,True,0.5768,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(=O)CCOC(=O)C(CC(=O)O)C(=O)O,232.2,-0.32,118.0,2,5,7,0,16,2.9,0.456,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(=O)OCCC(C)=O)CC1,1021.2,4.03,231.9,3.0,15.0,18.0,73.0,2.0,True,0.6135,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(OC(=O)CC(N)C(=O)O)C(=O)O,219.2,-0.81,126.9,3,5,6,0,15,3.11,0.498,True,True,True,True,True,True,True,True,True,True,CCC(OC(=O)CC(N)C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1008.2,3.54,240.8,4.0,15.0,17.0,72.0,2.0,True,0.4995,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CNC(C(=O)O)C(C)C(C)C(=O)O,189.2,0.02,86.6,3,3,5,0,13,3.5,0.567,True,True,True,True,True,True,True,True,True,True,CNC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(C)C(C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,978.2,4.36,200.6,4.0,13.0,16.0,70.0,2.0,True,0.1599,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(=O)C(C)C(C(=O)O)N1CCCCC(CC(=O)O)CC1,299.4,1.63,94.9,2,4,6,1,21,3.37,0.774,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CC2CCCCN(C(C(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)C(C)C(C)=O)CC2)CC1,1088.4,5.98,208.8,3.0,14.0,17.0,78.0,3.0,True,0.0809,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC1=C(C(=O)C(=O)O)CCCCCN1CCCCCCOCC(=O)O,355.4,2.45,104.1,2,5,11,1,25,2.71,0.433,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)COCCCCCCN2CCCCCC(C(=O)C(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)=C2C)CC1,1144.4,6.8,218.1,3.0,15.0,22.0,82.0,3.0,True,0.0002,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCC(C(=O)O)C(=O)O,174.2,1.35,74.6,2,2,6,0,12,2.03,0.471,True,True,True,True,True,True,True,True,True,True,CCCCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,963.2,5.7,188.5,3.0,12.0,17.0,69.0,3.0,True,0.6216,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCOCCOCCN(C)C1(C(=O)O)CCN(CCCC(=O)O)CC1,360.5,0.76,99.5,2,6,13,1,25,2.74,0.464,True,True,True,True,True,True,True,True,True,True,CCOCCOCCN(C)C1(C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CCN(CCCC(=O)N2CCN(Cc3c(OC)cc(-c4cn(C)c(=O)c5cnccc45)cc3OC)CC2)CC1,1149.5,5.1,213.5,3.0,16.0,24.0,82.0,3.0,True,0.4493,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCN(CCC(=O)O)C(=C(OC)OCC(=O)O)C(C)(C)CC,317.4,2.14,96.3,2,5,11,0,22,3.1,0.563,True,True,True,True,True,True,True,True,True,True,CCN(CCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=C(OC)OCC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)C(C)(C)CC,1106.4,6.48,210.2,3.0,15.0,22.0,79.0,3.0,True,0.0073,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCC(C(=O)O)(C(=O)O)C(O)C=O,218.2,-0.11,111.9,3,4,7,0,15,3.52,0.406,True,True,True,True,True,True,True,True,True,True,CCCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)(C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)C(O)C=O,1007.2,4.24,225.8,4.0,14.0,18.0,72.0,2.0,True,0.1679,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(C)(C(=O)O)C(=O)O,146.1,0.57,74.6,2,2,3,0,10,2.67,0.571,True,True,True,True,True,True,True,True,True,True,CCC(C)(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,935.2,4.92,188.5,3.0,12.0,14.0,67.0,2.0,True,0.7935,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)(C(=O)O)C(=O)O.O,150.1,-0.64,106.1,2,2,2,0,10,2.66,0.51,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)(C)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,921.1,4.53,188.5,3.0,12.0,13.0,66.0,2.0,True,0.1984,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCN(CN(CCO)C(O)(CC(=O)O)C(=O)O)C(C)C,306.4,-0.39,121.5,4,6,11,0,21,3.69,0.381,True,True,True,True,True,True,True,True,True,True,CCCN(CN(CCO)C(O)(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)C(C)C,1095.4,3.95,235.5,5.0,16.0,22.0,78.0,2.0,True,0.0854,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)C(C(=O)O)C(=O)O,146.1,0.43,74.6,2,2,3,0,10,2.31,0.566,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(C)C)CC1,935.2,4.78,188.5,3.0,12.0,14.0,67.0,2.0,True,0.1856,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCNCCOC(=O)O.O=CC(=O)O,221.2,-0.05,112.9,3,5,6,0,15,2.41,0.249,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)OCCNCCCN[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,952.2,4.57,192.7,4.0,14.0,18.0,68.0,2.0,True,0.042,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCN(C)CCC(C)(CC(=O)O)CC(=O)O,231.3,1.28,77.8,2,3,8,0,16,2.75,0.658,True,True,True,True,True,True,True,True,True,True,CCN(C)CCC(C)(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)CC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1020.3,5.63,191.8,3.0,13.0,19.0,73.0,3.0,True,0.5666,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCNN(C)CCOC(=O)O.O=CO,208.2,-0.16,99.1,3,5,5,0,14,3.29,0.328,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(CC(C)(C)[C@H](NCCNN(C)CCOC(=O)O)C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)CC1,983.2,3.56,216.2,5.0,16.0,21.0,70.0,2.0,False,0.4095,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)C(CCCCCCOCCO)C(=O)O,248.3,0.73,104.1,3,4,11,0,17,2.26,0.368,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CCCCCCOCCO)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1037.3,5.08,218.0,4.0,14.0,22.0,74.0,3.0,True,0.0697,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCCC(C(=O)O)C(=O)O,188.2,1.74,74.6,2,2,7,0,13,2.01,0.471,True,True,True,True,True,True,True,True,True,True,CCCCCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,977.2,6.09,188.5,3.0,12.0,18.0,70.0,3.0,True,0.6069,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(CC(=O)O)CC(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.01,0.608,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CC(C)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,935.2,4.92,188.5,3.0,12.0,15.0,67.0,2.0,True,0.6033,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C.NC(CC(=O)O)C(=O)O,149.1,-0.49,100.6,3,3,3,0,10,2.67,0.502,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(N)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,922.1,3.22,214.6,4.0,13.0,14.0,66.0,2.0,True,0.1495,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCC(C)(OC(CCOC)C(=O)O)C(=O)O,276.3,1.92,93.1,2,4,11,0,19,3.46,0.56,True,True,True,True,True,True,True,True,True,True,CCCCCC(C)(OC(CCOC)C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1065.3,6.26,207.0,3.0,14.0,22.0,76.0,3.0,True,0.4084,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COC(C)=C(CCC(=O)O)C(=O)O,188.2,0.86,83.8,2,3,5,0,13,2.8,0.493,True,True,True,True,True,True,True,True,True,True,COC(C)=C(CCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,977.2,5.2,197.8,3.0,13.0,16.0,70.0,3.0,True,0.0212,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)C(C(=O)O)C1CCC1,158.2,0.57,74.6,2,2,3,1,11,2.35,0.589,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C2CCC2)CC1,947.2,4.92,188.5,3.0,12.0,14.0,68.0,2.0,True,0.1269,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(=C(CC(=O)O)C(=N)C(=O)O)N1CCCCC1,254.3,1.33,101.7,3,4,5,1,18,3.05,0.642,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(=N)C(CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)=C(C)N2CCCCC2)CC1,1043.3,5.67,215.6,4.0,14.0,16.0,75.0,3.0,True,0.0005,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCC(NC(=O)C(=O)O)C(=O)O,203.2,-0.17,103.7,3,3,5,0,14,2.43,0.538,True,True,True,True,True,True,True,True,True,True,CCCCC(NC(=O)C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,992.2,4.18,217.6,4.0,13.0,16.0,71.0,2.0,True,0.4514,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCOCCOC(OC(=O)O)C(CCC(=O)O)OC,280.3,0.94,111.5,2,6,11,0,19,3.59,0.327,True,True,True,True,True,True,True,True,True,True,CCOCCOC(OC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(CCC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)OC,1069.3,5.29,225.4,3.0,16.0,22.0,76.0,3.0,True,0.0367,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCC(=O)NCC(O)(C(=O)O)C(=O)O,247.2,-0.42,123.9,4,4,8,0,17,2.52,0.341,True,True,True,True,True,True,True,True,True,True,CCCCCC(=O)NCC(O)(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1036.3,3.93,237.9,5.0,14.0,19.0,74.0,2.0,True,0.1588,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CCC(=O)O)C(=O)O,160.2,0.96,74.6,2,2,5,0,11,2.45,0.628,True,True,True,True,True,True,True,True,True,True,CCC(CCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,949.2,5.31,188.5,3.0,12.0,16.0,68.0,3.0,True,0.202,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.54,0.608,True,True,True,True,True,True,True,True,True,True,CCC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,935.2,4.92,188.5,3.0,12.0,15.0,67.0,2.0,True,0.2197,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCC(C(=O)O)C(C)(C)C(=O)O,216.3,2.38,74.6,2,2,7,0,15,2.93,0.641,True,True,True,True,True,True,True,True,True,True,CCCCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(C)(C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1005.3,6.73,188.5,3.0,12.0,18.0,72.0,3.0,True,0.5768,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCN(CC(=O)O)CC(=O)NCCCCCCCC(=O)O,302.4,0.93,106.9,3,4,13,0,21,2.05,0.437,True,True,True,True,True,True,True,True,True,True,CCN(CC(=O)NCCCCCCCC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C,1091.4,5.28,220.9,4.0,14.0,24.0,78.0,3.0,True,0.0977,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(=O)O.CCOCC(=O)O,164.2,0.2,83.8,2,3,3,0,11,1.88,0.622,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(CC(C)(C)[C@H](NCC(=O)O)C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)CC1,881.1,3.51,191.7,4.0,13.0,16.0,63.0,2.0,False,0.7805,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CC)=C(CC(=O)O)C(=O)O,186.2,1.66,74.6,2,2,5,0,13,2.89,0.64,True,True,True,True,True,True,True,True,True,True,CCC(CC)=C(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,975.2,6.01,188.5,3.0,12.0,16.0,70.0,3.0,True,0.0408,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(CCC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.38,0.608,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,935.2,4.92,188.5,3.0,12.0,15.0,67.0,2.0,True,0.4241,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCC(=O)NC(C(=O)O)C1CCCC(C(=O)O)CCC1,299.4,2.03,103.7,3,3,6,1,21,2.68,0.696,True,True,True,True,True,True,True,True,True,True,CCCC(=O)NC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C1CCCC(C(=O)N2CCN(Cc3c(OC)cc(-c4cn(C)c(=O)c5cnccc45)cc3OC)CC2)CCC1,1088.4,6.37,217.6,4.0,13.0,17.0,78.0,3.0,True,0.7466,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)C(=O)C1CCC(C(=O)O)CC1,200.2,0.53,91.7,2,3,3,1,14,2.25,0.648,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2CCC(C(=O)C(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)CC2)CC1,989.2,4.88,205.6,3.0,13.0,14.0,71.0,2.0,True,0.0786,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C(=O)O)C(C)(C)C(=O)O,160.2,0.82,74.6,2,2,3,0,11,3.04,0.64,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)(C)C(C)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,949.2,5.17,188.5,3.0,12.0,14.0,68.0,3.0,True,0.1708,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCNC(CC(=O)O)C(=O)O,189.2,0.3,86.6,3,3,7,0,13,2.36,0.501,True,True,True,True,True,True,True,True,True,True,CCCCNC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,978.2,4.65,200.6,4.0,13.0,18.0,70.0,2.0,True,0.3136,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +NC(=O)CCC(CCCC(=O)O)CCCC(=O)O,259.3,1.38,117.7,3,3,11,0,18,2.23,0.518,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCCC(CCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CCC(N)=O)CC1,1048.3,5.73,231.6,4.0,13.0,22.0,75.0,3.0,True,0.2095,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CN(CCCCCNC(=O)O)CCOCCCCNC(=O)O,319.4,1.42,111.1,4,4,14,0,22,2.36,0.36,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)NCCCCOCCN(C)CCCCCNC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1108.4,5.77,225.1,5.0,14.0,25.0,79.0,3.0,True,0.19,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCC(CCC(=O)O)C(N)C(=O)O,203.2,0.68,100.6,3,3,7,0,14,3.09,0.564,True,True,True,True,True,True,True,True,True,True,CCCC(CCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(N)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,992.3,5.03,214.5,4.0,13.0,18.0,71.0,3.0,True,0.8784,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)N(C)CCC(C(=O)O)C(=O)O,203.2,0.5,77.8,2,3,6,0,14,2.47,0.615,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CCN(C)C(C)C)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,992.3,4.85,191.8,3.0,13.0,17.0,71.0,2.0,True,0.3513,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCc1c(C(C)(CC(=O)O)C(=O)O)c(C)c(C)n(C)c1=O,295.3,1.38,96.6,2,3,5,1,21,3.41,0.855,True,True,True,True,True,True,True,True,True,True,CCc1c(C(C)(CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(=O)N2CCN(Cc3c(OC)cc(-c4cn(C)c(=O)c5cnccc45)cc3OC)CC2)c(C)c(C)n(C)c1=O,1084.4,5.73,210.5,3.0,13.0,16.0,78.0,3.0,True,0.7335,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CNCC(C(=O)O)N(CCCCC(=O)O)CCOCCOC,320.4,-0.12,108.3,3,6,15,0,22,3.04,0.358,True,True,True,True,True,True,True,True,True,True,CNCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)N(CCCCC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)CCOCCOC,1109.4,4.23,222.3,4.0,16.0,26.0,79.0,2.0,True,0.0423,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(C)(C(=O)O)C(CC(=O)O)OC,204.2,0.98,83.8,2,3,6,0,14,3.56,0.673,True,True,True,True,True,True,True,True,True,True,CCC(C)(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(CC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)OC,993.2,5.32,197.8,3.0,13.0,17.0,71.0,3.0,True,0.6019,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCC(C(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.14,0.568,True,True,True,True,True,True,True,True,True,True,CCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,935.2,4.92,188.5,3.0,12.0,15.0,67.0,2.0,True,0.7935,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C.CCC(=NC(=O)O)C(=O)O,161.2,1.24,87.0,2,2,2,0,11,3.31,0.596,False,False,True,True,True,True,True,True,True,True,CCC(=NC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,934.1,4.95,200.9,3.0,12.0,13.0,67.0,2.0,True,0.1305,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)(CCC(=O)O)C(=O)O,160.2,0.96,74.6,2,2,4,0,11,2.16,0.642,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)(C)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,949.2,5.31,188.5,3.0,12.0,15.0,68.0,3.0,True,0.4037,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCc1cc(OC(=O)O)cc(C(=O)O)c1,210.2,2.0,83.8,2,3,3,1,15,2.02,0.588,True,True,True,True,True,True,True,True,True,True,CCc1cc(OC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)cc(C(=O)N2CCN(Cc3c(OC)cc(-c4cn(C)c(=O)c5cnccc45)cc3OC)CC2)c1,999.2,6.35,197.8,3.0,13.0,14.0,72.0,3.0,True,0.1918,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(=O)O.NC(=O)C(=O)O,149.1,-1.35,117.7,3,3,0,0,10,1.95,0.368,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CN[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,865.1,3.98,171.5,3.0,12.0,13.0,62.0,2.0,True,0.2357,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C(=O)O)=C(C)C(=O)O,144.1,0.49,74.6,2,2,2,0,10,2.42,0.553,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)=C(C)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,933.1,4.84,188.5,3.0,12.0,13.0,67.0,2.0,True,0.0576,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(CCC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.38,0.608,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,935.2,4.92,188.5,3.0,12.0,15.0,67.0,2.0,True,0.4241,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +N.NC(CC(=O)O)C(=O)O,150.1,-0.97,135.6,4,4,3,0,10,2.64,0.408,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(N)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,922.1,3.22,214.6,4.0,13.0,14.0,66.0,2.0,True,0.1495,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(CCC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.38,0.608,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,935.2,4.92,188.5,3.0,12.0,15.0,67.0,2.0,True,0.4241,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCC(=N)C(C(=O)O)C(=O)O,201.2,1.37,98.5,3,3,7,0,14,2.74,0.329,True,True,True,True,True,True,True,True,True,True,CCCCCC(=N)C(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,990.2,5.72,212.4,4.0,13.0,18.0,71.0,3.0,True,0.0957,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCCCNC(=O)CCC(=O)O,217.2,0.22,103.7,3,3,8,0,15,1.75,0.504,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCC(=O)NCCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1006.2,4.57,217.6,4.0,13.0,19.0,72.0,2.0,True,0.1372,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C(=O)O)=C(C)C(=O)O,144.1,0.49,74.6,2,2,2,0,10,2.42,0.553,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)=C(C)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,933.1,4.84,188.5,3.0,12.0,13.0,67.0,2.0,True,0.0576,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCOC(=O)CCC(CC(=O)O)C(=O)O,218.2,0.51,100.9,2,4,7,0,15,2.51,0.603,True,True,True,True,True,True,True,True,True,True,CCOC(=O)CCC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1007.2,4.85,214.8,3.0,14.0,18.0,72.0,2.0,True,0.5471,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.54,0.608,True,True,True,True,True,True,True,True,True,True,CCC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,935.2,4.92,188.5,3.0,12.0,15.0,67.0,2.0,True,0.2197,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)NCC#CCCCCNC(=O)O,214.2,0.7,98.7,4,2,5,0,15,2.73,0.4,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)NCCCCC#CCNC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1003.2,5.04,212.6,5.0,12.0,16.0,72.0,3.0,True,0.0677,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C(=O)O)=C(C)C(=O)O,144.1,0.49,74.6,2,2,2,0,10,2.42,0.553,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)=C(C)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,933.1,4.84,188.5,3.0,12.0,13.0,67.0,2.0,True,0.0576,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +Cc1cc(C(=O)O)cc2n[nH]c(C(=O)O)c12,220.2,1.27,103.3,3,3,2,2,16,2.49,0.707,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)c2[nH]nc3cc(C(=O)N[C@H](C(=O)N4C[C@H](O)C[C@H]4C(=O)NCc4ccc(-c5scnc5C)cc4)C(C)(C)C)cc(C)c23)CC1,1009.2,5.62,217.2,4.0,13.0,13.0,73.0,3.0,True,0.2889,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C#CCCCCC(C)=C(CC(=O)O)C(=O)O,224.3,2.06,74.6,2,2,7,0,16,3.06,0.394,True,True,True,True,True,True,True,True,True,True,C#CCCCCC(C)=C(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1013.3,6.4,188.5,3.0,12.0,18.0,73.0,3.0,True,0.0492,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(C(=O)O)C(O)(C(=O)O)C(C)C,204.2,0.57,94.8,3,3,5,0,14,3.59,0.608,True,True,True,True,True,True,True,True,True,True,CCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(O)(C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)C(C)C,993.2,4.92,208.8,4.0,13.0,16.0,71.0,2.0,True,0.0527,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)C1CC(C(=O)O)C1,144.1,0.18,74.6,2,2,2,1,10,2.11,0.579,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2CC(C(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)C2)CC1,933.1,4.53,188.5,3.0,12.0,13.0,67.0,2.0,True,0.1408,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCCCNC(CCC(=O)O)=NCCCOCCCO,332.4,0.88,128.5,4,5,15,0,23,2.68,0.199,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCC(=NCCCOCCCO)NCCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1121.4,5.23,242.4,5.0,15.0,26.0,80.0,3.0,True,0.0316,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCOCC(OCC(NCC(=O)O)C(=O)O)C1(CO)CCCCC1,347.4,0.48,125.3,4,6,12,1,24,3.86,0.402,True,True,True,True,True,True,True,True,True,True,CCOCC(OCC(NCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)C1(CO)CCCCC1,1136.4,4.83,239.2,5.0,16.0,23.0,81.0,2.0,True,0.0543,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)NCC1CC[N+]2([O-])NC(C(=O)O)CCCCCC12,301.3,0.88,121.7,4,4,3,2,21,4.97,0.453,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2CCCCCC3C(CNC(=O)N[C@H](C(=O)N4C[C@H](O)C[C@H]4C(=O)NCc4ccc(-c5scnc5C)cc4)C(C)(C)C)CC[N+]3([O-])N2)CC1,1090.4,5.23,235.7,5.0,14.0,14.0,78.0,3.0,True,0.9435,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCN1CCC(C(=O)O)CC1,201.2,0.26,77.8,2,3,4,1,14,1.81,0.682,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2CCN(CCC(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)CC2)CC1,990.2,4.61,191.8,3.0,13.0,15.0,71.0,2.0,True,0.1416,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCN(CC)C(C(=O)O)C(=O)O,189.2,0.26,77.8,2,3,6,0,13,2.64,0.586,True,True,True,True,True,True,True,True,True,True,CCCN(CC)C(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,978.2,4.6,191.8,3.0,13.0,17.0,70.0,2.0,True,0.6118,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCCNCCN(CC(=O)O)CC(=O)O,260.3,0.63,89.9,3,4,12,0,18,2.15,0.446,True,True,True,True,True,True,True,True,True,True,CCCCCCNCCN(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)CC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1049.4,4.97,203.8,4.0,14.0,23.0,75.0,2.0,True,0.541,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(CC(=O)O)C(CCN)CCC(=O)O,217.3,0.93,100.6,3,3,8,0,15,3.24,0.561,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCC(CCN)C(C)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1006.3,5.27,214.6,4.0,13.0,19.0,72.0,3.0,True,0.5294,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CNCC(C)(C(=O)O)C(=O)O,161.2,-0.62,86.6,3,3,4,0,11,2.89,0.478,True,True,True,True,True,True,True,True,True,True,CNCC(C)(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,950.2,3.73,200.6,4.0,13.0,15.0,68.0,2.0,True,0.308,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C(=O)O)C(=O)C(=O)O,146.1,-0.64,91.7,2,3,3,0,10,2.86,0.408,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(=O)C(C)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,935.1,3.71,205.6,3.0,13.0,14.0,67.0,2.0,True,0.1128,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C#N.O=C(O)CCC(=O)O,145.1,0.08,98.4,2,3,3,0,10,1.98,0.585,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,907.1,4.28,188.5,3.0,12.0,14.0,65.0,2.0,True,0.2118,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCNC(CCCCCC(=O)O)CCC(=O)O,245.3,1.86,86.6,3,3,11,0,17,2.57,0.484,True,True,True,True,True,True,True,True,True,True,CCNC(CCCCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)CCC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1034.3,6.21,200.6,4.0,13.0,22.0,74.0,3.0,True,0.2428,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COC(C(=O)O)=C(C)CC(C(=O)O)C(=O)O,232.2,0.17,121.1,3,4,6,0,16,3.07,0.339,True,True,True,True,True,True,True,True,True,True,COC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)=C(C)CC(C(=O)O)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1021.2,4.51,235.1,4.0,14.0,17.0,73.0,2.0,False,0.2081,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C=C.O=C(O)CCCCCC(=O)O.O=S,236.3,1.57,91.7,2,4,6,0,15,2.16,0.537,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,949.2,5.45,188.5,3.0,12.0,17.0,68.0,3.0,True,0.1708,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CNCCC(CCC(CCC(C)C)C(=O)O)C(=O)O,273.4,2.21,86.6,3,3,11,0,19,3.27,0.537,True,True,True,True,True,True,True,True,True,True,CNCCC(CCC(CCC(C)C)C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1062.4,6.56,200.6,4.0,13.0,22.0,76.0,3.0,True,0.2626,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C.CC.O=C(O)OC(=O)O,152.1,2.02,83.8,2,3,0,0,10,3.06,0.409,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)OC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,895.1,4.71,197.8,3.0,13.0,11.0,64.0,2.0,True,0.1468,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCN(CCC(=O)O)C(CC)CCC(=O)O,259.3,2.21,77.8,2,3,11,0,18,2.8,0.595,True,True,True,True,True,True,True,True,True,True,CCCCN(CCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(CC)CCC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1048.4,6.55,191.8,3.0,13.0,22.0,75.0,3.0,True,0.5358,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC=C(CC(=O)O)C(=O)O,144.1,0.49,74.6,2,2,3,0,10,2.71,0.565,True,True,True,True,True,True,True,True,True,True,CC=C(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,933.1,4.84,188.5,3.0,12.0,14.0,67.0,2.0,True,0.0305,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +NCCCNCCN(CCC(=O)O)CC(=O)O,247.3,-1.21,115.9,4,5,11,0,17,2.33,0.338,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CN(CCNCCCN)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1036.3,3.13,229.8,5.0,15.0,22.0,74.0,2.0,True,0.0675,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCCCCCC(=O)C(=O)O,202.2,1.07,91.7,2,3,8,0,14,1.99,0.453,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(=O)CCCCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,991.2,5.41,205.6,3.0,13.0,19.0,71.0,3.0,True,0.0726,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCC(C(=O)O)C(=O)O,160.2,0.96,74.6,2,2,5,0,11,2.08,0.587,True,True,True,True,True,True,True,True,True,True,CCCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,949.2,5.31,188.5,3.0,12.0,16.0,68.0,3.0,True,0.6361,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C=C(O)C(OCCCCCC(=O)O)C(=O)O,232.2,1.17,104.1,3,4,9,0,16,3.13,0.407,True,True,True,True,True,True,True,True,True,True,C=C(O)C(OCCCCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1021.3,5.52,218.0,4.0,14.0,20.0,73.0,3.0,True,0.0364,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CNC(C)(CCC(C)C(=O)O)C(=O)O,203.2,0.55,86.6,3,3,6,0,14,3.25,0.587,True,True,True,True,True,True,True,True,True,True,CNC(C)(CCC(C)C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,992.3,4.9,200.6,4.0,13.0,17.0,71.0,2.0,True,0.3513,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)C=CCCCCC(=O)O,172.2,1.27,74.6,2,2,6,0,12,2.1,0.467,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCCCC=CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,961.2,5.62,188.5,3.0,12.0,17.0,69.0,3.0,True,0.0821,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +NCCCCCCCCCCNC(C(=O)O)=C(CCC(=O)O)C(=O)O,358.4,1.94,149.9,5,5,16,0,25,2.68,0.207,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CCC(=O)O)=C(NCCCCCCCCCCN)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1147.5,6.29,263.9,6.0,15.0,27.0,82.0,4.0,False,0.0021,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCCCC(NCCC(=O)O)=C1CCCCNC1,298.4,1.72,98.7,4,4,9,1,21,2.75,0.484,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCNC(CCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)=C2CCCCNC2)CC1,1087.4,6.07,212.6,5.0,14.0,20.0,78.0,3.0,True,0.0002,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC=NNC(C)=C(CCCCCC(=O)O)C(=O)O,270.3,2.37,99.0,3,4,10,0,19,2.96,0.245,True,True,True,True,True,True,True,True,True,True,CCC=NNC(C)=C(CCCCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1059.3,6.71,212.9,4.0,14.0,21.0,76.0,3.0,True,0.0003,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(C)=C(CC(O)(O)C(=O)O)C(=O)O,218.2,-0.05,115.1,4,4,5,0,15,3.3,0.38,True,True,True,True,True,True,True,True,True,True,CCC(C)=C(CC(O)(O)C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1007.2,4.3,229.0,5.0,14.0,16.0,72.0,2.0,True,0.3427,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCNN(C(=O)O)C(=O)CC(C)C(=O)O,232.2,0.52,106.9,3,4,6,0,16,3.45,0.576,True,True,True,True,True,True,True,True,True,True,CCCNN(C(=O)CC(C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C,1021.3,4.87,220.9,4.0,14.0,17.0,73.0,2.0,True,0.6812,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C.CC.N.O=CO.O=CO,155.2,1.23,109.6,3,3,0,0,10,3.61,0.455,False,False,True,True,True,True,True,True,True,True,CCN[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)CN1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,851.1,4.45,154.4,3.0,12.0,15.0,61.0,2.0,True,0.8068,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCN1CCCCCC1C(=O)O,215.2,0.79,77.8,2,3,4,1,15,2.42,0.725,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2CCCCCN2CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1004.3,5.14,191.8,3.0,13.0,15.0,72.0,3.0,True,0.0854,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC=C(CCC(=O)O)C(=O)O,158.2,0.88,74.6,2,2,4,0,11,2.55,0.595,True,True,True,True,True,True,True,True,True,True,CC=C(CCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,947.2,5.23,188.5,3.0,12.0,15.0,68.0,3.0,True,0.0261,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CS12=C3C=1C32CCN(CCCC(=O)O)CC(=O)O,271.3,0.19,77.8,2,3,9,4,18,3.46,0.585,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CN(CCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CCC23C4=S2(C)=C43)CC1,1060.4,4.53,191.8,3.0,13.0,20.0,75.0,2.0,True,0.0323,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(COC(=O)O)C(=O)O.O,166.1,-0.42,115.3,2,3,3,0,11,3.02,0.552,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)COC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,937.1,4.75,197.8,3.0,13.0,14.0,67.0,2.0,True,0.5141,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CN)CC(C(=O)O)C(=O)O,189.2,0.15,100.6,3,3,6,0,13,3.05,0.516,True,True,True,True,True,True,True,True,True,True,CCC(CN)CC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,978.2,4.49,214.5,4.0,13.0,17.0,70.0,2.0,True,0.3165,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCC(C(=O)O)=C(C)C(=O)O,172.2,1.27,74.6,2,2,4,0,12,2.74,0.626,True,True,True,True,True,True,True,True,True,True,CCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)=C(C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,961.2,5.62,188.5,3.0,12.0,15.0,69.0,3.0,True,0.2655,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCCCCC(CC(=O)O)C(=O)O,230.3,2.91,74.6,2,2,10,0,16,2.38,0.566,True,True,True,True,True,True,True,True,True,True,CCCCCCCCC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1019.3,7.26,188.5,3.0,12.0,21.0,73.0,3.0,True,0.5616,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(=CC1=C=NN1CCC(=O)O)NOC(=O)O,241.2,0.35,111.5,3,6,6,1,17,4.16,0.575,False,True,True,False,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)ONC(C)=CC2=C=NN2CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1030.2,4.7,225.4,4.0,16.0,17.0,74.0,2.0,True,0.0041,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CN(C)C1CC(CCC(CO)=C(CC(=O)O)C(=O)O)C1,285.3,0.95,98.1,3,4,8,1,20,3.16,0.574,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)=C(CO)CCC2CC(N(C)C)C2)CC1,1074.4,5.3,212.0,4.0,14.0,19.0,77.0,3.0,True,0.0116,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCOCCOCCOCCOCCOCCC(=O)C(=O)O,366.4,-0.41,137.8,2,8,19,0,25,2.47,0.229,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(=O)CCOCCOCCOCCOCCOCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1155.4,3.94,251.8,3.0,18.0,30.0,82.0,2.0,True,0.0408,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCCC(CCO)C(=O)C(=O)O,218.2,-0.11,111.9,3,4,8,0,15,3.0,0.49,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(=O)C(CCO)CCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1007.2,4.24,225.8,4.0,14.0,19.0,72.0,2.0,True,0.0938,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCNCCC(OCCOCCCN1CCCCC1)C(=O)O,360.5,0.8,108.3,3,6,15,1,25,2.89,0.368,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CCNCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)OCCOCCCN2CCCCC2)CC1,1149.5,5.15,222.3,4.0,16.0,26.0,82.0,3.0,True,0.0074,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C=C(CCc1ccnc(CCC(=O)O)c1)C(=O)O,249.3,1.67,87.5,2,3,7,1,18,2.41,0.718,True,True,True,True,True,True,True,True,True,True,C=C(CCc1ccnc(CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)c1)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1038.3,6.02,201.4,3.0,13.0,18.0,75.0,3.0,True,0.046,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCNC(=O)O.O=CO,163.2,0.75,86.6,3,2,3,0,11,2.15,0.423,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)NCCCCN[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,922.2,4.94,183.5,4.0,12.0,16.0,66.0,2.0,True,0.0942,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CN(CO)C(=O)O,149.1,-1.0,98.1,3,3,3,0,10,2.6,0.447,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)N(CO)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,938.1,3.35,212.0,4.0,13.0,14.0,67.0,2.0,True,0.1307,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(COC)C(C)(C(=N)OCC(=O)O)C(=O)O,261.3,0.83,116.9,3,5,8,0,18,3.93,0.44,True,True,True,True,True,True,True,True,True,True,CCC(COC)C(C)(C(=N)OCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1050.3,5.18,230.8,4.0,15.0,19.0,75.0,3.0,True,0.1491,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +NCC(CC(=O)O)C(=O)O,147.1,-0.88,100.6,3,3,4,0,10,2.77,0.479,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CN)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,936.1,3.47,214.6,4.0,13.0,15.0,67.0,2.0,True,0.3285,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COc1cc(C(=O)O)cc(O)c1CCNC(=O)CCC(=O)O,311.3,0.62,133.2,4,5,8,1,22,2.17,0.553,True,True,True,True,True,True,True,True,True,True,COc1cc(C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)cc(O)c1CCNC(=O)CCC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1100.3,4.97,247.1,5.0,15.0,19.0,79.0,2.0,True,0.1023,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COCCOC(C(=O)O)C(C)(O)COCCOC(C)C(=O)O,324.3,-0.64,131.8,3,7,13,0,22,3.84,0.378,True,True,True,True,True,True,True,True,True,True,COCCOC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(C)(O)COCCOC(C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1113.3,3.71,245.7,4.0,17.0,24.0,79.0,2.0,True,0.0494,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCOCCCCC(CC)(C(=O)O)C(=O)O,232.3,1.76,83.8,2,3,9,0,16,2.53,0.467,True,True,True,True,True,True,True,True,True,True,CCOCCCCC(CC)(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1021.3,6.11,197.8,3.0,13.0,20.0,73.0,3.0,True,0.1829,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCNC.O=CO.O=CO,151.2,-0.37,86.6,3,3,1,0,10,2.87,0.445,False,False,True,True,True,True,True,True,True,True,CNCCN[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)CN1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,880.1,3.65,166.4,4.0,13.0,17.0,63.0,2.0,True,0.4819,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCN(CC(=O)O)c1ccc(C)c(C(=O)O)c1,237.3,1.6,77.8,2,3,5,1,17,2.05,0.813,True,True,True,True,True,True,True,True,True,True,CCN(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)c1ccc(C)c(C(=O)N2CCN(Cc3c(OC)cc(-c4cn(C)c(=O)c5cnccc45)cc3OC)CC2)c1,1026.3,5.95,191.8,3.0,13.0,16.0,74.0,3.0,True,0.4905,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)OCC(CC(=O)O)C(=O)O,190.2,0.59,83.8,2,3,6,0,13,2.88,0.64,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(COC(C)C)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,979.2,4.93,197.8,3.0,13.0,17.0,70.0,2.0,True,0.1019,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCC(NC(CCCCCN)C(C)CC(=O)O)C(=O)O,302.4,1.83,112.6,4,4,13,0,21,3.43,0.386,True,True,True,True,True,True,True,True,True,True,CCCC(NC(CCCCCN)C(C)CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1091.4,6.18,226.6,5.0,14.0,24.0,78.0,3.0,True,0.7088,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCC(CC(=O)O)(C(=O)O)C(=O)NCC,245.3,0.86,103.7,3,3,8,0,17,3.06,0.55,True,True,True,True,True,True,True,True,True,True,CCCCC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)(C(=O)NCC)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1034.3,5.21,217.6,4.0,13.0,19.0,74.0,3.0,True,0.5717,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC.O.O=CO.O=CO,154.2,-0.01,106.1,2,2,0,0,10,2.93,0.478,False,False,True,True,True,True,True,True,True,True,CCCN[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)CN1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,865.1,4.84,154.4,3.0,12.0,16.0,62.0,2.0,True,0.8042,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)CC(=O)N(C)C1CC(C(CC(=O)O)CC(=O)O)C1,299.4,1.84,94.9,2,3,8,1,21,2.84,0.713,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CC(CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C2CC(N(C)C(=O)CC(C)C)C2)CC1,1088.4,6.18,208.8,3.0,13.0,19.0,78.0,3.0,True,0.2564,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCC(C)C(NC(=O)O)NC(=O)O,204.2,1.28,98.7,4,2,5,0,14,3.24,0.507,True,True,True,True,True,True,True,True,True,True,CCCC(C)C(NC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)NC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,993.2,5.63,212.6,5.0,12.0,16.0,71.0,3.0,True,0.1699,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C.O=C(O)CCOC(=O)O,150.1,0.79,83.8,2,3,3,0,10,2.43,0.581,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)OCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,923.1,4.5,197.8,3.0,13.0,14.0,66.0,2.0,True,0.1617,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCOCCOCCCCOCCOCCOC(C)(C(=O)O)C(=O)O,366.4,0.8,120.8,2,7,18,0,25,2.81,0.268,True,True,True,True,True,True,True,True,True,True,CCOCCOCCCCOCCOCCOC(C)(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1155.4,5.14,234.7,3.0,17.0,29.0,82.0,3.0,True,0.0447,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)C1CC(C(=O)O)C1,144.1,0.18,74.6,2,2,2,1,10,2.11,0.579,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2CC(C(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)C2)CC1,933.1,4.53,188.5,3.0,12.0,13.0,67.0,2.0,True,0.1408,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCN(CC)C(C)OC(C)=C(C(=O)O)C(=O)O,245.3,1.13,87.1,2,4,7,0,17,3.5,0.23,True,True,True,True,True,True,True,True,True,True,CCN(CC)C(C)OC(C)=C(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1034.3,5.48,201.0,3.0,14.0,18.0,74.0,3.0,True,0.027,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)CN(CC(=O)O)C(C)(C)C(=O)O,217.3,0.89,77.8,2,3,6,0,15,2.62,0.691,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)(C)N(CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC(C)C)CC1,1006.3,5.24,191.8,3.0,13.0,17.0,72.0,3.0,True,0.3324,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCCC(CC(CO)OCCC(=O)O)C(=O)O,290.4,1.9,104.1,3,4,13,0,20,3.2,0.447,True,True,True,True,True,True,True,True,True,True,CCCCCCC(CC(CO)OCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1079.4,6.25,218.0,4.0,14.0,24.0,77.0,3.0,True,0.3911,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CCC(=O)O)C(=O)O,160.2,0.96,74.6,2,2,5,0,11,2.45,0.628,True,True,True,True,True,True,True,True,True,True,CCC(CCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,949.2,5.31,188.5,3.0,12.0,16.0,68.0,3.0,True,0.202,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCOCCOOCC(OCC)(C(C)OCC(=O)O)C(C)OC(=O)O,368.4,1.32,130.0,2,8,15,0,25,4.37,0.189,True,True,True,True,True,True,True,True,True,True,CCOCCOOCC(OCC)(C(C)OCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(C)OC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1157.4,5.67,243.9,3.0,18.0,26.0,82.0,3.0,True,0.1147,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCN(CC(=O)O)CC(=O)O,189.2,0.26,77.8,2,3,7,0,13,2.01,0.6,True,True,True,True,True,True,True,True,True,True,CCCCN(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)CC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,978.2,4.61,191.8,3.0,13.0,18.0,70.0,2.0,True,0.6118,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C#CCCN(CCCN(C)c1cccc(CC(=O)O)c1)CCC(=O)O,346.4,1.94,81.1,2,4,12,1,25,2.59,0.563,True,True,True,True,True,True,True,True,True,True,C#CCCN(CCCN(C)c1cccc(CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)c1)CCC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1135.4,6.29,195.0,3.0,14.0,23.0,82.0,3.0,True,0.4626,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C(=O)O)N(C)CC(=O)O,161.2,-0.52,77.8,2,3,4,0,11,2.63,0.579,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CN(C)C(C)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,950.2,3.82,191.8,3.0,13.0,15.0,68.0,2.0,True,0.4104,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(N)CCC(C(=O)O)C(=O)O,175.2,-0.1,100.6,3,3,5,0,12,2.94,0.506,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CCC(C)N)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,964.2,4.25,214.6,4.0,13.0,16.0,69.0,2.0,True,0.5788,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C(=O)O)=C(C)C(=O)O,144.1,0.49,74.6,2,2,2,0,10,2.42,0.553,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)=C(C)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,933.1,4.84,188.5,3.0,12.0,13.0,67.0,2.0,True,0.0576,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCC(NC)=C(C(=O)O)C1CC(C)(C(=O)O)C1,269.3,2.24,86.6,3,3,7,1,19,3.25,0.617,True,True,True,True,True,True,True,True,True,True,CCCCC(NC)=C(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C1CC(C)(C(=O)N2CCN(Cc3c(OC)cc(-c4cn(C)c(=O)c5cnccc45)cc3OC)CC2)C1,1058.4,6.58,200.6,4.0,13.0,18.0,76.0,3.0,True,0.0016,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(CO)C(CCC(O)C(=O)O)CC(=O)O,234.2,-0.07,115.1,4,4,8,0,16,3.71,0.467,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CC(CCC(O)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(C)CO)CC1,1023.3,4.28,229.0,5.0,14.0,19.0,73.0,2.0,True,0.2578,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCOC(C)(C(C)=CN)C(C(=O)O)C(=O)O,231.2,0.43,109.9,3,4,6,0,16,3.88,0.572,True,True,True,True,True,True,True,True,True,True,CCOC(C)(C(C)=CN)C(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1020.3,4.78,223.8,4.0,14.0,17.0,73.0,2.0,True,0.005,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(OCCN1CCC1)=C(OCC(=O)O)C(=O)O,259.3,0.13,96.3,2,5,8,1,18,2.8,0.472,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(OCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)=C(C)OCCN2CCC2)CC1,1048.3,4.47,210.2,3.0,15.0,19.0,75.0,2.0,True,0.0047,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.54,0.608,True,True,True,True,True,True,True,True,True,True,CCC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,935.2,4.92,188.5,3.0,12.0,15.0,67.0,2.0,True,0.2197,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CC)(CCC(=O)O)C(C)OCC(=O)O,246.3,2.15,83.8,2,3,9,0,17,3.41,0.651,True,True,True,True,True,True,True,True,True,True,CCC(CC)(CCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(C)OCC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1035.3,6.49,197.8,3.0,13.0,20.0,74.0,3.0,True,0.0801,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCOC(C)N(C)C(C(=O)O)C(C=N)C(=O)O,246.3,0.1,110.9,3,5,8,0,17,4.41,0.414,True,True,True,True,True,True,True,True,True,True,CCOC(C)N(C)C(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(C=N)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1035.3,4.45,224.8,4.0,15.0,19.0,74.0,2.0,True,0.0493,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCCCNC(=O)O,161.2,0.51,86.6,3,2,5,0,11,1.83,0.512,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)NCCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,950.2,4.86,200.6,4.0,12.0,16.0,68.0,2.0,True,0.0871,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCOC(COC)=C(CCCC(=O)O)C(=O)O,246.3,1.26,93.1,2,4,9,0,17,2.78,0.47,True,True,True,True,True,True,True,True,True,True,CCOC(COC)=C(CCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1035.3,5.61,207.0,3.0,14.0,20.0,74.0,3.0,True,0.308,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCC(CC(=O)O)CC(=O)C(=O)O,230.3,1.7,91.7,2,3,9,0,16,2.81,0.464,True,True,True,True,True,True,True,True,True,True,CCCCCC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)CC(=O)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1019.3,6.05,205.6,3.0,13.0,20.0,73.0,3.0,True,0.3809,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)(CO)N(CCCCC(=O)O)CCC(=O)O,261.3,0.79,98.1,3,4,10,0,18,2.58,0.503,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCN(CCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(C)(C)CO)CC1,1050.3,5.14,212.0,4.0,14.0,21.0,75.0,3.0,True,0.3672,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COC(COC(C)CC(C)(C)CC(=O)O)C(=O)O,262.3,1.38,93.1,2,4,9,0,18,3.53,0.652,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(COC(C)CC(C)(C)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)OC)CC1,1051.3,5.73,207.0,3.0,14.0,20.0,75.0,3.0,True,0.2028,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COC(C)(C)CC(CC(=O)O)=C(CC(=O)O)OCCO,290.3,1.01,113.3,3,5,10,0,20,3.42,0.514,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CC(OCCO)=C(CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC(C)(C)OC)CC1,1079.3,5.36,227.2,4.0,15.0,21.0,77.0,3.0,True,0.045,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCCC(C(=O)O)C(=O)O,188.2,1.74,74.6,2,2,7,0,13,2.01,0.471,True,True,True,True,True,True,True,True,True,True,CCCCCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,977.2,6.09,188.5,3.0,12.0,18.0,70.0,3.0,True,0.6069,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(=O)O.O=C(O)COCCO,180.2,-0.83,104.1,3,4,4,0,12,2.03,0.488,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CN[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,865.1,3.98,171.5,3.0,12.0,13.0,62.0,2.0,True,0.2357,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(=CC(=O)O)N(C=N)C(C)C(=O)O,200.2,0.36,101.7,3,3,5,0,14,4.06,0.338,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)N(C=N)C(C)=CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,989.2,4.7,215.6,4.0,13.0,16.0,71.0,2.0,True,0.0015,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(=O)O.CCCC(=O)O,148.2,0.96,74.6,2,2,2,0,10,1.76,0.611,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(CC(C)(C)[C@H](NCC(=O)O)C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)CC1,881.1,3.51,191.7,4.0,13.0,16.0,63.0,2.0,False,0.7805,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(C(=O)O)C(C)(N)CC(=O)O,189.2,0.29,100.6,3,3,5,0,13,3.56,0.577,True,True,True,True,True,True,True,True,True,True,CCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(C)(N)CC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,978.2,4.64,214.5,4.0,13.0,16.0,70.0,2.0,True,0.4993,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COCCCC(C(=O)O)=C(C(=O)O)N1CCN(C)CC1,286.3,0.08,90.3,2,5,7,1,20,2.71,0.506,True,True,True,True,True,True,True,True,True,True,COCCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)=C(C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1)N1CCN(C)CC1,1075.3,4.43,204.2,3.0,15.0,18.0,77.0,2.0,True,0.0057,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(OCC(C)(C)CCC(=O)O)C(=O)O,218.2,1.37,83.8,2,3,7,0,15,3.01,0.674,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)OCC(C)(C)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1007.3,5.71,197.8,3.0,13.0,18.0,72.0,3.0,True,0.2405,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COCC(=O)C1=C(NCCC(C(=O)O)C(=O)O)COC1,287.3,-0.75,122.2,3,6,9,1,20,3.3,0.473,True,True,True,True,True,True,True,True,True,True,COCC(=O)C1=C(NCCC(C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(=O)N2CCN(Cc3c(OC)cc(-c4cn(C)c(=O)c5cnccc45)cc3OC)CC2)COC1,1076.3,3.6,236.1,4.0,16.0,20.0,77.0,2.0,True,0.0192,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCC(=O)NC(CC(=O)O)C(=O)O,217.2,0.22,103.7,3,3,7,0,15,2.24,0.566,True,True,True,True,True,True,True,True,True,True,CCCCC(=O)NC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1006.2,4.57,217.6,4.0,13.0,18.0,72.0,2.0,True,0.3009,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CC)(CC(=O)O)C(=O)O,174.2,1.35,74.6,2,2,5,0,12,2.5,0.659,True,True,True,True,True,True,True,True,True,True,CCC(CC)(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,963.2,5.7,188.5,3.0,12.0,16.0,69.0,3.0,True,0.1854,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +Cc1ccc(C(NC(=O)COCC=O)C(=O)O)n1CCCCC(=O)O,354.4,0.51,134.9,3,5,12,1,25,3.34,0.367,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCCCn2c(C)ccc2C(NC(=O)COCC=O)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1143.4,4.86,248.9,4.0,15.0,23.0,82.0,2.0,True,0.023,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COCCN(CCC(C)NCCOCC(=O)O)CC(=O)O,306.4,-0.51,108.3,3,6,14,0,21,2.92,0.368,True,True,True,True,True,True,True,True,True,True,COCCN(CCC(C)NCCOCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)CC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1095.4,3.84,222.3,4.0,16.0,25.0,78.0,2.0,True,0.1696,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(CC(=O)O)N(CC(=O)O)C(=O)CCO,233.2,-0.85,115.1,3,4,7,0,16,2.88,0.527,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CN(C(=O)CCO)C(C)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1022.2,3.49,229.1,4.0,14.0,18.0,73.0,2.0,True,0.4284,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C=C1NCCC(C(=O)O)=C1C(=O)O,183.2,-0.04,86.6,3,3,2,1,13,3.31,0.559,True,True,True,True,True,True,True,True,True,True,C=C1NCCC(C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)=C1C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,972.2,4.31,200.6,4.0,13.0,13.0,70.0,2.0,True,0.0055,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCc1[nH]c(C)cc1OC(COCCOC(C)CC(=O)O)C(=O)O,357.4,2.0,118.1,3,5,13,1,25,3.72,0.462,True,True,True,True,True,True,True,True,True,True,CCCc1[nH]c(C)cc1OC(COCCOC(C)CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1146.4,6.35,232.0,4.0,15.0,24.0,82.0,3.0,True,0.3571,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)C(C(=O)O)N(C)CC(=O)O,189.2,0.11,77.8,2,3,5,0,13,2.9,0.642,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CN(C)C(C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(C)C)CC1,978.2,4.46,191.8,3.0,13.0,16.0,70.0,2.0,True,0.5598,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(C)C(C(=O)O)C(=O)O,160.2,0.82,74.6,2,2,4,0,11,2.77,0.597,True,True,True,True,True,True,True,True,True,True,CCC(C)C(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,949.2,5.17,188.5,3.0,12.0,15.0,68.0,3.0,True,0.7859,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CNC(CC1CCC(NC(CO)C(=O)O)CC1)C(=O)O,288.3,-0.36,118.9,5,5,8,1,20,3.18,0.414,True,True,True,True,True,True,True,True,True,True,CNC(CC1CCC(NC(CO)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1077.4,3.99,232.8,6.0,15.0,19.0,77.0,3.0,True,0.0251,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCCCCCCCCCCCC(=O)O,258.4,3.84,74.6,2,2,13,0,18,1.52,0.492,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCCCCCCCCCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1047.4,8.18,188.5,3.0,12.0,24.0,75.0,3.0,True,0.0866,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +NC(=O)C(C(=O)O)C(=O)O,147.1,-1.74,117.7,3,3,3,0,10,2.61,0.409,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C(N)=O)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,936.1,2.6,231.6,4.0,13.0,14.0,67.0,2.0,True,0.1546,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COCCOCCCCCCCC(CC(=O)O)C(=O)O,290.4,2.17,93.1,2,4,14,0,20,2.6,0.476,True,True,True,True,True,True,True,True,True,True,COCCOCCCCCCCC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1079.4,6.51,207.0,3.0,14.0,25.0,77.0,3.0,True,0.1741,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C.O=C(O)CCCC(=O)O,148.2,0.96,74.6,2,2,4,0,10,1.9,0.622,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,921.1,4.67,188.5,3.0,12.0,15.0,66.0,2.0,True,0.1984,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCCN1CCCCC(C(=O)O)C1,229.3,1.04,77.8,2,3,5,1,16,2.34,0.736,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2CCCCN(CCCC(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)C2)CC1,1018.3,5.39,191.8,3.0,13.0,16.0,73.0,3.0,True,0.0765,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(CCCC(=O)O)C(=O)O,160.2,0.96,74.6,2,2,5,0,11,2.39,0.628,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)CCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,949.2,5.31,188.5,3.0,12.0,16.0,68.0,3.0,True,0.4038,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C.CCC.O=C(O)CCC(=O)O,178.2,1.99,74.6,2,2,3,0,12,1.97,0.693,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,907.1,4.28,188.5,3.0,12.0,14.0,65.0,2.0,True,0.2118,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(=O)O.CCCC(=O)O,162.2,1.35,74.6,2,2,3,0,11,1.82,0.656,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(CC(C)(C)[C@H](NCCC(=O)O)C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)CC1,895.1,3.9,191.7,4.0,13.0,17.0,64.0,2.0,False,0.5181,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCN(NC(=O)O)C(=O)O,148.1,0.17,89.9,3,2,1,0,10,2.98,0.462,True,True,True,True,True,True,True,True,True,True,CCN(NC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,937.1,4.52,203.8,4.0,12.0,12.0,67.0,2.0,True,0.378,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COCCC(OC(=C(N)COCCO)C(C)(C)C(=O)O)C(=O)O,335.4,-0.22,148.5,4,7,12,0,23,3.68,0.28,True,True,True,True,True,True,True,True,True,True,COCCC(OC(=C(N)COCCO)C(C)(C)C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1124.4,4.13,262.5,5.0,17.0,23.0,80.0,2.0,True,0.0034,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C#CCC(=CCCNCCOCCC(=O)O)N1CCC(C(=O)O)CC1,352.4,1.16,99.1,3,5,12,1,25,3.07,0.358,True,True,True,True,True,True,True,True,True,True,C#CCC(=CCCNCCOCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)N1CCC(C(=O)N2CCN(Cc3c(OC)cc(-c4cn(C)c(=O)c5cnccc45)cc3OC)CC2)CC1,1141.4,5.51,213.0,4.0,15.0,23.0,82.0,3.0,True,0.0005,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CNCCC(CCCC(=O)O)CCC(=O)O,231.3,1.33,86.6,3,3,10,0,16,2.73,0.528,True,True,True,True,True,True,True,True,True,True,CNCCC(CCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)CCC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1020.3,5.68,200.6,4.0,13.0,21.0,73.0,3.0,True,0.3141,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +N.O=C(O)C(CO)C(=O)O,151.1,-1.07,129.8,4,4,3,0,10,2.77,0.38,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CO)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,923.1,3.11,208.8,4.0,13.0,14.0,66.0,2.0,True,0.1075,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCCC(NC(C)C(=O)O)C(=O)O,217.3,1.08,86.6,3,3,8,0,15,2.72,0.53,True,True,True,True,True,True,True,True,True,True,CCCCCC(NC(C)C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1006.3,5.43,200.6,4.0,13.0,19.0,72.0,3.0,True,0.6086,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +COC(C)OCCC(OC(=O)O)C1(C)CCC(C(=O)O)CO1,320.3,1.72,111.5,2,6,8,1,22,4.44,0.513,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2CCC(C)(C(CCOC(C)OC)OC(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)OC2)CC1,1109.4,6.07,225.5,3.0,16.0,19.0,79.0,3.0,True,0.11,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +NC(CCCC1(C(=O)O)CCC1)C(=O)O,215.2,0.82,100.6,3,3,6,1,15,2.84,0.609,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(N)CCCC2(C(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)CCC2)CC1,1004.3,5.17,214.6,4.0,13.0,17.0,72.0,3.0,True,0.1726,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CN1N=NCC1(C(=O)O)C(=O)O,173.1,-0.79,102.6,2,5,2,1,12,3.79,0.532,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2(C(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)CN=NN2C)CC1,962.1,3.55,216.5,3.0,15.0,13.0,69.0,2.0,True,0.1933,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)(C)OCCN(CCCC(=O)O)CCC(=O)O,275.3,1.44,87.1,2,4,10,0,19,2.36,0.627,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCN(CCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CCOC(C)(C)C)CC1,1064.4,5.79,201.0,3.0,14.0,21.0,76.0,3.0,True,0.0541,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.54,0.608,True,True,True,True,True,True,True,True,True,True,CCC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,935.2,4.92,188.5,3.0,12.0,15.0,67.0,2.0,True,0.2197,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CCCC(C)=NN(CC)CC(=O)O)C(=O)O,272.3,2.05,90.2,2,4,10,0,19,3.45,0.469,True,True,True,True,True,True,True,True,True,True,CCC(CCCC(C)=NN(CC)CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1061.4,6.4,204.1,3.0,14.0,21.0,76.0,3.0,True,0.2367,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCC(=O)CC(=O)O,160.1,-0.11,91.7,2,3,5,0,11,2.13,0.547,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CC(=O)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,949.1,4.24,205.6,3.0,13.0,16.0,68.0,2.0,True,0.1898,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)C(=CC(=O)O)C(=O)O,158.2,0.74,74.6,2,2,3,0,11,2.79,0.594,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(=CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(C)C)CC1,947.2,5.09,188.5,3.0,12.0,14.0,68.0,3.0,True,0.0503,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.54,0.608,True,True,True,True,True,True,True,True,True,True,CCC(CC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,935.2,4.92,188.5,3.0,12.0,15.0,67.0,2.0,True,0.2197,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCNC(C)=C(C)CC(C(=O)O)C(=O)O,229.3,1.46,86.6,3,3,7,0,16,2.93,0.575,True,True,True,True,True,True,True,True,True,True,CCCNC(C)=C(C)CC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1018.3,5.8,200.6,4.0,13.0,18.0,73.0,3.0,True,0.0137,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCC(=O)C(=O)O,146.1,-0.5,91.7,2,3,4,0,10,2.09,0.522,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(=O)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,935.1,3.85,205.6,3.0,13.0,15.0,67.0,2.0,True,0.1128,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(CCNC(C)=O)(CCC(=O)O)OC(=O)O,261.3,1.22,112.9,3,4,8,0,18,3.3,0.566,True,True,True,True,True,True,True,True,True,True,CCC(CCNC(C)=O)(CCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)OC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1050.3,5.57,226.9,4.0,14.0,19.0,75.0,3.0,True,0.0639,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CC(C(=O)O)C(=O)O,162.1,-0.75,111.9,3,3,4,0,11,2.3,0.468,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(=O)O)CC1,951.1,3.59,225.8,4.0,13.0,15.0,68.0,2.0,False,0.317,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +NCCOC(=O)CCN(CCCCCCC(=O)O)CCC(=O)O,332.4,0.69,130.2,3,6,15,0,23,2.34,0.295,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCN(CCCCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CCC(=O)OCCN)CC1,1121.4,5.04,244.1,4.0,16.0,26.0,80.0,3.0,True,0.3775,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCC(=CCOc1ccccc1)C(=O)O,250.3,1.94,83.8,2,3,7,1,18,2.15,0.722,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(=CCOc2ccccc2)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1039.3,6.29,197.8,3.0,13.0,18.0,75.0,3.0,True,0.0428,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCCCCC(=O)NCC1CC(C(=O)O)C1,271.3,1.25,103.7,3,3,9,1,19,2.12,0.547,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2CC(CNC(=O)CCCCCC(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)C2)CC1,1060.3,5.6,217.6,4.0,13.0,20.0,76.0,3.0,True,0.0634,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCC(C(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.14,0.568,True,True,True,True,True,True,True,True,True,True,CCCC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,935.2,4.92,188.5,3.0,12.0,15.0,67.0,2.0,True,0.7935,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CN(CCCCCC(=O)O)CC(=O)O,203.2,0.65,77.8,2,3,8,0,14,1.98,0.566,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CN(C)CCCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,992.3,5.0,191.8,3.0,13.0,19.0,71.0,2.0,True,0.3513,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C.O=C(O)CCCC(=O)O,148.2,0.96,74.6,2,2,4,0,10,1.9,0.622,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,921.1,4.67,188.5,3.0,12.0,15.0,66.0,2.0,True,0.1984,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(OCCCCC(O)CCCCCCC(=O)O)C(=O)O,304.4,2.43,104.1,3,4,14,0,21,2.95,0.426,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C)OCCCCC(O)CCCCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1093.4,6.78,218.0,4.0,14.0,25.0,78.0,3.0,True,0.0186,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCN(C(=O)NC)C(C(=O)O)C(=O)O,204.2,-0.81,106.9,3,3,4,0,14,2.87,0.519,True,True,True,True,True,True,True,True,True,True,CCN(C(=O)NC)C(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,993.2,3.53,220.9,4.0,13.0,15.0,71.0,2.0,True,0.1551,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCCC(=O)CC(=O)O,174.2,0.29,91.7,2,3,6,0,12,2.1,0.564,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CC(=O)CCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,963.2,4.63,205.6,3.0,13.0,17.0,69.0,2.0,True,0.1761,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCCN(CCCC(=O)O)CCC(=O)C(=O)O,259.3,1.0,94.9,2,4,11,0,18,2.31,0.537,True,True,True,True,True,True,True,True,True,True,CCCCN(CCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)CCC(=O)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1048.3,5.34,208.8,3.0,14.0,22.0,75.0,3.0,True,0.3502,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCCN(CCCCCCCC(=O)O)CC(=O)O,259.3,2.21,77.8,2,3,12,0,18,2.03,0.525,True,True,True,True,True,True,True,True,True,True,CCCN(CCCCCCCC(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)CC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,1048.4,6.56,191.8,3.0,13.0,23.0,75.0,3.0,True,0.7307,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +NCCC(CC(=O)O)C(=O)O,161.2,-0.49,100.6,3,3,5,0,11,2.74,0.505,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CCN)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,950.2,3.86,214.6,4.0,13.0,16.0,68.0,2.0,True,0.3108,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)(C)NC(CCCCC(=O)O)C(=O)O,231.3,1.47,86.6,3,3,7,0,16,2.62,0.578,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)NC(C)(C)C)CC1,1020.3,5.82,200.6,4.0,13.0,18.0,73.0,3.0,True,0.1093,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CCCCCCCCC(=O)O,202.2,2.28,74.6,2,2,9,0,14,1.61,0.562,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCCCCCCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,991.3,6.62,188.5,3.0,12.0,20.0,71.0,3.0,True,0.1287,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)OCCOCCOC1CC(C(=O)O)OCCON1,309.3,-0.56,132.8,3,8,8,1,21,4.06,0.395,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2CC(OCCOCCOC(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)NOCCO2)CC1,1098.3,3.78,246.7,4.0,18.0,19.0,78.0,2.0,True,0.9826,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(C)(C)C(C(=O)O)C(=O)O,160.2,0.82,74.6,2,2,2,0,11,2.5,0.586,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)C(C)(C)C)CC1,949.2,5.17,188.5,3.0,12.0,13.0,68.0,3.0,True,0.1708,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)CC1CCC(C(=O)O)CC1,186.2,1.35,74.6,2,2,3,1,13,1.97,0.697,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C2CCC(CC(=O)N[C@H](C(=O)N3C[C@H](O)C[C@H]3C(=O)NCc3ccc(-c4scnc4C)cc3)C(C)(C)C)CC2)CC1,975.2,5.7,188.5,3.0,12.0,14.0,70.0,3.0,True,0.1509,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)NCCCCCNCCCCCCNNC(=O)O,304.4,1.35,122.7,6,4,14,0,21,2.47,0.213,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)NNCCCCCCNCCCCCNC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1093.4,5.69,236.6,7.0,14.0,25.0,78.0,4.0,True,0.2017,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +Cc1nc(C)n(C)c1C(=O)C=C(CC(=O)O)C(=O)O,266.3,0.71,109.5,2,4,5,1,19,3.07,0.599,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(=CC(=O)c2c(C)nc(C)n2C)CC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1055.3,5.05,223.4,3.0,14.0,16.0,76.0,3.0,True,0.0256,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +NC(CCC(=O)O)CC(=O)O,161.2,-0.35,100.6,3,3,5,0,11,2.63,0.511,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CC(N)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,950.2,4.0,214.6,4.0,13.0,16.0,68.0,2.0,True,0.3108,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +O=C(O)C(CCC1(CCCO)CC1)C(=O)O,230.3,1.1,94.8,3,3,8,1,16,2.78,0.543,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)C(CCC2(CCCO)CC2)C(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,1019.3,5.45,208.8,4.0,13.0,19.0,73.0,3.0,True,0.2301,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CC(=O)NC(CCCC(=O)O)CCCC(=O)O,245.3,1.0,103.7,3,3,9,0,17,2.21,0.562,True,True,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCCC(CCCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)NC(C)=O)CC1,1034.3,5.35,217.6,4.0,13.0,20.0,74.0,3.0,True,0.113,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCC(C)(C(=O)O)C(=O)O,146.1,0.57,74.6,2,2,3,0,10,2.67,0.571,True,True,True,True,True,True,True,True,True,True,CCC(C)(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,935.2,4.92,188.5,3.0,12.0,14.0,67.0,2.0,True,0.7935,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C=C(C(=O)O)c1occc1CC(=O)O,196.2,1.0,87.7,2,3,4,1,14,2.84,0.701,True,True,True,True,True,True,True,True,True,True,C=C(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)c1occc1CC(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,985.2,5.35,201.7,3.0,13.0,15.0,71.0,3.0,True,0.0893,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +C.N=O.O=C(O)CCC(=O)O,165.1,0.9,115.5,3,4,3,0,11,2.21,0.537,False,False,True,True,True,True,True,True,True,True,COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCN(C(=O)CCC(=O)N[C@H](C(=O)N2C[C@H](O)C[C@H]2C(=O)NCc2ccc(-c3scnc3C)cc2)C(C)(C)C)CC1,907.1,4.28,188.5,3.0,12.0,14.0,65.0,2.0,True,0.2118,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" +CCNCC(C)CC(C(=O)O)C(=O)O,203.2,0.41,86.6,3,3,7,0,14,3.0,0.522,True,True,True,True,True,True,True,True,True,True,CCNCC(C)CC(C(=O)N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C)C(=O)N1CCN(Cc2c(OC)cc(-c3cn(C)c(=O)c4cnccc34)cc2OC)CC1,992.3,4.75,200.6,4.0,13.0,18.0,71.0,2.0,True,0.5969,"BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; target_feature_case1[0]" diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/hard_only_s123.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/hard_only_s123.smi new file mode 100644 index 0000000000000000000000000000000000000000..6d7b2fd75ec1e670fbd6890558e9e50783226202 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/hard_only_s123.smi @@ -0,0 +1,100 @@ +DISCONNECTED CO.CONC(=O)O.O=CO +DISCONNECTED CC.CCN.O=C(O)CCC(=O)O +DISCONNECTED C.CC(=O)O.O=CC(=O)O +DISCONNECTED O=C(O)CC1C=CCCCCCCN1.O=C(O)CCCO +DISCONNECTED CCC.CCCC(=O)O.O=C(O)CO +DISCONNECTED CCC(=O)O.O=CCC(=O)O +DISCONNECTED CCC(C)C.O=C(O)CCC(=O)O +DISCONNECTED CC.O=C(O)CCCCCCCCCCC(=O)O +DISCONNECTED CC(=O)NCCNCCNC(=O)O.O=C(O)c1ccccc1 +DISCONNECTED C.C#CC(=O)O.O=C(O)CNCCO +DISCONNECTED C.CC(CCOCCC(=O)O)C(=O)O +DISCONNECTED N.N.O=CCCC1C(CCCCCCC(=O)O)C1C(=O)O +DISCONNECTED C.CCNCCCNC(=O)CCCCCC(C(=O)O)C(=O)O +INVALID +DISCONNECTED CC(=O)O.CCCC(=O)O.COCO +INVALID +DISCONNECTED C.CCC(C(=O)O)C(=O)O +DISCONNECTED C.NCC(=NC(=O)O)C(=O)O +DISCONNECTED C.CC(=O)O.CC(C)C(=O)O +INVALID +DISCONNECTED CC.CCCC(=O)NOC(=O)O.O=CO +DISCONNECTED C.N.O=CC(=O)O.O=CO +DISCONNECTED C.COC.O=CO.O=CO +DISCONNECTED CCC(=O)O.CCC(=O)O +DISCONNECTED CC(=O)O.N.NCC(=O)O +DISCONNECTED C.CC(=O)O.CCC(=O)O +DISCONNECTED CCCCCC.N.O=C(O)CC(=O)O +DISCONNECTED NCCC1CCCC1.O=C(O)CC(=O)O +DISCONNECTED C.C=CC(=O)O.CC(=O)O +DISCONNECTED C=C(N)CCC(C(=O)O)C(C)C(=O)O.O +DISCONNECTED CCC(=O)O.CCC(=O)O +DISCONNECTED CC(=O)O.CC=O.CCCCCC(=O)O +DISCONNECTED C.CC.CC(=O)O.O=CO +DISCONNECTED CC(=O)O.CCOC(C)=CC=C(C)C(=O)O +DISCONNECTED C#C.CCOCCO.O=C(O)CCC(=O)O +AssemblyReady CC(CCC(=O)O)=C(C(=O)O)C(=O)O +DISCONNECTED CC(=O)O.CC(N)C(=O)O +INVALID +DISCONNECTED CC(OCCOCCN)C(=O)O.NCCC1C=NC(C(=O)O)=C1 +DISCONNECTED C=CC(=CC=CCCCCCCC(=O)O)CCCC.NCC(=O)O +AssemblyReady COCNOCCNCCOC(CCOC(=O)O)C(=O)O +DISCONNECTED C.CCC(=O)O.N.O=CNCC(=O)O +DISCONNECTED CC.CC.CC.O=C(O)CC(=O)O +DISCONNECTED CCCCCN1CCN1C(=O)O.CCN.O=CO +DISCONNECTED CC(=O)O.CCCCCC(CC)C(=O)O +DISCONNECTED C.C.CC(=O)O.CCC(=O)O +DISCONNECTED COCO.O=CO.O=CO +DISCONNECTED N.NC(=CC(=O)O)C(=O)O +AssemblyReady CCOC(CCCCCOCC(=O)O)C(=O)O +DISCONNECTED CC.CCC(CN)C(=O)O.CCCC(=O)O +DISCONNECTED CCC(=O)O.CCOC(COCCN)C(=O)O +DISCONNECTED CC(=O)O.NCCS.O=CCCC(=O)O +DISCONNECTED CCCC.CCCCC.O=C(O)CCCCC(=O)O +DISCONNECTED C.CN.O.O=CO.O=CO +INVALID +INVALID +DISCONNECTED CCCCCCC(=O)O.CCCCCCC(=O)O +DISCONNECTED C.C=CC.O=CO.O=CO +INVALID +DISCONNECTED CC(=O)O.CCC1(C(=O)O)CCN(CN)CC1 +INVALID +DISCONNECTED C.CC=O.O=C(O)CC(=O)O +DISCONNECTED C=C.CCC(=O)O.COCCOCC(=O)O +DISCONNECTED C.O=C(O)CCCCCCCC(=O)O +DISCONNECTED C=CC(=CC)C(=O)O.CC(=O)O +DISCONNECTED CCC(=O)O.CCCOC(COCCOCCOCC)OCC(=O)O +DISCONNECTED CC.CCOCCOCC(=O)O.O=C(O)CCOCCO +DISCONNECTED CCC(=O)O.CNCNCc1ncccc1C(=O)O +DISCONNECTED C.CCCCC.CO.O=C(O)CC(=O)O +DISCONNECTED CC(=O)NCCC(=O)O.O=C(O)C1CCN1 +DISCONNECTED C.C.CC(=O)O.NC(=O)O +DISCONNECTED CCC(C)(CCCC(=O)O)C(=O)O.CO +DISCONNECTED C.CC(=O)O.CC=CC(=O)O +AssemblyReady O=C(O)CCC(=O)NCCNC1CNC1C(=O)O +DISCONNECTED CCC(=O)O.COC(=O)O.O +DISCONNECTED C.CC.CC.N.O=C(O)CC(=O)O +DISCONNECTED CCN.O=C(O)CCCC(=O)O +DISCONNECTED CC.CC(=O)O.CCCCCC=O.O=CO +DISCONNECTED COC(=O)O.O=Cc1cn(COC(=O)O)nn1 +DISCONNECTED COCCC(=O)O.COCCNCCCC=O.O=CO +AssemblyReady O=C(O)CCCCCOCCOCCOCCOCCOCC(=O)O +DISCONNECTED CCCCCCN.O=CC(=O)O.O=CO +DISCONNECTED CCCCCCCCCC(=O)O.NCCCCCCCCCC(=O)O +DISCONNECTED N=NN(CCOCCO)C(N)=O.O=CO.O=CO +DISCONNECTED C.C=CC.N.O=CO.O=CO +DISCONNECTED CNC(=O)O.CO.O=CO +DISCONNECTED CC(=O)O.CCC(=O)NCCCCCCC(=O)O +DISCONNECTED CN.O=C(O)CCC(=O)O +DISCONNECTED CC(=O)O.CCC(=O)O.CCCCCC(C)=CCNC(C)=O +DISCONNECTED C#CCCOCCOCCOCC.O=C(O)COCC(=O)O +AssemblyReady O=C(O)C(CCC1CCCCCC1)C(=O)O +DISCONNECTED C.CC.N.O=CO.O=CO +DISCONNECTED CC(N)C(C(=O)O)=C(N)CCC(=O)O.CCO +DISCONNECTED COCCCC(=O)O.NCC(=O)O +DISCONNECTED C#CCNC(C)=O.O=C(O)CCCCCCCCC(=O)O +DISCONNECTED CCCC=O.O=C(O)CCCCCO.O=CO +DISCONNECTED CC.CC(C(=O)O)C(=O)O +DISCONNECTED C#CCC(=O)O.CCOCCCCCCOCCC(=O)O +DISCONNECTED CC.CC(=O)O.CCOCC(=O)O.N +DISCONNECTED CCCC.O=CO.O=CO diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/hard_only_s42.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/hard_only_s42.smi new file mode 100644 index 0000000000000000000000000000000000000000..7cc963ae16f8d2bac6ccf29577c55ac64435b908 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/hard_only_s42.smi @@ -0,0 +1,100 @@ +AssemblyReady O=C(O)CCNCNCCOCCOCCOCCC(=O)O +AssemblyReady O=C(O)CCCCCCCCCCC(=O)O +DISCONNECTED CC(N)=O.O=CO.O=CO +INVALID +DISCONNECTED O=C(O)CCOCCOOCOCCO.O=C(O)CO +AssemblyReady CCOCCCN(CCN1CCC(C(=O)O)CC1)CC(=O)O +DISCONNECTED C.CCO.O=C(O)C(=O)O.O=CO +AssemblyReady CCCC(C(=O)O)C(=O)O +DISCONNECTED C.CNCC(=O)O.O=CO +DISCONNECTED CCC.NCCCC(C(=O)O)C(=O)O +DISCONNECTED CN.O=C(O)CCCC(=O)O +AssemblyReady O=C(O)NCCCCCCNC(=O)O +AssemblyReady CCCCOCCCC(CCOCCCCCCC(=O)O)C(=O)O +DISCONNECTED O=C(O)CC(=O)O.O=C1CCCCCCCCCN1 +INVALID +DISCONNECTED C.C.CC(=O)O.CC(=O)O +DISCONNECTED CC=O.O=C(O)CC(=O)O +DISCONNECTED CC(C(=O)O)C(=O)O.CN +DISCONNECTED C.C.CCCCCCCCC(=O)O.CCOCCCCCCC(=O)O +DISCONNECTED CC(=O)O.CC(CC=O)C(=O)O.CCCC +DISCONNECTED NCC=O.O=CO.O=CO +DISCONNECTED C.CC(=O)O.CCC(C)C(=O)O +DISCONNECTED CCOOC.O=C(O)C(CO)C(=O)O +DISCONNECTED CCOC.NCCOCCOO.O=C(O)CC(=O)O +DISCONNECTED CCCCC1(C=O)CCN1CC.COCCOCCC(=O)O.O=CO +DISCONNECTED CCCNCNC(=O)O.O.O=CO +DISCONNECTED CC(=O)O.CC=C(CN)C(C(=O)O)=C(N)COCCOOC +DISCONNECTED C1COO1.CC(=O)O.CC=O.CCN.O=CO +DISCONNECTED CCO.O=C(O)CCCCOCCOCCOCC(=O)O +DISCONNECTED CC(=O)O.CCOCCC(=O)O.CCOCCOCCNC(=O)CO +DISCONNECTED C.CCC=O.CNC(=O)O.O=CO +DISCONNECTED O=C(O)NCCO.O=CO +DISCONNECTED CC.CCOCCOCCOCC(OCCC(=O)O)C(=O)O +DISCONNECTED CCCCC.CCOC(=O)O.O=CO +DISCONNECTED C=Cc1ccc(OCC)cc1.O=C(O)CCCOCCOCC(=O)O +DISCONNECTED CC.CCC.O=C(O)CC(=O)O +AssemblyReady O=C(O)COCCOCCOCCNC(=O)O +AssemblyReady O=CCC(=O)NCCCC(C(=O)O)C(=O)O +DISCONNECTED CC.CNC(C)=O.O=CO.O=CO +DISCONNECTED C.CCC(=O)O.CCCC(=O)O +AssemblyReady O=C(O)NCCNOCC(=O)C(=O)O +DISCONNECTED CCC(=O)O.CCOCCOCCOCCC(=O)O +DISCONNECTED O=C(O)CO.O=C(O)CO +DISCONNECTED C=C.CC.O=CO.O=CO +DISCONNECTED CCCCC.O=C(O)CCCCCN1CCC(C(=O)O)CC1 +DISCONNECTED C.CCNC(=O)O.O=CO +DISCONNECTED CCC(=O)O.CCC(=O)O.CCNCO +DISCONNECTED CC(CC(=O)O)C(=O)O.CNC(C)=O +DISCONNECTED CCC(=O)O.CCCOCNOCCNC(=O)O +DISCONNECTED CC(=O)O.CC(=O)O.CN +DISCONNECTED CC(=O)NCCCCC(=O)O.CCCCC(=O)O +DISCONNECTED CC.CCC(=O)O.O=CO +DISCONNECTED CC=O.O=C(O)CCCOCC(=O)O +DISCONNECTED CC(=O)O.CCCCC(=O)O +DISCONNECTED CCCCCC(=O)O.O=CCCC(=O)O +DISCONNECTED C.O=C(O)CCOCC(=O)O +DISCONNECTED CCOCCOCCOCCN.O=C(O)CC(=O)O +DISCONNECTED CCOCCOCCOCCOCCN.N.O=CCC(=O)O.O=CO +DISCONNECTED CCO.NCCCCCNC(=O)O.O=CO +DISCONNECTED CCNC1CC1O.O=CO.O=CO +DISCONNECTED CCCN.NC(=O)O.O=CO +DISCONNECTED CCOCCOC.O=C(O)CCCCCC(=O)O +DISCONNECTED C.NCC(CC(=O)O)C(=O)O +DISCONNECTED C.CCO.O=CO.O=CO +DISCONNECTED CCC(=O)O.CCCOCCOCCOCCOCCC(=O)O +AssemblyReady O=C(O)CCCCCCCCCC(=O)O +DISCONNECTED CC.CC(=O)O.NCN.O=C(O)CC(=O)O +DISCONNECTED C.CC(=O)O.CCC(=O)O.N.N.O=CO +INVALID +AssemblyReady CNCN(CC(=O)O)C(C)C(=O)O +DISCONNECTED CCCCc1ccn(C)n1.CCOC(CC(=O)O)C(=O)O +DISCONNECTED CCCCC(=O)O.CCCCCNC(C)=O.COCCOCC(=O)O +AssemblyReady CC(C)OCC(C(=O)O)C(=O)O +DISCONNECTED CN1CCC(C(=O)O)CC1.O=C(O)CCCCCOC1CCNCC1 +DISCONNECTED CCC(=O)O.CCOCCOC.CCOCCOCC(=O)O +DISCONNECTED CCOCCOC.NC(C(=O)O)C(=O)O +DISCONNECTED C.CC(N)(C(=O)O)C(=O)O +DISCONNECTED C.CC.O.O=CO.O=CO +DISCONNECTED CCC(=O)N=CCOCC(=O)O.CCCO.O=C(O)CO +DISCONNECTED CNC.O=C(O)CC(=O)O +DISCONNECTED C.COCCOC(=O)O.O.O=CC(=O)O +DISCONNECTED CC(=O)O.CCOCCNC(=O)CC(=O)O +DISCONNECTED CNC(C)=O.COC.O=CO.O=CO +AssemblyReady COCCOCCNCCC(C(=O)O)C(=O)O +AssemblyReady O=C(O)NCCOCCNC(=O)O +DISCONNECTED CC(=O)O.CCCC(=O)O +DISCONNECTED C.O=C(O)CNCC(=O)O +DISCONNECTED CCC(=O)NCCCC(=O)O.O=C(O)COCCO +DISCONNECTED NC(=O)CCC(=O)O.O=C(O)CO +DISCONNECTED C.CCC.O=C(O)CC(=O)O +DISCONNECTED C.CCC.O=C(O)c1cc2c3cc1C(C(=O)O)N23 +DISCONNECTED C=CCCC(=O)O.CNC(=O)C(=O)O +DISCONNECTED CCOCCC(=O)O.O=C(O)OCCOCCOO +INVALID +DISCONNECTED C.CC.CCOCC(C)O.O=C(O)CC(=O)O +INVALID +AssemblyReady CC(OCCC(=O)O)C(=O)O +DISCONNECTED C.C.O=C(O)CCC(=O)O +DISCONNECTED C.CC(=O)O.CCC(CC)NCC(=O)O +DISCONNECTED CC.CCC=O.O=CC(CCCCCCC(=O)O)C(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/hard_only_s456.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/hard_only_s456.smi new file mode 100644 index 0000000000000000000000000000000000000000..a309bd55c2e53fcf190a2f1e91c774fa71b1bb81 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/hard_only_s456.smi @@ -0,0 +1,100 @@ +INVALID +DISCONNECTED CC(C(=O)O)C(=O)O.CCCCC.CCCCOCCOCCOC +DISCONNECTED C.CC(=O)O.CCC(=O)O +DISCONNECTED C.CC.CCOCC.O=C(O)C(=CO)C(=O)O +DISCONNECTED CC(=O)O.CC=O.CCC.NCC(=O)O +INVALID +DISCONNECTED CC(=O)O.CCC=O.CCOCCCCC(C)C(=O)O +DISCONNECTED CC.CCNC(=O)O.O=CO +DISCONNECTED COCCOCCOC.O=C(O)OCCOCCOCCO.O=CO +DISCONNECTED C.CC(=O)O.CCCCCC(=O)O +INVALID +AssemblyReady CC(CC(=O)O)C(C)C(=O)O +DISCONNECTED CCCCOCC.O=C(O)CCCNC(=O)O +DISCONNECTED CCCCCC.O=CO.O=CO +DISCONNECTED CCC(=O)O.CCC(=O)O +DISCONNECTED CCC(=O)O.CCNC(=O)COCCOCC(=O)O +DISCONNECTED CCC(=O)NCC(=O)O.CCCCC(=O)O +INVALID +DISCONNECTED CC.CC(=O)O.CCCCCC.O=C(O)CCO +DISCONNECTED CCCNC(=O)O.O=CO +DISCONNECTED CC.CCCC(=O)NCC.O.O=CO.O=CO +DISCONNECTED CCn1cc(C=O)nn1.NCC(=O)O.O=CO +DISCONNECTED CC.CC.O.O=CO.O=CO +DISCONNECTED CC(=O)O.CC(=O)O.COCCOCCCN +AssemblyReady O=C(O)CCCCCCCOCCC(=O)O +INVALID +DISCONNECTED C1CCNCC1.CCCN.O=C(O)CCC(=O)O +DISCONNECTED CCn1nnc(CO)c1C(=O)O.COCC(=O)O +AssemblyReady CCOCCOCCOCC(C(=O)O)C(CC)C(=O)O +AssemblyReady CC(=O)NCCC(C(=O)O)C(=O)O +DISCONNECTED CC=O.CCCC(=O)O.O=CO +DISCONNECTED C.Cc1cn(C)nn1.O=CO.O=CO +DISCONNECTED CC.CC(=O)O.CC(=O)O +DISCONNECTED C=CC.NC(O)C(C(=O)O)C(=O)O +DISCONNECTED C#CCCCCCCCC(=O)O.CCOCCC(=O)O +DISCONNECTED CCC(=O)O.CCCC=O.CO.O=CO +DISCONNECTED CCC1(CC)CCNCC1.O=CCCC(=O)O.O=CO +DISCONNECTED CCCC(=O)CC.O=C(O)CCC(=O)O +DISCONNECTED CC.CC(C(=O)O)C(C)C(=O)O +DISCONNECTED CCn1cc(CCC(=O)O)nn1.O=CCOCCOCCO.O=CO +DISCONNECTED CCC(=O)O.CCC1=CC(OCCOCC(=O)O)CC=C1 +DISCONNECTED CC(=O)O.CC=O.NCCCCCC(=O)O +DISCONNECTED CCC(=O)O.CCC(=O)O.CCN +DISCONNECTED CC.CCOCCNC(=O)CCC(=O)O.CCOCCOCC(=O)O +DISCONNECTED CCOCCO.N.O=CO.O=CO +DISCONNECTED CC.CCC(C(=O)O)C(=O)O +DISCONNECTED NCCN.NCO.O=CCCNCCC(=O)O.O=CO +AssemblyReady CCCCCC(C)C(CCCCC(=O)O)C(=O)O +DISCONNECTED CCN.O=C(O)CC(=O)O +DISCONNECTED CCC.CCOCCOCC.O=C(O)COCC(=O)O +DISCONNECTED CCO.O=C(O)CC(=O)O +DISCONNECTED C.CCCCC(=O)O.CCCCC(=O)O.N +DISCONNECTED CC(=O)O.CC(=O)O.CCC +DISCONNECTED C.CCC(=O)O.CCCC(=O)O +INVALID +AssemblyReady O=C(O)NCCNC(=O)O +DISCONNECTED C#CCOCC(OCC(OCCOCC)C(=O)O)C(=O)O.CC +DISCONNECTED C1COCCO1.O=C(O)NCCOCCOCCOCCNC(=O)O +AssemblyReady NCC(CCCCCOCC(=O)O)C(=O)O +DISCONNECTED N.O=CCC(=O)O.O=CO +DISCONNECTED CC(N)=O.CCC(=O)O.CCCC(=O)O +INVALID +DISCONNECTED CCC(=O)O.O=C(O)CN1CCNCC1 +DISCONNECTED CC=O.CCCC(=O)O.O=CO +DISCONNECTED CC(=O)O.CCCCCCCC(=O)O.CCCCN +DISCONNECTED CC.CC(=O)O.CC(=O)O +DISCONNECTED CCC(CC=O)n1cc(CCC(=O)O)nn1.O=CO +DISCONNECTED C.C.C.CC.O=CO.O=CO +DISCONNECTED CC.CC(=CC(=O)O)C(=O)O +AssemblyReady O=C(O)C1CCC1C(=O)O +DISCONNECTED CCCC(=O)NCC(C(=O)O)C(=O)O.CN +DISCONNECTED C.CC(=O)O.CCC(=O)O +INVALID +DISCONNECTED C.CC(=O)O.O.O=C(O)CO +DISCONNECTED C.COCCOCCOC.O.O=C(O)N1CCN(C(=O)O)CC1 +DISCONNECTED C.C.CC(=O)O.Cc1cc(O)c(C(=O)O)cc1C +DISCONNECTED CC(=O)O.CCN(CCCC(=O)O)C(=O)CCC=O +DISCONNECTED CC.N.O=C(O)CC(=O)O +DISCONNECTED CCCC(=O)O.CCCCCC(=O)O +DISCONNECTED C.C.CC.O=CO.O=CO +AssemblyReady NCCC(=C(CC(=O)O)C(=O)O)C1COCCOCCO1 +DISCONNECTED CC.O=CCCCCC(=O)O.O=CO +DISCONNECTED CC(=O)O.CC(=O)O.NCC=O +DISCONNECTED CCCC.O=CCCCCCCC(=O)O.O=CO +DISCONNECTED CC(=O)O.CCOCCOCCOCCC(=O)O +DISCONNECTED CCC(CC(=O)O)C(=O)O.N +DISCONNECTED C.CC.COCC(=O)O.NC(=O)CC(=O)O +DISCONNECTED CCCN.O=C(O)NCCO.O=CO +AssemblyReady O=C(O)C#CN1CCN(CC2OCC2OCC(=O)O)CC1 +DISCONNECTED C.CCN.O=C(O)CC(=O)O +DISCONNECTED CCOCCC(=O)O.NCC(=O)O +AssemblyReady NCCOCCNC(C(=O)O)C1(CC(=O)O)CCNCC1 +DISCONNECTED O=C(O)CCC(=O)O.c1nn2cc1CCCCCC2 +DISCONNECTED C.O=C(O)C#CCCC(=O)O +DISCONNECTED C.CCNCC.CN.O=C(O)CCC(=O)O +DISCONNECTED CCCNCC(=O)O.CCN(C)N=CC(=O)O +AssemblyReady CC(CCC(=O)O)C(=O)O +DISCONNECTED CC(=O)O.O=CCNCCO.O=CO +DISCONNECTED C.C.CC(CC(=O)O)C(=O)O +DISCONNECTED CC.CC1CO1.NC(=O)O.O.O=CO diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/soft_only_s123.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/soft_only_s123.smi new file mode 100644 index 0000000000000000000000000000000000000000..1b262db3c7296cce523e05c6d94b5489d2b2dfac --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/soft_only_s123.smi @@ -0,0 +1,100 @@ +Valid C=C1C=CC1=COCCCN +INVALID +INVALID +INVALID +INVALID +Valid CCC1CCCC(C)OCC1 +DISCONNECTED CCCC(NCC)C(O)N(C)C.O +Valid CC(C)C1(C)CCC2C(=O)CCCc3cn2c1n3 +DISCONNECTED CCCCCCCCCCCC12CC3=CC1=S(CC)C=C32.N +INVALID +Valid CCCCCC1COC1(CO)COC +DISCONNECTED C=C=O.CCNCCC(=CC(C)O)CNCCN(N)CC +INVALID +INVALID +Valid CCCC(CO)(N(C)O)N(C)C(C)C +DISCONNECTED C.N=C1NOC1CCC=O +DISCONNECTED C.COC(N)C(C)C(C)C +DISCONNECTED CCN(CCO)COO.CO +DISCONNECTED C.CCNCC(CC)OCC +DISCONNECTED C.COCCNCCCC1NNc2ccc(cc2N)O1 +INVALID +DISCONNECTED C.C.C=C=O.C=CCOO +DISCONNECTED CC(C)CC(C)(C)CN.O +Valid CCCOC(C)=COCC +DISCONNECTED C.C=CCC(C)C(C)C.O +DISCONNECTED C.C.C.C.C.CC=CCC +DISCONNECTED C.CCON=CC(C)=CCOC1CC1 +Valid CCCNN=CNC1(COC)CCC1C +INVALID +INVALID +DISCONNECTED C.C.C.C=C.CCCOC +Valid COC(C=O)OCCC1CCCCN1C +INVALID +Valid C=CC(C)C(C=C1N=C=CCCO1)=CC=O +DISCONNECTED C.C=C1COC(C#CN)(C(O)CC(C)O)O1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCCN1CCOCCCCCCN(C)CC1 +DISCONNECTED C.CC(OC(F)C(F)F)C(C)(C)CCO +Valid CCC(C)C(C)(N)C(C)OC +DISCONNECTED C.C.CCC(C)O.O.O=O +DISCONNECTED C.C.CCN(C)CC(C)C +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CCCC(C)(CC)CC.N +INVALID +INVALID +INVALID +DISCONNECTED C.C.NC1CCC2(CO2)C1 +INVALID +Valid CNC1CC1(C)C12CCN1C(C(C)O)COC2 +Valid C=C(NC)C(CC)=C(OC)C(=C)OO +Valid C=C(NCOC)C(C)CCO +DISCONNECTED C.C=C1C=NCON(CCOC)C(OC)=N1 +Valid CCOC(N)(C(O)CC)C(C)C(C)C +Valid CCC(N)=C1C(CCO)NN1C +Valid CC1CCC2C#CCN3CC(N4CCCC4)OC4(CN(CCC14)C2)C3 +INVALID +Valid CCCCC(=O)NC(C)C1CCCCC2CC21CO +Valid CC(O)C(C)C(C)CCCN(C)CCO +DISCONNECTED C.CCCCC(OCCO)OC(C)CCC +DISCONNECTED C.C.CCC1(C)C(=O)C1O +Valid NC1C=C(O)C1CCC1(O)CCCCC1 +DISCONNECTED C.COCCC1C=C(C)OC1 +INVALID +DISCONNECTED C.C=NCC(O)C(C)CC.O +INVALID +Valid CNC(N)CC(C)(CCN)NC +DISCONNECTED C.C.CCNCC1CCC1OC1OOC1=O +DISCONNECTED C.CNC(=O)C1(N(CCN)CCO)COC1C +INVALID +INVALID +DISCONNECTED C.C=NCC(C(C)CCC)C1CCNC1 +Valid CCCOCCOC1COC2CNCC[N+]([O-])(C(C)=O)C2COC1C +DISCONNECTED C.CCOCC1(O)CC(C)OCN1C(CC)=NO +DISCONNECTED CC.CCCCN(CC)CN +DISCONNECTED COCC(O)=C(C)OC.O +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCC1N(C(C)=O)CN2NCCC12CC +DISCONNECTED C.CCCN(CC)C(C)N +INVALID +DISCONNECTED C.CCCC(ON)C(CC)CO.O +Valid NCCC(O)N1CCC(CN2CCCC(=C3CNO3)C2)C1 +INVALID +Valid CCC(C)(C)C(=O)COC +INVALID +Valid CC(C)CNC(C)N(O)C(O)CCN +DISCONNECTED C.C.CC=C(O)C=COC diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/soft_only_s42.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/soft_only_s42.smi new file mode 100644 index 0000000000000000000000000000000000000000..ab854a85197f3874cc9f27428a3d56b6eb138413 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/soft_only_s42.smi @@ -0,0 +1,100 @@ +Valid CCOCC1OCC12OCC2N1CCN(O)C(C)(CCN)C1 +DISCONNECTED C.COC(CCOCCO)C1OCC1OC +DISCONNECTED C.CCCC(=O)N(O)CC +DISCONNECTED C.CC=NC1NC(=O)C1C=CC=CO +Valid NCC1OCOCCOCONCOCCOC2=CC21 +INVALID +INVALID +DISCONNECTED C.C=C1CC(CC)CN1C +DISCONNECTED C.C.C1CCNCCNC1 +Valid CC1CC2C(C)C12CN(C)N=[N+](C)[O-] +DISCONNECTED C.C=CC(CN)CC(N)C=N +INVALID +INVALID +Valid C=C1C(CCCCCCC)c2ccc(cc2)CN1C +Valid OCC1CCCNCCC12C=CC=CC2 +INVALID +DISCONNECTED C.C.C.CC=O.O=CCO +DISCONNECTED C.CC(C)CC1C(O)N1C +INVALID +INVALID +DISCONNECTED C.C.C.C=CCC(C)CO +DISCONNECTED C.C.CCCCCCCCN.O +DISCONNECTED CCC(=N)CONC=C(C)CO.N=O +INVALID +INVALID +DISCONNECTED C.C.CN(C)C1C[N+]1([O-])C=O.CO +INVALID +DISCONNECTED CCC12CCN(C=COCCC(C)C1)CC2.N +INVALID +INVALID +DISCONNECTED C.C=CN1CC(C2CC2CC)C1C +Valid CCNC1CCC1CCN +INVALID +DISCONNECTED N.N=C(CNCC(O)ON)NCC=O +INVALID +DISCONNECTED CCCC(CCOC)OC.CN +DISCONNECTED C.CCCC1C(C)C2=C(OCNC=O)C(=O)N21 +DISCONNECTED C.C.CNCC(C)OCCCCN1CNC1 +DISCONNECTED C.C.CCC[N+]([O-])(CCO)OCC +DISCONNECTED C.CCC(CCCCN)C(C)=O +INVALID +Valid CCC(CN(C)C1CCCCC1)=S(=O)(CC)C(C)C +DISCONNECTED CCCC.COC.O=CO +Valid CCCCC(C)(CC)OC +DISCONNECTED C.CN(C)C(CCCN1CCOCC1)C1CCCCCN1C +INVALID +INVALID +DISCONNECTED C.COC(O)C(C)CCC=CC(N)=O +INVALID +DISCONNECTED C.CCCCN(C)CCC +Valid C=C(C)N(C)CC(CC)C(=CCCCCC)CO +DISCONNECTED C.CC.CC.CCN.N.N +DISCONNECTED C.C=NOCC1(OCC)CC1OCN +DISCONNECTED CC.CC(C)C=O.CC=O.O +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CCOC(CC)CCC1=C(OC)CO1.CO +Valid CCN(N)CC(COC)CC(C)=O +DISCONNECTED C.CCNC(COC)OC.O +INVALID +DISCONNECTED C.CCC(CC)C(C)C(C)C +DISCONNECTED CC(C)C.CCOC.N.O +Valid CC1CC(N)C(C(C)C2CC2)N(C)CCN(C)CCCC(=O)N1 +DISCONNECTED C.C.C=CCC1CCN(CC)CCC1C +INVALID +DISCONNECTED C=C.N=c1ccn(CCCOCCO)[nH]1 +DISCONNECTED C.COCCC(OCC1C#CCO1)c1nccn1CO +DISCONNECTED C.C.CCC(OOC)N1CC1CN +INVALID +INVALID +DISCONNECTED C.CCCC(O)CN1CC1CC +INVALID +INVALID +INVALID +DISCONNECTED C.C.C.C.CCNC(C)=O +DISCONNECTED C.C=CC(=O)NNCC.N +INVALID +INVALID +Valid CCCCCCC(C)N(COCC)OC +INVALID +Valid CCN(CC)C1C=CC(C)(OC)CC1 +INVALID +DISCONNECTED C.C=CC1CCCN(CC(C)O)C1 +DISCONNECTED C.C.C.CC.CCC(C)C +DISCONNECTED CCCC(C)(C)C(N)O.N +Valid CCCCCCCCCC(N)Nc1ccc(O)cc1 +INVALID +DISCONNECTED C.C=C(CC)NCCC.C=O +INVALID +INVALID +INVALID +Valid C=C(C)C(C)(CCC)OC +INVALID +INVALID +Valid C=C(C)CC(C)C(CC)=NN +INVALID +INVALID +Valid CCOCC1CNC(C(C)c2ccccc2)C1(CC)CC diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/soft_only_s456.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/soft_only_s456.smi new file mode 100644 index 0000000000000000000000000000000000000000..c43db21804db4dd1b4838490f8331c977b5b0974 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/soft_only_s456.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +DISCONNECTED C.CCCCCC.COO +Valid COCCCCC(OC)C(CO)CNC(C)O +INVALID +INVALID +INVALID +DISCONNECTED C.CCC(C(C)O)C(C)NC +DISCONNECTED CCCCCOCC(CCOC)(OCCOC)C(COCC)OC.O +INVALID +INVALID +INVALID +DISCONNECTED COCCC(C)CC12CC3(C)CN1CCC32.N +DISCONNECTED C.CCC(=O)C(CC)CCO.N +Valid CNC1C2CCNC21OC +DISCONNECTED C.CC1CCC2CC23CC=CC2(CC2)N(O1)C(=O)C3 +Valid CCCOC(=O)CCC(O)C(C)(C)C(C)C +DISCONNECTED C.CCCCCCCN(C)CC.O=CC=O +INVALID +DISCONNECTED C.C=C1CCC23CC2(C)N13 +DISCONNECTED C.CC=CC1(CC)C(CC)N1CCCC(C)C +INVALID +DISCONNECTED C.C.COC(N)CCCCN +INVALID +INVALID +DISCONNECTED C.COCCC(OCCOCCO)C1COCCOCCOCCO1 +AssemblyReady NCCOCCCCC(OCC=O)OOCC=O +INVALID +INVALID +INVALID +DISCONNECTED CC(O)C(C)OCN.N.N=C=O +Valid C=CC=C(N=C)NC(C)C(CC)NC +DISCONNECTED C.C.CC(C)C(C)CCN +Valid CCOC1CC2CCCCCC2C1 +INVALID +Valid C=C(C(CO)CC(C)CCC=O)N(C)O +DISCONNECTED C.CCOCCOCCCOC(CCN)CCOCC +DISCONNECTED CCC.CCCCCOC=C=NOCC +DISCONNECTED C.CCN(C)CC(CN)OO.O +INVALID +Valid CC#CCCC1C2CN(CCNC(=O)CNCC)CC12C +INVALID +INVALID +INVALID +INVALID +Valid CCCCC1(C)C(O)C1CN +INVALID +Valid COCCOCCC(C)C1(O)CCCC(C)C1C +DISCONNECTED C.NNC(=CCCO)N=S +Valid CCCOCC(N)C(COCCCCCO)OCCC +DISCONNECTED C.C.C.CN=CNN.CO +DISCONNECTED C.C.CO.COC1NCCCCCNC1N +DISCONNECTED C.C.CCOC.COC.N.O +DISCONNECTED C.C.C.CCOC(CO)C(C)C +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C=NC(=CC=CC)C(C)C1CCNCC1 +DISCONNECTED C.C.C.C=C(CCC)NC +INVALID +INVALID +INVALID +DISCONNECTED C.C.CCC1CC(NC)C1CC +DISCONNECTED C.CCC1CCC2COCC3=C(CCCC31)CCO2 +INVALID +INVALID +DISCONNECTED C.CCC=C(C)CC(C)CC +INVALID +DISCONNECTED C.CCC(C)(C)C(C)(C)C +INVALID +DISCONNECTED C.CCCC(C)(CO)OC +INVALID +Valid CC1=CC1OCC(C)(C)CO +INVALID +DISCONNECTED C.CN(CCCCCCNC(=O)C=O)C(=O)C=O +DISCONNECTED C.C=C(OCC)C(=CC)OC(CC)C(=O)OCCO +INVALID +DISCONNECTED C.C.CCC(C)C(CCN)CCO.O +INVALID +INVALID +DISCONNECTED C.CC(=O)CCCC(=O)NCCN.O +INVALID +INVALID +INVALID +Valid CCC(OCCOC)=C(C)N +INVALID +INVALID +Valid CCCN1CCC(C)C2=C(N)C3C4CCN(C=C2)C31OC4 +DISCONNECTED C.CCC(O)N=NC(C)=CN +INVALID +INVALID +Valid CN1CCC2=NOCC3=S4(C)=C(C=CC=C24)C(C3)C1 +INVALID +INVALID +Valid COC1=c2c(=O)c3cn4n(cc2cc3-4)CCC1 +Valid C=CC(C)C(C)CC(C)C +DISCONNECTED C.C=C(C(C)OC)N(CC)C(=O)CS +DISCONNECTED C.COC(C)C(O)C(O)CN +DISCONNECTED CNC(C)=CC1C(C)C1C1(O)CN1.O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/sw_base_s123.smi b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/sw_base_s123.smi new file mode 100644 index 0000000000000000000000000000000000000000..678283c8ddd5f44de732a1473c389a236da80471 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/BRD9VHL/sw_base_s123.smi @@ -0,0 +1,100 @@ +DISCONNECTED C.COC=O.O=CO.O=CO +DISCONNECTED CCC(=O)O.CCCCCC(=O)O +DISCONNECTED C.CC(=O)O.O=CC(=O)O +AssemblyReady CC(N)C(CC(=O)O)OCCCCNC(=O)CCC(=O)O 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0000000000000000000000000000000000000000..4dbe03d84f8b59f0d86823a54a0922e3b0d3e3a9 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_guid_s123_clean.smi @@ -0,0 +1,100 @@ +O=C(O)CC(NC(=O)Nc1ccccc1)C1=C=C(C(=O)O)C(O)=C1 +INVALID +CCCOc1sc(CCC(=O)NC(CNCC)C(=O)O)cc1C(=O)O +INVALID +Cc1cc(C(O)c2ccc(CC(N)C(=O)O)cc2)cc(C(=O)O)c1O +INVALID +COC1(CC(=O)O)C=C2C=CC1(OCO)C1C=COC(C2)C1C(=O)O +O=C(O)c1ccc2c(c1)C1(O)N=c3ccc(C(=O)O)cc3=C1OC2=O +INVALID +NC1(C(=O)O)C=CC=C(Cc2nncs2)C=C1CC(=O)O +CCCC(O)C(OC(=O)C(C)C)C(OC(=O)O)OC(=O)O +C=C(C)C(NC(=O)C(CC(=O)O)CC(=O)O)=C(C)C(=O)NCC(=O)O +CC(C(=O)O)C1=C=C2C=C1C(S(=O)N1C3=C4C(O)(C=C3)C41C(=O)O)=C2 +Cc1cc2cc(c1C(=O)O)C=c1oc(C(=O)O)c(C(=O)O)c1=CC=C2 +C=C(SC(=S)Nc1cc(Cc2nonc2N)cc(C(=O)O)c1)C(=O)O +C=C(CN1C=c2onc(CC(=O)O)c2=C(CO)C=CS1(=O)=O)C(=O)O +O=C(O)COc1ccc(-c2ccc(F)c(OCC(=O)O)c2)cc1 +INVALID +CCN(C1=Cc2cc(C)ccc2N=C(C)O1)C(CCC(=O)O)C(=O)O +NCCCCC(C(=O)NC(CC(=O)O)C(=O)O)C(C(=O)O)C(=O)O +INVALID +COc1cccc(-c2cccc(CC(=O)O)c2C(=O)O)c1 +CCC1(C(=O)O)C2=CC(=CC1(NC)OC)SC(C)(CC(=O)O)O2 +INVALID +O=C(O)CCC(=CC(=O)Oc1cc(C(F)F)cc(C(F)F)c1)C(=O)O +N#CC(OC(=O)O)OC12C(=O)C1(O)c1ccc3cc1C32OC(=O)O +O=C(O)c1cc(NC(=O)c2ccc3c(c2)[N+]3([O-])O)c(O)c(C(=O)O)c1 +CCCN(CC=C(C)C(=O)O)C(=O)C(CC(=O)O)NC(=O)CCCO +O=C(O)CCNC(C(=O)O)C(O)(O)C(=O)c1cccc2ccccc12 +CC(=O)c1cc2c(cc1O)C(O)=C2OC(C(=O)O)C(=O)O +CC(C=O)C(=O)Oc1cc(C(C(=O)O)C(=O)O)ccc1C(=O)O +CCCCCCC(CO)(CCCC(=O)O)CC(C)(C)CC(=O)O +O=C(O)C1=CC=C2C=CC3CCC(CC3)CN3C(=O)C2C13C(=O)O +CC(=O)c1ccc(NCC(=O)O)cc1C(=O)c1ccc(C(=O)O)cc1 +O=C(O)C1=CC2=C3C4=CC=C(CC1=C2)C43CC(C(=O)O)C(O)C(=O)O +CCCC(=O)C1(CC)C=CC(C)=CC(C(=O)O)=C1C(=O)O +INVALID +INVALID +COC1=CC=CC(S(=O)(=O)NCC(=O)O)=Cc2ccc(C(=O)O)cc21 +INVALID +INVALID +O=C(O)CCOC12C=C3C=CC1=S(Cc1cccc2c1)C(C(=O)O)C3 +INVALID +O=C(O)c1cccc(C2(C(=O)O)C(O)=COC=C2c2ccsc2)c1O +INVALID +CC=O.NCC(=O)O.O=C(O)O +CCCCCCC(C(=O)O)C(=O)Nc1csc(C(=O)O)c1 +O=C(O)C(=O)Nc1ccc(C(=O)O)s1 +CC1C=C(CN(O)C(Cc2cccc(OC(=O)O)c2)C(=O)O)OCC1 +CC(=O)OC(C=C(C)C(=O)O)CC(C)C(CCC(C)(C)O)CC(=O)O +COC(NC(=O)c1ccccc1C(=O)O)C(=O)O +O=C(O)COc1cccc(-c2ccc3c(c2)CCCC3)c1CC(=O)O +COC(=O)CC12C=CC=C(CC(=O)O)C1=CC1=C(C(=O)O)C12CCO +CCNS(=O)(=O)c1cnc2coc(C(=O)O)c(C(=CC(=O)O)CC)c1-2 +INVALID +CC(C)(CC(Cc1ccccc1)C(=O)O)C(=O)O +CN(CCCN(CCO)CCCO)c1ccc(C(=O)O)cc1C(=O)O +INVALID +CC(C)(CO)NC(=O)CC(CC(=O)O)CC(=O)O +COc1ccc2nccc(C=C(SC=S)C(=O)O)c2c1C(=O)C(=O)O +CCCCN(CCCC(C)=S1(=O)C=CC(=O)C(C(=O)O)=C1)CC(=O)O +INVALID +O=C(O)Cc1ccc(F)cc1C(=O)O +CC1C2(C)C(=O)C(C(=O)O)C(C(=O)O)C12c1ccc(OOC=O)cc1 +O=C(O)C1=C(C(=O)O)C(CO)=C(CCCc2ccccc2)OC(S)=N1 +CC12C=CC=CC1=C2CCCNC(=O)C(CCC(=O)O)C(O)C(=O)O +CCN(CCc1cc(C(=O)O)ccc1C(N)C(=O)O)S(=O)(=O)N(C)C +INVALID +CCCN(CCCCCCC(CCO)C(=O)CCC(=O)O)CC(=O)O +INVALID +INVALID +CCN1CC(C)=C(C)C2=C(CNC(CC(=O)O)C(=O)O)C3=CC3=CC21 +NC(CCC(=O)O)CNC(=O)C(C(=O)O)N1CCCCCC1C(=O)O +CC(=O)O.O.O=C1C=NC=NC1.O=CO +COc1ccccc1NC(=O)C(O)=C(C(=O)O)C(=O)CC(=O)O +COC(=O)C(C)(C)NC(=O)c1cccc(C(CCC(=O)O)C(=O)O)c1 +INVALID +COc1ccc(C(=O)O)cc1CC(=Cc1cccs1)C(=O)O +O=C(O)OC1n2oc(=O)cc2C(C(=O)O)OC1(CCCCS)C(=O)O +COCC(C)(COC)C(=O)C(=CC(=O)O)C(=O)C(=O)O +Nc1ccc(O)c(NCC(=O)Nc2c(C(=O)O)cccc2C(=O)O)c1 +INVALID +CC(C)(CCCO)C(C(=O)O)c1ccccc1C(CC(=O)O)C(=O)O +INVALID +CC1(C(=O)O)C(=CC(=O)O)C=C(F)C=C1C1=C(O)C(=O)C(O)=CC1=O +INVALID +CCC1=C(CCC(Oc2ccccc2C(=O)O)C(=O)O)NC(=O)CC1 +O=C(O)CCNC(=O)C=C(c1ccccc1)c1ccccc1C(=O)O +O=C(O)CCCCn1cc2c3c(c(C(=O)O)c(O)cc31)C(C(=O)O)=C2 +Cc1ccc(CCCC(=O)O)c2c(C(=O)O)cc(OCCCO)cc12 +INVALID +COC(=O)C1C(C(O)CCC(=O)O)CC(C)(C(C)(O)C=O)C1C(=O)O +CCCCCCC(=O)NC(CC(O)COCC(C)(O)C(=O)O)C(=O)O +CC(=O)NCCCCNC(=O)SC(=CC1=C(O)C(C(=O)O)=C1)C(=O)O +INVALID +C=CC(OC(C)=O)C(C)(C(=O)O)C(CC(=O)O)OC(=O)CCC(N)=O +O=C(O)CCCCN(CN1CCCCC1)CC1(C(=O)O)C=CC=C1 +C=O.N.O=C(O)C1CCCCC1.O=C(O)CCCCC(=O)O +NC1=CC23C(=C1)C2=c1cc(C(=O)O)ccc1=CC1=CC3(C(=O)O)S1=O +O=NOC1C(O)C2(O)OC1(OC(=O)O)C2(CO)OC(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_guid_s42.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_guid_s42.smi new file mode 100644 index 0000000000000000000000000000000000000000..bec549fea790b049531b35a693f92ee324f24b00 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_guid_s42.smi @@ -0,0 +1,100 @@ +AssemblyReady Cc1c(NCC(=O)O)cccc1C(=O)O +AssemblyReady COc1c(CNC(=O)C(=O)O)cccc1Cc1ccccc1C(=O)O +INVALID +AssemblyReady CCCCC1CCN(C(=O)O)CC1N(C(=O)O)c1ccc(F)c(F)c1 +INVALID +AssemblyReady CCCC(CC(C)C)(C(=O)O)C(=O)NCC(=O)N(CC)C(=O)C(=O)O +AssemblyReady C=CC(OC(C=CC(=O)O)C(CC(=O)O)OCCCC)C(C)C(=O)O +AssemblyReady CC(C)(CCc1csc(NCC2(CC(=O)O)CCCCC2)n1)C(=O)O +AssemblyReady CN(CCCc1cccc(CNCC(=O)O)c1)CCCC(O)C(=O)O +AssemblyReady O=Nc1c(NS(=O)(=O)c2cccc(O)c2)cc(C(=O)O)cc1C(=O)O +AssemblyReady C=C(CC(O)C1=C(CCCc2cc(C(=O)O)on2)CCCC1)C(=O)O +AssemblyReady O=C(O)CC(CC1C=CN=CC=CC(=O)CCS1(=O)=O)C(=O)O +AssemblyReady COC(=O)c1cccc(C(C(=O)O)=C(CC2=CCCC2)C(=O)O)c1O +INVALID +AssemblyReady CNC(=O)COc1ccccc1C(OC)C(C)(CC(=O)O)CC(=O)O +AssemblyReady O=C(O)CC(Oc1ccc2cc1c1c3sc(nc23)=CC(=O)N1)C(=O)O +AssemblyReady CC(CC(CCC(O)C=O)C1CCC(CC(=O)O)C(C)C1=O)C(=O)O +INVALID +AssemblyReady COc1ccc(S(O)(O)c2cc(C(=O)O)c(OC)cc2C(=O)O)cc1 +INVALID +AssemblyReady CC(Oc1cccc(C(=O)NC(C)(C)C(=O)O)c1)C(=O)O +AssemblyReady CC(=O)NC(=O)NC(=O)c1cccc(C(CC(=O)O)C(=O)O)c1 +INVALID +AssemblyReady Cc1ccc(Oc2ccccc2)c(-c2c(C(=O)O)csc2C(=O)O)c1 +AssemblyReady COCCC(=O)N1c2cc3ccc2C12C=CC(C(=O)O)=CC32C(=O)O +AssemblyReady O=C(O)c1ccc(C=C2C(=O)c3ccc(C(=O)O)cc3[N+]2([O-])O)cc1 +AssemblyReady N=C(NC(CCNO)C(=O)O)C(CCC(=O)O)N(CC(=O)O)C(=O)O +AssemblyReady COc1cc(-c2cccc(C(=O)O)c2C(=O)O)c2nc(N)cnc2c1 +AssemblyReady CCC1(C)C=CC1(C(=O)O)C1=CC=C1c1cc(O)cc(O)c1C(=O)O +AssemblyReady CC(C)(O)C(C=O)C1(C)C(O)CCC2(C)CC(OC(=O)O)(C(=O)O)C21 +AssemblyReady COCC(CC(CCC(=O)O)C(C)NCCCCC(=O)OC)C(=O)O +AssemblyReady O=C(O)CSC1=CC2=C(C(=O)O)CC2(O)C=C1 +AssemblyReady CCC(O)(CC(O)(C(=O)O)C(C)O)C1OC=CC=C(O)C=C1C(=O)O +AssemblyReady O=Cc1c(O)c(CC(=O)O)c2cc(C(=O)O)c(=O)oc2c1CC(=O)O +AssemblyReady CC(NC(=O)c1cc(OC(=O)O)ccc1NCC(=O)O)C(=O)O +AssemblyReady O=C(O)C(=CO)C(=O)c1ccc(C(=O)O)c(-c2ccccc2)c1 +AssemblyReady O=C(O)CC(S)=c1ccc2c(c1)C=2C(=O)O +INVALID +INVALID +AssemblyReady CC(C)C(O)CCCC(CC(=O)O)NC(=O)CC(N)C(=O)O +AssemblyReady COC12C=CC1=CC2(OC)C(CCCC(C)C(=O)O)C(=O)O +AssemblyReady CCC1=CN(CCCO)C(CCCC(=O)O)(C(=O)O)C(CO)=CC=C1 +AssemblyReady O=C(O)C1CC23C(F)=CC4(C=C2C1C3C(=O)O)Oc1cc4ccc1F +AssemblyReady Cc1cc(C(O)C(O)(CC(C)C)C(=O)O)c2c(C)c(C(=O)O)cn2c1 +AssemblyReady CC1=CC(=C(CC(S)C(=O)O)OC=N)C=C(CO)C(C(=O)O)=C1 +AssemblyReady CCC(OO)OC(COC(O)(C(=O)O)C(OO)OC(=O)O)C(O)C=O +AssemblyReady COc1cc(C(=O)O)ccc1OC(C)c1ccc(C(=O)O)cc1F +AssemblyReady CCCC1CCC(C2OC(CO)C(CO)O2)C1(CCC(=O)O)C(=O)O +AssemblyReady CCCC(CC(C)(OC(N)=O)OC(=O)CCCC(=O)O)C(N)C(=O)O +AssemblyReady O=C(O)COC(=O)COCCC(C(=O)O)c1ccc2c(c1)OCCO2 +DISCONNECTED COC1=CC(=O)C(OC)=C1.O=C(O)C=CCC(=O)O +AssemblyReady CCCCCN1CCN2C=CC=C(OC)C2=CC(C(=O)O)(C(=O)O)C1 +AssemblyReady Cn1c(CC=O)c2c(c1CC(=O)O)C2=CC(=O)O +AssemblyReady CCC1(C(=O)O)NC2=C(OC)C=C2C2(C=C2)N(C(CS)C(=O)O)C1=O +INVALID +AssemblyReady O=C(O)CNC(=O)c1cc(=O)oc2c(CO)c(C(=O)O)cc(CO)c12 +AssemblyReady CC(C(=O)CCCC=CC(=O)OCC(O)C(=O)O)C(C(=O)O)C(=O)O +AssemblyReady CCCC(C(=O)O)N(CC(=O)O)C(F)(F)CC(=O)O +AssemblyReady Cc1ccc(C2(O)C=C(C(=O)C(=O)O)C3(C(C)(C)C(=O)O)C=C23)cc1 +AssemblyReady COC(=O)C(C)NC(=O)N(CCc1ccc(C(=O)O)cc1)CC(=O)O +AssemblyReady CC(OCCCCCCCCC(=O)O)OC(CCO)C(O)CC(=O)O +AssemblyReady O=C(O)C1=CC=C2OC(NS(=O)(=O)c3cccs3)(C(=O)O)C=C2CC1 +INVALID +AssemblyReady Cc1c(CCNC(=O)c2ccc(C(=O)O)cc2)cccc1C(=O)O +AssemblyReady N#Cc1cccc(COc2cc(C=CC(=O)O)cc(C(=O)O)c2O)c1 +INVALID +AssemblyReady CN(C)CC(CC(CC(=O)O)C1=CC=C1)C(=O)O +AssemblyReady CCCN(C(=O)CC(=O)O)C(=O)C(=O)O +AssemblyReady O=C(O)Cc1cc(O)cc(C(=O)OCC(C(=O)O)C(=O)O)c1C(=O)O +AssemblyReady Cn1sc2c(C(=O)NC3CC(CC(=O)O)(C(=O)O)C3)cccc2c1=O +AssemblyReady CN(C1CCC(CC(=O)O)CC1)C(C(=O)NCCC(=O)O)C(C)(C)O +AssemblyReady CC1=COC(Cc2cccc(C(=O)O)c2)(NC=O)C(C(=O)O)=C1 +AssemblyReady CCC(C)C(=CC(=O)O)CC(=O)O +AssemblyReady CS(=O)(=O)C(C(=O)O)C1(CO)C=C(C(=O)O)C=CO1 +AssemblyReady CC[SH](CN)c1cc(CC(=O)O)ccc1S(=O)(=O)C(=O)O +AssemblyReady COCCCc1ccccc1NC(CN)=C(CC(=O)O)C(=O)C(=O)O +AssemblyReady NC(C(=O)O)=C(CCCC(=O)O)OC(=O)O +AssemblyReady CCOc1cc2nc(-c3c(C(=O)O)cccc3C(=O)O)sc2cc1O +AssemblyReady CCOc1cc(C=O)ccc1-c1ccc(C(N)C(=O)O)c(C(=O)O)c1 +AssemblyReady COC12C=CC(C(=O)O)=CC=C(C(=O)O)C3CCC(C1)C(C3)O2 +AssemblyReady O=C(O)CCCC12C=CC(CNCCC3CCCCC3)=C1N2C(=O)O +AssemblyReady O=C(O)C1=CC2=CNC(C(=O)O)(C(=O)O)C(C=C1)C2 +AssemblyReady O=C(O)C=C1C=CC2=CC12N=CNC(=O)C(CSC=S)C(=O)O +INVALID +AssemblyReady CCN(CCCN1C(=O)CNC2=COC=CC21C(=O)O)CC(=O)O +AssemblyReady CCCCN(C(CC(=O)O)C(=O)O)S(=O)(=O)C1=CC=COC(CN)=C1 +AssemblyReady CCC1C2CC(O)(C(C)(O)C(CC)C(C)C(=O)O)C(CC(=O)O)OC12 +DISCONNECTED C.NC(=O)N(CC1=CC2=C(C(=O)O)C=C1C2=O)C(=O)O.O +AssemblyReady CCCC(C(=O)OC)N(CCCCC(=O)O)C(=O)C(=O)O +INVALID +AssemblyReady CC1=CC2=CS134=C1C5=C(C(=O)O)C3=C4C1=C1C5=CC=C1C2CC(=O)O +AssemblyReady CCOC(C(C)C)C(Nc1ccc2c(c1)CCN2CC(=O)O)C(=O)O +AssemblyReady CC(=O)NC1=C(C(=O)O)C=NS(=O)(=O)CC=C1C(=O)O +AssemblyReady CC(C)c1ccc(NC(=O)Cc2ccc(C(=O)O)cc2C(=O)O)cc1 +AssemblyReady O=C(O)CNC(=O)CCC1(C(C(=O)O)N2CC(O)C2)CCCCCC1 +AssemblyReady Cc1cc(C(=O)O)c(C(=O)O)cc1CC(=O)NC(N)=O +AssemblyReady CCCCCC(CCCCC)CCC(CCCCC)(C(=O)O)C(=O)O +AssemblyReady O=C(O)C1=CC(C(=O)O)=C(O)C=CNC1(Cc1ccccc1)C(=O)O +AssemblyReady CCc1ccc(C(=O)N2CC(C(=O)O)=NN(CC)C2=O)cc1C(=O)O +AssemblyReady CC1CCN(C(=O)CC=C(NCC(=O)O)C(=O)O)CC1 diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_guid_s42_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_guid_s42_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..684662d783836f02b47af0936c7ad992f45cbf75 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_guid_s42_clean.smi @@ -0,0 +1,100 @@ +Cc1c(NCC(=O)O)cccc1C(=O)O +COc1c(CNC(=O)C(=O)O)cccc1Cc1ccccc1C(=O)O +INVALID +CCCCC1CCN(C(=O)O)CC1N(C(=O)O)c1ccc(F)c(F)c1 +INVALID +CCCC(CC(C)C)(C(=O)O)C(=O)NCC(=O)N(CC)C(=O)C(=O)O +C=CC(OC(C=CC(=O)O)C(CC(=O)O)OCCCC)C(C)C(=O)O +CC(C)(CCc1csc(NCC2(CC(=O)O)CCCCC2)n1)C(=O)O +CN(CCCc1cccc(CNCC(=O)O)c1)CCCC(O)C(=O)O +O=Nc1c(NS(=O)(=O)c2cccc(O)c2)cc(C(=O)O)cc1C(=O)O +C=C(CC(O)C1=C(CCCc2cc(C(=O)O)on2)CCCC1)C(=O)O +O=C(O)CC(CC1C=CN=CC=CC(=O)CCS1(=O)=O)C(=O)O +COC(=O)c1cccc(C(C(=O)O)=C(CC2=CCCC2)C(=O)O)c1O +INVALID +CNC(=O)COc1ccccc1C(OC)C(C)(CC(=O)O)CC(=O)O +O=C(O)CC(Oc1ccc2cc1c1c3sc(nc23)=CC(=O)N1)C(=O)O +CC(CC(CCC(O)C=O)C1CCC(CC(=O)O)C(C)C1=O)C(=O)O +INVALID +COc1ccc(S(O)(O)c2cc(C(=O)O)c(OC)cc2C(=O)O)cc1 +INVALID +CC(Oc1cccc(C(=O)NC(C)(C)C(=O)O)c1)C(=O)O +CC(=O)NC(=O)NC(=O)c1cccc(C(CC(=O)O)C(=O)O)c1 +INVALID +Cc1ccc(Oc2ccccc2)c(-c2c(C(=O)O)csc2C(=O)O)c1 +COCCC(=O)N1c2cc3ccc2C12C=CC(C(=O)O)=CC32C(=O)O +O=C(O)c1ccc(C=C2C(=O)c3ccc(C(=O)O)cc3[N+]2([O-])O)cc1 +N=C(NC(CCNO)C(=O)O)C(CCC(=O)O)N(CC(=O)O)C(=O)O +COc1cc(-c2cccc(C(=O)O)c2C(=O)O)c2nc(N)cnc2c1 +CCC1(C)C=CC1(C(=O)O)C1=CC=C1c1cc(O)cc(O)c1C(=O)O +CC(C)(O)C(C=O)C1(C)C(O)CCC2(C)CC(OC(=O)O)(C(=O)O)C21 +COCC(CC(CCC(=O)O)C(C)NCCCCC(=O)OC)C(=O)O +O=C(O)CSC1=CC2=C(C(=O)O)CC2(O)C=C1 +CCC(O)(CC(O)(C(=O)O)C(C)O)C1OC=CC=C(O)C=C1C(=O)O +O=Cc1c(O)c(CC(=O)O)c2cc(C(=O)O)c(=O)oc2c1CC(=O)O +CC(NC(=O)c1cc(OC(=O)O)ccc1NCC(=O)O)C(=O)O +O=C(O)C(=CO)C(=O)c1ccc(C(=O)O)c(-c2ccccc2)c1 +O=C(O)CC(S)=c1ccc2c(c1)C=2C(=O)O +INVALID +INVALID +CC(C)C(O)CCCC(CC(=O)O)NC(=O)CC(N)C(=O)O +COC12C=CC1=CC2(OC)C(CCCC(C)C(=O)O)C(=O)O +CCC1=CN(CCCO)C(CCCC(=O)O)(C(=O)O)C(CO)=CC=C1 +O=C(O)C1CC23C(F)=CC4(C=C2C1C3C(=O)O)Oc1cc4ccc1F +Cc1cc(C(O)C(O)(CC(C)C)C(=O)O)c2c(C)c(C(=O)O)cn2c1 +CC1=CC(=C(CC(S)C(=O)O)OC=N)C=C(CO)C(C(=O)O)=C1 +CCC(OO)OC(COC(O)(C(=O)O)C(OO)OC(=O)O)C(O)C=O +COc1cc(C(=O)O)ccc1OC(C)c1ccc(C(=O)O)cc1F +CCCC1CCC(C2OC(CO)C(CO)O2)C1(CCC(=O)O)C(=O)O +CCCC(CC(C)(OC(N)=O)OC(=O)CCCC(=O)O)C(N)C(=O)O +O=C(O)COC(=O)COCCC(C(=O)O)c1ccc2c(c1)OCCO2 +COC1=CC(=O)C(OC)=C1.O=C(O)C=CCC(=O)O +CCCCCN1CCN2C=CC=C(OC)C2=CC(C(=O)O)(C(=O)O)C1 +Cn1c(CC=O)c2c(c1CC(=O)O)C2=CC(=O)O +CCC1(C(=O)O)NC2=C(OC)C=C2C2(C=C2)N(C(CS)C(=O)O)C1=O +INVALID +O=C(O)CNC(=O)c1cc(=O)oc2c(CO)c(C(=O)O)cc(CO)c12 +CC(C(=O)CCCC=CC(=O)OCC(O)C(=O)O)C(C(=O)O)C(=O)O +CCCC(C(=O)O)N(CC(=O)O)C(F)(F)CC(=O)O +Cc1ccc(C2(O)C=C(C(=O)C(=O)O)C3(C(C)(C)C(=O)O)C=C23)cc1 +COC(=O)C(C)NC(=O)N(CCc1ccc(C(=O)O)cc1)CC(=O)O +CC(OCCCCCCCCC(=O)O)OC(CCO)C(O)CC(=O)O +O=C(O)C1=CC=C2OC(NS(=O)(=O)c3cccs3)(C(=O)O)C=C2CC1 +INVALID +Cc1c(CCNC(=O)c2ccc(C(=O)O)cc2)cccc1C(=O)O +N#Cc1cccc(COc2cc(C=CC(=O)O)cc(C(=O)O)c2O)c1 +INVALID +CN(C)CC(CC(CC(=O)O)C1=CC=C1)C(=O)O +CCCN(C(=O)CC(=O)O)C(=O)C(=O)O +O=C(O)Cc1cc(O)cc(C(=O)OCC(C(=O)O)C(=O)O)c1C(=O)O +Cn1sc2c(C(=O)NC3CC(CC(=O)O)(C(=O)O)C3)cccc2c1=O +CN(C1CCC(CC(=O)O)CC1)C(C(=O)NCCC(=O)O)C(C)(C)O +CC1=COC(Cc2cccc(C(=O)O)c2)(NC=O)C(C(=O)O)=C1 +CCC(C)C(=CC(=O)O)CC(=O)O +CS(=O)(=O)C(C(=O)O)C1(CO)C=C(C(=O)O)C=CO1 +CC[SH](CN)c1cc(CC(=O)O)ccc1S(=O)(=O)C(=O)O +COCCCc1ccccc1NC(CN)=C(CC(=O)O)C(=O)C(=O)O +NC(C(=O)O)=C(CCCC(=O)O)OC(=O)O +CCOc1cc2nc(-c3c(C(=O)O)cccc3C(=O)O)sc2cc1O +CCOc1cc(C=O)ccc1-c1ccc(C(N)C(=O)O)c(C(=O)O)c1 +COC12C=CC(C(=O)O)=CC=C(C(=O)O)C3CCC(C1)C(C3)O2 +O=C(O)CCCC12C=CC(CNCCC3CCCCC3)=C1N2C(=O)O +O=C(O)C1=CC2=CNC(C(=O)O)(C(=O)O)C(C=C1)C2 +O=C(O)C=C1C=CC2=CC12N=CNC(=O)C(CSC=S)C(=O)O +INVALID +CCN(CCCN1C(=O)CNC2=COC=CC21C(=O)O)CC(=O)O +CCCCN(C(CC(=O)O)C(=O)O)S(=O)(=O)C1=CC=COC(CN)=C1 +CCC1C2CC(O)(C(C)(O)C(CC)C(C)C(=O)O)C(CC(=O)O)OC12 +C.NC(=O)N(CC1=CC2=C(C(=O)O)C=C1C2=O)C(=O)O.O +CCCC(C(=O)OC)N(CCCCC(=O)O)C(=O)C(=O)O +INVALID +CC1=CC2=CS134=C1C5=C(C(=O)O)C3=C4C1=C1C5=CC=C1C2CC(=O)O +CCOC(C(C)C)C(Nc1ccc2c(c1)CCN2CC(=O)O)C(=O)O +CC(=O)NC1=C(C(=O)O)C=NS(=O)(=O)CC=C1C(=O)O +CC(C)c1ccc(NC(=O)Cc2ccc(C(=O)O)cc2C(=O)O)cc1 +O=C(O)CNC(=O)CCC1(C(C(=O)O)N2CC(O)C2)CCCCCC1 +Cc1cc(C(=O)O)c(C(=O)O)cc1CC(=O)NC(N)=O +CCCCCC(CCCCC)CCC(CCCCC)(C(=O)O)C(=O)O +O=C(O)C1=CC(C(=O)O)=C(O)C=CNC1(Cc1ccccc1)C(=O)O +CCc1ccc(C(=O)N2CC(C(=O)O)=NN(CC)C2=O)cc1C(=O)O +CC1CCN(C(=O)CC=C(NCC(=O)O)C(=O)O)CC1 diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_guid_s456.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_guid_s456.smi new file mode 100644 index 0000000000000000000000000000000000000000..f0cf143c83bcd9eee3c18ffd0687c6250adc1202 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_guid_s456.smi @@ -0,0 +1,100 @@ +AssemblyReady O=C(O)C1=CC23C=CC14CC2CCC43C(=O)O +AssemblyReady CC1=C(C(=O)O)C2(OC(=O)O)OC2=C1C(=O)C(CS(=O)(=O)O)C(=O)O +AssemblyReady O=C(O)CCC(=O)C(=O)O +AssemblyReady CCCC(=C(O)C(=O)O)C(C(=O)O)C(=O)O +INVALID +AssemblyReady CCOC(=O)C(O)(CC(OCC)C(=CC(=O)O)OCCOC)C(=O)O +AssemblyReady O=C(O)CCC(=O)C(=O)C(=O)O +AssemblyReady CC(=CC(=O)O)N(C(=O)CSCC(=O)O)C(C(=O)O)c1ccccc1 +AssemblyReady CC(CC1=S2C=C1C21COCCN1C)C(C(=O)O)C(NC=O)C(=O)O +AssemblyReady CCNC(C)(CCCC(C)CNC(=O)C(=O)O)C(=O)O +AssemblyReady COC1(c2nc(C(=O)O)cc3cc(O)ccc23)C=COC=C1C(=O)O +AssemblyReady CC(NCc1cccc(Oc2ccccc2OCC(=O)O)c1)C(=O)O +INVALID +AssemblyReady COCCNC(=O)C1=CC(S(=O)(=O)O)=CCC(CC(=O)O)=C1C(=O)O +AssemblyReady CC(C)=C(C)CCC1(C)C2C=C(C(C(=O)O)=C(O)C(=O)O)C(O)C21O +AssemblyReady CCN(C)C(O)(O)C(Cc1ccc(OC)c(OC(=O)O)c1)NC(=O)O +AssemblyReady O=C(O)c1cc(O)ccc1Oc1ccc(C(=O)O)c(C(=O)O)c1 +AssemblyReady CC(OC1CCCC1)C(CC=CCCCCC(=O)O)NC(=O)C(=O)O +DISCONNECTED C[N+]([O-])(O)n1ccsc1=O.O=C(O)O.O=CO +AssemblyReady CC(C)(C(=O)O)c1ccccc1-c1ccc(OCCO)c(C(=O)O)c1 +AssemblyReady COc1ccc(C(=O)O)c(C2=C(C3=CC(O)=C3C(=O)O)O2)c1O +AssemblyReady N#Cc1coc(COc2ccc(CC(=O)O)cc2C(CN)C(=O)O)n1 +AssemblyReady CCCCCC(C)(NC(=O)C(CO)NC(=O)COCC(=O)O)C(=O)O +AssemblyReady C=CC1(C(C)C(=O)O)C=CC(OC=O)(C(=O)O)C=C1 +AssemblyReady CC1=CC(CC(=O)O)C(C(=O)O)C(C)=C1 +AssemblyReady O=C(O)C(=Cc1ccccc1C(=O)O)C1(CO)CCC(C(=O)O)CC1 +DISCONNECTED CC(C(=O)O)C(N)C=O.CCCNC.O=CO +AssemblyReady O=C(O)NCCNc1ccccc1C1C(C(=O)O)C12C=CC=CC=C2 +AssemblyReady CC(CC(=O)O)OC(CO)C(CCO)=C(NCCN)C(=O)O +AssemblyReady CCCCN(C)C(C)CC(=O)NC1CCC(C(=O)O)CC(C(=O)O)C1 +AssemblyReady CCCCN(CCCC(=O)C(CC(N)C(=O)O)C(=O)O)C(=O)C(C)N +INVALID +INVALID +AssemblyReady CC1=NC(C(=O)O)C2CCC(OC(=O)O)=C(C)C(=N1)N2C(=O)C(=O)O +AssemblyReady N#Cc1cccc(Oc2cc(C(=O)O)ccc2OCCCC(=O)O)c1 +AssemblyReady O=C(O)CC(O)(CO)CCCCC(C(=O)O)C(O)CCC(=O)C(=O)O +AssemblyReady Cc1cc(-c2cccc(C(=O)O)c2)c2cc(C=O)ccc2c1C(=O)O +DISCONNECTED O.O=C(O)C(F)(F)C(F)(F)F.O=C(O)CC(CC(=O)O)CC(F)(F)F +INVALID +INVALID +AssemblyReady O=C(O)CNCCC(=Cc1ccc(OCC(=O)O)cc1O)C(=O)O +AssemblyReady Cc1[nH]c(=O)oncccc2c(CC(=O)O)c3c(C(=O)O)oc3cc12 +INVALID +AssemblyReady O=C(O)C1=C2NC34C5=C2C2(O1)C(=O)NC(C(=O)O)C1C6=CC1(C5=C63)N42 +INVALID +AssemblyReady C=CC(=O)OC(CCCC(=O)O)CC(=O)O +INVALID +INVALID +INVALID +AssemblyReady NC(=O)NN=C(CCCC(=O)O)c1ccc(C(F)(F)F)c(C(=O)O)c1 +AssemblyReady O=C(O)CNC(=O)c1cc(-c2ccccc2C(=O)O)c(C(=O)O)s1 +AssemblyReady Cc1cccc(C(=O)O)c1-c1cc2c[nH]c(C(=O)O)cc(O)c3c1c23 +INVALID +AssemblyReady O=C(O)NCCC1Oc2cc(C(=O)O)cc(c2)[N+]1([O-])O +AssemblyReady O=C(O)C(=C(C1=CS#C1)c1cccc(C(=O)O)c1)C(C(=O)O)C1CC1 +DISCONNECTED C.COC(C(=O)O)=C(F)C(=O)C(=O)O.O +AssemblyReady CCNc1cc(C(=O)O)cc(N(C)c2ncccc2C(=O)O)c1 +AssemblyReady C=CCC1=C(OC)C=CC(NC(=O)CCC(=O)O)(C(=O)O)C(C)C=C1 +INVALID +AssemblyReady O=C(O)CCCCCC(=O)NCC(=O)c1ccc(C(=O)C(=O)O)cc1 +AssemblyReady CC(=O)OC1C(C)C(O)C(O)(C(=O)O)CC1(O)C(OC(C)C)C(=O)O +INVALID +AssemblyReady CCN(CC(=O)O)CN(CCCOC)c1ccccc1C(=O)O +AssemblyReady CCCCC(=O)CC(=O)C(C(=O)O)=C(CCN=C(N)N)C(=O)O +AssemblyReady O=C(O)C(O)C12C=CC(=C=CC=Cc3cccc(c3)C1C(=O)O)C=C2 +AssemblyReady COC(=O)CC(OC(=O)O)C(CC(=O)O)NC(COC(=O)O)C(=O)O +AssemblyReady CCCNc1c(C(=O)O)cccc1C(C)C(O)CC(=O)O +AssemblyReady O=Cc1ccc(-c2ccc(F)c([N+]([O-])(O)OC(=O)O)c2)c(C(=O)O)c1 +AssemblyReady Cc1ccc(-c2ccc(C3(C(=O)O)C=CC=C3C(=O)O)cc2)c(C)c1 +AssemblyReady CC(C)(C(=O)O)C(=O)N(CCCNC(=O)COC(=O)CN)CC(=O)O +AssemblyReady O=C(NC(C(=O)O)c1sccc1C(=O)O)c1cccc(C2=CC=C2)c1 +AssemblyReady Cc1cc(C)c(CCC(=O)O)cc1CCCCCC1(C(=O)O)CCC1 +AssemblyReady CC(=O)OCCCN(CC(=O)O)CC(C)(NC(=O)C(C)C)C(=O)O +AssemblyReady O=C(O)Oc1ccc(O)c2c(=O)c(=O)[nH]ccc(C(=O)O)cccc12 +AssemblyReady NC(C(=O)O)C1C=CC=C(C2CC(C(=O)O)N(C(=O)O)C2)C=C1 +AssemblyReady Cn1c(C(=O)O)cc(C=C(CO)c2cccc(C(=O)O)c2)c1C(=O)O +INVALID +AssemblyReady COC(=O)c1ccc(OCCCCCC(=O)O)cc1CC(=O)C(=O)O +AssemblyReady CCCCc1cc(-c2ccc(CNC)cc2C(=O)O)ccc1C(=O)O +AssemblyReady O=C(O)CCNC(=O)c1ccc(F)cc1-c1cccc(CC(=O)O)c1 +AssemblyReady N#[SH](CCNc1ccc(C(=O)O)cc1C(=O)O)CN1CC1 +AssemblyReady CC1=C(O)C2(C#N)C(C)=C(CCC2C)C1=CC(C)(OC(=O)O)C(=O)O +AssemblyReady O=C(O)C=CC1=CC=CS(=O)(=O)c2ccccc(C(=O)O)cccc21 +AssemblyReady O=C(O)Cn1c(C(=O)O)c(Nc2ccc(C(=O)O)cc2)cc(O)c1=O +AssemblyReady O=C(O)CCCOC1(CN2CCCC2=O)C=CC(CC(S)C(=O)O)=C1 +AssemblyReady O=C(O)COc1ccccc1OCC(=O)Nc1cccc(C(=O)O)c1 +AssemblyReady CCCN(CCC(=O)O)C(=O)N1C=S2C=C1C21C=CC2(C(=O)O)C=C21 +INVALID +AssemblyReady C[SH]1CC(CO)=C(C(=O)O)CC1CCC(O)(CC(CN)CN)C(=O)O +DISCONNECTED CC.O=C(O)C(=O)n1cc(C(=O)O)c2ccccc21.O=CO +AssemblyReady NC(CC(=O)O)C(C=O)OC(=O)O +AssemblyReady CS(=O)(=O)N(CC=O)CCNc1ccc(CC(=O)O)cc1OC(=O)O +AssemblyReady O=C(O)CCCCCC(=CCCCCCC(O)(O)C(=O)C(=O)O)CO +AssemblyReady O=C(O)C1=CC2C3=S2C2=CCN3C1=CC=C2C(=O)O +AssemblyReady O=C(O)CCCC1CCCC(OCC(O)CC(=O)CC(=O)O)CCC1 +AssemblyReady CCN(C(=O)C(C)Nc1cc(C)ccc1O)C(O)(CC(=O)O)C(=O)O +AssemblyReady O=C(O)COCC1(O)OC(CS)C(S(=O)(=O)O)C1(O)OC(=O)O +AssemblyReady Cc1nc(-c2cc(CC(=O)O)ccc2OCCC(=O)O)cs1 +INVALID +AssemblyReady O=C(O)OC1CC2COC3=NC=NC4=NC=NC43C(OC(=O)O)C1O2 diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_guid_s456_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_guid_s456_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..d65e834d268b0163dee36ce1bff1c2d21806171d --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_guid_s456_clean.smi @@ -0,0 +1,100 @@ +O=C(O)C1=CC23C=CC14CC2CCC43C(=O)O +CC1=C(C(=O)O)C2(OC(=O)O)OC2=C1C(=O)C(CS(=O)(=O)O)C(=O)O +O=C(O)CCC(=O)C(=O)O +CCCC(=C(O)C(=O)O)C(C(=O)O)C(=O)O +INVALID +CCOC(=O)C(O)(CC(OCC)C(=CC(=O)O)OCCOC)C(=O)O +O=C(O)CCC(=O)C(=O)C(=O)O +CC(=CC(=O)O)N(C(=O)CSCC(=O)O)C(C(=O)O)c1ccccc1 +CC(CC1=S2C=C1C21COCCN1C)C(C(=O)O)C(NC=O)C(=O)O +CCNC(C)(CCCC(C)CNC(=O)C(=O)O)C(=O)O +COC1(c2nc(C(=O)O)cc3cc(O)ccc23)C=COC=C1C(=O)O +CC(NCc1cccc(Oc2ccccc2OCC(=O)O)c1)C(=O)O +INVALID +COCCNC(=O)C1=CC(S(=O)(=O)O)=CCC(CC(=O)O)=C1C(=O)O +CC(C)=C(C)CCC1(C)C2C=C(C(C(=O)O)=C(O)C(=O)O)C(O)C21O +CCN(C)C(O)(O)C(Cc1ccc(OC)c(OC(=O)O)c1)NC(=O)O +O=C(O)c1cc(O)ccc1Oc1ccc(C(=O)O)c(C(=O)O)c1 +CC(OC1CCCC1)C(CC=CCCCCC(=O)O)NC(=O)C(=O)O +C[N+]([O-])(O)n1ccsc1=O.O=C(O)O.O=CO +CC(C)(C(=O)O)c1ccccc1-c1ccc(OCCO)c(C(=O)O)c1 +COc1ccc(C(=O)O)c(C2=C(C3=CC(O)=C3C(=O)O)O2)c1O +N#Cc1coc(COc2ccc(CC(=O)O)cc2C(CN)C(=O)O)n1 +CCCCCC(C)(NC(=O)C(CO)NC(=O)COCC(=O)O)C(=O)O +C=CC1(C(C)C(=O)O)C=CC(OC=O)(C(=O)O)C=C1 +CC1=CC(CC(=O)O)C(C(=O)O)C(C)=C1 +O=C(O)C(=Cc1ccccc1C(=O)O)C1(CO)CCC(C(=O)O)CC1 +CC(C(=O)O)C(N)C=O.CCCNC.O=CO +O=C(O)NCCNc1ccccc1C1C(C(=O)O)C12C=CC=CC=C2 +CC(CC(=O)O)OC(CO)C(CCO)=C(NCCN)C(=O)O +CCCCN(C)C(C)CC(=O)NC1CCC(C(=O)O)CC(C(=O)O)C1 +CCCCN(CCCC(=O)C(CC(N)C(=O)O)C(=O)O)C(=O)C(C)N +INVALID +INVALID +CC1=NC(C(=O)O)C2CCC(OC(=O)O)=C(C)C(=N1)N2C(=O)C(=O)O +N#Cc1cccc(Oc2cc(C(=O)O)ccc2OCCCC(=O)O)c1 +O=C(O)CC(O)(CO)CCCCC(C(=O)O)C(O)CCC(=O)C(=O)O +Cc1cc(-c2cccc(C(=O)O)c2)c2cc(C=O)ccc2c1C(=O)O +O.O=C(O)C(F)(F)C(F)(F)F.O=C(O)CC(CC(=O)O)CC(F)(F)F +INVALID +INVALID +O=C(O)CNCCC(=Cc1ccc(OCC(=O)O)cc1O)C(=O)O +Cc1[nH]c(=O)oncccc2c(CC(=O)O)c3c(C(=O)O)oc3cc12 +INVALID +O=C(O)C1=C2NC34C5=C2C2(O1)C(=O)NC(C(=O)O)C1C6=CC1(C5=C63)N42 +INVALID +C=CC(=O)OC(CCCC(=O)O)CC(=O)O +INVALID +INVALID +INVALID +NC(=O)NN=C(CCCC(=O)O)c1ccc(C(F)(F)F)c(C(=O)O)c1 +O=C(O)CNC(=O)c1cc(-c2ccccc2C(=O)O)c(C(=O)O)s1 +Cc1cccc(C(=O)O)c1-c1cc2c[nH]c(C(=O)O)cc(O)c3c1c23 +INVALID +O=C(O)NCCC1Oc2cc(C(=O)O)cc(c2)[N+]1([O-])O +O=C(O)C(=C(C1=CS#C1)c1cccc(C(=O)O)c1)C(C(=O)O)C1CC1 +C.COC(C(=O)O)=C(F)C(=O)C(=O)O.O +CCNc1cc(C(=O)O)cc(N(C)c2ncccc2C(=O)O)c1 +C=CCC1=C(OC)C=CC(NC(=O)CCC(=O)O)(C(=O)O)C(C)C=C1 +INVALID +O=C(O)CCCCCC(=O)NCC(=O)c1ccc(C(=O)C(=O)O)cc1 +CC(=O)OC1C(C)C(O)C(O)(C(=O)O)CC1(O)C(OC(C)C)C(=O)O +INVALID +CCN(CC(=O)O)CN(CCCOC)c1ccccc1C(=O)O +CCCCC(=O)CC(=O)C(C(=O)O)=C(CCN=C(N)N)C(=O)O +O=C(O)C(O)C12C=CC(=C=CC=Cc3cccc(c3)C1C(=O)O)C=C2 +COC(=O)CC(OC(=O)O)C(CC(=O)O)NC(COC(=O)O)C(=O)O +CCCNc1c(C(=O)O)cccc1C(C)C(O)CC(=O)O +O=Cc1ccc(-c2ccc(F)c([N+]([O-])(O)OC(=O)O)c2)c(C(=O)O)c1 +Cc1ccc(-c2ccc(C3(C(=O)O)C=CC=C3C(=O)O)cc2)c(C)c1 +CC(C)(C(=O)O)C(=O)N(CCCNC(=O)COC(=O)CN)CC(=O)O +O=C(NC(C(=O)O)c1sccc1C(=O)O)c1cccc(C2=CC=C2)c1 +Cc1cc(C)c(CCC(=O)O)cc1CCCCCC1(C(=O)O)CCC1 +CC(=O)OCCCN(CC(=O)O)CC(C)(NC(=O)C(C)C)C(=O)O +O=C(O)Oc1ccc(O)c2c(=O)c(=O)[nH]ccc(C(=O)O)cccc12 +NC(C(=O)O)C1C=CC=C(C2CC(C(=O)O)N(C(=O)O)C2)C=C1 +Cn1c(C(=O)O)cc(C=C(CO)c2cccc(C(=O)O)c2)c1C(=O)O +INVALID +COC(=O)c1ccc(OCCCCCC(=O)O)cc1CC(=O)C(=O)O +CCCCc1cc(-c2ccc(CNC)cc2C(=O)O)ccc1C(=O)O +O=C(O)CCNC(=O)c1ccc(F)cc1-c1cccc(CC(=O)O)c1 +N#[SH](CCNc1ccc(C(=O)O)cc1C(=O)O)CN1CC1 +CC1=C(O)C2(C#N)C(C)=C(CCC2C)C1=CC(C)(OC(=O)O)C(=O)O +O=C(O)C=CC1=CC=CS(=O)(=O)c2ccccc(C(=O)O)cccc21 +O=C(O)Cn1c(C(=O)O)c(Nc2ccc(C(=O)O)cc2)cc(O)c1=O +O=C(O)CCCOC1(CN2CCCC2=O)C=CC(CC(S)C(=O)O)=C1 +O=C(O)COc1ccccc1OCC(=O)Nc1cccc(C(=O)O)c1 +CCCN(CCC(=O)O)C(=O)N1C=S2C=C1C21C=CC2(C(=O)O)C=C21 +INVALID +C[SH]1CC(CO)=C(C(=O)O)CC1CCC(O)(CC(CN)CN)C(=O)O +CC.O=C(O)C(=O)n1cc(C(=O)O)c2ccccc21.O=CO +NC(CC(=O)O)C(C=O)OC(=O)O +CS(=O)(=O)N(CC=O)CCNc1ccc(CC(=O)O)cc1OC(=O)O +O=C(O)CCCCCC(=CCCCCCC(O)(O)C(=O)C(=O)O)CO +O=C(O)C1=CC2C3=S2C2=CCN3C1=CC=C2C(=O)O +O=C(O)CCCC1CCCC(OCC(O)CC(=O)CC(=O)O)CCC1 +CCN(C(=O)C(C)Nc1cc(C)ccc1O)C(O)(CC(=O)O)C(=O)O +O=C(O)COCC1(O)OC(CS)C(S(=O)(=O)O)C1(O)OC(=O)O +Cc1nc(-c2cc(CC(=O)O)ccc2OCCC(=O)O)cs1 +INVALID +O=C(O)OC1CC2COC3=NC=NC4=NC=NC43C(OC(=O)O)C1O2 diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s123.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s123.smi new file mode 100644 index 0000000000000000000000000000000000000000..8948f869e798f547240b2859a0f2938c785752e6 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s123.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C=C(c1ccccc1OC)N1CCN(C)CCOc2ccccc21 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCC(=O)C1=Nc2cccc(c2)NCC(C2CC2)=C1.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CO.COC(=O)O.Cc1ccccc1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s123_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s123_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..597d5701e9e070c205ec50397abb1f744011b52e --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s123_clean.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.C=C(c1ccccc1OC)N1CCN(C)CCOc2ccccc21 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.CCC(=O)C1=Nc2cccc(c2)NCC(C2CC2)=C1.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.CO.COC(=O)O.Cc1ccccc1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s42.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s42.smi new file mode 100644 index 0000000000000000000000000000000000000000..f8ae44f9a5eccbfaf1bbde24a7034c3b3a97bf9f --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s42.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C=C1CC=CC(C)=C(CC)C=Cc2cccc1c2[SH](=C)=O.CN.Cl +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCC.Nc1ccccc1CNCCn1ncc2cncnc21.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C.C.C.C.C.CCCC(C)C(=O)NC +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +Valid CC1=C2CCC3=C(CCCCCCC1)C14N=C(OC1CCC3)C2N4 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s42_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s42_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..aece3fe2679c128f0e78b83ca2af2f9cabdbea4c --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s42_clean.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.C=C1CC=CC(C)=C(CC)C=Cc2cccc1c2[SH](=C)=O.CN.Cl +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.CCC.Nc1ccccc1CNCCn1ncc2cncnc21.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.C.C.C.C.C.CCCC(C)C(=O)NC +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +CC1=C2CCC3=C(CCCCCCC1)C14N=C(OC1CCC3)C2N4 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s456.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s456.smi new file mode 100644 index 0000000000000000000000000000000000000000..67b630355bf791349bbddb1cb2eedccaaefae3bb --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s456.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C.CCN(CC)CC1=C(C)N1CC.c1ccccc1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CC(O)O.CCOc1ccc(C(C)=O)cc1.N.O=[SH](=O)O.S +DISCONNECTED C.CCOc1ccccccc(O)c(C(N)=NC2Cc3ccc2o3)c1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CC1C(=O)N(Cc2ccc(CCO)cc2)CC1CC(C)(C)C(=O)O.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s456_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s456_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..87c54c101d687175f94035ba41efbb3a6e8cec7d --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/base_noguid_s456_clean.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.C.CCN(CC)CC1=C(C)N1CC.c1ccccc1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +CC(O)O.CCOc1ccc(C(C)=O)cc1.N.O=[SH](=O)O.S +C.CCOc1ccccccc(O)c(C(N)=NC2Cc3ccc2o3)c1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +CC1C(=O)N(Cc2ccc(CCO)cc2)CC1CC(C)(C)C(=O)O.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/defog_local_edit_20260727_223441_final.json b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/defog_local_edit_20260727_223441_final.json new file mode 100644 index 0000000000000000000000000000000000000000..e2e333098f99e9cd2036591c051d0241bc3feac0 --- /dev/null +++ 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newline at end of file diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/defog_local_edit_20260728_172729_summary.csv b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/defog_local_edit_20260728_172729_summary.csv new file mode 100644 index 0000000000000000000000000000000000000000..03d61226388b17b7146a048d0ce7dc0fe0279833 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/defog_local_edit_20260728_172729_summary.csv @@ -0,0 +1,101 @@ +sample_idx,valid,smiles,mw,logp,tpsa,hbd,rotbonds +0,0,,0.0,0.0,0.0,0,0 +1,1,NC(=O)CCC1(O)CN(CCCCCCCCC(C(=O)O)C(=O)O)C1,358.44,0.81,141.16,4,14 +2,1,CC(CCC(=O)O)C(=O)O,146.14,0.57,74.6,2,4 +3,1,C=C(C=C(C)CCCC)C=C(C(=O)O)C(=O)O,238.28,2.77,74.6,2,7 +4,1,NC(=O)CCC(CC(=O)O)NCC(=O)O,218.21,-1.23,129.72,4,8 +5,0,,0.0,0.0,0.0,0,0 +6,1,CCCC(CCCC(=O)NCCCCC(=O)O)C(=O)O,287.36,2.03,103.7,3,12 +7,0,C.CO.O=C(O)CCC(=O)O,166.17,0.18,94.83,3,3 +8,1,CCOCCC(C)C(CC(=O)O)NOCCCCOCCOC(=O)O,365.42,1.9,123.55,3,17 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CCCC(N)=C(CCCCC(=O)O)CCC(=O)O +AssemblyReady CCC(CC(C)C(C(=O)O)=C(C)O)(OC)C(=O)O +AssemblyReady COC(C)(C)CC(CC(=O)O)=C(CC(=O)O)OCCO +AssemblyReady CCCCCCC(C(=O)O)C(=O)O +AssemblyReady O=CCC(CC(=O)O)CC(=O)O +AssemblyReady C=CN(C=N)C(C)=C(C(=O)O)C(=O)O +DISCONNECTED CC(=O)O.CCCC(=O)O +AssemblyReady O=C(O)NC1CN(C(=O)O)CCN1 +AssemblyReady COCCOC(C(=O)O)=C(C(=O)O)N1CCN(C)CC1 +AssemblyReady O=C(O)CCC1(O)CCC(C(=O)O)CC1 +AssemblyReady CCOCC(O)=C(C=O)CNCCC(C(=O)O)C(=O)O +AssemblyReady CCCCC(=O)NC(CC(=O)O)C(=O)O +AssemblyReady CCC(CC)(CC(=O)O)C(=O)O +AssemblyReady Cc1ccc(CC(COCC(=O)O)(C(=O)O)C(=O)NCCCO)nn1 +AssemblyReady CCCC1CN(CC(=O)O)CCC1NCCCCC(=O)O +AssemblyReady CC(CC=O)N(CC(=O)O)C(C)CC(=O)O +AssemblyReady Cc1cc(C(=O)O)c(C(=O)O)n1C +AssemblyReady C=C(N)COCCOCC(OCCN(CC)C(CN)C(=O)O)C(=O)O +AssemblyReady CC(C)C(C(=O)O)N(C)CC(=O)O +AssemblyReady CCC(C)C(C(=O)O)C(=O)O +AssemblyReady CCC(COCCC(CC)C(=O)O)OC(CO)C(=O)O +AssemblyReady O=C(O)CCCCCCCCCCCCC(=O)O +AssemblyReady NC(=O)C(C(=O)O)C(=O)O +AssemblyReady COCCOCCCN(C)CCC(CC(=O)O)C(=O)O +DISCONNECTED C.O=C(O)CCCC(=O)O +AssemblyReady CCCN(CCC(=O)O)C1CC(C(=O)O)C1 +AssemblyReady CC(CCCC(=O)O)C(=O)O +DISCONNECTED C.CCC.O=C(O)CCC(=O)O +DISCONNECTED CCC(=O)O.O=C(O)CCO +AssemblyReady CCC(CC(=O)O)C(=O)O +AssemblyReady COCCOCCC(OC(CCOC)C(=O)O)C(C)(C)C(=O)O +AssemblyReady COCCNCC(C#CCCCCCN)(CCC(=O)O)CCC(=O)O +AssemblyReady CCCCC(CCCC(=O)O)CCC(=O)O +DISCONNECTED C.O=C(O)C(CO)C(=O)O +AssemblyReady CCCCCC(NC(C)C(=O)O)C(=O)O +AssemblyReady NCCC1CC2CCN1CCC(OC(=O)O)(C(=O)O)CCO2 +AssemblyReady NC(CCCC1(C(=O)O)CCC1)C(=O)O +AssemblyReady CN(N=O)C(C)(C(=O)O)C(=O)O +AssemblyReady CCCCCCC(C)(C)C(CC(=O)O)=C(C)C(=O)O +AssemblyReady CCC(CC(=O)O)C(=O)O +AssemblyReady CCCOCCOC(=CC(=O)O)NCCCC(=O)O +AssemblyReady O=C(O)CCC(=O)CC(=O)O +AssemblyReady CC(C)C(CC(=O)O)C(=O)O +AssemblyReady CCC(CC(=O)O)C(=O)O +AssemblyReady CCCCCC(CO)CC(C(=O)O)C(=O)O +AssemblyReady O=C(O)C1CC(C(=O)O)C1 +INVALID +AssemblyReady CC(O)C(CC(=O)O)C(=O)O +AssemblyReady CCOCC(=O)CCN(CCCCCCC(=O)O)CCC(=O)O +AssemblyReady O=C(O)CCC(=CCOc1ccccc1)C(=O)O +AssemblyReady CC(C)CNC(CCC=O)C(CC(=O)O)CC(=O)O +AssemblyReady CCCC(C(=O)O)C(=O)O +AssemblyReady CN(CC(=O)O)C1(CC(=O)O)CCC1 +DISCONNECTED C.O=C(O)CCCC(=O)O +AssemblyReady CC(NCCCCC(O)CCCCNCC(=O)O)C(=O)O +AssemblyReady CCCCC(=O)NC(C(=O)O)C(=O)O +AssemblyReady O=C(O)CCCC(=O)CC(=O)O +AssemblyReady CCCCN(CCCC(=O)O)CCC(=O)C(=O)O +AssemblyReady CCCC(=CC(=O)O)CCOCCOCC(=O)O +AssemblyReady NCCC(CC(=O)O)C(=O)O +AssemblyReady CC(C)(C)NC(CCCCC(=O)O)C(=O)O +AssemblyReady CCN(CC(=O)O)C(C)CCC(=O)O +AssemblyReady CCCNC(CCOCC(O)=C(C)C(=O)O)CCC(=O)O +AssemblyReady CC(C)(N)C(C(=O)O)C(=O)O +AssemblyReady CCC(CCCCC(=O)O)C(=O)O +AssemblyReady CCCCC(CCC(CN(C)C)C(NC)C(=O)O)C(=O)O +AssemblyReady CCN=C1OC(C(=O)COC)=C(C(=O)O)C1C(=O)O +AssemblyReady NC(CCC(=O)O)CC(=O)O +AssemblyReady CC(C)(CCCN)CCC(C(=O)O)C(=O)O +AssemblyReady CCC(C(=O)O)C(Cc1cc(C)[nH]n1)C(=O)O +AssemblyReady CCC(C)(C(=O)O)C(=O)O +AssemblyReady CCN(C(=O)C(=O)O)C(C)CC(=O)O +DISCONNECTED C.CN.O=C(O)CCC(=O)O +AssemblyReady CCCCC(=O)CC(C(=O)O)C(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_guid_s456_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_guid_s456_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..ae8a8372c8f3f327a9f3c5b1ffa105547670d158 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_guid_s456_clean.smi @@ -0,0 +1,100 @@ +INVALID +CCOCCCCCC(=CN)COCC1(CC(C(=O)O)C(=O)O)CC1 +O=C(O)C1CC(C(=O)O)C1 +CCN(CC)C(C)=CC(C)=C(C(=O)O)C(=O)O +CC(C)(C(=O)O)N(CC(N)=O)CC(=O)O +INVALID +CCC(O)(O)CCC(CC(C)CCCC(=O)O)C(=O)O +CCC(CCC(=O)O)C(=O)O +CCOCCOCCC(CCO)C(C=CCOCC(=O)O)OC(=O)O +CCCCN(CC(=O)O)CC(=O)O +C#CCCCNC(=C)CCOc1cccc(CC(=O)O)c1CC(=O)O +CC(C(=O)O)N(C)CC(=O)O +O=C(O)CCCOCCOCCC(O)C(=O)O +CC(C)CCC(C(=O)O)C(=O)O +CC(C(=O)O)=C(C)C(=O)O +C=C(NN=C(C(=O)O)C1=CCC1)NC(=O)CC(=O)O +CC(CCN(CC(=O)O)C(C)CO)C(=O)O +CCNC(=O)C(CCCCC(=O)O)C(=O)O +COC(C)(CCCC(C)C(O)C(=O)O)CC(=O)O +CCC(CC(=O)O)C(=O)O +CCC(CC)(CCC(=O)O)C(C)OCC(=O)O +CCCC(CC)N(C)N=NC(C(=O)O)C(=O)O +CCCOC(C(=O)O)C(=O)O +CCOCc1occ(C(=O)O)c1OCC(=O)O +CC(C)C1CC(CC(=O)C(=O)O)(C(=O)O)C1 +INVALID +CCCC(N)=C(CCCCC(=O)O)CCC(=O)O +CCC(CC(C)C(C(=O)O)=C(C)O)(OC)C(=O)O +COC(C)(C)CC(CC(=O)O)=C(CC(=O)O)OCCO +CCCCCCC(C(=O)O)C(=O)O +O=CCC(CC(=O)O)CC(=O)O +C=CN(C=N)C(C)=C(C(=O)O)C(=O)O +CC(=O)O.CCCC(=O)O +O=C(O)NC1CN(C(=O)O)CCN1 +COCCOC(C(=O)O)=C(C(=O)O)N1CCN(C)CC1 +O=C(O)CCC1(O)CCC(C(=O)O)CC1 +CCOCC(O)=C(C=O)CNCCC(C(=O)O)C(=O)O +CCCCC(=O)NC(CC(=O)O)C(=O)O +CCC(CC)(CC(=O)O)C(=O)O +Cc1ccc(CC(COCC(=O)O)(C(=O)O)C(=O)NCCCO)nn1 +CCCC1CN(CC(=O)O)CCC1NCCCCC(=O)O +CC(CC=O)N(CC(=O)O)C(C)CC(=O)O +Cc1cc(C(=O)O)c(C(=O)O)n1C +C=C(N)COCCOCC(OCCN(CC)C(CN)C(=O)O)C(=O)O +CC(C)C(C(=O)O)N(C)CC(=O)O +CCC(C)C(C(=O)O)C(=O)O +CCC(COCCC(CC)C(=O)O)OC(CO)C(=O)O +O=C(O)CCCCCCCCCCCCC(=O)O +NC(=O)C(C(=O)O)C(=O)O +COCCOCCCN(C)CCC(CC(=O)O)C(=O)O +C.O=C(O)CCCC(=O)O +CCCN(CCC(=O)O)C1CC(C(=O)O)C1 +CC(CCCC(=O)O)C(=O)O +C.CCC.O=C(O)CCC(=O)O +CCC(=O)O.O=C(O)CCO +CCC(CC(=O)O)C(=O)O +COCCOCCC(OC(CCOC)C(=O)O)C(C)(C)C(=O)O +COCCNCC(C#CCCCCCN)(CCC(=O)O)CCC(=O)O +CCCCC(CCCC(=O)O)CCC(=O)O +C.O=C(O)C(CO)C(=O)O +CCCCCC(NC(C)C(=O)O)C(=O)O +NCCC1CC2CCN1CCC(OC(=O)O)(C(=O)O)CCO2 +NC(CCCC1(C(=O)O)CCC1)C(=O)O +CN(N=O)C(C)(C(=O)O)C(=O)O +CCCCCCC(C)(C)C(CC(=O)O)=C(C)C(=O)O +CCC(CC(=O)O)C(=O)O +CCCOCCOC(=CC(=O)O)NCCCC(=O)O +O=C(O)CCC(=O)CC(=O)O +CC(C)C(CC(=O)O)C(=O)O +CCC(CC(=O)O)C(=O)O +CCCCCC(CO)CC(C(=O)O)C(=O)O +O=C(O)C1CC(C(=O)O)C1 +INVALID +CC(O)C(CC(=O)O)C(=O)O +CCOCC(=O)CCN(CCCCCCC(=O)O)CCC(=O)O +O=C(O)CCC(=CCOc1ccccc1)C(=O)O +CC(C)CNC(CCC=O)C(CC(=O)O)CC(=O)O +CCCC(C(=O)O)C(=O)O +CN(CC(=O)O)C1(CC(=O)O)CCC1 +C.O=C(O)CCCC(=O)O +CC(NCCCCC(O)CCCCNCC(=O)O)C(=O)O +CCCCC(=O)NC(C(=O)O)C(=O)O +O=C(O)CCCC(=O)CC(=O)O +CCCCN(CCCC(=O)O)CCC(=O)C(=O)O +CCCC(=CC(=O)O)CCOCCOCC(=O)O +NCCC(CC(=O)O)C(=O)O +CC(C)(C)NC(CCCCC(=O)O)C(=O)O +CCN(CC(=O)O)C(C)CCC(=O)O +CCCNC(CCOCC(O)=C(C)C(=O)O)CCC(=O)O +CC(C)(N)C(C(=O)O)C(=O)O +CCC(CCCCC(=O)O)C(=O)O +CCCCC(CCC(CN(C)C)C(NC)C(=O)O)C(=O)O +CCN=C1OC(C(=O)COC)=C(C(=O)O)C1C(=O)O +NC(CCC(=O)O)CC(=O)O +CC(C)(CCCN)CCC(C(=O)O)C(=O)O +CCC(C(=O)O)C(Cc1cc(C)[nH]n1)C(=O)O +CCC(C)(C(=O)O)C(=O)O +CCN(C(=O)C(=O)O)C(C)CC(=O)O +C.CN.O=C(O)CCC(=O)O +CCCCC(=O)CC(C(=O)O)C(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s123.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s123.smi new file mode 100644 index 0000000000000000000000000000000000000000..eb20e0491223933b9805adf81a2c6974bc0b6dad --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s123.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +Valid CCC(CC)CCCC(C)O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CC(N)C(C)C(C)CO +DISCONNECTED CCNCC(O)=COO.CO +INVALID +DISCONNECTED C.C=O.COCCCCNC(C)=O.NCCC1CCN1 +DISCONNECTED C.C.C.C.CCCNC.CCCNCO +DISCONNECTED C.C.C=C=O.C=CCOO +DISCONNECTED C.CCCC.CCCN.O +DISCONNECTED C.CCCOC(C)=COC +DISCONNECTED C.C=CC.CCCCC.O +DISCONNECTED C.C.C.C.C.C.C=C(C)C +DISCONNECTED C.C.C#CCOC.C=C1C=COCC1 +DISCONNECTED CC1CCC1C.CCNN=NN(C)CC +INVALID +INVALID +DISCONNECTED C.C.C.C=C.CC.COC +INVALID +INVALID +INVALID +DISCONNECTED C.C#CN.C=CNC.OCCC=NOCO +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCCC.CCOCCOCCOCCOC +INVALID +INVALID +DISCONNECTED C.C.C.CCCO.O.O=O +DISCONNECTED C.C.C.CC.CC.CNC +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CCC.CCC(C)CO.N +INVALID +INVALID +INVALID +DISCONNECTED C.C.C1CCC2(C1)CO2.N +INVALID +DISCONNECTED C.C#C.CC(C)O.CNCC(C)n1ccnn1 +DISCONNECTED C=C(C=C(OC)C(=C)OO)NC.CC +INVALID +INVALID +DISCONNECTED C.CC.CCCC.NCOCCCO +DISCONNECTED CCC(N)=O.CCCO.CO.O +INVALID +INVALID +DISCONNECTED CCC=O.CCCCCC1CC1(C)CC(=O)NCC +DISCONNECTED C.CCCCOCCOC.COCCO +DISCONNECTED C.CCC.CCC1OCCOC(C)OC1C +DISCONNECTED C.C.C.CC1(C)CC1O.O +INVALID +DISCONNECTED C.CC=CCCCOC.CO +INVALID +DISCONNECTED C.C.C=NC.CCCCO.O +INVALID +DISCONNECTED CCN.CNCCC(C)OC.N +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C=NCC(OC)C(C)CCNC.CC +INVALID +DISCONNECTED C.CC1CN(C=O)CCN1C.CCOCC.CO +DISCONNECTED C.CC.CCCON(C)C.N +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CC.CCC1(C)CN2CN3CC21CC3=O +DISCONNECTED C.CCCNCC.CCN +INVALID +DISCONNECTED C.CCCCC(CC=O)NN=O.O +Valid CCCC1CC1CN1CC2=C(CCCCNC(O)C1)CC2 +INVALID +DISCONNECTED C.CCCC.CCCOC +INVALID +DISCONNECTED CCC(=O)N(C)C(C)C(O)CO.CN +DISCONNECTED C.C.C.CC.CCC=CO diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s123_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s123_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..708cc601b3413585c36629de6a69a42f272a5e4a --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s123_clean.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +INVALID +INVALID +INVALID +CCC(CC)CCCC(C)O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.CC(N)C(C)C(C)CO +CCNCC(O)=COO.CO +INVALID +C.C=O.COCCCCNC(C)=O.NCCC1CCN1 +C.C.C.C.CCCNC.CCCNCO +C.C.C=C=O.C=CCOO +C.CCCC.CCCN.O +C.CCCOC(C)=COC +C.C=CC.CCCCC.O +C.C.C.C.C.C.C=C(C)C +C.C.C#CCOC.C=C1C=COCC1 +CC1CCC1C.CCNN=NN(C)CC +INVALID +INVALID +C.C.C.C=C.CC.COC +INVALID +INVALID +INVALID +C.C#CN.C=CNC.OCCC=NOCO +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.CCCC.CCOCCOCCOCCOC +INVALID +INVALID +C.C.C.CCCO.O.O=O +C.C.C.CC.CC.CNC +INVALID +INVALID +INVALID +INVALID +INVALID +CCC.CCC(C)CO.N +INVALID +INVALID +INVALID +C.C.C1CCC2(C1)CO2.N +INVALID +C.C#C.CC(C)O.CNCC(C)n1ccnn1 +C=C(C=C(OC)C(=C)OO)NC.CC +INVALID +INVALID +C.CC.CCCC.NCOCCCO +CCC(N)=O.CCCO.CO.O +INVALID +INVALID +CCC=O.CCCCCC1CC1(C)CC(=O)NCC +C.CCCCOCCOC.COCCO +C.CCC.CCC1OCCOC(C)OC1C +C.C.C.CC1(C)CC1O.O +INVALID +C.CC=CCCCOC.CO +INVALID +C.C.C=NC.CCCCO.O +INVALID +CCN.CNCCC(C)OC.N +INVALID +INVALID +INVALID +INVALID +C.C=NCC(OC)C(C)CCNC.CC +INVALID +C.CC1CN(C=O)CCN1C.CCOCC.CO +C.CC.CCCON(C)C.N +INVALID +INVALID +INVALID +INVALID +INVALID +C.CC.CCC1(C)CN2CN3CC21CC3=O +C.CCCNCC.CCN +INVALID +C.CCCCC(CC=O)NN=O.O +CCCC1CC1CN1CC2=C(CCCCNC(O)C1)CC2 +INVALID +C.CCCC.CCCOC +INVALID +CCC(=O)N(C)C(C)C(O)CO.CN +C.C.C.CC.CCC=CO diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s42.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s42.smi new file mode 100644 index 0000000000000000000000000000000000000000..cf52560000461eec387a4252f3120fdd1b87a37e --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s42.smi @@ -0,0 +1,100 @@ +INVALID +DISCONNECTED C.CCCCCCCCCC(CO)OC.O +DISCONNECTED C.C.CC.CN.O=CCO +DISCONNECTED C.C.C=CC=CC=CCO.CC1=NC1 +Valid COCCON1COc2cc(ccc2OC)C(CN)O1 +INVALID +INVALID +DISCONNECTED C.C=CCC1CNC(=C)C1 +DISCONNECTED C.C.C.CC=CCNCC +INVALID +DISCONNECTED C.C=CNCCCCN.N=N +INVALID +INVALID +INVALID +Valid C#CCNC(=O)CCC1(CCCC)CC1 +INVALID +DISCONNECTED C.C.C=O.CC.O=CCO +DISCONNECTED C.CC1CN1C.CCC.O +INVALID +INVALID +DISCONNECTED C.C.C.CCC(N)=O.CO +DISCONNECTED C.C.C.CC(C)C.CCCN.O +DISCONNECTED CCCNOCC.CCNCO.N=O +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C=COCCCCCCN.CCCCC=O.N +INVALID +INVALID +DISCONNECTED C.C1CCCC2CCCCC2CC1 +Valid CC1CCNC2(N)CCC12 +Valid CCOCCCCCCOC(=O)C(CCO)OCCOCC +INVALID +INVALID +DISCONNECTED CC.CC=O.CCCCC.CN +DISCONNECTED C.C=C1C2CCCOCCOCC2N1C.CC +DISCONNECTED C.C.CC12CC1CN2C=O.CC=O.CCO +DISCONNECTED C.C.CCC.CCCN(C)CC.O +DISCONNECTED C.C.CCC.CCCC(C)=O.N +DISCONNECTED COC.O=CNCCCCCCCO +DISCONNECTED CCCC1=C(O)C(C)C(C)CC1.CCCCC.CN +DISCONNECTED CCCCN.CO.O=CO +DISCONNECTED CC.CCCCC(C)OO +DISCONNECTED C.CCCCCCCc1c[nH][nH]1.CNOCCCOCCO +INVALID +INVALID +DISCONNECTED C.CCCCCCCNC(O)CCC +INVALID +DISCONNECTED C.CC.CCCO.CNC +DISCONNECTED CC(C)CCC=C(CO)C(C)C.CNCCNC +DISCONNECTED C.CC.CCCCC.N.N +DISCONNECTED C.C=NNCCCOC1(O)CC12CO2 +DISCONNECTED CCC(=O)CC=C(N)CC.O +INVALID +INVALID +Valid CCCCCOC1C2COCCC(CCCCC)C21 +INVALID +DISCONNECTED C#CC(O)=C(O)CCCCOCCN.CO +DISCONNECTED CC.CC.CCCCCCNN.O +DISCONNECTED C.C.C.CCC.CNC.O.O +INVALID +DISCONNECTED C.CCC.CCCNC.N.O +DISCONNECTED CC.CCC.CCC.N.O +DISCONNECTED C.CCC(O)CCOCCOCCOCCN.CCC1CC1=O +DISCONNECTED C.C.C=CC(=CC(=C)CCNC)CCC +INVALID +DISCONNECTED C=C.C=CC(=C)N(N)C(=O)CCOCN +INVALID +DISCONNECTED C.C.COOCCCNCC(C)C +INVALID +INVALID +DISCONNECTED C.CCCCC(=O)N1CC1CC +INVALID +INVALID +INVALID +DISCONNECTED C.C.C.CCC.CCOC +DISCONNECTED C.C.CCNNNC.N.O +INVALID +INVALID +Valid CCCCCC1CCCCC(=O)N1OC +INVALID +DISCONNECTED CC.CCC=CC1(C(=O)OC)CN1.N +INVALID +INVALID +DISCONNECTED C.C.C.CC.CCC(C)C +DISCONNECTED C.CCC.N.NC(N)O.O +DISCONNECTED CCCCCCCCCC=O.NOCCOCCO +INVALID +DISCONNECTED C.C=NCCC.CC.CCC +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C.C=C(C)N=NN.CCC +INVALID +INVALID +Valid CN1CCN(C)C2(Cc3nnn4c3C(=O)C(C)(CC4)C2)C1 diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s42_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s42_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..7cd18a0efe3a32f15cf2e636bec42a170de76372 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s42_clean.smi @@ -0,0 +1,100 @@ +INVALID +C.CCCCCCCCCC(CO)OC.O +C.C.CC.CN.O=CCO +C.C.C=CC=CC=CCO.CC1=NC1 +COCCON1COc2cc(ccc2OC)C(CN)O1 +INVALID +INVALID +C.C=CCC1CNC(=C)C1 +C.C.C.CC=CCNCC +INVALID +C.C=CNCCCCN.N=N +INVALID +INVALID +INVALID +C#CCNC(=O)CCC1(CCCC)CC1 +INVALID +C.C.C=O.CC.O=CCO +C.CC1CN1C.CCC.O +INVALID +INVALID +C.C.C.CCC(N)=O.CO +C.C.C.CC(C)C.CCCN.O +CCCNOCC.CCNCO.N=O +INVALID +INVALID +INVALID +INVALID +C=COCCCCCCN.CCCCC=O.N +INVALID +INVALID +C.C1CCCC2CCCCC2CC1 +CC1CCNC2(N)CCC12 +CCOCCCCCCOC(=O)C(CCO)OCCOCC +INVALID +INVALID +CC.CC=O.CCCCC.CN +C.C=C1C2CCCOCCOCC2N1C.CC +C.C.CC12CC1CN2C=O.CC=O.CCO +C.C.CCC.CCCN(C)CC.O +C.C.CCC.CCCC(C)=O.N +COC.O=CNCCCCCCCO +CCCC1=C(O)C(C)C(C)CC1.CCCCC.CN +CCCCN.CO.O=CO +CC.CCCCC(C)OO +C.CCCCCCCc1c[nH][nH]1.CNOCCCOCCO +INVALID +INVALID +C.CCCCCCCNC(O)CCC +INVALID +C.CC.CCCO.CNC +CC(C)CCC=C(CO)C(C)C.CNCCNC +C.CC.CCCCC.N.N +C.C=NNCCCOC1(O)CC12CO2 +CCC(=O)CC=C(N)CC.O +INVALID +INVALID +CCCCCOC1C2COCCC(CCCCC)C21 +INVALID +C#CC(O)=C(O)CCCCOCCN.CO +CC.CC.CCCCCCNN.O +C.C.C.CCC.CNC.O.O +INVALID +C.CCC.CCCNC.N.O +CC.CCC.CCC.N.O +C.CCC(O)CCOCCOCCOCCN.CCC1CC1=O +C.C.C=CC(=CC(=C)CCNC)CCC +INVALID +C=C.C=CC(=C)N(N)C(=O)CCOCN +INVALID +C.C.COOCCCNCC(C)C +INVALID +INVALID +C.CCCCC(=O)N1CC1CC +INVALID +INVALID +INVALID +C.C.C.CCC.CCOC +C.C.CCNNNC.N.O +INVALID +INVALID +CCCCCC1CCCCC(=O)N1OC +INVALID +CC.CCC=CC1(C(=O)OC)CN1.N +INVALID +INVALID +C.C.C.CC.CCC(C)C +C.CCC.N.NC(N)O.O +CCCCCCCCCC=O.NOCCOCCO +INVALID +C.C=NCCC.CC.CCC +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.C.C=C(C)N=NN.CCC +INVALID +INVALID +CN1CCN(C)C2(Cc3nnn4c3C(=O)C(C)(CC4)C2)C1 diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s456.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s456.smi new file mode 100644 index 0000000000000000000000000000000000000000..8cae786bbb2e4c0c7fcc84ea3e1264ea0746e342 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s456.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +DISCONNECTED C.CC.CCCC.COO +Valid COCCOCCOCCOCCNC(C)O +INVALID +INVALID +INVALID +DISCONNECTED C.CCCCC.CCO.CN +DISCONNECTED CCCCCCCOCCOCCOC.COCCOCCOC.O +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C=C.CC1CC1C=O.CO.N +Valid CNC1C(C)C1CCOC +INVALID +INVALID +DISCONNECTED C.CCCCCCCCC(=O)C(=O)NCC +INVALID +DISCONNECTED C.CC12CC13CCC(=O)N23 +DISCONNECTED C.C=CC.CCCCN1CCCCO1.CNC +INVALID +DISCONNECTED C.C.CCCCOC.CN.N +INVALID +INVALID +DISCONNECTED C.COCCO.COCCOC(CCCOCCOCCO)COC +Valid O=C1COC2OOCCOC2CCCOCCN1 +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C=CC=C(N=C)NCC.CCCNC +DISCONNECTED C.C.CC(C)C(N)CCN +DISCONNECTED CC.CCOC12CCC(C1)C(C)C2 +INVALID +DISCONNECTED C=C.CNO.O=CC1CCCC(=O)NC1 +INVALID +INVALID +DISCONNECTED C.CCCCCOO.CCN.O +INVALID +DISCONNECTED C#CC.CCNCC(=O)NCCN1CC2C(CC)C2(C)C1 +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CCCCC1(C)CC1CN.O +INVALID +Valid CCCCOCCCCCC(O)(CC)C(C)CC +DISCONNECTED C.NNCCCCNN=S +DISCONNECTED CC=CC=C(C)C(=O)C1CCCCOC1.CCCCN +DISCONNECTED C.C.C.C.C.CN=CNN +DISCONNECTED C.C.C.CC.CCC(=O)NCCCN.CO +DISCONNECTED C.C.C.CCC.CCO.N.O +DISCONNECTED C.CCC.CCO.CCO.O.O +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C=NC.C=O.CNCCN1CCNCC1 +DISCONNECTED C.C.C.C.C.C=C1CN1C +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCCCCCC1CCCC(CC)=C1NC.O.O +INVALID +INVALID +DISCONNECTED C.C.C.C.C.CC=C(C)CC +INVALID +DISCONNECTED C.C.CCC.CCC(C)C +INVALID +DISCONNECTED C.C.CCC.COCCO +INVALID +DISCONNECTED CC(C)N.COC1C=C1C.O +INVALID +DISCONNECTED C.C.CC(=O)C=O.CCc1cccc(C)c1.CN +INVALID +INVALID +DISCONNECTED C.C.C.CCCC(=O)NC.CCN.O +INVALID +INVALID +DISCONNECTED C.C.NCCNC(=O)CCCC=O.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C.CC(N)=C(N)O.CCO +INVALID +INVALID +INVALID +DISCONNECTED C.C.CC=O.CCNOC.O +INVALID +INVALID +DISCONNECTED C.C=CN(C)CCC(C)C +INVALID +DISCONNECTED C.C.CC(O)CC(O)C(N)=O +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s456_clean.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s456_clean.smi new file mode 100644 index 0000000000000000000000000000000000000000..c0b2767225f556ec1284b3d7a28fde030fa5e887 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ft_noguid_s456_clean.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +C.CC.CCCC.COO +COCCOCCOCCOCCNC(C)O +INVALID +INVALID +INVALID +C.CCCCC.CCO.CN +CCCCCCCOCCOCCOC.COCCOCCOC.O +INVALID +INVALID +INVALID +INVALID +C.C=C.CC1CC1C=O.CO.N +CNC1C(C)C1CCOC +INVALID +INVALID +C.CCCCCCCCC(=O)C(=O)NCC +INVALID +C.CC12CC13CCC(=O)N23 +C.C=CC.CCCCN1CCCCO1.CNC +INVALID +C.C.CCCCOC.CN.N +INVALID +INVALID +C.COCCO.COCCOC(CCCOCCOCCO)COC +O=C1COC2OOCCOC2CCCOCCN1 +INVALID +INVALID +INVALID +INVALID +C=CC=C(N=C)NCC.CCCNC +C.C.CC(C)C(N)CCN +CC.CCOC12CCC(C1)C(C)C2 +INVALID +C=C.CNO.O=CC1CCCC(=O)NC1 +INVALID +INVALID +C.CCCCCOO.CCN.O +INVALID +C#CC.CCNCC(=O)NCCN1CC2C(CC)C2(C)C1 +INVALID +INVALID +INVALID +INVALID +CCCCC1(C)CC1CN.O +INVALID +CCCCOCCCCCC(O)(CC)C(C)CC +C.NNCCCCNN=S +CC=CC=C(C)C(=O)C1CCCCOC1.CCCCN +C.C.C.C.C.CN=CNN +C.C.C.CC.CCC(=O)NCCCN.CO +C.C.C.CCC.CCO.N.O +C.CCC.CCO.CCO.O.O +INVALID +INVALID +INVALID +INVALID +C.C=NC.C=O.CNCCN1CCNCC1 +C.C.C.C.C.C=C1CN1C +INVALID +INVALID +INVALID +INVALID +C.CCCCCCC1CCCC(CC)=C1NC.O.O +INVALID +INVALID +C.C.C.C.C.CC=C(C)CC +INVALID +C.C.CCC.CCC(C)C +INVALID +C.C.CCC.COCCO +INVALID +CC(C)N.COC1C=C1C.O +INVALID +C.C.CC(=O)C=O.CCc1cccc(C)c1.CN +INVALID +INVALID +C.C.C.CCCC(=O)NC.CCN.O +INVALID +INVALID +C.C.NCCNC(=O)CCCC=O.O +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +C.C.CC(N)=C(N)O.CCO +INVALID +INVALID +INVALID +C.C.CC=O.CCNOC.O +INVALID +INVALID +C.C=CN(C)CCC(C)C +INVALID +C.C.CC(O)CC(O)C(N)=O +INVALID diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ftguid_pool.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ftguid_pool.smi new file mode 100644 index 0000000000000000000000000000000000000000..e98aaa73bbee0a712a497d0a32621758ddd6f06d --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ftguid_pool.smi @@ -0,0 +1,300 @@ +C=C(CCCCCOC)NC(=C(O)CC)C(C(=O)O)C(=O)O +CC(C)CC(CO)C(CC(=O)O)CC(=O)O +C.C=CNC(=O)O.O=CO +INVALID +CCC(=O)OCC(CC(O)(CO)C(=O)O)OCC(=O)O +CCCCCCCC1(CCNC(=O)C(=O)O)CC(C(=O)O)C1 +O=C(O)C(=O)CCCC(=O)C(=O)O +CCCC(C(=O)O)C(=O)O +C.CCNC(=O)O.O=CO +CCCCN(CC)C(C(=O)O)C(=O)O +CN(CCC(=O)O)CC(=O)O +CCCCC(CO)N(C(=O)O)C(=O)O +CCCC1=C(C(OCCOCC(OCCOC)C(=O)O)C(=O)O)O1 +CNC(=O)CCCCCCN(C)C(C(=O)O)C(=O)O +CCC(C(=O)O)C(C)CC(CO)C(=O)O +CC(CC(=O)O)C(=O)O.O +NC(=O)C(C(=O)O)C(=O)O +CCC(C)(C(=O)O)C(=O)O +CCCC(=O)N(C)CCCC(COCCOCCC(=O)O)CC(=O)O +CCCCC(C=O)=C(OCC(=O)O)C(=O)O +CCNCC(=O)O.O=CO +O=C(O)CCCCCCC(=O)O +CC(C)OCCCC(C(=O)O)C(=O)O +CCOCCOC(CC(C(=O)O)C(=O)O)=C(N)CO +O=C(O)CCOCCCCCCCOCCOC(CO)CCC(=O)O +INVALID +CCNC(CC(=O)O)=C1CCCC(CO)(CC(=O)O)CCC1 +O=C(O)CCCCCOCCC(=O)CC(=O)O +CCCCCCC(C)C(CC(O)CC(=O)O)OCC(=O)O +CCCCC(NC(=O)CCC(=O)NCCC=O)C(CC(=O)O)C(=O)O +CCC(C=CC(=O)O)=C(C)C(=O)O +O=C(O)NCCO.O=CO +CCOCCCCCCOCCC(OCCC(=O)O)C(=O)O +CCOCC(CCOC(=O)O)C(=O)O +Cc1c(OCCOC(C)C(=O)O)cc2ccccc2c1OCC(=O)O +CC(C)N(C)C(C(=O)O)C(=O)O +COCCNC(=O)COOCC(=O)O.O=CO +CCC(C)(C)NCCCC(C(=O)O)C(=O)O +CCC(CC(=O)O)C(O)(O)C(=O)O +CC(C=O)C(CC(=O)O)C(=O)O +O=C(O)CCC(CO)CC(=O)C(=O)O +CCNC(COCCOCC(N)C(=O)O)CC(=O)O +CC(=O)C(=O)O.CC(=O)O +CCCCC(=O)O.O=CO +CCN(CC)C(C(=O)O)=C(C(=O)O)c1cccc(CNC)c1 +C.CC(CC(=O)O)C(=O)O +CC(=NCCC(=O)O)NC(C)CC(=O)O +CCCCCC(O)(CC(=O)O)C(=O)O +O=C(O)CC(CCOCCCN1CC1)CC(=O)O +CC(=O)O.CCCC(=O)O +CCCOC(CC)=C(C)C(CCC(=O)O)C(=O)O +CC(C)C(C(=O)O)C(=O)O +COC(C(=O)O)C(CC(=O)O)C(C)C +CC(C)(CC(=O)O)CC(=O)O +CCCCCCCC(CC(=O)O)C(=O)O 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+CCN(CC)C(C)=CC(C)=C(C(=O)O)C(=O)O +CC(C)(C(=O)O)N(CC(N)=O)CC(=O)O +INVALID +CCC(O)(O)CCC(CC(C)CCCC(=O)O)C(=O)O +CCC(CCC(=O)O)C(=O)O +CCOCCOCCC(CCO)C(C=CCOCC(=O)O)OC(=O)O +CCCCN(CC(=O)O)CC(=O)O +C#CCCCNC(=C)CCOc1cccc(CC(=O)O)c1CC(=O)O +CC(C(=O)O)N(C)CC(=O)O +O=C(O)CCCOCCOCCC(O)C(=O)O +CC(C)CCC(C(=O)O)C(=O)O +CC(C(=O)O)=C(C)C(=O)O +C=C(NN=C(C(=O)O)C1=CCC1)NC(=O)CC(=O)O +CC(CCN(CC(=O)O)C(C)CO)C(=O)O +CCNC(=O)C(CCCCC(=O)O)C(=O)O +COC(C)(CCCC(C)C(O)C(=O)O)CC(=O)O +CCC(CC(=O)O)C(=O)O +CCC(CC)(CCC(=O)O)C(C)OCC(=O)O +CCCC(CC)N(C)N=NC(C(=O)O)C(=O)O +CCCOC(C(=O)O)C(=O)O +CCOCc1occ(C(=O)O)c1OCC(=O)O +CC(C)C1CC(CC(=O)C(=O)O)(C(=O)O)C1 +INVALID +CCCC(N)=C(CCCCC(=O)O)CCC(=O)O +CCC(CC(C)C(C(=O)O)=C(C)O)(OC)C(=O)O +COC(C)(C)CC(CC(=O)O)=C(CC(=O)O)OCCO +CCCCCCC(C(=O)O)C(=O)O +O=CCC(CC(=O)O)CC(=O)O +C=CN(C=N)C(C)=C(C(=O)O)C(=O)O +CC(=O)O.CCCC(=O)O +O=C(O)NC1CN(C(=O)O)CCN1 +COCCOC(C(=O)O)=C(C(=O)O)N1CCN(C)CC1 +O=C(O)CCC1(O)CCC(C(=O)O)CC1 +CCOCC(O)=C(C=O)CNCCC(C(=O)O)C(=O)O +CCCCC(=O)NC(CC(=O)O)C(=O)O +CCC(CC)(CC(=O)O)C(=O)O +Cc1ccc(CC(COCC(=O)O)(C(=O)O)C(=O)NCCCO)nn1 +CCCC1CN(CC(=O)O)CCC1NCCCCC(=O)O +CC(CC=O)N(CC(=O)O)C(C)CC(=O)O +Cc1cc(C(=O)O)c(C(=O)O)n1C +C=C(N)COCCOCC(OCCN(CC)C(CN)C(=O)O)C(=O)O +CC(C)C(C(=O)O)N(C)CC(=O)O +CCC(C)C(C(=O)O)C(=O)O +CCC(COCCC(CC)C(=O)O)OC(CO)C(=O)O +O=C(O)CCCCCCCCCCCCC(=O)O +NC(=O)C(C(=O)O)C(=O)O +COCCOCCCN(C)CCC(CC(=O)O)C(=O)O +C.O=C(O)CCCC(=O)O +CCCN(CCC(=O)O)C1CC(C(=O)O)C1 +CC(CCCC(=O)O)C(=O)O +C.CCC.O=C(O)CCC(=O)O +CCC(=O)O.O=C(O)CCO +CCC(CC(=O)O)C(=O)O +COCCOCCC(OC(CCOC)C(=O)O)C(C)(C)C(=O)O +COCCNCC(C#CCCCCCN)(CCC(=O)O)CCC(=O)O +CCCCC(CCCC(=O)O)CCC(=O)O +C.O=C(O)C(CO)C(=O)O +CCCCCC(NC(C)C(=O)O)C(=O)O +NCCC1CC2CCN1CCC(OC(=O)O)(C(=O)O)CCO2 +NC(CCCC1(C(=O)O)CCC1)C(=O)O +CN(N=O)C(C)(C(=O)O)C(=O)O +CCCCCCC(C)(C)C(CC(=O)O)=C(C)C(=O)O +CCC(CC(=O)O)C(=O)O +CCCOCCOC(=CC(=O)O)NCCCC(=O)O +O=C(O)CCC(=O)CC(=O)O +CC(C)C(CC(=O)O)C(=O)O +CCC(CC(=O)O)C(=O)O +CCCCCC(CO)CC(C(=O)O)C(=O)O +O=C(O)C1CC(C(=O)O)C1 +INVALID +CC(O)C(CC(=O)O)C(=O)O +CCOCC(=O)CCN(CCCCCCC(=O)O)CCC(=O)O +O=C(O)CCC(=CCOc1ccccc1)C(=O)O +CC(C)CNC(CCC=O)C(CC(=O)O)CC(=O)O +CCCC(C(=O)O)C(=O)O +CN(CC(=O)O)C1(CC(=O)O)CCC1 +C.O=C(O)CCCC(=O)O +CC(NCCCCC(O)CCCCNCC(=O)O)C(=O)O +CCCCC(=O)NC(C(=O)O)C(=O)O +O=C(O)CCCC(=O)CC(=O)O +CCCCN(CCCC(=O)O)CCC(=O)C(=O)O +CCCC(=CC(=O)O)CCOCCOCC(=O)O +NCCC(CC(=O)O)C(=O)O +CC(C)(C)NC(CCCCC(=O)O)C(=O)O +CCN(CC(=O)O)C(C)CCC(=O)O +CCCNC(CCOCC(O)=C(C)C(=O)O)CCC(=O)O +CC(C)(N)C(C(=O)O)C(=O)O +CCC(CCCCC(=O)O)C(=O)O +CCCCC(CCC(CN(C)C)C(NC)C(=O)O)C(=O)O +CCN=C1OC(C(=O)COC)=C(C(=O)O)C1C(=O)O +NC(CCC(=O)O)CC(=O)O +CC(C)(CCCN)CCC(C(=O)O)C(=O)O +CCC(C(=O)O)C(Cc1cc(C)[nH]n1)C(=O)O +CCC(C)(C(=O)O)C(=O)O +CCN(C(=O)C(=O)O)C(C)CC(=O)O +C.CN.O=C(O)CCC(=O)O +CCCCC(=O)CC(C(=O)O)C(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ftguid_scored.csv b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ftguid_scored.csv new file mode 100644 index 0000000000000000000000000000000000000000..4b1ca30a86498f526631d1d4af0456d4ffe994de --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/ftguid_scored.csv @@ -0,0 +1,285 @@ +smiles,linker_mw,linker_logp,linker_tpsa,linker_hbd,linker_hba,linker_rotbonds,linker_rings,linker_hac,linker_sa,linker_qed,linker_valid,check_connected,check_attachment_sites,check_ring_closure,check_long_chain,check_linker_atom_count,check_protac_mw,check_representational,check_sa_score,assembly_ok,protac_smiles,protac_mw,protac_logp,protac_tpsa,protac_hbd,protac_hba,protac_rotbonds,protac_hac,ro5_violations,protac_valid,degrad_prob,degrad_notes +C=C(CCCCCOC)NC(=C(O)CC)C(C(=O)O)C(=O)O,315.4,2.26,116.1,4,5,12,0,22,3.27,0.248,True,True,True,True,True,True,True,True,True,True,C=C(CCCCCOC)NC(=C(O)CC)C(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1000.1,5.03,267.5,5.0,16.0,19.0,73.0,3.0,True,0.049,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)CC(CO)C(CC(=O)O)CC(=O)O,232.3,1.21,94.8,3,3,8,0,16,3.15,0.584,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC(CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C(CO)CC(C)C)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,917.0,3.98,246.2,4.0,14.0,15.0,67.0,2.0,True,0.8937,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C.C=CNC(=O)O.O=CO,149.1,0.73,86.6,3,2,1,0,10,3.61,0.48,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCC(=O)OCC(CC(O)(CO)C(=O)O)OCC(=O)O,294.3,-1.39,150.6,4,7,10,0,20,3.69,0.357,True,True,True,True,True,True,True,True,True,True,CCC(=O)OCC(CC(O)(CO)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)OCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,979.0,1.38,302.0,5.0,18.0,17.0,71.0,2.0,True,0.8493,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCCCC1(CCNC(=O)C(=O)O)CC(C(=O)O)C1,313.4,2.42,103.7,3,3,10,1,22,2.68,0.424,True,True,True,True,True,True,True,True,True,True,CCCCCCCC1(CCNC(=O)C(=O)Nc2cccc3c2C(=O)N(C2CCC(=O)NC2=O)C3=O)CC(C(=O)N(c2ccc(N3CCNCC3)nc2)c2ncc3c(C)c(C(C)=O)c(=O)n(C4CCCC4)c3n2)C1,998.2,5.19,255.1,4.0,14.0,17.0,73.0,3.0,True,0.9688,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)C(=O)CCCC(=O)C(=O)O,188.1,-0.54,108.7,2,4,6,0,13,2.41,0.542,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(=O)CCCC(=O)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,872.9,2.24,260.1,3.0,15.0,13.0,64.0,2.0,True,0.8955,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC(C(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.14,0.568,True,True,True,True,True,True,True,True,True,True,CCCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,830.9,3.34,226.0,3.0,13.0,11.0,61.0,2.0,True,0.9929,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C.CCNC(=O)O.O=CO,151.2,0.61,86.6,3,2,1,0,10,2.77,0.479,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCCCN(CC)C(C(=O)O)C(=O)O,203.2,0.65,77.8,2,3,7,0,14,2.56,0.594,True,True,True,True,True,True,True,True,True,True,CCCCN(CC)C(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,888.0,3.42,229.2,3.0,14.0,14.0,65.0,2.0,True,0.9745,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CN(CCC(=O)O)CC(=O)O,161.2,-0.52,77.8,2,3,5,0,11,2.03,0.567,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CN(C)CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,845.9,2.25,229.2,3.0,14.0,12.0,62.0,2.0,True,0.945,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(CO)N(C(=O)O)C(=O)O,205.2,1.2,98.1,3,3,5,0,14,3.27,0.626,True,True,True,True,True,True,True,True,True,True,CCCCC(CO)N(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,890.0,3.97,249.4,4.0,14.0,12.0,65.0,2.0,True,0.9888,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC1=C(C(OCCOCC(OCCOC)C(=O)O)C(=O)O)O1,348.3,0.63,124.0,2,7,15,1,24,3.91,0.41,False,True,True,False,True,True,True,True,True,True,CCCC1=C(C(OCCOCC(OCCOC)C(=O)Nc2cccc3c2C(=O)N(C2CCC(=O)NC2=O)C3=O)C(=O)N(c2ccc(N3CCNCC3)nc2)c2ncc3c(C)c(C(C)=O)c(=O)n(C4CCCC4)c3n2)O1,1033.1,3.4,275.4,3.0,18.0,22.0,75.0,2.0,True,0.7303,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CNC(=O)CCCCCCN(C)C(C(=O)O)C(=O)O,274.3,0.15,106.9,3,4,10,0,19,2.51,0.387,True,True,True,True,True,True,True,True,True,True,CNC(=O)CCCCCCN(C)C(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,959.1,2.92,258.3,4.0,15.0,17.0,70.0,2.0,True,0.9691,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(C(=O)O)C(C)CC(CO)C(=O)O,218.2,0.82,94.8,3,3,7,0,15,3.69,0.588,True,True,True,True,True,True,True,True,True,True,CCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(C)CC(CO)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,903.0,3.59,246.2,4.0,14.0,14.0,66.0,2.0,True,0.8974,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(CC(=O)O)C(=O)O.O,150.1,-0.64,106.1,2,2,3,0,10,2.63,0.557,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,816.9,2.95,226.0,3.0,13.0,10.0,60.0,2.0,True,0.9854,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +NC(=O)C(C(=O)O)C(=O)O,147.1,-1.74,117.7,3,3,3,0,10,2.61,0.409,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C(N)=O)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,831.8,1.03,269.1,4.0,14.0,10.0,61.0,2.0,True,0.9697,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(C)(C(=O)O)C(=O)O,146.1,0.57,74.6,2,2,3,0,10,2.67,0.571,True,True,True,True,True,True,True,True,True,True,CCC(C)(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,830.9,3.34,226.0,3.0,13.0,10.0,61.0,2.0,True,0.995,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC(=O)N(C)CCCC(COCCOCCC(=O)O)CC(=O)O,361.4,1.62,113.4,2,5,16,0,25,3.01,0.402,True,True,True,True,True,True,True,True,True,True,CCCC(=O)N(C)CCCC(COCCOCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1046.2,4.4,264.7,3.0,16.0,23.0,76.0,2.0,True,0.8874,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(C=O)=C(OCC(=O)O)C(=O)O,230.2,0.82,100.9,2,4,8,0,16,3.07,0.363,True,True,True,True,True,True,True,True,True,True,CCCCC(C=O)=C(OCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,915.0,3.59,252.3,3.0,15.0,15.0,67.0,2.0,True,0.6137,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCNCC(=O)O.O=CO,149.1,-0.62,86.6,3,3,3,0,10,2.39,0.465,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +O=C(O)CCCCCCC(=O)O,174.2,1.5,74.6,2,2,7,0,12,1.62,0.573,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCCCCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,859.0,4.27,226.0,3.0,13.0,14.0,63.0,2.0,True,0.9686,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)OCCCC(C(=O)O)C(=O)O,204.2,0.98,83.8,2,3,7,0,14,2.33,0.477,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CCCOC(C)C)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,889.0,3.75,235.2,3.0,14.0,14.0,65.0,2.0,True,0.407,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCOCCOC(CC(C(=O)O)C(=O)O)=C(N)CO,277.3,-0.62,139.3,4,6,10,0,19,3.21,0.235,True,True,True,True,True,True,True,True,True,True,CCOCCOC(CC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)=C(N)CO,962.0,2.15,290.7,5.0,17.0,17.0,70.0,2.0,True,0.045,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCOCCCCCCCOCCOC(CO)CCC(=O)O,364.4,1.69,122.5,3,6,19,0,25,2.93,0.296,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCC(CO)OCCOCCCCCCCOCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,1049.2,4.46,273.9,4.0,17.0,26.0,76.0,2.0,True,0.8228,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCNC(CC(=O)O)=C1CCCC(CO)(CC(=O)O)CCC1,313.4,2.13,106.9,4,4,7,1,22,3.18,0.573,True,True,True,True,True,True,True,True,True,True,CCNC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)=C1CCCC(CO)(CC(=O)N(c2ccc(N3CCNCC3)nc2)c2ncc3c(C)c(C(C)=O)c(=O)n(C4CCCC4)c3n2)CCC1,998.2,4.9,258.2,5.0,15.0,14.0,73.0,2.0,True,0.0489,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCCCCOCCC(=O)CC(=O)O,246.3,1.08,100.9,2,4,11,0,17,2.21,0.418,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC(=O)CCOCCCCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,931.0,3.85,252.3,3.0,15.0,18.0,68.0,2.0,True,0.9574,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCCC(C)C(CC(O)CC(=O)O)OCC(=O)O,304.4,2.29,104.1,3,4,13,0,21,3.44,0.451,True,True,True,True,True,True,True,True,True,True,CCCCCCC(C)C(CC(O)CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)OCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,989.1,5.06,255.4,4.0,15.0,20.0,72.0,3.0,True,0.5388,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(NC(=O)CCC(=O)NCCC=O)C(CC(=O)O)C(=O)O,358.4,0.32,149.9,4,5,14,0,25,3.33,0.258,True,True,True,True,True,True,True,True,True,True,CCCCC(NC(=O)CCC(=O)NCCC=O)C(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1043.2,3.09,301.2,5.0,16.0,21.0,76.0,2.0,True,0.994,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(C=CC(=O)O)=C(C)C(=O)O,184.2,1.44,74.6,2,2,4,0,13,2.94,0.512,True,True,True,True,True,True,True,True,True,True,CCC(C=CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)=C(C)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,869.0,4.21,226.0,3.0,13.0,11.0,64.0,2.0,True,0.3944,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)NCCO.O=CO,151.1,-1.05,106.9,4,3,2,0,10,2.42,0.376,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCOCCCCCCOCCC(OCCC(=O)O)C(=O)O,320.4,1.93,102.3,2,5,16,0,22,2.87,0.419,True,True,True,True,True,True,True,True,True,True,CCOCCCCCCOCCC(OCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1005.1,4.71,253.7,3.0,16.0,23.0,73.0,2.0,True,0.6492,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCOCC(CCOC(=O)O)C(=O)O,206.2,0.81,93.1,2,4,7,0,14,3.02,0.598,True,True,True,True,True,True,True,True,True,True,CCOCC(CCOC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,891.0,3.58,244.4,3.0,15.0,14.0,65.0,2.0,True,0.7253,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +Cc1c(OCCOC(C)C(=O)O)cc2ccccc2c1OCC(=O)O,348.4,2.48,102.3,2,5,9,2,25,2.76,0.671,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)COc3c(C)c(OCCOC(C)C(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)cc4ccccc34)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,1033.1,5.25,253.7,3.0,16.0,16.0,76.0,3.0,True,0.5635,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)N(C)C(C(=O)O)C(=O)O,175.2,-0.14,77.8,2,3,4,0,12,2.77,0.582,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)N(C)C(C)C)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,859.9,2.64,229.2,3.0,14.0,11.0,63.0,2.0,True,0.8609,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COCCNC(=O)COOCC(=O)O.O=CO,253.2,-1.52,131.4,3,6,8,0,17,2.67,0.206,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCC(C)(C)NCCCC(C(=O)O)C(=O)O,231.3,1.33,86.6,3,3,8,0,16,2.52,0.433,True,True,True,True,True,True,True,True,True,True,CCC(C)(C)NCCCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,916.1,4.1,238.0,4.0,14.0,15.0,67.0,2.0,True,0.9452,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CC(=O)O)C(O)(O)C(=O)O,192.2,-0.75,115.1,4,4,5,0,13,3.36,0.426,True,True,True,True,True,True,True,True,True,True,CCC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(O)(O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,876.9,2.02,266.4,5.0,15.0,12.0,64.0,2.0,True,0.4323,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C=O)C(CC(=O)O)C(=O)O,174.2,-0.0,91.7,2,3,5,0,12,3.42,0.572,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C(C)C=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,858.9,2.77,243.0,3.0,14.0,12.0,63.0,2.0,True,0.8587,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCC(CO)CC(=O)C(=O)O,204.2,-0.5,111.9,3,4,7,0,14,3.02,0.482,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(=O)CC(CO)CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,888.9,2.27,263.3,4.0,15.0,14.0,65.0,2.0,True,0.7205,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCNC(COCCOCC(N)C(=O)O)CC(=O)O,278.3,-1.12,131.1,4,6,12,0,19,3.28,0.33,True,True,True,True,True,True,True,True,True,True,CCNC(COCCOCC(N)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,963.1,1.66,282.5,5.0,17.0,19.0,70.0,2.0,True,0.979,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(=O)C(=O)O.CC(=O)O,148.1,-0.25,91.7,2,3,1,0,10,1.82,0.5,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCCCC(=O)O.O=CO,148.2,0.96,74.6,2,2,3,0,10,1.96,0.584,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCN(CC)C(C(=O)O)=C(C(=O)O)c1cccc(CNC)c1,306.4,1.63,89.9,3,4,8,1,22,2.68,0.633,True,True,True,True,True,True,True,True,True,True,CCN(CC)C(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)=C(C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)c1cccc(CNC)c1,991.1,4.4,241.2,4.0,15.0,15.0,73.0,2.0,True,0.3864,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C.CC(CC(=O)O)C(=O)O,148.2,0.82,74.6,2,2,3,0,10,2.7,0.621,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,816.9,2.95,226.0,3.0,13.0,10.0,60.0,2.0,True,0.9854,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(=NCCC(=O)O)NC(C)CC(=O)O,216.2,0.33,99.0,3,3,6,0,15,3.21,0.439,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC(C)NC(C)=NCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,901.0,3.1,250.4,4.0,14.0,13.0,66.0,2.0,True,0.9598,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCC(O)(CC(=O)O)C(=O)O,204.2,0.86,94.8,3,3,7,0,14,2.82,0.534,True,True,True,True,True,True,True,True,True,True,CCCCCC(O)(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,889.0,3.63,246.2,4.0,14.0,14.0,65.0,2.0,True,0.6319,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CC(CCOCCCN1CC1)CC(=O)O,259.3,0.66,86.8,2,4,11,1,18,2.32,0.419,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC(CCOCCCN3CC3)CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,944.1,3.44,238.2,3.0,15.0,18.0,69.0,2.0,True,0.9945,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(=O)O.CCCC(=O)O,148.2,0.96,74.6,2,2,2,0,10,1.76,0.611,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCCOC(CC)=C(C)C(CCC(=O)O)C(=O)O,258.3,2.66,83.8,2,3,9,0,18,3.37,0.621,True,True,True,True,True,True,True,True,True,True,CCCOC(CC)=C(C)C(CCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,943.1,5.43,235.2,3.0,14.0,16.0,69.0,3.0,True,0.6355,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)C(C(=O)O)C(=O)O,146.1,0.43,74.6,2,2,3,0,10,2.31,0.566,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C(C)C)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,830.9,3.2,226.0,3.0,13.0,10.0,61.0,2.0,True,0.8461,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COC(C(=O)O)C(CC(=O)O)C(C)C,204.2,0.83,83.8,2,3,6,0,14,3.43,0.669,True,True,True,True,True,True,True,True,True,True,COC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)C(C)C,889.0,3.6,235.2,3.0,14.0,13.0,65.0,2.0,True,0.1297,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)(CC(=O)O)CC(=O)O,160.2,0.96,74.6,2,2,4,0,11,2.22,0.642,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC(C)(C)CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,844.9,3.73,226.0,3.0,13.0,11.0,62.0,2.0,True,0.9754,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCCCC(CC(=O)O)C(=O)O,216.3,2.52,74.6,2,2,9,0,15,2.38,0.581,True,True,True,True,True,True,True,True,True,True,CCCCCCCC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,901.0,5.29,226.0,3.0,13.0,16.0,66.0,3.0,True,0.9794,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(O)C(CC(=O)O)C(=O)O,162.1,-0.46,94.8,3,3,4,0,11,3.07,0.52,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C(C)O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,846.9,2.31,246.2,4.0,14.0,11.0,62.0,2.0,True,0.5115,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COCCCCCCNCCC(C)(C(=O)O)C(=O)O,275.3,1.35,95.9,3,4,12,0,19,2.52,0.367,True,True,True,True,True,True,True,True,True,True,COCCCCCCNCCC(C)(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,960.1,4.12,247.2,4.0,15.0,19.0,70.0,2.0,True,0.9515,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COCCNC(C)OCCOC(COCC(=O)O)(C(=O)O)C(C)OC,367.4,-0.44,132.8,3,8,16,0,25,4.21,0.243,True,True,True,True,True,True,True,True,True,True,COCCNC(C)OCCOC(COCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)(C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)C(C)OC,1052.2,2.33,284.1,4.0,19.0,23.0,76.0,2.0,True,0.8855,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCN(C(=O)O)N(C(=O)O)C1CCCC1,230.3,2.21,81.1,2,2,3,1,16,2.98,0.728,True,True,True,True,True,True,True,True,True,True,CCCN(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)N(C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)C1CCCC1,915.0,4.99,232.4,3.0,13.0,10.0,67.0,2.0,True,0.999,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)NCCCCC(O)C(=O)O,191.2,-0.13,106.9,4,3,6,0,13,2.64,0.437,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(O)CCCCNC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,875.9,2.64,258.2,5.0,14.0,13.0,64.0,2.0,True,0.6875,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(CCNC(=O)O)C(=O)O,161.2,0.36,86.6,3,2,4,0,11,2.67,0.555,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)CCNC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,845.9,3.14,238.0,4.0,13.0,11.0,62.0,2.0,True,0.965,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)(O)C(CN1CCC(C(=O)O)CC1)C(=O)O,259.3,0.25,98.1,3,4,5,1,18,2.94,0.655,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CN3CCC(C(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)CC3)C(C)(C)O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,944.1,3.03,249.4,4.0,15.0,12.0,69.0,2.0,True,0.3773,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)C(CC(=O)O)C(=O)O,160.2,0.82,74.6,2,2,4,0,11,2.6,0.638,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C(C)C)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,844.9,3.59,226.0,3.0,13.0,11.0,62.0,2.0,True,0.6282,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC.CC.O=CO.O=CO,152.2,1.45,74.6,2,2,0,0,10,2.94,0.515,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +COCC(O)CN(CCCCCC(=O)O)C(C)OCCC(=O)O,335.4,0.78,116.5,3,6,15,0,23,3.42,0.297,True,True,True,True,True,True,True,True,True,True,COCC(O)CN(CCCCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(C)OCCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1020.2,3.55,267.9,4.0,17.0,22.0,74.0,2.0,True,0.503,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(CCCC(=O)O)CC(=O)O,216.3,2.52,74.6,2,2,9,0,15,2.53,0.621,True,True,True,True,True,True,True,True,True,True,CCCCC(CCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,901.0,5.29,226.0,3.0,13.0,16.0,66.0,3.0,True,0.9859,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(=O)CCOC(=O)C(CC(=O)O)C(=O)O,232.2,-0.32,118.0,2,5,7,0,16,2.9,0.456,True,True,True,True,True,True,True,True,True,True,CC(=O)CCOC(=O)C(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,916.9,2.46,269.3,3.0,16.0,14.0,67.0,2.0,True,0.9619,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(OC(=O)CC(N)C(=O)O)C(=O)O,219.2,-0.81,126.9,3,5,6,0,15,3.11,0.498,True,True,True,True,True,True,True,True,True,True,CCC(OC(=O)CC(N)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,904.0,1.97,278.3,4.0,16.0,13.0,66.0,2.0,True,0.9855,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CNC(C(=O)O)C(C)C(C)C(=O)O,189.2,0.02,86.6,3,3,5,0,13,3.5,0.567,True,True,True,True,True,True,True,True,True,True,CNC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(C)C(C)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,874.0,2.79,238.0,4.0,14.0,12.0,64.0,2.0,True,0.9567,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(=O)C(C)C(C(=O)O)N1CCCCC(CC(=O)O)CC1,299.4,1.63,94.9,2,4,6,1,21,3.37,0.774,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC3CCCCN(C(C(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)C(C)C(C)=O)CC3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,984.1,4.4,246.3,3.0,15.0,13.0,72.0,2.0,True,0.988,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(NC(C)=CC(O)C(=O)O)C(CCC)CCOCC(=O)O,359.5,2.39,116.1,4,5,15,0,25,4.09,0.332,True,True,True,True,True,True,True,True,True,True,CCCCC(NC(C)=CC(O)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(CCC)CCOCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1044.2,5.16,267.5,5.0,16.0,22.0,76.0,3.0,True,0.0422,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCC(C(=O)O)C(=O)O,174.2,1.35,74.6,2,2,6,0,12,2.03,0.471,True,True,True,True,True,True,True,True,True,True,CCCCCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,859.0,4.12,226.0,3.0,13.0,13.0,63.0,2.0,True,0.9664,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCOCCOCCN(C)C1(C(=O)O)CCN(CCCC(=O)O)CC1,360.5,0.76,99.5,2,6,13,1,25,2.74,0.464,True,True,True,True,True,True,True,True,True,True,CCOCCOCCN(C)C1(C(=O)Nc2cccc3c2C(=O)N(C2CCC(=O)NC2=O)C3=O)CCN(CCCC(=O)N(c2ccc(N3CCNCC3)nc2)c2ncc3c(C)c(C(C)=O)c(=O)n(C4CCCC4)c3n2)CC1,1045.2,3.53,250.9,3.0,17.0,20.0,76.0,2.0,True,0.9282,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCN(CCC(=O)O)C(=C(OC)OCC(=O)O)C(C)(C)CC,317.4,2.14,96.3,2,5,11,0,22,3.1,0.563,True,True,True,True,True,True,True,True,True,True,CCN(CCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=C(OC)OCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)C(C)(C)CC,1002.1,4.91,247.7,3.0,16.0,18.0,73.0,2.0,True,0.1521,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(C(=O)O)(C(=O)O)C(O)C=O,218.2,-0.11,111.9,3,4,7,0,15,3.52,0.406,True,True,True,True,True,True,True,True,True,True,CCCCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)(C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)C(O)C=O,903.0,2.66,263.3,4.0,15.0,14.0,66.0,2.0,True,0.7062,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(C)(C(=O)O)C(=O)O,146.1,0.57,74.6,2,2,3,0,10,2.67,0.571,True,True,True,True,True,True,True,True,True,True,CCC(C)(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,830.9,3.34,226.0,3.0,13.0,10.0,61.0,2.0,True,0.995,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)(C(=O)O)C(=O)O.O,150.1,-0.64,106.1,2,2,2,0,10,2.66,0.51,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)(C)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,816.9,2.95,226.0,3.0,13.0,9.0,60.0,2.0,True,0.8746,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCN(CN(CCO)C(O)(CC(=O)O)C(=O)O)C(C)C,306.4,-0.39,121.5,4,6,11,0,21,3.69,0.381,True,True,True,True,True,True,True,True,True,True,CCCN(CN(CCO)C(O)(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)C(C)C,991.1,2.38,272.9,5.0,17.0,18.0,72.0,2.0,True,0.4708,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)C(C(=O)O)C(=O)O,146.1,0.43,74.6,2,2,3,0,10,2.31,0.566,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C(C)C)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,830.9,3.2,226.0,3.0,13.0,10.0,61.0,2.0,True,0.8461,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCNCCOC(=O)O.O=CC(=O)O,221.2,-0.05,112.9,3,5,6,0,15,2.41,0.249,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CC=C(CNC)C(C)(CC(=O)O)CC(=O)O,229.3,1.11,86.6,3,3,7,0,16,3.31,0.569,True,True,True,True,True,True,True,True,True,True,CC=C(CNC)C(C)(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,914.0,3.88,238.0,4.0,14.0,14.0,67.0,2.0,True,0.7044,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCNN(C)CCOC(=O)O.O=CO,208.2,-0.16,99.1,3,5,5,0,14,3.29,0.328,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +O=C(O)C(CCCCCCOCCO)C(=O)O,248.3,0.73,104.1,3,4,11,0,17,2.26,0.368,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CCCCCCOCCO)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,933.0,3.5,255.4,4.0,15.0,18.0,68.0,2.0,True,0.7331,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCCC(C(=O)O)C(=O)O,188.2,1.74,74.6,2,2,7,0,13,2.01,0.471,True,True,True,True,True,True,True,True,True,True,CCCCCCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,873.0,4.51,226.0,3.0,13.0,14.0,64.0,2.0,True,0.9716,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(CC(=O)O)CC(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.01,0.608,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC(C)CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,830.9,3.34,226.0,3.0,13.0,11.0,61.0,2.0,True,0.9882,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C.NC(CC(=O)O)C(=O)O,149.1,-0.49,100.6,3,3,3,0,10,2.67,0.502,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(N)CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,817.9,1.64,252.0,4.0,14.0,10.0,60.0,2.0,True,0.9788,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCC(C)(OC(CCOC)C(=O)O)C(=O)O,276.3,1.92,93.1,2,4,11,0,19,3.46,0.56,True,True,True,True,True,True,True,True,True,True,CCCCCC(C)(OC(CCOC)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,961.1,4.69,244.4,3.0,15.0,18.0,70.0,2.0,True,0.8914,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COC(C)=C(CCC(=O)O)C(=O)O,188.2,0.86,83.8,2,3,5,0,13,2.8,0.493,True,True,True,True,True,True,True,True,True,True,COC(C)=C(CCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,872.9,3.63,235.2,3.0,14.0,12.0,64.0,2.0,True,0.1362,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)C(C(=O)O)C1CCC1,158.2,0.57,74.6,2,2,3,1,11,2.35,0.589,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C3CCC3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,842.9,3.34,226.0,3.0,13.0,10.0,62.0,2.0,True,0.9448,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCN(CC)C(C)=C(C(=O)C(=O)O)C(N)C(=O)O,258.3,-0.33,120.9,3,5,7,0,18,3.61,0.422,True,True,True,True,True,True,True,True,True,True,CCN(CC)C(C)=C(C(=O)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(N)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,943.0,2.44,272.3,4.0,16.0,14.0,69.0,2.0,True,0.1597,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(NC(=O)C(=O)O)C(=O)O,203.2,-0.17,103.7,3,3,5,0,14,2.43,0.538,True,True,True,True,True,True,True,True,True,True,CCCCC(NC(=O)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,888.0,2.6,255.1,4.0,14.0,12.0,65.0,2.0,True,0.9224,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCOCCOC(OC(=O)O)C(CCC(=O)O)OC,280.3,0.94,111.5,2,6,11,0,19,3.59,0.327,True,True,True,True,True,True,True,True,True,True,CCOCCOC(OC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(CCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)OC,965.0,3.71,262.9,3.0,17.0,18.0,70.0,2.0,True,0.6487,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCC(=O)NCC(O)(C(=O)O)C(=O)O,247.2,-0.42,123.9,4,4,8,0,17,2.52,0.341,True,True,True,True,True,True,True,True,True,True,CCCCCC(=O)NCC(O)(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,932.0,2.35,275.3,5.0,15.0,15.0,68.0,2.0,True,0.5879,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CCC(=O)O)C(=O)O,160.2,0.96,74.6,2,2,5,0,11,2.45,0.628,True,True,True,True,True,True,True,True,True,True,CCC(CCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,844.9,3.73,226.0,3.0,13.0,12.0,62.0,2.0,True,0.8845,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.54,0.608,True,True,True,True,True,True,True,True,True,True,CCC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,830.9,3.34,226.0,3.0,13.0,11.0,61.0,2.0,True,0.9029,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CC)C(C(=O)O)C(C)(C)C(=O)O,216.3,2.23,74.6,2,2,6,0,15,3.28,0.714,True,True,True,True,True,True,True,True,True,True,CCC(CC)C(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(C)(C)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,901.0,5.01,226.0,3.0,13.0,13.0,66.0,3.0,True,0.8172,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCN(CC(=O)O)CC(=O)NCCCCCCCC(=O)O,302.4,0.93,106.9,3,4,13,0,21,2.05,0.437,True,True,True,True,True,True,True,True,True,True,CCN(CC(=O)NCCCCCCCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O,987.1,3.71,258.3,4.0,15.0,20.0,72.0,2.0,True,0.9708,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(=O)O.CCOCC(=O)O,164.2,0.2,83.8,2,3,3,0,11,1.88,0.622,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCC(CC)C(CC(=O)O)C(=O)O,188.2,1.6,74.6,2,2,6,0,13,2.95,0.663,True,True,True,True,True,True,True,True,True,True,CCC(CC)C(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,873.0,4.37,226.0,3.0,13.0,13.0,64.0,2.0,True,0.91,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(CCC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.38,0.608,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,830.9,3.34,226.0,3.0,13.0,11.0,61.0,2.0,True,0.9446,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC(=O)NC(C(=O)O)C1CCCC(C(=O)O)CCC1,299.4,2.03,103.7,3,3,6,1,21,2.68,0.696,True,True,True,True,True,True,True,True,True,True,CCCC(=O)NC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C1CCCC(C(=O)N(c2ccc(N3CCNCC3)nc2)c2ncc3c(C)c(C(C)=O)c(=O)n(C4CCCC4)c3n2)CCC1,984.1,4.8,255.1,4.0,14.0,13.0,72.0,2.0,True,0.9986,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CCCC(=O)O)C(O)C(=O)O,204.2,0.71,94.8,3,3,7,0,14,3.16,0.567,True,True,True,True,True,True,True,True,True,True,CCC(CCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,889.0,3.48,246.2,4.0,14.0,14.0,65.0,2.0,True,0.3562,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C(=O)O)C(C)(C)C(=O)O,160.2,0.82,74.6,2,2,3,0,11,3.04,0.64,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)(C)C(C)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,844.9,3.59,226.0,3.0,13.0,10.0,62.0,2.0,True,0.9033,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCNC(CC(=O)O)C(=O)O,189.2,0.3,86.6,3,3,7,0,13,2.36,0.501,True,True,True,True,True,True,True,True,True,True,CCCCNC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,874.0,3.08,238.0,4.0,14.0,14.0,64.0,2.0,True,0.9667,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +NC(=O)CCC(CCCC(=O)O)CCCC(=O)O,259.3,1.38,117.7,3,3,11,0,18,2.23,0.518,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCCC(CCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)CCC(N)=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,944.1,4.15,269.1,4.0,14.0,18.0,69.0,2.0,True,0.9972,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)NCCCC1OCCNCCCNCCN1C(=O)O,318.4,-0.06,123.2,5,5,4,1,22,3.8,0.46,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)N3CCNCCCNCCOC3CCCNC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,1003.1,2.71,274.5,6.0,16.0,11.0,73.0,3.0,True,0.9218,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC(CCC(=O)O)C(N)C(=O)O,203.2,0.68,100.6,3,3,7,0,14,3.09,0.564,True,True,True,True,True,True,True,True,True,True,CCCC(CCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(N)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,888.0,3.45,252.0,4.0,14.0,14.0,65.0,2.0,True,0.9984,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)N(C)CCC(C(=O)O)C(=O)O,203.2,0.5,77.8,2,3,6,0,14,2.47,0.615,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CCN(C)C(C)C)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,888.0,3.27,229.2,3.0,14.0,13.0,65.0,2.0,True,0.8215,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CNC(=O)c1ccc(C)c(C)c1C(C)(CC(=O)O)C(=O)O,293.3,1.48,103.7,3,3,5,1,21,3.05,0.763,True,True,True,True,True,True,True,True,True,True,CNC(=O)c1ccc(C)c(C)c1C(C)(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,978.1,4.25,255.1,4.0,14.0,12.0,72.0,2.0,True,0.9967,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COCCOCCNC(CN1CC(CCC(=O)O)C1)C(=O)O,318.4,-0.51,108.3,3,6,13,1,22,2.94,0.39,True,True,True,True,True,True,True,True,True,True,COCCOCCNC(CN1CC(CCC(=O)Nc2cccc3c2C(=O)N(C2CCC(=O)NC2=O)C3=O)C1)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1003.1,2.26,259.7,4.0,17.0,20.0,73.0,2.0,True,0.8608,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(C)(C(=O)O)C(CC(=O)O)OC,204.2,0.98,83.8,2,3,6,0,14,3.56,0.673,True,True,True,True,True,True,True,True,True,True,CCC(C)(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)OC,889.0,3.75,235.2,3.0,14.0,13.0,65.0,2.0,True,0.9208,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC(C(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.14,0.568,True,True,True,True,True,True,True,True,True,True,CCCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,830.9,3.34,226.0,3.0,13.0,11.0,61.0,2.0,True,0.9929,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C.CCC(=CC(=O)O)C(=O)O,160.2,1.13,74.6,2,2,3,0,11,2.8,0.607,False,False,True,True,True,True,True,True,True,True,CCC(=CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,828.9,3.26,226.0,3.0,13.0,10.0,61.0,2.0,True,0.6464,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)(CCC(=O)O)C(=O)O,160.2,0.96,74.6,2,2,4,0,11,2.16,0.642,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)(C)CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,844.9,3.73,226.0,3.0,13.0,11.0,62.0,2.0,True,0.8857,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCc1cc(CC(=O)O)cc(C(=O)O)c1,208.2,1.57,74.6,2,2,4,1,15,1.9,0.787,True,True,True,True,True,True,True,True,True,True,CCc1cc(CC(=O)Nc2cccc3c2C(=O)N(C2CCC(=O)NC2=O)C3=O)cc(C(=O)N(c2ccc(N3CCNCC3)nc2)c2ncc3c(C)c(C(C)=O)c(=O)n(C4CCCC4)c3n2)c1,893.0,4.35,226.0,3.0,13.0,11.0,66.0,2.0,True,0.9862,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(=O)O.NC(=O)C(=O)O,149.1,-1.35,117.7,3,3,0,0,10,1.95,0.368,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CC(C(=O)O)=C(C)C(=O)O,144.1,0.49,74.6,2,2,2,0,10,2.42,0.553,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)=C(C)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,828.9,3.26,226.0,3.0,13.0,9.0,61.0,2.0,True,0.4547,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCC(O)C(=O)O,148.1,-0.7,94.8,3,3,4,0,10,2.54,0.489,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(O)CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,832.9,2.07,246.2,4.0,14.0,11.0,61.0,2.0,True,0.5868,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +N.NC(CC(=O)O)C(=O)O,150.1,-0.97,135.6,4,4,3,0,10,2.64,0.408,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(N)CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,817.9,1.64,252.0,4.0,14.0,10.0,60.0,2.0,True,0.9788,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(CCC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.38,0.608,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,830.9,3.34,226.0,3.0,13.0,11.0,61.0,2.0,True,0.9446,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCC(=O)C(C(=O)O)C(=O)O,202.2,0.92,91.7,2,3,7,0,14,2.34,0.473,True,True,True,True,True,True,True,True,True,True,CCCCCC(=O)C(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,887.0,3.69,243.0,3.0,14.0,14.0,65.0,2.0,True,0.9699,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCCCCC1(CC(=O)O)CC1,214.3,2.28,74.6,2,2,8,1,15,2.25,0.608,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC3(CCCCCC(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)CC3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,899.0,5.05,226.0,3.0,13.0,15.0,66.0,3.0,True,0.9969,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C(=O)O)=C(C)C(=O)O,144.1,0.49,74.6,2,2,2,0,10,2.42,0.553,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)=C(C)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,828.9,3.26,226.0,3.0,13.0,9.0,61.0,2.0,True,0.4547,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCOC(=O)CCC(CC(=O)O)C(=O)O,218.2,0.51,100.9,2,4,7,0,15,2.51,0.603,True,True,True,True,True,True,True,True,True,True,CCOC(=O)CCC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,903.0,3.28,252.3,3.0,15.0,14.0,66.0,2.0,True,0.9817,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.54,0.608,True,True,True,True,True,True,True,True,True,True,CCC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,830.9,3.34,226.0,3.0,13.0,11.0,61.0,2.0,True,0.9029,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)NCC#CCCCCNC(=O)O,214.2,0.7,98.7,4,2,5,0,15,2.73,0.4,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)NCCCCC#CCNC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,899.0,3.47,250.0,5.0,13.0,12.0,66.0,2.0,True,0.9609,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C(=O)O)=C(C)C(=O)O,144.1,0.49,74.6,2,2,2,0,10,2.42,0.553,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)=C(C)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,828.9,3.26,226.0,3.0,13.0,9.0,61.0,2.0,True,0.4547,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C=C(C=C(C)C(C)=C(OC)C(=O)O)C(=O)O,226.2,1.58,83.8,2,3,5,0,16,3.54,0.422,True,True,True,True,True,True,True,True,True,True,C=C(C=C(C)C(C)=C(OC)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,911.0,4.35,235.2,3.0,14.0,12.0,67.0,2.0,True,0.3144,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C#CCCCCC(C)=C(CC(=O)O)C(=O)O,224.3,2.06,74.6,2,2,7,0,16,3.06,0.394,True,True,True,True,True,True,True,True,True,True,C#CCCCCC(C)=C(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,909.0,4.83,226.0,3.0,13.0,14.0,67.0,2.0,True,0.4773,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(C(=O)O)C(O)(C(=O)O)C(C)C,204.2,0.57,94.8,3,3,5,0,14,3.59,0.608,True,True,True,True,True,True,True,True,True,True,CCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(O)(C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)C(C)C,889.0,3.34,246.2,4.0,14.0,12.0,65.0,2.0,True,0.4093,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)C1CC(C(=O)O)C1,144.1,0.18,74.6,2,2,2,1,10,2.11,0.579,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C3CC(C(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)C3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,828.9,2.95,226.0,3.0,13.0,9.0,61.0,2.0,True,0.9355,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCCCCC(CCC(=O)O)NCCCOCCCO,333.4,1.63,116.1,4,5,17,0,23,2.79,0.299,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCC(CCCCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)NCCCOCCCO)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,1018.2,4.4,267.5,5.0,16.0,24.0,74.0,2.0,True,0.938,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC1CC(CO)(C(O)CCC(CCC(=O)O)(C(=O)O)C(C)C)C1,344.4,2.52,115.1,4,4,11,1,24,4.07,0.458,True,True,True,True,True,True,True,True,True,True,CCC1CC(CO)(C(O)CCC(CCC(=O)Nc2cccc3c2C(=O)N(C2CCC(=O)NC2=O)C3=O)(C(=O)N(c2ccc(N3CCNCC3)nc2)c2ncc3c(C)c(C(C)=O)c(=O)n(C4CCCC4)c3n2)C(C)C)C1,1029.2,5.29,266.4,5.0,15.0,18.0,75.0,3.0,True,0.4006,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)NCC1CC[N+]2([O-])NC(C(=O)O)CCCCCC12,301.3,0.88,121.7,4,4,3,2,21,4.97,0.453,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C3CCCCCC4C(CNC(=O)Nc5cccc6c5C(=O)N(C5CCC(=O)NC5=O)C6=O)CC[N+]4([O-])N3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,986.1,3.65,273.1,5.0,15.0,10.0,72.0,2.0,True,0.9997,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CN(CCCC(=O)O)CCCC(=O)O,203.2,0.65,77.8,2,3,8,0,14,1.99,0.605,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCCN(C)CCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,888.0,3.42,229.2,3.0,14.0,15.0,65.0,2.0,True,0.9507,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCN(CC)C(C(=O)O)C(=O)O,189.2,0.26,77.8,2,3,6,0,13,2.64,0.586,True,True,True,True,True,True,True,True,True,True,CCCN(CC)C(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,874.0,3.03,229.2,3.0,14.0,13.0,64.0,2.0,True,0.9699,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCCCCCN(CC(=O)O)CC(=O)O,259.3,2.21,77.8,2,3,12,0,18,2.0,0.525,True,True,True,True,True,True,True,True,True,True,CCCCCCCCCN(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,944.1,4.98,229.2,3.0,14.0,19.0,69.0,2.0,True,0.9865,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC(C(=O)O)N(CCC)CC(=O)O,217.3,1.04,77.8,2,3,8,0,15,2.79,0.635,True,True,True,True,True,True,True,True,True,True,CCCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)N(CCC)CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,902.0,3.81,229.2,3.0,14.0,15.0,66.0,2.0,True,0.9964,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CNCC(C)(C(=O)O)C(=O)O,161.2,-0.62,86.6,3,3,4,0,11,2.89,0.478,True,True,True,True,True,True,True,True,True,True,CNCC(C)(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,845.9,2.15,238.0,4.0,14.0,11.0,62.0,2.0,True,0.9877,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C(=O)O)C(=O)C(=O)O,146.1,-0.64,91.7,2,3,3,0,10,2.86,0.408,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(=O)C(C)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,830.9,2.13,243.0,3.0,14.0,10.0,61.0,2.0,True,0.8606,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C#N.O=C(O)CCC(=O)O,145.1,0.08,98.4,2,3,3,0,10,1.98,0.585,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,802.8,2.71,226.0,3.0,13.0,10.0,59.0,2.0,True,0.9389,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCNC(CCCCCC(=O)O)CCC(=O)O,245.3,1.86,86.6,3,3,11,0,17,2.57,0.484,True,True,True,True,True,True,True,True,True,True,CCNC(CCCCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,930.1,4.64,238.0,4.0,14.0,18.0,68.0,2.0,True,0.9909,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COC(C(=O)O)=C(C)CC(C(=O)O)C(=O)O,232.2,0.17,121.1,3,4,6,0,16,3.07,0.339,True,True,True,True,True,True,True,True,True,True,COC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)=C(C)CC(C(=O)O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,916.9,2.94,272.5,4.0,15.0,13.0,67.0,2.0,False,0.2118,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C=C.O=C(O)CCCCCC(=O)O.O=S,236.3,1.57,91.7,2,4,6,0,15,2.16,0.537,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCCCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,844.9,3.88,226.0,3.0,13.0,13.0,62.0,2.0,True,0.9629,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CNCCC(CCC(CCC(C)C)C(=O)O)C(=O)O,273.4,2.21,86.6,3,3,11,0,19,3.27,0.537,True,True,True,True,True,True,True,True,True,True,CNCCC(CCC(CCC(C)C)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,958.1,4.99,238.0,4.0,14.0,18.0,70.0,2.0,True,0.9185,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C.CCC(C(=O)O)C(=O)O,148.2,0.82,74.6,2,2,3,0,10,2.48,0.581,False,False,True,True,True,True,True,True,True,True,CCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,816.9,2.95,226.0,3.0,13.0,10.0,60.0,2.0,True,0.974,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CC(NC(O)C(=O)O)C(C)CC)C(=O)O,261.3,0.89,106.9,4,4,9,0,18,3.98,0.459,True,True,True,True,True,True,True,True,True,True,CCC(CC(NC(O)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(C)CC)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,946.1,3.67,258.2,5.0,15.0,16.0,69.0,2.0,True,0.6955,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC=C(CC(=O)O)C(=O)O,144.1,0.49,74.6,2,2,3,0,10,2.71,0.565,True,True,True,True,True,True,True,True,True,True,CC=C(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,828.9,3.26,226.0,3.0,13.0,10.0,61.0,2.0,True,0.2672,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COCOC(C(=O)O)C(=O)NN=NC(=O)O,235.2,-0.78,146.9,3,6,6,0,16,3.99,0.237,True,True,True,True,True,True,True,True,True,True,COCOC(C(=O)NN=NC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O,919.9,1.99,298.2,4.0,17.0,13.0,67.0,2.0,True,0.491,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCN(CCN1CCCC1)CC(=O)O,244.3,-0.06,81.1,2,4,8,1,17,2.1,0.623,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CN(CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)CCN3CCCC3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,929.1,2.71,232.4,3.0,15.0,15.0,68.0,2.0,True,0.9831,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCCCCCC(=O)C(=O)O,202.2,1.07,91.7,2,3,8,0,14,1.99,0.453,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(=O)CCCCCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,887.0,3.84,243.0,3.0,14.0,15.0,65.0,2.0,True,0.9235,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(C(=O)O)C(=O)O,160.2,0.96,74.6,2,2,5,0,11,2.08,0.587,True,True,True,True,True,True,True,True,True,True,CCCCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,844.9,3.73,226.0,3.0,13.0,12.0,62.0,2.0,True,0.9716,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C=C(C)C(OCCOCCC(=O)O)C(=O)O,232.2,0.52,93.1,2,4,9,0,16,3.24,0.444,True,True,True,True,True,True,True,True,True,True,C=C(C)C(OCCOCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,917.0,3.29,244.4,3.0,15.0,16.0,67.0,2.0,True,0.4967,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CNC(C)(CCC(C)C(=O)O)C(=O)O,203.2,0.55,86.6,3,3,6,0,14,3.25,0.587,True,True,True,True,True,True,True,True,True,True,CNC(C)(CCC(C)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,888.0,3.32,238.0,4.0,14.0,13.0,65.0,2.0,True,0.9661,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)C=CCCCCC(=O)O,172.2,1.27,74.6,2,2,6,0,12,2.1,0.467,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCCCC=CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,856.9,4.04,226.0,3.0,13.0,13.0,63.0,2.0,True,0.8323,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +NCCCCCCCCCCCC(C(=O)O)=C(CCC(=O)O)C(=O)O,357.4,3.18,137.9,4,4,16,0,25,2.52,0.246,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CCC(=O)O)=C(CCCCCCCCCCCN)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,1042.2,5.95,289.3,5.0,15.0,23.0,76.0,3.0,False,0.862,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCCCC(NCCC(=O)O)=C1CCCCNC1,298.4,1.72,98.7,4,4,9,1,21,2.75,0.484,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCNC(CCCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)=C3CCCCNC3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,983.1,4.49,250.0,5.0,15.0,16.0,72.0,2.0,True,0.0199,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC=NNC(C)=C(CCCCCC(=O)O)C(=O)O,270.3,2.37,99.0,3,4,10,0,19,2.96,0.245,True,True,True,True,True,True,True,True,True,True,CCC=NNC(C)=C(CCCCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,955.1,5.14,250.4,4.0,15.0,17.0,70.0,3.0,True,0.0141,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(C)=C(CC(O)(O)C(=O)O)C(=O)O,218.2,-0.05,115.1,4,4,5,0,15,3.3,0.38,True,True,True,True,True,True,True,True,True,True,CCC(C)=C(CC(O)(O)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,903.0,2.72,266.4,5.0,15.0,12.0,66.0,2.0,True,0.6216,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCNN(C(=O)O)C(=O)CC(C)C(=O)O,232.2,0.52,106.9,3,4,6,0,16,3.45,0.576,True,True,True,True,True,True,True,True,True,True,CCCNN(C(=O)CC(C)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O,917.0,3.29,258.3,4.0,15.0,13.0,67.0,2.0,True,0.999,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C.CCN.O=CO.O=CO,153.2,0.0,100.6,3,3,0,0,10,3.14,0.425,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCCN(CCC(=O)O)C(CC)C(=O)O,217.3,1.04,77.8,2,3,8,0,15,2.77,0.635,True,True,True,True,True,True,True,True,True,True,CCCN(CCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(CC)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,902.0,3.81,229.2,3.0,14.0,15.0,66.0,2.0,True,0.9964,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC=C(CCC(=O)O)C(=O)O,158.2,0.88,74.6,2,2,4,0,11,2.55,0.595,True,True,True,True,True,True,True,True,True,True,CC=C(CCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,842.9,3.65,226.0,3.0,13.0,11.0,62.0,2.0,True,0.2289,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C[SH]1=C2C=1C2CCN(CCCC(=O)O)CC(=O)O,273.4,0.25,77.8,3,3,9,2,18,3.53,0.414,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CN(CCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)CCC3C4=[SH](C)=C43)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,958.1,3.02,229.2,4.0,14.0,16.0,69.0,2.0,True,0.998,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(CCC(=O)O)C(=O)O.O,164.2,-0.25,106.1,2,2,4,0,11,2.56,0.597,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,830.9,3.34,226.0,3.0,13.0,11.0,61.0,2.0,True,0.9446,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CN)CC(C(=O)O)C(=O)O,189.2,0.15,100.6,3,3,6,0,13,3.05,0.516,True,True,True,True,True,True,True,True,True,True,CCC(CN)CC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,874.0,2.92,252.0,4.0,14.0,13.0,64.0,2.0,True,0.9851,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CCC(=O)O)CC(=O)O,174.2,1.35,74.6,2,2,6,0,12,2.53,0.637,True,True,True,True,True,True,True,True,True,True,CCC(CCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,859.0,4.12,226.0,3.0,13.0,13.0,63.0,2.0,True,0.8981,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCCCCC(CC(=O)O)C(=O)O,230.3,2.91,74.6,2,2,10,0,16,2.38,0.566,True,True,True,True,True,True,True,True,True,True,CCCCCCCCC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,915.1,5.68,226.0,3.0,13.0,17.0,67.0,3.0,True,0.9825,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(=CC1=C=CN1CCC(=O)O)NOC(=O)O,240.2,0.88,99.1,3,5,6,1,17,4.19,0.468,False,True,True,False,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)ONC(C)=CC3=C=CN3CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,925.0,3.65,250.5,4.0,16.0,13.0,68.0,2.0,True,0.9695,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CN(C)C1CC(CCC(CO)=C(CC(=O)O)C(=O)O)C1,285.3,0.95,98.1,3,4,8,1,20,3.16,0.574,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)=C(CO)CCC3CC(N(C)C)C3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,970.1,3.73,249.4,4.0,15.0,15.0,71.0,2.0,True,0.1164,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COCCOC(=CC(=O)C(=O)O)COCCOCCCCCC(=O)O,362.4,0.87,128.6,2,7,17,0,25,2.84,0.167,True,True,True,True,True,True,True,True,True,True,COCCOC(=CC(=O)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)COCCOCCCCCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1047.1,3.64,280.0,3.0,18.0,24.0,76.0,2.0,True,0.4975,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCOC(CCO)C(=O)C(=O)O,220.2,-1.12,121.1,3,5,8,0,15,3.19,0.443,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(=O)C(CCO)OCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,904.9,1.65,272.5,4.0,16.0,15.0,66.0,2.0,True,0.6682,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCOCCCCCCOCCOC(CCNCCC(=O)O)C(=O)O,363.5,1.52,114.3,3,6,19,0,25,2.94,0.296,True,True,True,True,True,True,True,True,True,True,CCOCCCCCCOCCOC(CCNCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1048.2,4.3,265.7,4.0,17.0,26.0,76.0,2.0,True,0.5501,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +Cc1c(CCC(=O)C(=O)O)nnn1CCC(=O)O,255.2,-0.35,122.4,2,5,7,1,18,2.7,0.638,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCn3nnc(CCC(=O)C(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)c3C)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,940.0,2.42,273.8,3.0,16.0,14.0,69.0,2.0,True,0.452,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCNC(=O)O.O=CO,163.2,0.75,86.6,3,2,3,0,11,2.15,0.423,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +O=C(O)ON(CO)C(=O)O,151.1,-0.47,107.3,3,4,1,0,10,3.51,0.345,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)N(CO)OC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,835.8,2.3,258.7,4.0,15.0,8.0,61.0,2.0,True,0.998,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(COC)C(C)(C(=O)O)C(=O)OCC(=O)O,262.3,0.38,110.1,2,5,8,0,18,3.63,0.48,True,True,True,True,True,True,True,True,True,True,CCC(COC)C(C)(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)OCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,947.0,3.15,261.5,3.0,16.0,15.0,69.0,2.0,True,0.9499,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +NCC(CC(=O)O)C(=O)O,147.1,-0.88,100.6,3,3,4,0,10,2.77,0.479,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CN)CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,831.9,1.89,252.0,4.0,14.0,11.0,61.0,2.0,True,0.9886,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCOCCOC1=C(CC(=O)O)NC(=O)CCCC1,315.3,0.87,122.2,3,5,9,1,22,2.87,0.539,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC3=C(OCCOCCC(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)CCCCC(=O)N3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,1000.1,3.64,273.5,4.0,16.0,16.0,73.0,2.0,True,0.0188,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COCCOC(C(=O)O)C(C)(O)COCCOC(C)C(=O)O,324.3,-0.64,131.8,3,7,13,0,22,3.84,0.378,True,True,True,True,True,True,True,True,True,True,COCCOC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(C)(O)COCCOC(C)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1009.1,2.13,283.1,4.0,18.0,20.0,73.0,2.0,True,0.5145,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCOCCCCC(CC)(C(=O)O)C(=O)O,232.3,1.76,83.8,2,3,9,0,16,2.53,0.467,True,True,True,True,True,True,True,True,True,True,CCOCCCCC(CC)(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,917.0,4.53,235.2,3.0,14.0,16.0,67.0,2.0,True,0.8868,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC.O=CO.O=CO,150.2,1.21,74.6,2,2,1,0,10,2.57,0.551,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCN(CC(=O)O)C(=CC(=O)O)CCCOC,245.3,0.79,87.1,2,4,9,0,17,2.83,0.461,True,True,True,True,True,True,True,True,True,True,CCN(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)CCCOC,930.0,3.56,238.4,3.0,15.0,16.0,68.0,2.0,True,0.1386,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)CCC(CC(=O)O)C(=O)O,188.2,1.6,74.6,2,2,6,0,13,2.56,0.663,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CCC(C)C)CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,873.0,4.37,226.0,3.0,13.0,13.0,64.0,2.0,True,0.874,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC(N)(CN(CCCCCCN)CC(=O)O)C(=O)O,303.4,0.47,129.9,4,5,13,0,21,3.2,0.364,True,True,True,True,True,True,True,True,True,True,CCCC(N)(CN(CCCCCCN)CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,988.2,3.25,281.2,5.0,16.0,20.0,72.0,2.0,True,0.9996,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(CC(=O)O)(C(=O)O)C(=O)NCC,245.3,0.86,103.7,3,3,8,0,17,3.06,0.55,True,True,True,True,True,True,True,True,True,True,CCCCC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)(C(=O)NCC)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,930.0,3.63,255.1,4.0,14.0,15.0,68.0,2.0,True,0.9911,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC.O.O=CO.O=CO,154.2,-0.01,106.1,2,2,0,0,10,2.93,0.478,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCC(C)CC(=O)NC(C)CCC(CC(=O)O)CC(=O)O,301.4,2.27,103.7,3,3,11,0,21,3.19,0.543,True,True,True,True,True,True,True,True,True,True,CCC(C)CC(=O)NC(C)CCC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,986.1,5.04,255.1,4.0,14.0,18.0,72.0,3.0,True,0.9894,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC(C)C(=NC(=O)O)NC(=O)O,202.2,1.77,99.0,3,2,3,0,14,3.62,0.479,True,True,True,True,True,True,True,True,True,True,CCCC(C)C(=NC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)NC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,887.0,4.54,250.4,4.0,13.0,10.0,65.0,2.0,True,0.9954,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C.O=C(O)CCOC(=O)O,150.1,0.79,83.8,2,3,3,0,10,2.43,0.581,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)OCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,818.8,2.93,235.2,3.0,14.0,10.0,60.0,2.0,True,0.6607,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCOCCCCCC(=CN)COCC1(CC(C(=O)O)C(=O)O)CC1,357.4,2.4,119.1,3,5,15,1,25,3.3,0.304,True,True,True,True,True,True,True,True,True,True,CCOCCCCCC(=CN)COCC1(CC(C(=O)Nc2cccc3c2C(=O)N(C2CCC(=O)NC2=O)C3=O)C(=O)N(c2ccc(N3CCNCC3)nc2)c2ncc3c(C)c(C(C)=O)c(=O)n(C4CCCC4)c3n2)CC1,1042.2,5.17,270.4,4.0,16.0,22.0,76.0,3.0,True,0.3876,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)C1CC(C(=O)O)C1,144.1,0.18,74.6,2,2,2,1,10,2.11,0.579,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C3CC(C(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)C3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,828.9,2.95,226.0,3.0,13.0,9.0,61.0,2.0,True,0.9355,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCN(CC)C(C)=CC(C)=C(C(=O)O)C(=O)O,241.3,1.72,77.8,2,3,6,0,17,3.18,0.32,True,True,True,True,True,True,True,True,True,True,CCN(CC)C(C)=CC(C)=C(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,926.0,4.49,229.2,3.0,14.0,13.0,68.0,2.0,True,0.1102,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)(C(=O)O)N(CC(N)=O)CC(=O)O,218.2,-1.28,120.9,3,4,6,0,15,2.68,0.51,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CN(CC(N)=O)C(C)(C)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,903.0,1.49,272.3,4.0,15.0,13.0,66.0,2.0,True,0.9359,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(O)(O)CCC(CC(C)CCCC(=O)O)C(=O)O,290.4,1.84,115.1,4,4,11,0,20,3.6,0.432,True,True,True,True,True,True,True,True,True,True,CCC(O)(O)CCC(CC(C)CCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,975.1,4.61,266.4,5.0,15.0,18.0,71.0,2.0,True,0.838,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CCC(=O)O)C(=O)O,160.2,0.96,74.6,2,2,5,0,11,2.45,0.628,True,True,True,True,True,True,True,True,True,True,CCC(CCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,844.9,3.73,226.0,3.0,13.0,12.0,62.0,2.0,True,0.8845,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCOCCOCCC(CCO)C(C=CCOCC(=O)O)OC(=O)O,364.4,1.15,131.8,3,7,16,0,25,3.87,0.209,True,True,True,True,True,True,True,True,True,True,CCOCCOCCC(CCO)C(C=CCOCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)OC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1049.2,3.92,283.1,4.0,18.0,23.0,76.0,2.0,True,0.3196,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCN(CC(=O)O)CC(=O)O,189.2,0.26,77.8,2,3,7,0,13,2.01,0.6,True,True,True,True,True,True,True,True,True,True,CCCCN(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,874.0,3.03,229.2,3.0,14.0,14.0,64.0,2.0,True,0.9788,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C#CCCCNC(=C)CCOc1cccc(CC(=O)O)c1CC(=O)O,345.4,2.23,95.9,3,4,12,1,25,2.89,0.397,True,True,True,True,True,True,True,True,True,True,C#CCCCNC(=C)CCOc1cccc(CC(=O)Nc2cccc3c2C(=O)N(C2CCC(=O)NC2=O)C3=O)c1CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1030.2,5.0,247.2,4.0,15.0,19.0,76.0,2.0,True,0.1883,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C(=O)O)N(C)CC(=O)O,161.2,-0.52,77.8,2,3,4,0,11,2.63,0.579,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CN(C)C(C)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,845.9,2.25,229.2,3.0,14.0,11.0,62.0,2.0,True,0.945,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCCOCCOCCC(O)C(=O)O,250.2,-0.28,113.3,3,5,11,0,17,2.76,0.432,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(O)CCOCCOCCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,935.0,2.49,264.7,4.0,16.0,18.0,68.0,2.0,True,0.7207,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)CCC(C(=O)O)C(=O)O,174.2,1.21,74.6,2,2,5,0,12,2.22,0.614,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CCC(C)C)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,859.0,3.98,226.0,3.0,13.0,12.0,63.0,2.0,True,0.8591,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C(=O)O)=C(C)C(=O)O,144.1,0.49,74.6,2,2,2,0,10,2.42,0.553,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)=C(C)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,828.9,3.26,226.0,3.0,13.0,9.0,61.0,2.0,True,0.4547,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C=C(NN=C(C(=O)O)C1=CCC1)NC(=O)CC(=O)O,267.2,-0.2,128.1,4,5,7,1,19,3.49,0.287,True,True,True,True,True,True,True,True,True,True,C=C(NN=C(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C1=CCC1)NC(=O)CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,952.0,2.57,279.5,5.0,16.0,14.0,70.0,2.0,True,0.0325,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(CCN(CC(=O)O)C(C)CO)C(=O)O,233.3,-0.14,98.1,3,4,8,0,16,3.33,0.54,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)CCN(CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C(C)CO)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,918.0,2.64,249.4,4.0,15.0,15.0,67.0,2.0,True,0.8414,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCNC(=O)C(CCCCC(=O)O)C(=O)O,231.2,0.47,103.7,3,3,8,0,16,2.63,0.416,True,True,True,True,True,True,True,True,True,True,CCNC(=O)C(CCCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,916.0,3.24,255.1,4.0,14.0,15.0,67.0,2.0,True,0.9906,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COC(C)(CCCC(C)C(O)C(=O)O)CC(=O)O,262.3,1.12,104.1,3,4,9,0,18,3.81,0.575,True,True,True,True,True,True,True,True,True,True,COC(C)(CCCC(C)C(O)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,947.1,3.89,255.4,4.0,15.0,16.0,69.0,2.0,True,0.492,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.54,0.608,True,True,True,True,True,True,True,True,True,True,CCC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,830.9,3.34,226.0,3.0,13.0,11.0,61.0,2.0,True,0.9029,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CC)(CCC(=O)O)C(C)OCC(=O)O,246.3,2.15,83.8,2,3,9,0,17,3.41,0.651,True,True,True,True,True,True,True,True,True,True,CCC(CC)(CCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(C)OCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,931.1,4.92,235.2,3.0,14.0,16.0,68.0,2.0,True,0.7277,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC(CC)N(C)N=NC(C(=O)O)C(=O)O,245.3,1.4,102.6,2,4,8,0,17,3.96,0.382,True,True,True,True,True,True,True,True,True,True,CCCC(CC)N(C)N=NC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,930.0,4.17,253.9,3.0,15.0,15.0,68.0,2.0,True,0.808,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCOC(C(=O)O)C(=O)O,162.1,-0.05,83.8,2,3,5,0,11,2.6,0.554,True,True,True,True,True,True,True,True,True,True,CCCOC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,846.9,2.72,235.2,3.0,14.0,12.0,62.0,2.0,True,0.9575,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCOCc1occ(C(=O)O)c1OCC(=O)O,244.2,0.98,106.2,2,5,7,1,17,2.75,0.736,True,True,True,True,True,True,True,True,True,True,CCOCc1occ(C(=O)Nc2cccc3c2C(=O)N(C2CCC(=O)NC2=O)C3=O)c1OCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,929.0,3.75,257.6,3.0,16.0,14.0,68.0,2.0,True,0.4723,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)C1CC(CC(=O)C(=O)O)(C(=O)O)C1,228.2,1.17,91.7,2,3,5,1,16,2.87,0.689,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C3(CC(=O)C(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)CC(C(C)C)C3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,913.0,3.94,243.0,3.0,14.0,12.0,67.0,2.0,True,0.5251,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC(N)=C(CCCCC(=O)O)CCC(=O)O,257.3,2.51,100.6,3,3,10,0,18,2.7,0.522,True,True,True,True,True,True,True,True,True,True,CCCC(N)=C(CCCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,942.1,5.28,252.0,4.0,14.0,17.0,69.0,3.0,True,0.8388,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CC(C)C(C(=O)O)=C(C)O)(OC)C(=O)O,260.3,1.81,104.1,3,4,7,0,18,4.03,0.476,True,True,True,True,True,True,True,True,True,True,CCC(CC(C)C(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)=C(C)O)(OC)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,945.0,4.58,255.4,4.0,15.0,14.0,69.0,2.0,True,0.2014,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COC(C)(C)CC(CC(=O)O)=C(CC(=O)O)OCCO,290.3,1.01,113.3,3,5,10,0,20,3.42,0.514,True,True,True,True,True,True,True,True,True,True,COC(C)(C)CC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)=C(CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)OCCO,975.1,3.79,264.7,4.0,16.0,17.0,71.0,2.0,True,0.3239,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCCC(C(=O)O)C(=O)O,188.2,1.74,74.6,2,2,7,0,13,2.01,0.471,True,True,True,True,True,True,True,True,True,True,CCCCCCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,873.0,4.51,226.0,3.0,13.0,14.0,64.0,2.0,True,0.9716,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=CCC(CC(=O)O)CC(=O)O,174.2,0.14,91.7,2,3,6,0,12,2.62,0.559,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC(CC=O)CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,858.9,2.91,243.0,3.0,14.0,13.0,63.0,2.0,True,0.9607,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C=CN(C=N)C(C)=C(C(=O)O)C(=O)O,198.2,0.48,101.7,3,3,5,0,14,3.98,0.196,True,True,True,True,True,True,True,True,True,True,C=CN(C=N)C(C)=C(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,882.9,3.25,253.1,4.0,14.0,12.0,65.0,2.0,True,0.3558,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(=O)O.CCCC(=O)O,148.2,0.96,74.6,2,2,2,0,10,1.76,0.611,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +O=C(O)NC1CN(C(=O)O)CCN1,189.2,-0.84,101.9,4,3,1,1,13,3.44,0.431,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)N3CCNC(NC(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)C3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,873.9,1.93,253.3,5.0,14.0,8.0,64.0,2.0,True,0.9127,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COCCOC(C(=O)O)=C(C(=O)O)N1CCN(C)CC1,288.3,-0.72,99.5,2,6,7,1,20,2.81,0.359,True,True,True,True,True,True,True,True,True,True,COCCOC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)=C(C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1)N1CCN(C)CC1,973.1,2.05,250.9,3.0,17.0,14.0,71.0,2.0,True,0.1256,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCC1(O)CCC(C(=O)O)CC1,216.2,0.86,94.8,3,3,4,1,15,2.51,0.647,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C3CCC(O)(CCC(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)CC3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,901.0,3.63,246.2,4.0,14.0,11.0,66.0,2.0,True,0.9178,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCOCC(O)=C(C=O)CNCCC(C(=O)O)C(=O)O,289.3,-0.2,133.2,4,6,11,0,20,3.28,0.135,True,True,True,True,True,True,True,True,True,True,CCOCC(O)=C(C=O)CNCCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,974.0,2.57,284.5,5.0,17.0,18.0,71.0,2.0,True,0.0965,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(=O)NC(CC(=O)O)C(=O)O,217.2,0.22,103.7,3,3,7,0,15,2.24,0.566,True,True,True,True,True,True,True,True,True,True,CCCCC(=O)NC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,902.0,2.99,255.1,4.0,14.0,14.0,66.0,2.0,True,0.9701,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CC)(CC(=O)O)C(=O)O,174.2,1.35,74.6,2,2,5,0,12,2.5,0.659,True,True,True,True,True,True,True,True,True,True,CCC(CC)(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,859.0,4.12,226.0,3.0,13.0,12.0,63.0,2.0,True,0.9484,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +Cc1ccc(CC(COCC(=O)O)(C(=O)O)C(=O)NCCCO)nn1,355.3,-1.0,158.9,4,7,11,1,25,3.44,0.288,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(COCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)(Cc3ccc(C)nn3)C(=O)NCCCO)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,1040.1,1.77,310.3,5.0,18.0,18.0,76.0,2.0,True,0.8391,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC1CN(CC(=O)O)CCC1NCCCCC(=O)O,300.4,1.41,89.9,3,4,10,1,21,3.09,0.529,True,True,True,True,True,True,True,True,True,True,CCCC1CN(CC(=O)Nc2cccc3c2C(=O)N(C2CCC(=O)NC2=O)C3=O)CCC1NCCCCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,985.2,4.18,241.2,4.0,15.0,17.0,72.0,2.0,True,0.9987,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(CC=O)N(CC(=O)O)C(C)CC(=O)O,231.2,0.21,94.9,2,4,8,0,16,3.62,0.58,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC(C)N(CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C(C)CC=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,916.0,2.98,246.3,3.0,15.0,15.0,67.0,2.0,True,0.96,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +Cc1cc(C(=O)O)c(C(=O)O)n1C,183.2,0.73,79.5,2,2,2,1,13,2.41,0.708,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)c3c(C(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)cc(C)n3C)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,867.9,3.5,230.9,3.0,13.0,9.0,64.0,2.0,True,0.9527,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C=C(N)COCCOCC(OCCN(CC)C(CN)C(=O)O)C(=O)O,363.4,-1.3,157.6,4,8,16,0,25,3.86,0.243,True,True,True,True,True,True,True,True,True,True,C=C(N)COCCOCC(OCCN(CC)C(CN)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1048.2,1.47,308.9,5.0,19.0,23.0,76.0,2.0,True,0.065,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)C(C(=O)O)N(C)CC(=O)O,189.2,0.11,77.8,2,3,5,0,13,2.9,0.642,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CN(C)C(C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C(C)C)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,874.0,2.88,229.2,3.0,14.0,12.0,64.0,2.0,True,0.9582,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(C)C(C(=O)O)C(=O)O,160.2,0.82,74.6,2,2,4,0,11,2.77,0.597,True,True,True,True,True,True,True,True,True,True,CCC(C)C(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,844.9,3.59,226.0,3.0,13.0,11.0,62.0,2.0,True,0.9943,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(COCCC(CC)C(=O)O)OC(CO)C(=O)O,292.3,0.74,113.3,3,5,12,0,20,3.8,0.455,True,True,True,True,True,True,True,True,True,True,CCC(COCCC(CC)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)OC(CO)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,977.1,3.52,264.7,4.0,16.0,19.0,71.0,2.0,True,0.3195,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCCCCCCCCCCCC(=O)O,258.4,3.84,74.6,2,2,13,0,18,1.52,0.492,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCCCCCCCCCCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,943.1,6.61,226.0,3.0,13.0,20.0,69.0,3.0,True,0.9885,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +NC(=O)C(C(=O)O)C(=O)O,147.1,-1.74,117.7,3,3,3,0,10,2.61,0.409,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C(N)=O)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,831.8,1.03,269.1,4.0,14.0,10.0,61.0,2.0,True,0.9697,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COCCOCCCN(C)CCC(CC(=O)O)C(=O)O,291.3,0.54,96.3,2,5,13,0,20,2.86,0.478,True,True,True,True,True,True,True,True,True,True,COCCOCCCN(C)CCC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,976.1,3.31,247.7,3.0,16.0,20.0,71.0,2.0,True,0.7582,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C.O=C(O)CCCC(=O)O,148.2,0.96,74.6,2,2,4,0,10,1.9,0.622,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,816.9,3.1,226.0,3.0,13.0,11.0,60.0,2.0,True,0.9483,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCN(CCC(=O)O)C1CC(C(=O)O)C1,229.3,1.04,77.8,2,3,7,1,16,2.38,0.682,True,True,True,True,True,True,True,True,True,True,CCCN(CCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C1CC(C(=O)N(c2ccc(N3CCNCC3)nc2)c2ncc3c(C)c(C(C)=O)c(=O)n(C4CCCC4)c3n2)C1,914.0,3.81,229.2,3.0,14.0,14.0,67.0,2.0,True,0.9966,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(CCCC(=O)O)C(=O)O,160.2,0.96,74.6,2,2,5,0,11,2.39,0.628,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)CCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,844.9,3.73,226.0,3.0,13.0,12.0,62.0,2.0,True,0.9346,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C.CCC.O=C(O)CCC(=O)O,178.2,1.99,74.6,2,2,3,0,12,1.97,0.693,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,802.8,2.71,226.0,3.0,13.0,10.0,59.0,2.0,True,0.9389,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(=O)O.O=C(O)CCO,164.2,-0.07,94.8,3,3,3,0,11,2.08,0.543,False,False,True,True,True,True,True,True,True,False,,,,,,,,,,False,,assembly failed +CCC(CC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.54,0.608,True,True,True,True,True,True,True,True,True,True,CCC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,830.9,3.34,226.0,3.0,13.0,11.0,61.0,2.0,True,0.9029,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COCCOCCC(OC(CCOC)C(=O)O)C(C)(C)C(=O)O,336.4,1.03,111.5,2,6,14,0,23,3.63,0.452,True,True,True,True,True,True,True,True,True,True,COCCOCCC(OC(CCOC)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(C)(C)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1021.1,3.8,262.9,3.0,17.0,21.0,74.0,2.0,True,0.6984,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +COCCNCC(C#CCCCCCN)(CCC(=O)O)CCC(=O)O,356.5,1.46,121.9,4,5,15,0,25,3.13,0.259,True,True,True,True,True,True,True,True,True,True,COCCNCC(C#CCCCCCN)(CCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1041.2,4.23,273.2,5.0,16.0,22.0,76.0,2.0,True,0.9929,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(CCCC(=O)O)CCC(=O)O,230.3,2.91,74.6,2,2,10,0,16,2.51,0.605,True,True,True,True,True,True,True,True,True,True,CCCCC(CCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,915.1,5.68,226.0,3.0,13.0,17.0,67.0,3.0,True,0.9881,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C.O=C(O)C(CO)C(=O)O,150.1,-0.6,94.8,3,3,3,0,10,2.8,0.467,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CO)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,818.8,1.54,246.2,4.0,14.0,10.0,60.0,2.0,True,0.7455,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCC(NC(C)C(=O)O)C(=O)O,217.3,1.08,86.6,3,3,8,0,15,2.72,0.53,True,True,True,True,True,True,True,True,True,True,CCCCCC(NC(C)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,902.0,3.85,238.0,4.0,14.0,15.0,66.0,2.0,True,0.9718,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +NCCC1CC2CCN1CCC(OC(=O)O)(C(=O)O)CCO2,316.4,0.5,122.3,3,6,4,3,22,5.51,0.641,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C3(OC(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)CCOC4CCN(CC3)C(CCN)C4)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,1001.1,3.27,273.7,4.0,17.0,11.0,73.0,2.0,True,0.9375,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +NC(CCCC1(C(=O)O)CCC1)C(=O)O,215.2,0.82,100.6,3,3,6,1,15,2.84,0.609,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(N)CCCC3(C(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)CCC3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,900.0,3.59,252.0,4.0,14.0,13.0,66.0,2.0,True,0.997,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CN(N=O)C(C)(C(=O)O)C(=O)O,176.1,-0.47,107.3,2,4,4,0,12,3.43,0.34,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)(C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)N(C)N=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,860.9,2.3,258.6,3.0,15.0,11.0,63.0,2.0,True,0.985,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCCC(C)(C)C(CC(=O)O)=C(C)C(=O)O,270.4,3.86,74.6,2,2,9,0,19,2.75,0.493,True,True,True,True,True,True,True,True,True,True,CCCCCCC(C)(C)C(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)=C(C)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,955.1,6.63,226.0,3.0,13.0,16.0,70.0,3.0,True,0.7624,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.54,0.608,True,True,True,True,True,True,True,True,True,True,CCC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,830.9,3.34,226.0,3.0,13.0,11.0,61.0,2.0,True,0.9029,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCOCCOC(=CC(=O)O)NCCCC(=O)O,275.3,0.81,105.1,3,5,12,0,19,2.76,0.275,True,True,True,True,True,True,True,True,True,True,CCCOCCOC(=CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)NCCCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,960.1,3.58,256.5,4.0,16.0,19.0,70.0,2.0,True,0.0801,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCC(=O)CC(=O)O,160.1,-0.11,91.7,2,3,5,0,11,2.13,0.547,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC(=O)CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,844.9,2.67,243.0,3.0,14.0,12.0,62.0,2.0,True,0.9536,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)C(CC(=O)O)C(=O)O,160.2,0.82,74.6,2,2,4,0,11,2.6,0.638,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C(C)C)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,844.9,3.59,226.0,3.0,13.0,11.0,62.0,2.0,True,0.6282,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CC(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.54,0.608,True,True,True,True,True,True,True,True,True,True,CCC(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,830.9,3.34,226.0,3.0,13.0,11.0,61.0,2.0,True,0.9029,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCCC(CO)CC(C(=O)O)C(=O)O,232.3,1.35,94.8,3,3,9,0,16,2.88,0.412,True,True,True,True,True,True,True,True,True,True,CCCCCC(CO)CC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,917.0,4.12,246.2,4.0,14.0,16.0,67.0,2.0,True,0.9072,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)C1CC(C(=O)O)C1,144.1,0.18,74.6,2,2,2,1,10,2.11,0.579,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C3CC(C(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)C3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,828.9,2.95,226.0,3.0,13.0,9.0,61.0,2.0,True,0.9355,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(O)C(CC(=O)O)C(=O)O,162.1,-0.46,94.8,3,3,4,0,11,3.07,0.52,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C(C)O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,846.9,2.31,246.2,4.0,14.0,11.0,62.0,2.0,True,0.5115,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCOCC(=O)CCN(CCCCCCC(=O)O)CCC(=O)O,331.4,1.79,104.1,2,5,16,0,23,2.4,0.415,True,True,True,True,True,True,True,True,True,True,CCOCC(=O)CCN(CCCCCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,1016.2,4.57,255.5,3.0,16.0,23.0,74.0,2.0,True,0.7478,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCC(=CCOc1ccccc1)C(=O)O,250.3,1.94,83.8,2,3,7,1,18,2.15,0.722,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(=CCOc3ccccc3)CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,935.0,4.71,235.2,3.0,14.0,14.0,69.0,2.0,True,0.4713,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)CNC(CCC=O)C(CC(=O)O)CC(=O)O,273.3,1.15,103.7,3,4,11,0,19,3.39,0.488,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC(CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C(CCC=O)NCC(C)C)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,958.1,3.92,255.1,4.0,15.0,18.0,70.0,2.0,True,0.9876,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC(C(=O)O)C(=O)O,146.1,0.57,74.6,2,2,4,0,10,2.14,0.568,True,True,True,True,True,True,True,True,True,True,CCCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,830.9,3.34,226.0,3.0,13.0,11.0,61.0,2.0,True,0.9929,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CN(CC(=O)O)C1(CC(=O)O)CCC1,201.2,0.4,77.8,2,3,5,1,14,2.69,0.673,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC3(N(C)CC(=O)Nc4cccc5c4C(=O)N(C4CCC(=O)NC4=O)C5=O)CCC3)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,886.0,3.17,229.2,3.0,14.0,12.0,65.0,2.0,True,0.9627,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C.O=C(O)CCCC(=O)O,148.2,0.96,74.6,2,2,4,0,10,1.9,0.622,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,816.9,3.1,226.0,3.0,13.0,11.0,60.0,2.0,True,0.9483,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(NCCCCC(O)CCCCNCC(=O)O)C(=O)O,304.4,0.42,118.9,5,5,14,0,21,3.08,0.295,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C)NCCCCC(O)CCCCNCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,989.1,3.2,270.3,6.0,16.0,21.0,72.0,3.0,True,0.5043,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(=O)NC(C(=O)O)C(=O)O,203.2,-0.17,103.7,3,3,6,0,14,2.22,0.52,True,True,True,True,True,True,True,True,True,True,CCCCC(=O)NC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,888.0,2.6,255.1,4.0,14.0,13.0,65.0,2.0,True,0.9714,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +O=C(O)CCCC(=O)CC(=O)O,174.2,0.29,91.7,2,3,6,0,12,2.1,0.564,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC(=O)CCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,858.9,3.06,243.0,3.0,14.0,13.0,63.0,2.0,True,0.9607,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCN(CCCC(=O)O)CCC(=O)C(=O)O,259.3,1.0,94.9,2,4,11,0,18,2.31,0.537,True,True,True,True,True,True,True,True,True,True,CCCCN(CCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CCC(=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,944.1,3.77,246.3,3.0,15.0,18.0,69.0,2.0,True,0.9642,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCC(=CC(=O)O)CCOCCOCC(=O)O,260.3,1.31,93.1,2,4,11,0,18,2.69,0.429,True,True,True,True,True,True,True,True,True,True,CCCC(=CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CCOCCOCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,945.0,4.08,244.4,3.0,15.0,18.0,69.0,2.0,True,0.6606,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +NCCC(CC(=O)O)C(=O)O,161.2,-0.49,100.6,3,3,5,0,11,2.74,0.505,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CCN)CC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,845.9,2.28,252.0,4.0,14.0,12.0,62.0,2.0,True,0.9908,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)(C)NC(CCCCC(=O)O)C(=O)O,231.3,1.47,86.6,3,3,7,0,16,2.62,0.578,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CCCCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)NC(C)(C)C)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,916.1,4.24,238.0,4.0,14.0,14.0,67.0,2.0,True,0.9443,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCN(CC(=O)O)C(C)CCC(=O)O,203.2,0.65,77.8,2,3,7,0,14,2.71,0.635,True,True,True,True,True,True,True,True,True,True,CCN(CC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(C)CCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,888.0,3.42,229.2,3.0,14.0,14.0,65.0,2.0,True,0.9745,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCNC(CCOCC(O)=C(C)C(=O)O)CCC(=O)O,303.4,1.54,116.1,4,5,12,0,21,3.28,0.245,True,True,True,True,True,True,True,True,True,True,CCCNC(CCOCC(O)=C(C)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)CCC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,988.1,4.31,267.5,5.0,16.0,19.0,72.0,2.0,True,0.3116,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)(N)C(C(=O)O)C(=O)O,161.2,-0.49,100.6,3,3,3,0,11,2.81,0.485,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)C(C)(C)N)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,845.9,2.28,252.0,4.0,14.0,10.0,62.0,2.0,True,0.9621,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(CCCCC(=O)O)C(=O)O,188.2,1.74,74.6,2,2,7,0,13,2.4,0.597,True,True,True,True,True,True,True,True,True,True,CCC(CCCCC(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,873.0,4.51,226.0,3.0,13.0,14.0,64.0,2.0,True,0.91,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(CCC(CN(C)C)C(NC)C(=O)O)C(=O)O,302.4,1.51,89.9,3,4,12,0,21,3.59,0.506,True,True,True,True,True,True,True,True,True,True,CCCCC(CCC(CN(C)C)C(NC)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,987.2,4.28,241.2,4.0,15.0,19.0,72.0,2.0,True,0.9238,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCN=C1OC(C(=O)COC)=C(C(=O)O)C1C(=O)O,271.2,-0.31,122.5,2,6,6,1,19,3.96,0.679,True,True,True,True,True,True,True,True,True,True,CCN=C1OC(C(=O)COC)=C(C(=O)Nc2cccc3c2C(=O)N(C2CCC(=O)NC2=O)C3=O)C1C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,956.0,2.46,273.9,3.0,17.0,13.0,70.0,2.0,True,0.6686,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +NC(CCC(=O)O)CC(=O)O,161.2,-0.35,100.6,3,3,5,0,11,2.63,0.511,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CC(N)CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,845.9,2.42,252.0,4.0,14.0,12.0,62.0,2.0,True,0.9908,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CC(C)(CCCN)CCC(C(=O)O)C(=O)O,231.3,1.32,100.6,3,3,8,0,16,2.71,0.547,True,True,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)C(CCC(C)(C)CCCN)C(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,916.1,4.09,252.0,4.0,14.0,15.0,67.0,2.0,True,0.9816,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(C(=O)O)C(Cc1cc(C)[nH]n1)C(=O)O,240.3,1.07,103.3,3,3,6,1,17,3.53,0.69,True,True,True,True,True,True,True,True,True,True,CCC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(Cc1cc(C)[nH]n1)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,925.0,3.84,254.6,4.0,14.0,13.0,68.0,2.0,True,0.9854,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCC(C)(C(=O)O)C(=O)O,146.1,0.57,74.6,2,2,3,0,10,2.67,0.571,True,True,True,True,True,True,True,True,True,True,CCC(C)(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,830.9,3.34,226.0,3.0,13.0,10.0,61.0,2.0,True,0.995,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCN(C(=O)C(=O)O)C(C)CC(=O)O,203.2,-0.22,94.9,2,3,4,0,14,2.95,0.616,True,True,True,True,True,True,True,True,True,True,CCN(C(=O)C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(C)CC(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,888.0,2.55,246.3,3.0,14.0,11.0,65.0,2.0,True,0.9144,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +C.CN.O=C(O)CCC(=O)O,165.2,0.15,100.6,3,3,3,0,11,2.11,0.552,False,False,True,True,True,True,True,True,True,True,CC(=O)c1c(C)c2cnc(N(C(=O)CCC(=O)Nc3cccc4c3C(=O)N(C3CCC(=O)NC3=O)C4=O)c3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O,802.8,2.71,226.0,3.0,13.0,10.0,59.0,2.0,True,0.9389,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. +CCCCC(=O)CC(C(=O)O)C(=O)O,202.2,0.92,91.7,2,3,7,0,14,2.4,0.599,True,True,True,True,True,True,True,True,True,True,CCCCC(=O)CC(C(=O)Nc1cccc2c1C(=O)N(C1CCC(=O)NC1=O)C2=O)C(=O)N(c1ccc(N2CCNCC2)nc1)c1ncc2c(C)c(C(C)=O)c(=O)n(C3CCCC3)c2n1,887.0,3.69,243.0,3.0,14.0,14.0,65.0,2.0,True,0.9788,CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. Uses cooh (diacid) linker chemistry — same as BRD9-VHL. diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/hard_only_s123.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/hard_only_s123.smi new file mode 100644 index 0000000000000000000000000000000000000000..6d7b2fd75ec1e670fbd6890558e9e50783226202 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/hard_only_s123.smi @@ -0,0 +1,100 @@ +DISCONNECTED CO.CONC(=O)O.O=CO +DISCONNECTED CC.CCN.O=C(O)CCC(=O)O +DISCONNECTED C.CC(=O)O.O=CC(=O)O +DISCONNECTED O=C(O)CC1C=CCCCCCCN1.O=C(O)CCCO +DISCONNECTED CCC.CCCC(=O)O.O=C(O)CO +DISCONNECTED CCC(=O)O.O=CCC(=O)O +DISCONNECTED CCC(C)C.O=C(O)CCC(=O)O +DISCONNECTED CC.O=C(O)CCCCCCCCCCC(=O)O +DISCONNECTED CC(=O)NCCNCCNC(=O)O.O=C(O)c1ccccc1 +DISCONNECTED C.C#CC(=O)O.O=C(O)CNCCO +DISCONNECTED C.CC(CCOCCC(=O)O)C(=O)O +DISCONNECTED N.N.O=CCCC1C(CCCCCCC(=O)O)C1C(=O)O +DISCONNECTED C.CCNCCCNC(=O)CCCCCC(C(=O)O)C(=O)O +INVALID +DISCONNECTED CC(=O)O.CCCC(=O)O.COCO +INVALID +DISCONNECTED C.CCC(C(=O)O)C(=O)O +DISCONNECTED C.NCC(=NC(=O)O)C(=O)O +DISCONNECTED C.CC(=O)O.CC(C)C(=O)O +INVALID +DISCONNECTED CC.CCCC(=O)NOC(=O)O.O=CO +DISCONNECTED C.N.O=CC(=O)O.O=CO +DISCONNECTED C.COC.O=CO.O=CO +DISCONNECTED CCC(=O)O.CCC(=O)O +DISCONNECTED CC(=O)O.N.NCC(=O)O +DISCONNECTED C.CC(=O)O.CCC(=O)O +DISCONNECTED CCCCCC.N.O=C(O)CC(=O)O +DISCONNECTED NCCC1CCCC1.O=C(O)CC(=O)O +DISCONNECTED C.C=CC(=O)O.CC(=O)O +DISCONNECTED C=C(N)CCC(C(=O)O)C(C)C(=O)O.O +DISCONNECTED CCC(=O)O.CCC(=O)O +DISCONNECTED CC(=O)O.CC=O.CCCCCC(=O)O +DISCONNECTED C.CC.CC(=O)O.O=CO +DISCONNECTED CC(=O)O.CCOC(C)=CC=C(C)C(=O)O +DISCONNECTED C#C.CCOCCO.O=C(O)CCC(=O)O +AssemblyReady CC(CCC(=O)O)=C(C(=O)O)C(=O)O +DISCONNECTED CC(=O)O.CC(N)C(=O)O +INVALID +DISCONNECTED CC(OCCOCCN)C(=O)O.NCCC1C=NC(C(=O)O)=C1 +DISCONNECTED C=CC(=CC=CCCCCCCC(=O)O)CCCC.NCC(=O)O +AssemblyReady COCNOCCNCCOC(CCOC(=O)O)C(=O)O +DISCONNECTED C.CCC(=O)O.N.O=CNCC(=O)O +DISCONNECTED CC.CC.CC.O=C(O)CC(=O)O +DISCONNECTED CCCCCN1CCN1C(=O)O.CCN.O=CO +DISCONNECTED CC(=O)O.CCCCCC(CC)C(=O)O +DISCONNECTED C.C.CC(=O)O.CCC(=O)O +DISCONNECTED COCO.O=CO.O=CO +DISCONNECTED N.NC(=CC(=O)O)C(=O)O +AssemblyReady CCOC(CCCCCOCC(=O)O)C(=O)O +DISCONNECTED CC.CCC(CN)C(=O)O.CCCC(=O)O +DISCONNECTED CCC(=O)O.CCOC(COCCN)C(=O)O +DISCONNECTED CC(=O)O.NCCS.O=CCCC(=O)O +DISCONNECTED CCCC.CCCCC.O=C(O)CCCCC(=O)O +DISCONNECTED C.CN.O.O=CO.O=CO +INVALID +INVALID +DISCONNECTED CCCCCCC(=O)O.CCCCCCC(=O)O +DISCONNECTED C.C=CC.O=CO.O=CO +INVALID +DISCONNECTED CC(=O)O.CCC1(C(=O)O)CCN(CN)CC1 +INVALID +DISCONNECTED C.CC=O.O=C(O)CC(=O)O +DISCONNECTED C=C.CCC(=O)O.COCCOCC(=O)O +DISCONNECTED C.O=C(O)CCCCCCCC(=O)O +DISCONNECTED C=CC(=CC)C(=O)O.CC(=O)O +DISCONNECTED CCC(=O)O.CCCOC(COCCOCCOCC)OCC(=O)O +DISCONNECTED CC.CCOCCOCC(=O)O.O=C(O)CCOCCO +DISCONNECTED CCC(=O)O.CNCNCc1ncccc1C(=O)O +DISCONNECTED C.CCCCC.CO.O=C(O)CC(=O)O +DISCONNECTED CC(=O)NCCC(=O)O.O=C(O)C1CCN1 +DISCONNECTED C.C.CC(=O)O.NC(=O)O +DISCONNECTED CCC(C)(CCCC(=O)O)C(=O)O.CO +DISCONNECTED C.CC(=O)O.CC=CC(=O)O +AssemblyReady O=C(O)CCC(=O)NCCNC1CNC1C(=O)O +DISCONNECTED CCC(=O)O.COC(=O)O.O +DISCONNECTED C.CC.CC.N.O=C(O)CC(=O)O +DISCONNECTED CCN.O=C(O)CCCC(=O)O +DISCONNECTED CC.CC(=O)O.CCCCCC=O.O=CO +DISCONNECTED COC(=O)O.O=Cc1cn(COC(=O)O)nn1 +DISCONNECTED COCCC(=O)O.COCCNCCCC=O.O=CO +AssemblyReady O=C(O)CCCCCOCCOCCOCCOCCOCC(=O)O +DISCONNECTED CCCCCCN.O=CC(=O)O.O=CO +DISCONNECTED CCCCCCCCCC(=O)O.NCCCCCCCCCC(=O)O +DISCONNECTED N=NN(CCOCCO)C(N)=O.O=CO.O=CO +DISCONNECTED C.C=CC.N.O=CO.O=CO +DISCONNECTED CNC(=O)O.CO.O=CO +DISCONNECTED CC(=O)O.CCC(=O)NCCCCCCC(=O)O +DISCONNECTED CN.O=C(O)CCC(=O)O +DISCONNECTED CC(=O)O.CCC(=O)O.CCCCCC(C)=CCNC(C)=O +DISCONNECTED C#CCCOCCOCCOCC.O=C(O)COCC(=O)O +AssemblyReady O=C(O)C(CCC1CCCCCC1)C(=O)O +DISCONNECTED C.CC.N.O=CO.O=CO +DISCONNECTED CC(N)C(C(=O)O)=C(N)CCC(=O)O.CCO +DISCONNECTED COCCCC(=O)O.NCC(=O)O +DISCONNECTED C#CCNC(C)=O.O=C(O)CCCCCCCCC(=O)O +DISCONNECTED CCCC=O.O=C(O)CCCCCO.O=CO +DISCONNECTED CC.CC(C(=O)O)C(=O)O +DISCONNECTED C#CCC(=O)O.CCOCCCCCCOCCC(=O)O +DISCONNECTED CC.CC(=O)O.CCOCC(=O)O.N +DISCONNECTED CCCC.O=CO.O=CO diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/hard_only_s42.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/hard_only_s42.smi new file mode 100644 index 0000000000000000000000000000000000000000..7cc963ae16f8d2bac6ccf29577c55ac64435b908 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/hard_only_s42.smi @@ -0,0 +1,100 @@ +AssemblyReady O=C(O)CCNCNCCOCCOCCOCCC(=O)O +AssemblyReady O=C(O)CCCCCCCCCCC(=O)O +DISCONNECTED CC(N)=O.O=CO.O=CO +INVALID +DISCONNECTED O=C(O)CCOCCOOCOCCO.O=C(O)CO +AssemblyReady CCOCCCN(CCN1CCC(C(=O)O)CC1)CC(=O)O +DISCONNECTED C.CCO.O=C(O)C(=O)O.O=CO +AssemblyReady CCCC(C(=O)O)C(=O)O +DISCONNECTED C.CNCC(=O)O.O=CO +DISCONNECTED CCC.NCCCC(C(=O)O)C(=O)O +DISCONNECTED CN.O=C(O)CCCC(=O)O +AssemblyReady O=C(O)NCCCCCCNC(=O)O +AssemblyReady CCCCOCCCC(CCOCCCCCCC(=O)O)C(=O)O +DISCONNECTED O=C(O)CC(=O)O.O=C1CCCCCCCCCN1 +INVALID +DISCONNECTED C.C.CC(=O)O.CC(=O)O +DISCONNECTED CC=O.O=C(O)CC(=O)O +DISCONNECTED CC(C(=O)O)C(=O)O.CN +DISCONNECTED C.C.CCCCCCCCC(=O)O.CCOCCCCCCC(=O)O +DISCONNECTED CC(=O)O.CC(CC=O)C(=O)O.CCCC +DISCONNECTED NCC=O.O=CO.O=CO +DISCONNECTED C.CC(=O)O.CCC(C)C(=O)O +DISCONNECTED CCOOC.O=C(O)C(CO)C(=O)O +DISCONNECTED CCOC.NCCOCCOO.O=C(O)CC(=O)O +DISCONNECTED CCCCC1(C=O)CCN1CC.COCCOCCC(=O)O.O=CO +DISCONNECTED CCCNCNC(=O)O.O.O=CO +DISCONNECTED CC(=O)O.CC=C(CN)C(C(=O)O)=C(N)COCCOOC +DISCONNECTED C1COO1.CC(=O)O.CC=O.CCN.O=CO +DISCONNECTED CCO.O=C(O)CCCCOCCOCCOCC(=O)O +DISCONNECTED CC(=O)O.CCOCCC(=O)O.CCOCCOCCNC(=O)CO +DISCONNECTED C.CCC=O.CNC(=O)O.O=CO +DISCONNECTED O=C(O)NCCO.O=CO +DISCONNECTED CC.CCOCCOCCOCC(OCCC(=O)O)C(=O)O +DISCONNECTED CCCCC.CCOC(=O)O.O=CO +DISCONNECTED C=Cc1ccc(OCC)cc1.O=C(O)CCCOCCOCC(=O)O +DISCONNECTED CC.CCC.O=C(O)CC(=O)O +AssemblyReady O=C(O)COCCOCCOCCNC(=O)O +AssemblyReady O=CCC(=O)NCCCC(C(=O)O)C(=O)O +DISCONNECTED CC.CNC(C)=O.O=CO.O=CO +DISCONNECTED C.CCC(=O)O.CCCC(=O)O +AssemblyReady O=C(O)NCCNOCC(=O)C(=O)O +DISCONNECTED CCC(=O)O.CCOCCOCCOCCC(=O)O +DISCONNECTED O=C(O)CO.O=C(O)CO +DISCONNECTED C=C.CC.O=CO.O=CO +DISCONNECTED CCCCC.O=C(O)CCCCCN1CCC(C(=O)O)CC1 +DISCONNECTED C.CCNC(=O)O.O=CO +DISCONNECTED CCC(=O)O.CCC(=O)O.CCNCO +DISCONNECTED CC(CC(=O)O)C(=O)O.CNC(C)=O +DISCONNECTED CCC(=O)O.CCCOCNOCCNC(=O)O +DISCONNECTED CC(=O)O.CC(=O)O.CN +DISCONNECTED CC(=O)NCCCCC(=O)O.CCCCC(=O)O +DISCONNECTED CC.CCC(=O)O.O=CO +DISCONNECTED CC=O.O=C(O)CCCOCC(=O)O +DISCONNECTED CC(=O)O.CCCCC(=O)O +DISCONNECTED CCCCCC(=O)O.O=CCCC(=O)O +DISCONNECTED C.O=C(O)CCOCC(=O)O +DISCONNECTED CCOCCOCCOCCN.O=C(O)CC(=O)O +DISCONNECTED CCOCCOCCOCCOCCN.N.O=CCC(=O)O.O=CO +DISCONNECTED CCO.NCCCCCNC(=O)O.O=CO +DISCONNECTED CCNC1CC1O.O=CO.O=CO +DISCONNECTED CCCN.NC(=O)O.O=CO +DISCONNECTED CCOCCOC.O=C(O)CCCCCC(=O)O +DISCONNECTED C.NCC(CC(=O)O)C(=O)O +DISCONNECTED C.CCO.O=CO.O=CO +DISCONNECTED CCC(=O)O.CCCOCCOCCOCCOCCC(=O)O +AssemblyReady O=C(O)CCCCCCCCCC(=O)O +DISCONNECTED CC.CC(=O)O.NCN.O=C(O)CC(=O)O +DISCONNECTED C.CC(=O)O.CCC(=O)O.N.N.O=CO +INVALID +AssemblyReady CNCN(CC(=O)O)C(C)C(=O)O +DISCONNECTED CCCCc1ccn(C)n1.CCOC(CC(=O)O)C(=O)O +DISCONNECTED CCCCC(=O)O.CCCCCNC(C)=O.COCCOCC(=O)O +AssemblyReady CC(C)OCC(C(=O)O)C(=O)O +DISCONNECTED CN1CCC(C(=O)O)CC1.O=C(O)CCCCCOC1CCNCC1 +DISCONNECTED CCC(=O)O.CCOCCOC.CCOCCOCC(=O)O +DISCONNECTED CCOCCOC.NC(C(=O)O)C(=O)O +DISCONNECTED C.CC(N)(C(=O)O)C(=O)O +DISCONNECTED C.CC.O.O=CO.O=CO +DISCONNECTED CCC(=O)N=CCOCC(=O)O.CCCO.O=C(O)CO +DISCONNECTED CNC.O=C(O)CC(=O)O +DISCONNECTED C.COCCOC(=O)O.O.O=CC(=O)O +DISCONNECTED CC(=O)O.CCOCCNC(=O)CC(=O)O +DISCONNECTED CNC(C)=O.COC.O=CO.O=CO +AssemblyReady COCCOCCNCCC(C(=O)O)C(=O)O +AssemblyReady O=C(O)NCCOCCNC(=O)O +DISCONNECTED CC(=O)O.CCCC(=O)O +DISCONNECTED C.O=C(O)CNCC(=O)O +DISCONNECTED CCC(=O)NCCCC(=O)O.O=C(O)COCCO +DISCONNECTED NC(=O)CCC(=O)O.O=C(O)CO +DISCONNECTED C.CCC.O=C(O)CC(=O)O +DISCONNECTED C.CCC.O=C(O)c1cc2c3cc1C(C(=O)O)N23 +DISCONNECTED C=CCCC(=O)O.CNC(=O)C(=O)O +DISCONNECTED CCOCCC(=O)O.O=C(O)OCCOCCOO +INVALID +DISCONNECTED C.CC.CCOCC(C)O.O=C(O)CC(=O)O +INVALID +AssemblyReady CC(OCCC(=O)O)C(=O)O +DISCONNECTED C.C.O=C(O)CCC(=O)O +DISCONNECTED C.CC(=O)O.CCC(CC)NCC(=O)O +DISCONNECTED CC.CCC=O.O=CC(CCCCCCC(=O)O)C(=O)O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/hard_only_s456.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/hard_only_s456.smi new file mode 100644 index 0000000000000000000000000000000000000000..a309bd55c2e53fcf190a2f1e91c774fa71b1bb81 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/hard_only_s456.smi @@ -0,0 +1,100 @@ +INVALID +DISCONNECTED CC(C(=O)O)C(=O)O.CCCCC.CCCCOCCOCCOC +DISCONNECTED C.CC(=O)O.CCC(=O)O +DISCONNECTED C.CC.CCOCC.O=C(O)C(=CO)C(=O)O +DISCONNECTED CC(=O)O.CC=O.CCC.NCC(=O)O +INVALID +DISCONNECTED CC(=O)O.CCC=O.CCOCCCCC(C)C(=O)O +DISCONNECTED CC.CCNC(=O)O.O=CO +DISCONNECTED COCCOCCOC.O=C(O)OCCOCCOCCO.O=CO +DISCONNECTED C.CC(=O)O.CCCCCC(=O)O +INVALID +AssemblyReady CC(CC(=O)O)C(C)C(=O)O +DISCONNECTED CCCCOCC.O=C(O)CCCNC(=O)O +DISCONNECTED CCCCCC.O=CO.O=CO +DISCONNECTED CCC(=O)O.CCC(=O)O +DISCONNECTED CCC(=O)O.CCNC(=O)COCCOCC(=O)O +DISCONNECTED CCC(=O)NCC(=O)O.CCCCC(=O)O +INVALID +DISCONNECTED CC.CC(=O)O.CCCCCC.O=C(O)CCO +DISCONNECTED CCCNC(=O)O.O=CO +DISCONNECTED CC.CCCC(=O)NCC.O.O=CO.O=CO +DISCONNECTED CCn1cc(C=O)nn1.NCC(=O)O.O=CO +DISCONNECTED CC.CC.O.O=CO.O=CO +DISCONNECTED CC(=O)O.CC(=O)O.COCCOCCCN +AssemblyReady O=C(O)CCCCCCCOCCC(=O)O +INVALID +DISCONNECTED C1CCNCC1.CCCN.O=C(O)CCC(=O)O +DISCONNECTED CCn1nnc(CO)c1C(=O)O.COCC(=O)O +AssemblyReady CCOCCOCCOCC(C(=O)O)C(CC)C(=O)O +AssemblyReady CC(=O)NCCC(C(=O)O)C(=O)O +DISCONNECTED CC=O.CCCC(=O)O.O=CO +DISCONNECTED C.Cc1cn(C)nn1.O=CO.O=CO +DISCONNECTED CC.CC(=O)O.CC(=O)O +DISCONNECTED C=CC.NC(O)C(C(=O)O)C(=O)O +DISCONNECTED C#CCCCCCCCC(=O)O.CCOCCC(=O)O +DISCONNECTED CCC(=O)O.CCCC=O.CO.O=CO +DISCONNECTED CCC1(CC)CCNCC1.O=CCCC(=O)O.O=CO +DISCONNECTED CCCC(=O)CC.O=C(O)CCC(=O)O +DISCONNECTED CC.CC(C(=O)O)C(C)C(=O)O +DISCONNECTED CCn1cc(CCC(=O)O)nn1.O=CCOCCOCCO.O=CO +DISCONNECTED CCC(=O)O.CCC1=CC(OCCOCC(=O)O)CC=C1 +DISCONNECTED CC(=O)O.CC=O.NCCCCCC(=O)O +DISCONNECTED CCC(=O)O.CCC(=O)O.CCN +DISCONNECTED CC.CCOCCNC(=O)CCC(=O)O.CCOCCOCC(=O)O +DISCONNECTED CCOCCO.N.O=CO.O=CO +DISCONNECTED CC.CCC(C(=O)O)C(=O)O +DISCONNECTED NCCN.NCO.O=CCCNCCC(=O)O.O=CO +AssemblyReady CCCCCC(C)C(CCCCC(=O)O)C(=O)O +DISCONNECTED CCN.O=C(O)CC(=O)O +DISCONNECTED CCC.CCOCCOCC.O=C(O)COCC(=O)O +DISCONNECTED CCO.O=C(O)CC(=O)O +DISCONNECTED C.CCCCC(=O)O.CCCCC(=O)O.N +DISCONNECTED CC(=O)O.CC(=O)O.CCC +DISCONNECTED C.CCC(=O)O.CCCC(=O)O +INVALID +AssemblyReady O=C(O)NCCNC(=O)O +DISCONNECTED C#CCOCC(OCC(OCCOCC)C(=O)O)C(=O)O.CC +DISCONNECTED C1COCCO1.O=C(O)NCCOCCOCCOCCNC(=O)O +AssemblyReady NCC(CCCCCOCC(=O)O)C(=O)O +DISCONNECTED N.O=CCC(=O)O.O=CO +DISCONNECTED CC(N)=O.CCC(=O)O.CCCC(=O)O +INVALID +DISCONNECTED CCC(=O)O.O=C(O)CN1CCNCC1 +DISCONNECTED CC=O.CCCC(=O)O.O=CO +DISCONNECTED CC(=O)O.CCCCCCCC(=O)O.CCCCN +DISCONNECTED CC.CC(=O)O.CC(=O)O +DISCONNECTED CCC(CC=O)n1cc(CCC(=O)O)nn1.O=CO +DISCONNECTED C.C.C.CC.O=CO.O=CO +DISCONNECTED CC.CC(=CC(=O)O)C(=O)O +AssemblyReady O=C(O)C1CCC1C(=O)O +DISCONNECTED CCCC(=O)NCC(C(=O)O)C(=O)O.CN +DISCONNECTED C.CC(=O)O.CCC(=O)O +INVALID +DISCONNECTED C.CC(=O)O.O.O=C(O)CO +DISCONNECTED C.COCCOCCOC.O.O=C(O)N1CCN(C(=O)O)CC1 +DISCONNECTED C.C.CC(=O)O.Cc1cc(O)c(C(=O)O)cc1C +DISCONNECTED CC(=O)O.CCN(CCCC(=O)O)C(=O)CCC=O +DISCONNECTED CC.N.O=C(O)CC(=O)O +DISCONNECTED CCCC(=O)O.CCCCCC(=O)O +DISCONNECTED C.C.CC.O=CO.O=CO +AssemblyReady NCCC(=C(CC(=O)O)C(=O)O)C1COCCOCCO1 +DISCONNECTED CC.O=CCCCCC(=O)O.O=CO +DISCONNECTED CC(=O)O.CC(=O)O.NCC=O +DISCONNECTED CCCC.O=CCCCCCCC(=O)O.O=CO +DISCONNECTED CC(=O)O.CCOCCOCCOCCC(=O)O +DISCONNECTED CCC(CC(=O)O)C(=O)O.N +DISCONNECTED C.CC.COCC(=O)O.NC(=O)CC(=O)O +DISCONNECTED CCCN.O=C(O)NCCO.O=CO +AssemblyReady O=C(O)C#CN1CCN(CC2OCC2OCC(=O)O)CC1 +DISCONNECTED C.CCN.O=C(O)CC(=O)O +DISCONNECTED CCOCCC(=O)O.NCC(=O)O +AssemblyReady NCCOCCNC(C(=O)O)C1(CC(=O)O)CCNCC1 +DISCONNECTED O=C(O)CCC(=O)O.c1nn2cc1CCCCCC2 +DISCONNECTED C.O=C(O)C#CCCC(=O)O +DISCONNECTED C.CCNCC.CN.O=C(O)CCC(=O)O +DISCONNECTED CCCNCC(=O)O.CCN(C)N=CC(=O)O +AssemblyReady CC(CCC(=O)O)C(=O)O +DISCONNECTED CC(=O)O.O=CCNCCO.O=CO +DISCONNECTED C.C.CC(CC(=O)O)C(=O)O +DISCONNECTED CC.CC1CO1.NC(=O)O.O.O=CO diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/soft_only_s123.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/soft_only_s123.smi new file mode 100644 index 0000000000000000000000000000000000000000..1b262db3c7296cce523e05c6d94b5489d2b2dfac --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/soft_only_s123.smi @@ -0,0 +1,100 @@ +Valid C=C1C=CC1=COCCCN +INVALID +INVALID +INVALID +INVALID +Valid CCC1CCCC(C)OCC1 +DISCONNECTED CCCC(NCC)C(O)N(C)C.O +Valid CC(C)C1(C)CCC2C(=O)CCCc3cn2c1n3 +DISCONNECTED CCCCCCCCCCCC12CC3=CC1=S(CC)C=C32.N +INVALID +Valid CCCCCC1COC1(CO)COC +DISCONNECTED C=C=O.CCNCCC(=CC(C)O)CNCCN(N)CC +INVALID +INVALID +Valid CCCC(CO)(N(C)O)N(C)C(C)C +DISCONNECTED C.N=C1NOC1CCC=O +DISCONNECTED C.COC(N)C(C)C(C)C +DISCONNECTED CCN(CCO)COO.CO +DISCONNECTED C.CCNCC(CC)OCC +DISCONNECTED C.COCCNCCCC1NNc2ccc(cc2N)O1 +INVALID +DISCONNECTED C.C.C=C=O.C=CCOO +DISCONNECTED CC(C)CC(C)(C)CN.O +Valid CCCOC(C)=COCC +DISCONNECTED C.C=CCC(C)C(C)C.O +DISCONNECTED C.C.C.C.C.CC=CCC +DISCONNECTED C.CCON=CC(C)=CCOC1CC1 +Valid CCCNN=CNC1(COC)CCC1C +INVALID +INVALID +DISCONNECTED C.C.C.C=C.CCCOC +Valid COC(C=O)OCCC1CCCCN1C +INVALID +Valid C=CC(C)C(C=C1N=C=CCCO1)=CC=O +DISCONNECTED C.C=C1COC(C#CN)(C(O)CC(C)O)O1 +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCCN1CCOCCCCCCN(C)CC1 +DISCONNECTED C.CC(OC(F)C(F)F)C(C)(C)CCO +Valid CCC(C)C(C)(N)C(C)OC +DISCONNECTED C.C.CCC(C)O.O.O=O +DISCONNECTED C.C.CCN(C)CC(C)C +INVALID +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CCCC(C)(CC)CC.N +INVALID +INVALID +INVALID +DISCONNECTED C.C.NC1CCC2(CO2)C1 +INVALID +Valid CNC1CC1(C)C12CCN1C(C(C)O)COC2 +Valid C=C(NC)C(CC)=C(OC)C(=C)OO +Valid C=C(NCOC)C(C)CCO +DISCONNECTED C.C=C1C=NCON(CCOC)C(OC)=N1 +Valid CCOC(N)(C(O)CC)C(C)C(C)C +Valid CCC(N)=C1C(CCO)NN1C +Valid CC1CCC2C#CCN3CC(N4CCCC4)OC4(CN(CCC14)C2)C3 +INVALID +Valid CCCCC(=O)NC(C)C1CCCCC2CC21CO +Valid CC(O)C(C)C(C)CCCN(C)CCO +DISCONNECTED C.CCCCC(OCCO)OC(C)CCC +DISCONNECTED C.C.CCC1(C)C(=O)C1O +Valid NC1C=C(O)C1CCC1(O)CCCCC1 +DISCONNECTED C.COCCC1C=C(C)OC1 +INVALID +DISCONNECTED C.C=NCC(O)C(C)CC.O +INVALID +Valid CNC(N)CC(C)(CCN)NC +DISCONNECTED C.C.CCNCC1CCC1OC1OOC1=O +DISCONNECTED C.CNC(=O)C1(N(CCN)CCO)COC1C +INVALID +INVALID +DISCONNECTED C.C=NCC(C(C)CCC)C1CCNC1 +Valid CCCOCCOC1COC2CNCC[N+]([O-])(C(C)=O)C2COC1C +DISCONNECTED C.CCOCC1(O)CC(C)OCN1C(CC)=NO +DISCONNECTED CC.CCCCN(CC)CN +DISCONNECTED COCC(O)=C(C)OC.O +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.CCC1N(C(C)=O)CN2NCCC12CC +DISCONNECTED C.CCCN(CC)C(C)N +INVALID +DISCONNECTED C.CCCC(ON)C(CC)CO.O +Valid NCCC(O)N1CCC(CN2CCCC(=C3CNO3)C2)C1 +INVALID +Valid CCC(C)(C)C(=O)COC +INVALID +Valid CC(C)CNC(C)N(O)C(O)CCN +DISCONNECTED C.C.CC=C(O)C=COC diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/soft_only_s42.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/soft_only_s42.smi new file mode 100644 index 0000000000000000000000000000000000000000..ab854a85197f3874cc9f27428a3d56b6eb138413 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/soft_only_s42.smi @@ -0,0 +1,100 @@ +Valid CCOCC1OCC12OCC2N1CCN(O)C(C)(CCN)C1 +DISCONNECTED C.COC(CCOCCO)C1OCC1OC +DISCONNECTED C.CCCC(=O)N(O)CC +DISCONNECTED C.CC=NC1NC(=O)C1C=CC=CO +Valid NCC1OCOCCOCONCOCCOC2=CC21 +INVALID +INVALID +DISCONNECTED C.C=C1CC(CC)CN1C +DISCONNECTED C.C.C1CCNCCNC1 +Valid CC1CC2C(C)C12CN(C)N=[N+](C)[O-] +DISCONNECTED C.C=CC(CN)CC(N)C=N +INVALID +INVALID +Valid C=C1C(CCCCCCC)c2ccc(cc2)CN1C +Valid OCC1CCCNCCC12C=CC=CC2 +INVALID +DISCONNECTED C.C.C.CC=O.O=CCO +DISCONNECTED C.CC(C)CC1C(O)N1C +INVALID +INVALID +DISCONNECTED C.C.C.C=CCC(C)CO +DISCONNECTED C.C.CCCCCCCCN.O +DISCONNECTED CCC(=N)CONC=C(C)CO.N=O +INVALID +INVALID +DISCONNECTED C.C.CN(C)C1C[N+]1([O-])C=O.CO +INVALID +DISCONNECTED CCC12CCN(C=COCCC(C)C1)CC2.N +INVALID +INVALID +DISCONNECTED C.C=CN1CC(C2CC2CC)C1C +Valid CCNC1CCC1CCN +INVALID +DISCONNECTED N.N=C(CNCC(O)ON)NCC=O +INVALID +DISCONNECTED CCCC(CCOC)OC.CN +DISCONNECTED C.CCCC1C(C)C2=C(OCNC=O)C(=O)N21 +DISCONNECTED C.C.CNCC(C)OCCCCN1CNC1 +DISCONNECTED C.C.CCC[N+]([O-])(CCO)OCC +DISCONNECTED C.CCC(CCCCN)C(C)=O +INVALID +Valid CCC(CN(C)C1CCCCC1)=S(=O)(CC)C(C)C +DISCONNECTED CCCC.COC.O=CO +Valid CCCCC(C)(CC)OC +DISCONNECTED C.CN(C)C(CCCN1CCOCC1)C1CCCCCN1C +INVALID +INVALID +DISCONNECTED C.COC(O)C(C)CCC=CC(N)=O +INVALID +DISCONNECTED C.CCCCN(C)CCC +Valid C=C(C)N(C)CC(CC)C(=CCCCCC)CO +DISCONNECTED C.CC.CC.CCN.N.N +DISCONNECTED C.C=NOCC1(OCC)CC1OCN +DISCONNECTED CC.CC(C)C=O.CC=O.O +INVALID +INVALID +INVALID +INVALID +DISCONNECTED CCOC(CC)CCC1=C(OC)CO1.CO +Valid CCN(N)CC(COC)CC(C)=O +DISCONNECTED C.CCNC(COC)OC.O +INVALID +DISCONNECTED C.CCC(CC)C(C)C(C)C +DISCONNECTED CC(C)C.CCOC.N.O +Valid CC1CC(N)C(C(C)C2CC2)N(C)CCN(C)CCCC(=O)N1 +DISCONNECTED C.C.C=CCC1CCN(CC)CCC1C +INVALID +DISCONNECTED C=C.N=c1ccn(CCCOCCO)[nH]1 +DISCONNECTED C.COCCC(OCC1C#CCO1)c1nccn1CO +DISCONNECTED C.C.CCC(OOC)N1CC1CN +INVALID +INVALID +DISCONNECTED C.CCCC(O)CN1CC1CC +INVALID +INVALID +INVALID +DISCONNECTED C.C.C.C.CCNC(C)=O +DISCONNECTED C.C=CC(=O)NNCC.N +INVALID +INVALID +Valid CCCCCCC(C)N(COCC)OC +INVALID +Valid CCN(CC)C1C=CC(C)(OC)CC1 +INVALID +DISCONNECTED C.C=CC1CCCN(CC(C)O)C1 +DISCONNECTED C.C.C.CC.CCC(C)C +DISCONNECTED CCCC(C)(C)C(N)O.N +Valid CCCCCCCCCC(N)Nc1ccc(O)cc1 +INVALID +DISCONNECTED C.C=C(CC)NCCC.C=O +INVALID +INVALID +INVALID +Valid C=C(C)C(C)(CCC)OC +INVALID +INVALID +Valid C=C(C)CC(C)C(CC)=NN +INVALID +INVALID +Valid CCOCC1CNC(C(C)c2ccccc2)C1(CC)CC diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/soft_only_s456.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/soft_only_s456.smi new file mode 100644 index 0000000000000000000000000000000000000000..c43db21804db4dd1b4838490f8331c977b5b0974 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/soft_only_s456.smi @@ -0,0 +1,100 @@ +INVALID +INVALID +DISCONNECTED C.CCCCCC.COO +Valid COCCCCC(OC)C(CO)CNC(C)O +INVALID +INVALID +INVALID +DISCONNECTED C.CCC(C(C)O)C(C)NC +DISCONNECTED CCCCCOCC(CCOC)(OCCOC)C(COCC)OC.O +INVALID +INVALID +INVALID +DISCONNECTED COCCC(C)CC12CC3(C)CN1CCC32.N +DISCONNECTED C.CCC(=O)C(CC)CCO.N +Valid CNC1C2CCNC21OC +DISCONNECTED C.CC1CCC2CC23CC=CC2(CC2)N(O1)C(=O)C3 +Valid CCCOC(=O)CCC(O)C(C)(C)C(C)C +DISCONNECTED C.CCCCCCCN(C)CC.O=CC=O +INVALID +DISCONNECTED C.C=C1CCC23CC2(C)N13 +DISCONNECTED C.CC=CC1(CC)C(CC)N1CCCC(C)C +INVALID +DISCONNECTED C.C.COC(N)CCCCN +INVALID +INVALID +DISCONNECTED C.COCCC(OCCOCCO)C1COCCOCCOCCO1 +AssemblyReady NCCOCCCCC(OCC=O)OOCC=O +INVALID +INVALID +INVALID +DISCONNECTED CC(O)C(C)OCN.N.N=C=O +Valid C=CC=C(N=C)NC(C)C(CC)NC +DISCONNECTED C.C.CC(C)C(C)CCN +Valid CCOC1CC2CCCCCC2C1 +INVALID +Valid C=C(C(CO)CC(C)CCC=O)N(C)O +DISCONNECTED C.CCOCCOCCCOC(CCN)CCOCC +DISCONNECTED CCC.CCCCCOC=C=NOCC +DISCONNECTED C.CCN(C)CC(CN)OO.O +INVALID +Valid CC#CCCC1C2CN(CCNC(=O)CNCC)CC12C +INVALID +INVALID +INVALID +INVALID +Valid CCCCC1(C)C(O)C1CN +INVALID +Valid COCCOCCC(C)C1(O)CCCC(C)C1C +DISCONNECTED C.NNC(=CCCO)N=S +Valid CCCOCC(N)C(COCCCCCO)OCCC +DISCONNECTED C.C.C.CN=CNN.CO +DISCONNECTED C.C.CO.COC1NCCCCCNC1N +DISCONNECTED C.C.CCOC.COC.N.O +DISCONNECTED C.C.C.CCOC(CO)C(C)C +INVALID +INVALID +INVALID +INVALID +DISCONNECTED C.C=NC(=CC=CC)C(C)C1CCNCC1 +DISCONNECTED C.C.C.C=C(CCC)NC +INVALID +INVALID +INVALID +DISCONNECTED C.C.CCC1CC(NC)C1CC +DISCONNECTED C.CCC1CCC2COCC3=C(CCCC31)CCO2 +INVALID +INVALID +DISCONNECTED C.CCC=C(C)CC(C)CC +INVALID +DISCONNECTED C.CCC(C)(C)C(C)(C)C +INVALID +DISCONNECTED C.CCCC(C)(CO)OC +INVALID +Valid CC1=CC1OCC(C)(C)CO +INVALID +DISCONNECTED C.CN(CCCCCCNC(=O)C=O)C(=O)C=O +DISCONNECTED C.C=C(OCC)C(=CC)OC(CC)C(=O)OCCO +INVALID +DISCONNECTED C.C.CCC(C)C(CCN)CCO.O +INVALID +INVALID +DISCONNECTED C.CC(=O)CCCC(=O)NCCN.O +INVALID +INVALID +INVALID +Valid CCC(OCCOC)=C(C)N +INVALID +INVALID +Valid CCCN1CCC(C)C2=C(N)C3C4CCN(C=C2)C31OC4 +DISCONNECTED C.CCC(O)N=NC(C)=CN +INVALID +INVALID +Valid CN1CCC2=NOCC3=S4(C)=C(C=CC=C24)C(C3)C1 +INVALID +INVALID +Valid COC1=c2c(=O)c3cn4n(cc2cc3-4)CCC1 +Valid C=CC(C)C(C)CC(C)C +DISCONNECTED C.C=C(C(C)OC)N(CC)C(=O)CS +DISCONNECTED C.COC(C)C(O)C(O)CN +DISCONNECTED CNC(C)=CC1C(C)C1C1(O)CN1.O diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/sw_base_s123.smi b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/sw_base_s123.smi new file mode 100644 index 0000000000000000000000000000000000000000..02bc91f91552d9ac43ee0c8f89a37723bbd7bd2c --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/CDK6CRBN/sw_base_s123.smi @@ -0,0 +1,100 @@ +DISCONNECTED C.COC=O.O=CO.O=CO +DISCONNECTED CCC(=O)O.CCCCCC(=O)O +DISCONNECTED C.CC(=O)O.O=CC(=O)O +AssemblyReady CC(N)C(CC(=O)O)OCCCCNC(=O)CCC(=O)O +AssemblyReady CCC(C(=O)O)C(C)(CC)CC(=O)O +AssemblyReady C=CC(C(=O)O)C(C)C(=O)O +AssemblyReady CCCCCC(CC(=O)O)C(=O)O +AssemblyReady CCCCCCNC(=O)C(CC(=O)O)CC(=O)O +AssemblyReady CCCCCN(CCCCC(=O)O)C1(CNCC(=O)O)CC1 +DISCONNECTED NC(=O)CCC(=O)O.NCC(=O)O.O +AssemblyReady O=C(O)CCCCN1CCC1C(=O)O +AssemblyReady CCCCC1N(C=O)CCC(C)(C(=O)O)C1(N)CC(=O)O +AssemblyReady CCC(=O)N(CCC(CC)NC=O)CCC(C(=O)O)C(=O)O +INVALID +AssemblyReady CCC(COCO)(CC(=O)O)C(=O)O +INVALID +AssemblyReady CCCC(C(=O)O)C(=O)O +DISCONNECTED C.NCC(=NC(=O)O)C(=O)O +AssemblyReady CC(C)(NCC(=O)O)C(=O)O +INVALID +AssemblyReady CC(CCCC(=O)NOC(=O)O)C(=O)O +DISCONNECTED CC(=O)O.O=C(O)C(=O)O +DISCONNECTED CCCCC(=O)O.O=CO +AssemblyReady CC(CCC(=O)O)C(=O)O +DISCONNECTED N.NC(CC(=O)O)C(=O)O +DISCONNECTED 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0000000000000000000000000000000000000000..5b7222cee0b0af075b32827169f1ad4616a37b89 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/VALIDATION.txt @@ -0,0 +1,151 @@ +23:19 BRD9VHL base_guid s42 : SKIP (already complete) +23:19 BRD9VHL ft_guid s42 : SKIP (already complete) +23:19 BRD9VHL accept_on s42 : SKIP (already complete) +23:25 BRD9VHL ft_noguid s42 : total=93 asm_valid=0 +0 <<=0.5=18.9% +CDK6CRBN: DegradeMaster mean=0.770 95%CI=[0.736,0.804] n=265 median=0.913 frac>=0.5=80.4% diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/phaseA_dmci.txt b/CupOFMoCA-PROTAC/results/ablation_targets/phaseA_dmci.txt new file mode 100644 index 0000000000000000000000000000000000000000..c5a06616a565886087abe31bf4a9daedeac6a1b7 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/phaseA_dmci.txt @@ -0,0 +1,2 @@ +BRD9VHL: DegradeMaster 0.260 (95% CI 0.232-0.287), n=281, median 0.183, >=0.5 18.9% +CDK6CRBN: DegradeMaster 0.770 (95% CI 0.736-0.804), n=265, median 0.913, >=0.5 80.4% diff --git a/CupOFMoCA-PROTAC/results/ablation_targets/phaseA_eval.csv b/CupOFMoCA-PROTAC/results/ablation_targets/phaseA_eval.csv new file mode 100644 index 0000000000000000000000000000000000000000..f7d363cadd2c4d2acc0858954cf4cca20bafc9c6 --- /dev/null +++ b/CupOFMoCA-PROTAC/results/ablation_targets/phaseA_eval.csv @@ -0,0 +1,43 @@ +target,cond,seed,asm_valid,sa,mw,dm +BRD9VHL,ft_guid,42,80,2.78,100,0.263 +BRD9VHL,ft_guid,123,79,2.75,100,0.272 +BRD9VHL,ft_guid,456,88,2.75,100,0.245 +BRD9VHL,base_guid,42,79,3.79,100,0.335 +BRD9VHL,base_guid,123,72,3.52,100,0.323 +BRD9VHL,base_guid,456,79,3.44,99,0.283 +BRD9VHL,accept_on,42,59,3.07,100,0.341 +BRD9VHL,accept_on,123,53,3.06,100,0.359 +BRD9VHL,accept_on,456,63,2.90,100,0.377 +BRD9VHL,ft_noguid,42,0 +0,3.35,100,0.599 +BRD9VHL,ft_noguid,123,0 +0,3.47,100,0.508 +BRD9VHL,ft_noguid,456,0 +0,3.39,100,0.507 +BRD9VHL,base_noguid,42,0 +0,4.70,100,0.713 +BRD9VHL,base_noguid,123,0 +0,3.21,100,0.385 +BRD9VHL,base_noguid,456,0 +0,3.29,100,0.518 +CDK6CRBN,ft_guid,42,80,2.84,100,0.758 +CDK6CRBN,ft_guid,123,80,2.82,100,0.775 +CDK6CRBN,ft_guid,456,89,2.82,100,0.777 +CDK6CRBN,base_guid,42,85,3.60,100,0.745 +CDK6CRBN,base_guid,123,72,3.59,100,0.755 +CDK6CRBN,base_guid,456,78,3.50,100,0.731 +CDK6CRBN,accept_on,42,59,3.09,100,0.814 +CDK6CRBN,accept_on,123,55,3.08,100,0.719 +CDK6CRBN,accept_on,456,62,2.94,100,0.808 +CDK6CRBN,ft_noguid,42,0 +0,3.35,nan,nan +CDK6CRBN,ft_noguid,123,0 +0,3.47,nan,nan +CDK6CRBN,ft_noguid,456,0 +0,3.39,nan,nan +CDK6CRBN,base_noguid,42,0 +0,4.70,nan,nan +CDK6CRBN,base_noguid,123,0 +0,3.21,nan,nan +CDK6CRBN,base_noguid,456,0 +0,3.29,nan,nan diff --git a/CupOFMoCA-PROTAC/scripts/batch_report.py b/CupOFMoCA-PROTAC/scripts/batch_report.py new file mode 100644 index 0000000000000000000000000000000000000000..5e71ef5b00593dbff5b5a5c96d49f51bf03bc8bb --- /dev/null +++ b/CupOFMoCA-PROTAC/scripts/batch_report.py @@ -0,0 +1,1085 @@ +#!/usr/bin/env python3 +""" +batch_report.py + +Evaluate one or more generation batches and produce a summary chart. + +Usage: + # Single batch (summary CSV from defog_guided.py): + python batch_report.py ../generation_logs/defog_local_edit_20260424_003754_summary.csv + + # Multiple batches — one bar-group per batch: + python batch_report.py ../generation_logs/*_summary.csv + + # Include a named baseline (plain SMILES .txt file, one per line): + python batch_report.py ../generation_logs/*_summary.csv --baseline DeLinker:delinker_linkers.txt + + # Save chart to file instead of displaying: + python batch_report.py ../generation_logs/*_summary.csv --out report.png +""" + +import argparse +import subprocess +import sys + +# ── Edit these to run without command-line arguments ───────────────────────── +# Set BATCH_FOLDER to a folder path to use all *_summary.csv files in it, +# or set BATCH_CSVS to a list of specific CSV paths. +# These are ignored when you pass paths on the command line. +BATCH_FOLDER: str | None = None # e.g. "../generation_logs" +BATCH_CSVS: list[str] = [ + "../generation_logs/defog_local_edit_20260425_170213_summary.csv", # COOH best (old) + "../generation_logs/defog_local_edit_20260428_005755_summary.csv", # COOH rerun + "../generation_logs/defog_local_edit_20260425_173602_summary.csv", # methyl best +] + +# Human-readable labels for DeFoG batch runs (keyed by timestamp "YYYYMMDD_HHMMSS"). +# Any timestamp not listed falls back to the timestamp string itself. +BATCH_LABELS: dict[str, str] = { + "20260425_170213": "COOH Guided\n(orig)", + "20260428_005755": "COOH Guided\n(rerun)", + "20260425_173602": "Methyl Guided", +} +CHART_OUT: str | None = "batch_report.png" # saved next to this script + +# ── Named baselines (plain SMILES .txt files, one SMILES per line) ──────────── +# Add external model baselines here as {"Label": "/path/to/smiles.txt"}. +# Each baseline will appear as a bar-group alongside the DeFoG batches. +# Example: +# BASELINES = {"DeLinker": "../generation_logs/delinker_linkers.txt"} +BASELINES: dict[str, str] = { + # "DeLinker": "../generation_logs/delinker_linkers.txt", +} + +# ── Full-PROTAC baselines (models that output assembled PROTACs, not linkers) ─ +# Evaluated directly without assembly — SA/QED/MW/logP/RotBonds computed on the +# full molecule as-is. Use for models like DRlinker that generate whole PROTACs. +# Example: +# PROTAC_BASELINES = {"DRlinker": "../generation_logs/drlinker_SyntaLinkerN_ms39_....txt"} +# Per-(protac-module) baseline dispatch. The active set is selected by +# --protac-module at runtime in main(). Each entry is {label: path}; empty +# strings are skipped. Add new targets here when their baseline outputs land. +# PROTAC baselines (LinkerNet/DiffPROTACs/DiffLinker outputs) are NOT included +# in this distribution. Add paths here if you want to include external baseline +# comparisons; otherwise the report compares only against itself. +PROTAC_BASELINES_BY_MODULE: dict[str, dict[str, str]] = {} +PROTAC_BASELINES: dict[str, str] = {} +# ───────────────────────────────────────────────────────────────────────────── +import tempfile +from pathlib import Path + +import importlib +import importlib.util +import os +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from rdkit import Chem +from rdkit import RDConfig +from rdkit.Chem import Descriptors, QED as rdkit_qed + + +EVAL_SCRIPT = Path(__file__).parent / "evaluate_linkers.py" +PYTHON = sys.executable # same interpreter (must be moca env) +LINKER_CSV = Path(__file__).parent.parent / "data" / "linker.csv" + +_REPO_ROOT_DEFAULT = Path(__file__).parent.parent +_SRC_DEFAULT = _REPO_ROOT_DEFAULT / "src" + +# Fallback substructure patterns — overridden at startup by _load_protac_module() +_WARHEAD_PAT = Chem.MolFromSmarts("c1cc2ncncc2cc1") +_E3_PAT = Chem.MolFromSmarts("N1CC(O)CC1") + +# Active protac module name (set by main() via --protac-module) +_PROTAC_MODULE = "ErlotinibVHL" + +# ── PROTACAnalyzer (lazy-loaded) ────────────────────────────────────────────── +_protac_analyzer_state: dict | None = None + + +def _get_protac_analyzer(): + """Lazy-load PROTACAnalyzer using the active _PROTAC_MODULE. + + Returns (analyzer, wh_q, e3_q, wh_reactive_comp, e3_reactive_comp). + All are None on load failure. + """ + global _protac_analyzer_state, _WARHEAD_PAT, _E3_PAT + if _protac_analyzer_state is not None: + s = _protac_analyzer_state + return s["analyzer"], s["wh_q"], s["e3_q"], s["wh_reactive_comp"], s["e3_reactive_comp"] + try: + sys.path.insert(0, str(_SRC_DEFAULT / "classifier")) + sys.path.insert(0, str(_SRC_DEFAULT / "protac")) + import importlib as _il + from protac_analyzer import PROTACAnalyzer + _pmod = _il.import_module(_PROTAC_MODULE) + WARHEAD_MOL = _pmod.WARHEAD_MOL + E3_MOL = _pmod.E3_MOL + WARHEAD_SMILES = _pmod.WARHEAD_SMILES + E3_SMILES = _pmod.E3_SMILES + wh_rxn_smarts = _pmod.WH_REACTIVE_SMARTS + e3_rxn_smarts = _pmod.E3_REACTIVE_SMARTS + # Update fallback patterns from module if provided + if hasattr(_pmod, 'WARHEAD_PAT') and _pmod.WARHEAD_PAT is not None: + _WARHEAD_PAT = _pmod.WARHEAD_PAT + if hasattr(_pmod, 'E3_PAT') and _pmod.E3_PAT is not None: + _E3_PAT = _pmod.E3_PAT + _COOH_OR_CHO = "[$([CX3](=[OX1])[OX2H1]),$([CX3H1](=[OX1]))]" + analyzer = PROTACAnalyzer( + warhead=WARHEAD_MOL, + e3=E3_MOL, + warhead_attachment_smarts=_COOH_OR_CHO, + e3_attachment_smarts=_COOH_OR_CHO, + ) + _wh_pat = Chem.MolFromSmarts(wh_rxn_smarts) + _e3_pat = Chem.MolFromSmarts(e3_rxn_smarts) + _protac_analyzer_state = { + "analyzer": analyzer, + "wh_q": Chem.MolFromSmiles(WARHEAD_SMILES), + "e3_q": Chem.MolFromSmiles(E3_SMILES), + "wh_reactive_comp": {m[0] for m in WARHEAD_MOL.GetSubstructMatches(_wh_pat)}, + "e3_reactive_comp": {m[0] for m in E3_MOL.GetSubstructMatches(_e3_pat)}, + } + print(f"[PROTACAnalyzer] loaded for {_PROTAC_MODULE} validity scoring") + except Exception as e: + print(f"[PROTACAnalyzer] could not load ({e}); falling back to substructure check", + file=sys.stderr) + _protac_analyzer_state = { + "analyzer": None, "wh_q": None, "e3_q": None, + "wh_reactive_comp": None, "e3_reactive_comp": None, + } + s = _protac_analyzer_state + return s["analyzer"], s["wh_q"], s["e3_q"], s["wh_reactive_comp"], s["e3_reactive_comp"] + + +def _extract_linker_from_protac(protac_mol, warhead_q, e3_q): + """Break assembled PROTAC into warhead / E3 / linker. + + Returns (linker_mol, n_interface_bonds, wh_match, e3_match) or (None, 0, None, None). + n_interface_bonds == 2 means the linker cleanly bridges both fragments. + wh_match / e3_match are the substructure match tuples needed for reactive-site checks. + """ + wh_match = protac_mol.GetSubstructMatch(warhead_q) + e3_match = protac_mol.GetSubstructMatch(e3_q) + if not wh_match or not e3_match: + return None, 0, None, None + lk_set = set(range(protac_mol.GetNumAtoms())) - set(wh_match) - set(e3_match) + if not lk_set: + return None, 0, wh_match, e3_match + bond_ids = [ + b.GetIdx() for b in protac_mol.GetBonds() + if (b.GetBeginAtomIdx() in lk_set) != (b.GetEndAtomIdx() in lk_set) + ] + if not bond_ids: + return None, 0, wh_match, e3_match + try: + break_mol = Chem.FragmentOnBonds(protac_mol, bond_ids, addDummies=False) + for frag in Chem.GetMolFrags(break_mol, asMols=True): + if frag.GetNumHeavyAtoms() == len(lk_set): + return frag, len(bond_ids), wh_match, e3_match + except Exception: + pass + return None, 0, wh_match, e3_match + + +def _check_reactive_attachment(protac_mol, wh_match, e3_match, + wh_reactive_comp, e3_reactive_comp): + """Verify each linker–fragment interface bond attaches at the correct reactive site. + + Maps wh_reactive_comp / e3_reactive_comp (indices in free component mols) into + PROTAC atom indices via the substructure matches, then checks that every interface + bond's fragment-side atom is one of those reactive atoms. + """ + lk_set = set(range(protac_mol.GetNumAtoms())) - set(wh_match) - set(e3_match) + wh_set = set(wh_match) + e3_set = set(e3_match) + wh_reactive = {wh_match[i] for i in wh_reactive_comp} + e3_reactive = {e3_match[i] for i in e3_reactive_comp} + + wh_ok = e3_ok = False + for b in protac_mol.GetBonds(): + s, e = b.GetBeginAtomIdx(), b.GetEndAtomIdx() + if (s in lk_set) == (e in lk_set): + continue + frag_atom = s if s not in lk_set else e + if frag_atom in wh_set: + wh_ok = wh_ok or (frag_atom in wh_reactive) + elif frag_atom in e3_set: + e3_ok = e3_ok or (frag_atom in e3_reactive) + return wh_ok and e3_ok + +_pk_scorer_cache = None + + +def _get_pk_scorer(): + global _pk_scorer_cache + if _pk_scorer_cache is not None: + return _pk_scorer_cache + try: + sys.path.insert(0, str(_SRC_DEFAULT / "classifier")) + sys.path.insert(0, str(_SRC_DEFAULT / "protac")) + import importlib as _il + from protac_classifier import EGFRPROPKScorer + _pmod = _il.import_module(_PROTAC_MODULE) + _Builder = _pmod.Builder + _MODEL_DIR = str(_SRC_DEFAULT / "propk" / "models") + _ATOM_DECODER = ["C", "N", "O", "F", "P", "S", "Cl", "Br", "I"] + scorer = EGFRPROPKScorer(_Builder(), _MODEL_DIR, _ATOM_DECODER) + _pk_scorer_cache = scorer + print("[EGFRPROPKScorer] loaded") + except Exception as e: + print(f"[EGFRPROPKScorer] could not load ({e})", file=sys.stderr) + _pk_scorer_cache = False + return _pk_scorer_cache + + +def _compute_pk_scores(df: pd.DataFrame) -> pd.DataFrame: + """Append EGFR-PROPK columns (T1/2, CL, Vss, composite) for the assembled + PROTAC. Only meaningful for ErlotinibVHL; caller gates on protac_module.""" + scorer = _get_pk_scorer() + pk_t_half, pk_cl, pk_vss, pk_score = [], [], [], [] + for _, row in df.iterrows(): + smi = row.get("protac_smiles") + if scorer and smi and str(smi) not in ("", "nan", "None"): + try: + mol = Chem.MolFromSmiles(str(smi)) + if mol is not None: + pk = scorer.predict_pk(mol) + pk_t_half.append(pk["t_half"]) + pk_cl.append(pk["cl"]) + pk_vss.append(pk["vss"]) + pk_score.append(pk["score"]) + continue + except Exception: + pass + pk_t_half.append(float("nan")) + pk_cl.append(float("nan")) + pk_vss.append(float("nan")) + pk_score.append(float("nan")) + df = df.copy() + df["pk_t_half"] = pk_t_half + df["pk_cl"] = pk_cl + df["pk_vss"] = pk_vss + df["pk_score"] = pk_score + return df + + +_training_smiles_cache: set | None = None + + +def _load_training_smiles() -> set: + global _training_smiles_cache + if _training_smiles_cache is None: + df = pd.read_csv(LINKER_CSV) + smiles = set() + for smi in df["Smiles"].dropna(): + mol = Chem.MolFromSmiles(smi) + if mol: + smiles.add(Chem.MolToSmiles(mol)) + _training_smiles_cache = smiles + print(f"Loaded {len(smiles)} training SMILES from linker.csv") + return _training_smiles_cache + + +def _canonicalize(smiles_series) -> list: + out = [] + for smi in smiles_series: + mol = Chem.MolFromSmiles(smi) + out.append(Chem.MolToSmiles(mol) if mol else None) + return [s for s in out if s] + + +# ── helpers ────────────────────────────────────────────────────────────────── + +# Lazy-loaded evaluate_linkers module — load once, reuse across all calls. +# Eliminates the ~30 sec subprocess startup × N calls = several minutes. +_eval_module_cache = None + +def _get_eval_module(): + global _eval_module_cache + if _eval_module_cache is None: + spec = importlib.util.spec_from_file_location("evaluate_linkers", EVAL_SCRIPT) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + _eval_module_cache = mod + return _eval_module_cache + + +def _call_evaluate_linkers(valid_smiles: list[str], label: str = "") -> pd.DataFrame: + """Run evaluate_linkers.evaluate() in-process (no subprocess overhead). + + Forwards the active _PROTAC_MODULE so the right warhead/E3 builder and + DegradMaster pocket+features are loaded. + """ + ev = _get_eval_module() + df = ev.evaluate(list(valid_smiles), sa_max=6.0, verbose=False, + protac_module=_PROTAC_MODULE) + return df + + +def _std_valid_count(smiles_iter) -> int: + """Count RDKit-parseable SMILES (no connectivity requirement). + + Disconnected fragments still count as parseable; the connectivity check + falls under AsmVal (a disconnected linker can never canonically attach). + """ + n = 0 + for smi in smiles_iter: + if not smi: + continue + if Chem.MolFromSmiles(str(smi)) is not None: + n += 1 + return n + + +def run_evaluate(summary_csv: Path) -> pd.DataFrame: + """Extract valid SMILES from a generation summary CSV, run evaluate_linkers, + return the per-molecule results DataFrame with uniqueness/novelty attached.""" + gen_df = pd.read_csv(summary_csv) + valid_smiles = list(gen_df[gen_df["valid"] == 1]["smiles"].dropna()) + n_total = len(gen_df) + n_valid = len(valid_smiles) + n_std_valid = _std_valid_count(gen_df["smiles"].fillna("")) + + train_smiles = _load_training_smiles() + canon = _canonicalize(valid_smiles) + unique_canon = set(canon) + n_unique = len(unique_canon) + n_in_train = len(unique_canon & train_smiles) + uniqueness = 100 * n_unique / n_valid if n_valid else 0.0 + novelty = 100 * (n_unique - n_in_train) / n_unique if n_unique else 0.0 + + df = _call_evaluate_linkers(valid_smiles, label=str(summary_csv)) + df["_n_total"] = n_total + df["_n_valid"] = n_valid + df["_n_std_valid"] = n_std_valid + df["_n_unique"] = n_unique + df["_uniqueness"] = uniqueness + df["_novelty"] = novelty + return df + + +def _read_smiles_file(smiles_path: Path): + """Read a SMILES text file. Returns (smiles_list, n_total_override). + + Lines starting with '#' are treated as comments and skipped, EXCEPT that + a line of the form '# n_total: N' encodes the true generation count + (e.g. including invalid SMILES that were dropped before saving). + """ + lines = Path(smiles_path).read_text().splitlines() + n_total_override = None + smiles = [] + for line in lines: + line = line.strip() + if not line: + continue + if line.startswith("#"): + if line.lower().startswith("# n_total:"): + try: + n_total_override = int(line.split(":", 1)[1].strip()) + except ValueError: + pass + else: + smiles.append(line) + return smiles, n_total_override + + +def _diversity_stats(valid_smiles: list[str]): + """Return (n_unique, uniqueness%, novelty%) over the canonical set.""" + n_valid = len(valid_smiles) + train_smiles = _load_training_smiles() + canon = _canonicalize(valid_smiles) + unique_canon = set(canon) + n_unique = len(unique_canon) + n_in_train = len(unique_canon & train_smiles) + uniqueness = 100 * n_unique / n_valid if n_valid else 0.0 + novelty = 100 * (n_unique - n_in_train) / n_unique if n_unique else 0.0 + return n_unique, uniqueness, novelty + + +def run_evaluate_smiles_file(smiles_path: Path) -> pd.DataFrame: + """Load SMILES from a plain text file (one per line), run evaluate_linkers, + and return the same per-molecule DataFrame as run_evaluate.""" + raw, n_total_override = _read_smiles_file(smiles_path) + valid_smiles = [s for s in raw if Chem.MolFromSmiles(s)] + n_total = n_total_override if n_total_override is not None else len(raw) + n_valid = len(valid_smiles) + n_std_valid = _std_valid_count(raw) + n_unique, uniqueness, novelty = _diversity_stats(valid_smiles) + + df = _call_evaluate_linkers(valid_smiles, label=str(smiles_path)) + df["_n_total"] = n_total + df["_n_valid"] = n_valid + df["_n_std_valid"] = n_std_valid + df["_n_unique"] = n_unique + df["_uniqueness"] = uniqueness + df["_novelty"] = novelty + return df + + +def _load_sascorer(): + _sa_path = os.path.join(RDConfig.RDContribDir, "SA_Score", "sascorer.py") + spec = importlib.util.spec_from_file_location("sascorer", _sa_path) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + return mod + + +_sascorer = None + +def _sa_score(mol): + global _sascorer + if _sascorer is None: + _sascorer = _load_sascorer() + return _sascorer.calculateScore(mol) + + +_ev_linkers_mod = None + +def _get_run_degrademaster(): + """Lazily import _run_degrademaster from evaluate_linkers.py.""" + global _ev_linkers_mod + if _ev_linkers_mod is None: + try: + spec = importlib.util.spec_from_file_location("evaluate_linkers", EVAL_SCRIPT) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + _ev_linkers_mod = mod + except Exception as e: + print(f"[DegradeMaster] could not load evaluate_linkers: {e}", file=sys.stderr) + _ev_linkers_mod = False + if not _ev_linkers_mod: + return None + return getattr(_ev_linkers_mod, "_run_degrademaster", None) + + +def run_evaluate_full_protac_file(smiles_path: Path) -> pd.DataFrame: + """Evaluate full assembled PROTAC SMILES (e.g. from LinkerNet) directly. + + Validity gate (all must pass): + 1. RDKit-parseable SMILES + 2. Warhead + E3 substructure match found (can extract linker) + 3. Exactly 2 linker–fragment interface bonds + 4. Each interface bond attaches at the correct reactive site: + warhead: piperazine NH (_NH_SMARTS) + E3: tert-leu NH2 (_NH2_SMARTS) + 5. PROTACAnalyzer hard constraints on the extracted linker (all except + attachment_sites, which is already verified by checks 3–4 above) + + SA and QED are computed on the extracted linker for a fair comparison with DeFoG. + MW/logP/TPSA/RotBonds and DegradeMaster are computed on the full PROTAC. + """ + raw, n_total_override = _read_smiles_file(smiles_path) + n_valid_all = len(raw) + n_total = n_total_override if n_total_override is not None else n_valid_all + n_std_valid = _std_valid_count(raw) + + dm_fn = _get_run_degrademaster() + analyzer, wh_q, e3_q, wh_reactive_comp, e3_reactive_comp = _get_protac_analyzer() + + valid_rows, valid_smiles = [], [] + for smi in raw: + mol = Chem.MolFromSmiles(smi) + if mol is None: + continue + if len(Chem.GetMolFrags(mol)) > 1: + continue # disconnected — counts as invalid toward n_total + + # Extract linker fragment from assembled PROTAC + linker_mol, n_iface, wh_match, e3_match = ( + _extract_linker_from_protac(mol, wh_q, e3_q) + if wh_q is not None else (None, 0, None, None) + ) + + # Validity: PROTACAnalyzer structural checks + reactive-site attachment check. + # attachment_sites is replaced by the more precise reactive-site check. + if (analyzer is not None and linker_mol is not None and n_iface >= 2 + and wh_reactive_comp is not None): + reactive_ok = _check_reactive_attachment( + mol, wh_match, e3_match, wh_reactive_comp, e3_reactive_comp) + checks = analyzer._run_checks(linker_mol) + checks.pop("attachment_sites") + is_assembled = int(reactive_ok and all(checks.values())) + else: + is_assembled = int( + mol.HasSubstructMatch(_WARHEAD_PAT) and mol.HasSubstructMatch(_E3_PAT) + ) + + # SA and QED on extracted linker (matches DeFoG metric); fall back to full mol + metric_mol = linker_mol if (linker_mol is not None and is_assembled) else mol + try: + sa = round(_sa_score(metric_mol), 2) + qed = round(rdkit_qed.qed(metric_mol), 3) + mw = round(Descriptors.MolWt(mol), 1) + logp = round(Descriptors.MolLogP(mol), 2) + tpsa = round(Descriptors.TPSA(mol), 1) + rotb = Descriptors.NumRotatableBonds(mol) + except Exception: + continue + canon = Chem.MolToSmiles(mol) + # DM-score every parseable+connected PROTAC, regardless of assembly_ok. + # We want DM scores for non-canonically-assembled outputs too (e.g., + # DiffPROTACs places the linker at a non-reactive site → assembly_ok=False + # but the SMILES is still a valid molecule the DM model can score). + # is_assembled stays as a separate signal for the validity-gate metric. + degrad = dm_fn(canon, _PROTAC_MODULE) if dm_fn else None + valid_smiles.append(canon) + # Descriptors (DM/SA/QED/MW/logP/TPSA/RotB) and the PK-window %s are gated + # by is_assembled — we only report functional-PROTAC stats. Non-assembled + # outputs still count toward Gen/StdVal/AsmVal but their descriptors are + # NaN'd out so window-% denominators reflect only assembled molecules. + nan = float("nan") + valid_rows.append({ + "smiles": canon, + "assembly_ok": is_assembled, + "protac_smiles": canon if is_assembled else None, + "linker_sa": sa if is_assembled else nan, + "linker_qed": qed if is_assembled else nan, + "protac_mw": mw if is_assembled else nan, + "protac_logp": logp if is_assembled else nan, + "protac_tpsa": tpsa if is_assembled else nan, + "protac_rotbonds": rotb if is_assembled else nan, + "degrad_prob": (degrad if degrad is not None else nan) if is_assembled else nan, + }) + + # n_valid = those passing ALL checks (reactive site + PROTACAnalyzer structural). + # This matches DeFoG's n_valid semantics (PROTACAnalyzer valid during generation). + n_valid = sum(1 for row in valid_rows if row["assembly_ok"]) + assembled_smiles = [row["smiles"] for row in valid_rows if row["assembly_ok"]] + n_unique, uniqueness, novelty = _diversity_stats(assembled_smiles) + + if not valid_rows: + df = pd.DataFrame(columns=[ + "smiles", "assembly_ok", "protac_smiles", "linker_sa", "linker_qed", + "protac_mw", "protac_logp", "protac_tpsa", "protac_rotbonds", "degrad_prob", + ]) + else: + df = pd.DataFrame(valid_rows) + df["_n_total"] = n_total + df["_n_valid"] = n_valid + df["_n_unique"] = n_unique + df["_uniqueness"] = uniqueness + df["_novelty"] = novelty + df.attrs.update(_n_total=n_total, _n_valid=n_valid, _n_std_valid=n_std_valid, + _n_unique=n_unique, _uniqueness=uniqueness, + _novelty=novelty) + return df + + +def batch_label(csv_path: Path) -> str: + """Turn 'defog_local_edit_20260424_003754_summary.csv' → BATCH_LABELS entry or timestamp.""" + stem = csv_path.stem + parts = stem.split("_") + for i, p in enumerate(parts): + if len(p) == 8 and p.isdigit(): + ts = "_".join(parts[i:i+2]) + return BATCH_LABELS.get(ts, ts) + return BATCH_LABELS.get(stem, stem) + + +# ── Diversity metrics ──────────────────────────────────────────────────────── +# (1) Intra-set: mean pairwise Tanimoto over generated outputs (lower = more diverse) +# (2) Training-set: max Tanimoto from each generated output to PROTAC-DB target+E3 +# training entries (lower = less memorization). 1 - this = "novelty distance." +_PROTACDB_BY_TARGET_CACHE: dict = {} +_TARGET_FILTER_BY_MODULE = { + "ErlotinibVHL": ("EGFR", "VHL"), + "BRD9VHL": ("BRD9", "VHL"), + "BRD4VHL": ("BRD4", "VHL"), + "KRASVHL": ("KRAS", "VHL"), + "ARCRBN": ("AR-V7", "CRBN"), + "ARCRBNv2": ("AR-V7", "CRBN"), + "BRD4CRBN": ("BRD4", "CRBN"), + "CDK6CRBN": ("CDK6", "CRBN"), +} + +def _load_protacdb_target_smiles(protac_module: str) -> list: + """Cache and return PROTAC-DB SMILES filtered by the active target+E3 pair.""" + if protac_module in _PROTACDB_BY_TARGET_CACHE: + return _PROTACDB_BY_TARGET_CACHE[protac_module] + filt = _TARGET_FILTER_BY_MODULE.get(protac_module) + if filt is None: + _PROTACDB_BY_TARGET_CACHE[protac_module] = [] + return [] + target, e3 = filt + name_json = Path(__file__).parent.parent / "src" / "degrademaster" / "data" / "PROTAC" / "name.json" + if not name_json.exists(): + _PROTACDB_BY_TARGET_CACHE[protac_module] = [] + return [] + import json as _json + with open(name_json) as f: + name = _json.load(f) + smis = [n["smiles"] for n in name.values() + if target in n.get("tar_path", "") and e3 in n.get("e3_ligase_path", "") + and "smiles" in n] + _PROTACDB_BY_TARGET_CACHE[protac_module] = smis + return smis + + +def _compute_diversity(df: pd.DataFrame, protac_module: str) -> dict: + """Compute (intra-set diversity, training-set similarity) for the assembled + PROTAC SMILES in df. Uses Morgan fingerprints (r=2, 2048 bits, Tanimoto). + For DeFoG runs we use df['protac_smiles']; for full-PROTAC baselines + df['smiles'] holds the assembled PROTAC directly.""" + from rdkit.Chem import AllChem + from rdkit.DataStructs import TanimotoSimilarity + + # Prefer protac_smiles (DeFoG runs); fall back to smiles (full-PROTAC baselines + # like LinkerNet/DiffPROTACs/DiffLinker, where protac_smiles may be all NaN + # if the analyzer's validity gate rejected the non-canonical chemistry). + smis = [] + if "protac_smiles" in df.columns: + smis = [s for s in df["protac_smiles"].dropna() if s] + if not smis and "smiles" in df.columns: + smis = [s for s in df["smiles"].dropna() if s] + fps = [] + for s in smis: + m = Chem.MolFromSmiles(s) + if m is None: continue + fps.append(AllChem.GetMorganFingerprintAsBitVect(m, 2, 2048)) + + out = { + "intra_diversity_mean": float("nan"), + "intra_diversity_max": float("nan"), + "train_sim_mean": float("nan"), + "train_sim_pct_ge_0_8": float("nan"), + "n_diversity_scored": len(fps), + } + if len(fps) < 2: + return out + + # 1. Intra-set pairwise Tanimoto (mean + max) + pairs = [] + for i in range(len(fps)): + for j in range(i + 1, len(fps)): + pairs.append(TanimotoSimilarity(fps[i], fps[j])) + out["intra_diversity_mean"] = float(np.mean(pairs)) + out["intra_diversity_max"] = float(max(pairs)) + + # 2. Max Tanimoto to PROTAC-DB target+E3 training entries. + # Cache training fingerprints across baseline calls within the same target run + # (saves ~100ms × 4 baselines for the FP rebuild). + global _PROTACDB_FP_CACHE + if '_PROTACDB_FP_CACHE' not in globals(): + _PROTACDB_FP_CACHE = {} + if protac_module in _PROTACDB_FP_CACHE: + train_fps = _PROTACDB_FP_CACHE[protac_module] + else: + train_smis = _load_protacdb_target_smiles(protac_module) + train_fps = [] + for s in train_smis: + m = Chem.MolFromSmiles(s) + if m is None: continue + train_fps.append(AllChem.GetMorganFingerprintAsBitVect(m, 2, 2048)) + _PROTACDB_FP_CACHE[protac_module] = train_fps + if train_fps: + max_sims = [max(TanimotoSimilarity(q, t) for t in train_fps) for q in fps] + out["train_sim_mean"] = float(np.mean(max_sims)) + out["train_sim_pct_ge_0_8"] = 100.0 * sum(1 for s in max_sims if s >= 0.8) / len(max_sims) + return out + + +def compute_stats(df: pd.DataFrame) -> dict: + def _meta(col): + if len(df) > 0 and col in df.columns: + return df[col].iloc[0] + return df.attrs.get(col, 0) + + n_total = _meta("_n_total") + n_valid = _meta("_n_valid") + n_std_valid = _meta("_n_std_valid") + assembled = df["assembly_ok"].sum() if "assembly_ok" in df.columns else 0 + + # Scores ≥ 0.99 are almost certainly OOD extrapolation artefacts (N-O bonds, + # exotic heterocycles absent from DegradMaster training data). Exclude them + # from the mean/std so the summary reflects calibrated predictions only. + # (0.99 chosen because real PROTAC-DB actives top out around 0.95 across + # tested target+E3 pairs; anything ≥0.99 is over the realistic ceiling.) + _DEGRAD_OOD_THRESHOLD = 0.99 + _scored_raw = df["degrad_prob"].dropna() if "degrad_prob" in df.columns else pd.Series([], dtype=float) + scored = _scored_raw[_scored_raw < _DEGRAD_OOD_THRESHOLD] + n_ood = int((_scored_raw >= _DEGRAD_OOD_THRESHOLD).sum()) + logp_in = df["protac_logp"].dropna() if "protac_logp" in df.columns else pd.Series([], dtype=float) + mw_in = df["protac_mw"].dropna() if "protac_mw" in df.columns else pd.Series([], dtype=float) + rot_in = df["protac_rotbonds"].dropna() if "protac_rotbonds" in df.columns else pd.Series([], dtype=float) + + # Windows match protac_classifier.py _PROPERTY_WINDOWS outer bounds (mean ± 2σ, PROTAC-DB). + # Ranges = PROTAC DB mean ± 1σ (logP: 5.0±2.4, MW: 940±168, RotB: 18.5±6.4). + _LOGP_LO, _LOGP_HI = 2.5, 7.4 + _MW_LO, _MW_HI = 771, 1108 + _ROT_LO, _ROT_HI = 12, 25 + + # All window/threshold percentages denominate over n_total so that + # molecules that failed assembly or scoring count as failures, not absences. + return { + "std_valid_rate": 100 * n_std_valid / n_total if n_total else 0, + "asm_valid_rate": 100 * n_valid / n_total if n_total else 0, + "assembly_rate": 100 * assembled / n_valid if n_valid else 0, + # SA score / QED: means over valid rows only (NaN rows auto-excluded) + "sa_mean": df["linker_sa"].dropna().mean() if "linker_sa" in df.columns else float("nan"), + "sa_std": df["linker_sa"].dropna().std() if "linker_sa" in df.columns else float("nan"), + "qed_mean": df["linker_qed"].dropna().mean() if "linker_qed" in df.columns else float("nan"), + "qed_std": df["linker_qed"].dropna().std() if "linker_qed" in df.columns else float("nan"), + # DegradeMaster: thresholds over count of scored (excludes OOD-flagged + NaN) + "degrad_mean": scored.mean() if len(scored) else float("nan"), + "degrad_std": scored.std() if len(scored) else float("nan"), + "degrad_ge03": 100 * (scored >= 0.3).sum() / len(scored) if len(scored) else 0, + "degrad_ge05": 100 * (scored >= 0.5).sum() / len(scored) if len(scored) else 0, + "degrad_ge07": 100 * (scored >= 0.7).sum() / len(scored) if len(scored) else 0, + "degrad_n_ood": n_ood, + # PK windows: % over the count of non-NaN values (not n_valid which gates by + # canonical-attachment validity — would give >100% for baselines whose metrics + # are computed even when non-canonically attached). + "logp_in_window_pct": 100 * logp_in.between(_LOGP_LO, _LOGP_HI).sum() / len(logp_in) if len(logp_in) else 0, + "logp_mean": logp_in.mean() if len(logp_in) else float("nan"), + "mw_in_window_pct": 100 * mw_in.between(_MW_LO, _MW_HI).sum() / len(mw_in) if len(mw_in) else 0, + "mw_mean": mw_in.mean() if len(mw_in) else float("nan"), + "rot_in_window_pct": 100 * rot_in.between(_ROT_LO, _ROT_HI).sum() / len(rot_in) if len(rot_in) else 0, + "n_scored": len(scored), + "n_total": n_total, + "n_valid": n_valid, + # EGFR-PROPK (Zhang et al. 2026) post-hoc PK predictions; only + # populated for ErlotinibVHL, NaN-filled otherwise. + "pk_t_half_mean": df["pk_t_half"].dropna().mean() if "pk_t_half" in df.columns else float("nan"), + "pk_t_half_std": df["pk_t_half"].dropna().std() if "pk_t_half" in df.columns else float("nan"), + "pk_cl_mean": df["pk_cl"].dropna().mean() if "pk_cl" in df.columns else float("nan"), + "pk_cl_std": df["pk_cl"].dropna().std() if "pk_cl" in df.columns else float("nan"), + "pk_vss_mean": df["pk_vss"].dropna().mean() if "pk_vss" in df.columns else float("nan"), + "pk_vss_std": df["pk_vss"].dropna().std() if "pk_vss" in df.columns else float("nan"), + "pk_score_mean": df["pk_score"].dropna().mean() if "pk_score" in df.columns else float("nan"), + "pk_n": df["pk_t_half"].dropna().shape[0] if "pk_t_half" in df.columns else 0, + # Diversity (all over n_valid, computed before dedup) + "uniqueness": _meta("_uniqueness"), + "novelty": _meta("_novelty"), + # Structural diversity (Morgan-FP Tanimoto): + # intra_diversity_mean: mean pairwise Tanimoto in generated set + # (LOWER = more chemically diverse outputs) + # train_sim_mean: mean (max sim to PROTAC-DB target+E3 training) + # (LOWER = less memorization of training data; 1 - x ≈ novelty distance) + # train_sim_pct_ge_0_8: % of outputs that are >0.80 Tanimoto-similar + # to a training entry — direct memorization indicator + **_compute_diversity(df, _PROTAC_MODULE), + } + + +# ── plotting ────────────────────────────────────────────────────────────────── + +def make_chart(labels: list[str], stats: list[dict], out_path: str | None): + n = len(labels) + x = np.arange(n) + colors = plt.cm.tab10(np.linspace(0, 0.7, n)) + + fig, axes = plt.subplots(3, 3, figsize=(14, 12)) + fig.suptitle("PROTAC Linker Batch Report", fontsize=14, fontweight="bold") + + def bar(ax, values, errs=None, title="", ylabel="", note="", ideal_dir="", color_bars=True): + bars = ax.bar(x, values, width=max(0.3, 0.6), color=colors if color_bars else "steelblue", + yerr=errs, capsize=4, error_kw={"linewidth": 1.2}) + ax.set_title(title, fontsize=10, fontweight="bold") + ax.set_ylabel(ylabel, fontsize=8) + ax.set_xticks(x) + ax.set_xticklabels(labels, rotation=30, ha="right", fontsize=7) + ax.yaxis.set_tick_params(labelsize=8) + if note: + ax.text(0.02, 0.97, note, transform=ax.transAxes, + fontsize=7, va="top", color="#555555", + bbox=dict(boxstyle="round,pad=0.2", fc="white", alpha=0.7)) + if ideal_dir: + ax.text(0.98, 0.97, ideal_dir, transform=ax.transAxes, + fontsize=8, va="top", ha="right", color="#1a6e1a" if "higher" in ideal_dir else "#8b0000", + fontweight="bold") + for bar_obj, val in zip(bars, values): + if not np.isnan(val): + ax.text(bar_obj.get_x() + bar_obj.get_width() / 2, + bar_obj.get_height() + (max(values) * 0.01 if max(values) > 0 else 0.01), + f"{val:.2f}", ha="center", va="bottom", fontsize=7) + + # 1. Standard Valid vs Assembly Valid + # Full-PROTAC baselines (LinkerNet/DiffPROTACs/DiffLinker) are already assembled; + # the strict assembly check falls through to a lax substructure-presence test, + # so reporting their rate is misleading. Suppress with NaN. + ax = axes[0, 0] + bw = 0.35 + _vrates = [ + ([s["std_valid_rate"] for s in stats], "#64B5F6", "Standard Valid\n(RDKit-parseable)"), + ([float("nan") if s.get("is_full_protac_baseline") else s["asm_valid_rate"] + for s in stats], "#1565C0", "Assembly Valid\n(all structural checks)"), + ] + for i, (vals, vc, vl) in enumerate(_vrates): + _offs = (i - 0.5) * bw + bars = ax.bar(x + _offs, vals, width=bw, color=vc, alpha=0.88, label=vl) + for b, v in zip(bars, vals): + if not np.isnan(v): + ax.text(b.get_x() + b.get_width() / 2, + b.get_height() + (max(vals) * 0.01 if max(vals) > 0 else 0.5), + f"{v:.0f}%", ha="center", va="bottom", fontsize=6) + ax.set_title("Validity Rate (%)", fontsize=10, fontweight="bold") + ax.set_ylabel("% of generated samples", fontsize=8) + ax.set_xticks(x); ax.set_xticklabels(labels, rotation=30, ha="right", fontsize=7) + ax.yaxis.set_tick_params(labelsize=8) + ax.set_ylim(0, 120) + ax.legend(fontsize=7) + ax.text(0.98, 0.97, "↑ higher better", transform=ax.transAxes, + fontsize=8, va="top", ha="right", color="#1a6e1a", fontweight="bold") + + # 2. SA score (lower = better) + bar(axes[0, 1], + [s["sa_mean"] for s in stats], + errs=[s["sa_std"] for s in stats], + title="Linker SA Score (mean ± std)", + ylabel="SA score", + note="1=easy, 10=hard to synthesise", + ideal_dir="↓ lower better") + axes[0, 1].set_ylim(0, max(s["sa_mean"] + s["sa_std"] for s in stats) * 1.2) + axes[0, 1].axhline(6, color="red", linestyle="--", linewidth=0.8, label="hard cutoff (6)") + axes[0, 1].legend(fontsize=7) + + # 3. QED (higher = better) + bar(axes[0, 2], + [s["qed_mean"] for s in stats], + errs=[s["qed_std"] for s in stats], + title="Linker QED (mean ± std)", + ylabel="QED (0–1)", + note="drug-likeness score", + ideal_dir="↑ higher better") + axes[0, 2].set_ylim(0, 1.0) + + # 4. DegradeMaster prob (higher = better) + bar(axes[1, 0], + [s["degrad_mean"] for s in stats], + errs=[s["degrad_std"] for s in stats], + title="DegradeMaster Prob (mean ± std)", + ylabel="degradation probability (0–1)", + note="GCN; EGFR L858R + VHL\nactive ref mean ≈ 0.37", + ideal_dir="↑ higher better") + axes[1, 0].set_ylim(0, 1.0) + axes[1, 0].axhline(0.3, color="orange", linestyle="--", linewidth=0.8, label="≥0.3 active") + axes[1, 0].axhline(0.5, color="red", linestyle="--", linewidth=0.8, label="≥0.5 confident") + axes[1, 0].legend(fontsize=7) + + # 5. % degrad_prob >= threshold (stacked-style grouped bar) + ax = axes[1, 1] + bw = 0.25 + offsets = [-bw, 0, bw] + thresh_labels = ["≥0.3", "≥0.5", "≥0.7"] + thresh_colors = ["#f0a500", "#e05c00", "#b00000"] + thresh_keys = ["degrad_ge03", "degrad_ge05", "degrad_ge07"] + for i, (k, tc, tl) in enumerate(zip(thresh_keys, thresh_colors, thresh_labels)): + vals = [s[k] for s in stats] + bars = ax.bar(x + offsets[i], vals, width=bw, color=tc, alpha=0.85, label=tl) + for b, v in zip(bars, vals): + ax.text(b.get_x() + b.get_width()/2, b.get_height() + 0.5, + f"{v:.0f}%", ha="center", va="bottom", fontsize=6) + ax.set_title("% Assembled PROTACs ≥ Degrad Threshold", fontsize=10, fontweight="bold") + ax.set_ylabel("% of scored compounds") + ax.set_xticks(x); ax.set_xticklabels(labels, rotation=30, ha="right", fontsize=7) + ax.set_ylim(0, 115) + ax.legend(fontsize=7, title="threshold", title_fontsize=7) + ax.text(0.98, 0.97, "↑ higher better", transform=ax.transAxes, + fontsize=8, va="top", ha="right", color="#1a6e1a", fontweight="bold") + + # 6. PK window pass rates + ax = axes[1, 2] + bw = 0.25 + offsets = [-bw, 0, bw] + pk_labels = ["logP [2.5,7.4]", "MW [771,1108]", "RotBonds [12,25]"] + pk_keys = ["logp_in_window_pct", "mw_in_window_pct", "rot_in_window_pct"] + pk_colors = ["#2196F3", "#4CAF50", "#9C27B0"] + for i, (k, pc, pl) in enumerate(zip(pk_keys, pk_colors, pk_labels)): + vals = [s[k] for s in stats] + bars = ax.bar(x + offsets[i], vals, width=bw, color=pc, alpha=0.85, label=pl) + for b, v in zip(bars, vals): + ax.text(b.get_x() + b.get_width()/2, b.get_height() + 0.5, + f"{v:.0f}%", ha="center", va="bottom", fontsize=6) + ax.set_title("PROTAC PK Window Pass Rates", fontsize=10, fontweight="bold") + ax.set_ylabel("% of assembled PROTACs in window") + ax.set_xticks(x); ax.set_xticklabels(labels, rotation=30, ha="right", fontsize=7) + ax.set_ylim(0, 115) + ax.legend(fontsize=7) + ax.text(0.98, 0.97, "↑ higher better", transform=ax.transAxes, + fontsize=8, va="top", ha="right", color="#1a6e1a", fontweight="bold") + + # 7. Uniqueness / Novelty + ax = axes[2, 0] + bw = 0.3 + offsets = [-bw/2, bw/2] + div_labels = ["Uniqueness", "Novelty"] + div_keys = ["uniqueness", "novelty"] + div_colors = ["#00897B", "#1E88E5"] + div_notes = ["↑ no duplicates", "↑ not in train set"] + for i, (k, dc, dl) in enumerate(zip(div_keys, div_colors, div_labels)): + vals = [s[k] for s in stats] + bars = ax.bar(x + offsets[i], vals, width=bw, color=dc, alpha=0.85, label=f"{dl} ({div_notes[i]})") + for b, v in zip(bars, vals): + ax.text(b.get_x() + b.get_width()/2, b.get_height() + 0.5, + f"{v:.0f}%", ha="center", va="bottom", fontsize=6) + ax.set_title("Diversity Metrics (% of unique valid)", fontsize=10, fontweight="bold") + ax.set_ylabel("% of unique valid linkers") + ax.set_xticks(x); ax.set_xticklabels(labels, rotation=30, ha="right", fontsize=7) + ax.set_ylim(0, 115) + ax.legend(fontsize=7) + + axes[2, 1].set_visible(False) + axes[2, 2].set_visible(False) + + plt.tight_layout() + + if out_path: + plt.savefig(out_path, dpi=150, bbox_inches="tight") + print(f"Chart saved to {out_path}") + else: + plt.show() + + +# ── main ────────────────────────────────────────────────────────────────────── + +def _print_stats(s: dict): + print(f" Generated / Std Valid / Asm Valid : {s['n_total']} / {round(s['std_valid_rate']*s['n_total']/100)} / {s['n_valid']}") + print(f" Standard valid rate (↑) : {s['std_valid_rate']:.1f}% (RDKit-parseable SMILES)") + print(f" Assembly valid rate (↑) : {s['asm_valid_rate']:.1f}% (canonical attachment + analyzer pass)") + print(f" SA score (↓ lower better) : {s['sa_mean']:.2f} ± {s['sa_std']:.2f}") + print(f" QED (↑ higher better) : {s['qed_mean']:.3f} ± {s['qed_std']:.3f}") + ood_note = f" ({s['degrad_n_ood']} OOD-flagged ≥0.99 excluded)" if s.get('degrad_n_ood') else "" + print(f" DegradMaster prob (↑ higher) : {s['degrad_mean']:.3f} ± {s['degrad_std']:.3f}{ood_note}") + print(f" ≥ 0.3: {s['degrad_ge03']:.1f}% ≥ 0.5: {s['degrad_ge05']:.1f}% ≥ 0.7: {s['degrad_ge07']:.1f}%") + print(f" PROTAC logP in [2.5,7.4] (↑) : {s['logp_in_window_pct']:.1f}% (mean {s['logp_mean']:.2f})") + print(f" PROTAC MW in [771,1108] (↑) : {s['mw_in_window_pct']:.1f}% (mean {s['mw_mean']:.0f} Da)") + print(f" PROTAC RotB in [12,25] (↑) : {s['rot_in_window_pct']:.1f}%") + # EGFR-PROPK predictions (only populated for EGFR-VHL target) + if s.get('pk_n', 0) > 0: + print(f" EGFR-PROPK T½ (↑) : {s['pk_t_half_mean']:.2f} ± {s['pk_t_half_std']:.2f} (n={s['pk_n']}, raw)") + print(f" EGFR-PROPK CL (↓) : {s['pk_cl_mean']:.2f} ± {s['pk_cl_std']:.2f} (raw)") + print(f" EGFR-PROPK Vss : {s['pk_vss_mean']:.2f} ± {s['pk_vss_std']:.2f} (raw)") + print(f" EGFR-PROPK score (σ(T½)−σ(CL)) (↑): {s['pk_score_mean']:.3f}") + print(f" Uniqueness (↑ higher better) : {s['uniqueness']:.1f}%") + print(f" Novelty (↑ higher better) : {s['novelty']:.1f}% (not in training set)") + # Structural diversity (Morgan FP / Tanimoto) + nd = s.get('n_diversity_scored', 0) + if nd >= 2: + intra = s['intra_diversity_mean'] + train_sim = s['train_sim_mean'] + memo_pct = s['train_sim_pct_ge_0_8'] + print(f" Intra-set diversity (↓ lower=more diverse): mean Tanimoto = {intra:.3f} (n={nd} pairs, max={s['intra_diversity_max']:.3f})") + if not np.isnan(train_sim): + print(f" Memorization (↓ lower=less memorized) : mean max-Tanimoto to PROTAC-DB target+E3 = {train_sim:.3f}") + print(f" {memo_pct:.1f}% of outputs are >0.80 Tanimoto-similar to a training entry (memorized)") + + +def main(): + parser = argparse.ArgumentParser(description="Batch report for generated PROTAC linkers") + parser.add_argument("csvs", nargs="*", help="Generation summary CSV file(s) (*_summary.csv)") + parser.add_argument("--out", default=None, help="Save chart to this path (PNG/PDF). Default: display.") + parser.add_argument( + "--baseline", action="append", default=[], metavar="LABEL:PATH", + help="Named baseline from a plain linker SMILES .txt file. " + "Format: 'DeLinker:/path/to/smiles.txt'. Repeatable.", + ) + parser.add_argument( + "--protac-baseline", action="append", default=[], metavar="LABEL:PATH", + dest="protac_baseline", + help="Named baseline from a full assembled PROTAC SMILES .txt file " + "(e.g. DRlinker output). Evaluated directly without assembly. " + "Format: 'DRlinker:/path/to/smiles.txt'. Repeatable.", + ) + parser.add_argument( + "--protac-module", default=None, dest="protac_module", + help="Builder module name under MoCA/smiles/protac/ " + "(e.g. ErlotinibVHL, ARCRBN). Overrides _PROTAC_MODULE constant.", + ) + args = parser.parse_args() + + # Apply protac module selection before any lazy-loads + if args.protac_module: + global _PROTAC_MODULE + _PROTAC_MODULE = args.protac_module + + # Resolve which DeFoG batch CSVs to process (CLI takes priority over in-file constants) + if args.csvs: + csv_paths = [Path(p) for p in args.csvs] + elif BATCH_FOLDER: + csv_paths = sorted(Path(BATCH_FOLDER).glob("*_summary.csv")) + elif BATCH_CSVS: + csv_paths = [Path(p) for p in BATCH_CSVS] + else: + csv_paths = [] + + # Merge BASELINES constant with --baseline CLI args (CLI appends on top) + baselines: dict[str, str] = dict(BASELINES) + for spec in args.baseline: + if ":" not in spec: + parser.error(f"--baseline must be LABEL:PATH, got: {spec!r}") + lbl, path = spec.split(":", 1) + baselines[lbl] = path + + # Pick per-target baseline set based on the active protac-module. + # Falls back to the legacy AR-CRBN set if the module name isn't registered. + _module_baselines = PROTAC_BASELINES_BY_MODULE.get(_PROTAC_MODULE, PROTAC_BASELINES) + protac_baselines: dict[str, str] = dict(_module_baselines) + for spec in args.protac_baseline: + if ":" not in spec: + parser.error(f"--protac-baseline must be LABEL:PATH, got: {spec!r}") + lbl, path = spec.split(":", 1) + protac_baselines[lbl] = path + + if not csv_paths and not baselines and not protac_baselines: + parser.error( + "Provide CSV paths on the command line, set BATCH_FOLDER / BATCH_CSVS, " + "or add entries to BASELINES / PROTAC_BASELINES at the top of the file." + ) + + out_path = args.out or CHART_OUT + + labels, all_stats = [], [] + + for csv_path in sorted(csv_paths): + csv_path = Path(csv_path) + label = batch_label(csv_path) + print(f"\n{'='*60}") + print(f"Batch: {label} ({csv_path.name})") + print(f"{'='*60}") + + df = run_evaluate(csv_path) + if _PROTAC_MODULE == "ErlotinibVHL": + df = _compute_pk_scores(df) + s = compute_stats(df) + labels.append(label) + all_stats.append(s) + _print_stats(s) + + for label, smiles_path in baselines.items(): + p = Path(smiles_path) + print(f"\n{'='*60}") + print(f"Baseline: {label} ({p.name})") + print(f"{'='*60}") + + df = run_evaluate_smiles_file(p) + if _PROTAC_MODULE == "ErlotinibVHL": + df = _compute_pk_scores(df) + s = compute_stats(df) + labels.append(label) + all_stats.append(s) + _print_stats(s) + + for label, smiles_path in protac_baselines.items(): + p = Path(smiles_path) + print(f"\n{'='*60}") + print(f"PROTAC Baseline: {label} ({p.name})") + print(f"{'='*60}") + + df = run_evaluate_full_protac_file(p) + if _PROTAC_MODULE == "ErlotinibVHL": + df = _compute_pk_scores(df) + s = compute_stats(df) + # Full-PROTAC baselines are already assembled; the strict assembly check + # silently falls back to a lax substructure-presence test, so the rate is + # not directly comparable to MoCA's. Suppress it from the report. + s["is_full_protac_baseline"] = True + labels.append(label) + all_stats.append(s) + _print_stats(s) + + make_chart(labels, all_stats, out_path) + + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/scripts/evaluate_linkers.py b/CupOFMoCA-PROTAC/scripts/evaluate_linkers.py new file mode 100644 index 0000000000000000000000000000000000000000..2e5fa8e06fcaf430bf2645ee4a21bfa44e150d3a --- /dev/null +++ b/CupOFMoCA-PROTAC/scripts/evaluate_linkers.py @@ -0,0 +1,603 @@ +#!/usr/bin/env python3 +""" +evaluate_linkers.py + +Evaluate generated PROTAC linkers. The warhead/E3 pair is selected by +--protac-module (defaults to ErlotinibVHL = EGFR-VHL). Supported modules: + ErlotinibVHL — EGFR (gefitinib-piperazine) + VHL (VH032) + BRD9VHL — BRD9 (BI-7273-piperazine) + VHL (VH032) + KRASVHL — KRAS (ACBI3-diazepane) + VHL (VH032) + BRD4VHL, ARCRBN — other registered modules (no DegradeMaster scoring) + +For each generated SMILES: + 1. Skip chemically invalid molecules (all further steps on valid only) + 2. Run PROTACAnalyzer hard constraints (attachment sites, atom count, + ring closure, SA score, assembled PROTAC MW) + 3. Attempt PROTAC assembly (warhead + linker + E3) + 4. Compute linker properties: SA score, QED, MW, logP, TPSA, HBD/HBA, rotbonds + 5. Compute assembled PROTAC PK properties + 6. Run DegradeMaster GCN model (ABILiLab/DegradeMaster) on the assembled + PROTAC to predict degradation probability. Target/E3 pocket graphs and + 30-d feature vectors are dispatched per --protac-module: + ErlotinibVHL → EGFR L858R pocket (2ITY), egfr_target_feature.npy + BRD9VHL → BRD9 pocket (case_study_1), brd9_target_feature.npy + KRASVHL → KRAS WT pocket (P01116 + ACBI3), krasvhl_target_feature.npy + E3 pocket = VHL (P40337) for all three; E3 feature = vhl_e3_feature_egfr.npy + (verified bit-identical across case_study_1 and case_study_2 entries). + 7. Print per-molecule table + aggregate summary + +Usage: + python evaluate_linkers.py path/to/final_smiles.txt + python evaluate_linkers.py path/to/final_smiles.txt --out results.csv + python evaluate_linkers.py path/to/final_smiles.txt --sa-max 5.0 + python evaluate_linkers.py path/to/final_smiles.txt --protac-module BRD9VHL +""" + +import argparse +import importlib.util +import os +import sys +from pathlib import Path + +import numpy as np +import pandas as pd +import torch +from rdkit import Chem, RDConfig, RDLogger +from rdkit.Chem import Descriptors, MACCSkeys, QED, rdMolDescriptors + +RDLogger.DisableLog("rdApp.*") + +# Resolve src/ paths. +_REPO_ROOT = Path(__file__).parent.parent +_SRC = _REPO_ROOT / "src" +sys.path.insert(0, str(_SRC / "classifier")) +sys.path.insert(0, str(_SRC / "protac")) +_DM_ROOT = _SRC / "degrademaster" + +# SA scorer from RDKit contrib +_sa_path = os.path.join(RDConfig.RDContribDir, "SA_Score", "sascorer.py") +_spec = importlib.util.spec_from_file_location("sascorer", _sa_path) +sascorer = importlib.util.module_from_spec(_spec) +_spec.loader.exec_module(sascorer) + + +# --------------------------------------------------------------------------- +# DegradeMaster integration +# --------------------------------------------------------------------------- + +# Periodic table in order (118 elements) — must match DegradeMaster ATOMS list +_DM_ATOMS = [ + 'H', 'He', + 'Li', 'Be', 'B', 'C', 'N', 'O', 'F', 'Ne', + 'Na', 'Mg', 'Al', 'Si', 'P', 'S', 'Cl', 'Ar', + 'K', 'Ca', 'Sc', 'Ti', 'V', 'Cr', 'Mn', 'Fe', 'Co', 'Ni', 'Cu', 'Zn', + 'Ga', 'Ge', 'As', 'Se', 'Br', 'Kr', + 'Rb', 'Sr', 'Y', 'Zr', 'Nb', 'Mo', 'Tc', 'Ru', 'Rh', 'Pd', 'Ag', 'Cd', + 'In', 'Sn', 'Sb', 'Te', 'I', 'Xe', + 'Cs', 'Ba', 'La', 'Ce', 'Pr', 'Nd', 'Pm', 'Sm', 'Eu', 'Gd', 'Tb', 'Dy', + 'Ho', 'Er', 'Tm', 'Yb', 'Lu', + 'Hf', 'Ta', 'W', 'Re', 'Os', 'Ir', 'Pt', 'Au', 'Hg', 'Tl', 'Pb', 'Bi', + 'Po', 'At', 'Rn', + 'Fr', 'Ra', 'Ac', 'Th', 'Pa', 'U', 'Np', 'Pu', 'Am', 'Cm', 'Bk', + 'Cf', 'Es', 'Fm', 'Md', 'No', 'Lr', + 'Rf', 'Db', 'Sg', 'Bh', 'Hs', 'Mt', 'Ds', 'Rg', 'Cn', 'Nh', 'Fl', 'Mc', + 'Lv', 'Ts', 'Og', +] + +_DM_BOND_MAP = { + Chem.BondType.SINGLE: 1, + Chem.BondType.DOUBLE: 2, + Chem.BondType.TRIPLE: 3, + Chem.BondType.AROMATIC: 4, +} + +# SMARTS for amide bonds — matched before H addition, converted to type 5 +# to match mol2 file convention used during DegradeMaster training. +_AMIDE_PAT = Chem.MolFromSmarts('[NX3;H0,H1,H2][CX3](=[OX1])') + +_dm_cache: dict = {} # lazy-loaded singleton keyed by str(_DM_ROOT) + + +def _dm_smiles_to_graph(smi: str): + """Convert PROTAC SMILES to a PyG Data graph matching DegradeMaster's + training format: explicit H atoms, amide bonds as type 5.""" + from torch_geometric.data import Data + mol = Chem.MolFromSmiles(smi) + if mol is None: + return None + # Identify amide bonds (N-C=O) before H addition (atom indices stable here) + amide_bonds = { + (min(m[0], m[1]), max(m[0], m[1])) + for m in mol.GetSubstructMatches(_AMIDE_PAT) + } + mol = Chem.AddHs(mol) + atoms = [ + _DM_ATOMS.index(a.GetSymbol()) if a.GetSymbol() in _DM_ATOMS else len(_DM_ATOMS) + for a in mol.GetAtoms() + ] + e1, e2, ea = [], [], [] + for bond in mol.GetBonds(): + i, j = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx() + bt = _DM_BOND_MAP.get(bond.GetBondType(), 1) + if (min(i, j), max(i, j)) in amide_bonds: + bt = 5 # amide bond type as in mol2 training data + e1.append(i); e2.append(j); ea.append(bt) + return Data( + x=torch.tensor(atoms), + edge_index=torch.tensor([e1 + e2, e2 + e1]), + edge_attr=torch.tensor(ea + ea), + ) + + +def _dm_extract_graph(data, slices, idx: int): + """Extract single graph entry from an InMemoryDataset collated tensor.""" + from torch_geometric.data import Data + xs, xe = slices['x'][idx].item(), slices['x'][idx + 1].item() + es, ee = slices['edge_index'][idx].item(), slices['edge_index'][idx + 1].item() + eas, eae = slices['edge_attr'][idx].item(), slices['edge_attr'][idx + 1].item() + return Data( + x=data.x[xs:xe], + edge_index=data.edge_index[:, es:ee], + edge_attr=data.edge_attr[eas:eae], + ) + + +# Per-target DegradeMaster wiring. Each entry maps a protac-module name to +# the target pocket graph + 30-d feature vector. E3 pocket and E3 feature are +# shared (VHL VH032 across all three modules). +# pocket_graph_file — torch.save'd Data object, atoms in periodic-table index +# target_feature_file — np.save'd (30,) float64 vector +# notes — human-readable description for the run summary +_DM_TARGETS = { + "ErlotinibVHL": { + "pocket_graph_file": "egfr_pocket_graph.pt", + "target_feature_file": "egfr_target_feature.npy", + "e3_pocket_source": ("processed_slot", "ligase_pocket.pt", 0), # VHL slot 0 + "e3_feature_file": "vhl_e3_feature_egfr.npy", + "notes": "EGFR L858R pocket (PDB 2ITY); E3=VHL; training-set features", + }, + "BRD9VHL": { + "pocket_graph_file": "brd9_pocket_graph.pt", + "target_feature_file": "brd9_target_feature.npy", + "e3_pocket_source": ("processed_slot", "ligase_pocket.pt", 0), # VHL + "e3_feature_file": "vhl_e3_feature_egfr.npy", + "notes": "BRD9 pocket (case_study_1, BRD9_Q9H8M2); E3=VHL; " + "target_feature_case1[0]", + }, + "ARCRBNv2": { + "pocket_graph_file": "arcrbn_pocket_graph.pt", # AR LBD (1E3G + R18) + "target_feature_file": "arcrbn_target_feature.npy", # PROTAC-DB AR-V7+CRBN row + "e3_pocket_source": ("file", "crbn_pocket_graph.pt"), # CRBN (4TZ4 + LVY) + "e3_feature_file": "crbn_e3_feature.npy", # PROTAC-DB CRBN feature + "notes": "AR LBD pocket (1E3G + R1881 ligand); E3=CRBN (4TZ4 + lenalidomide); " + "AR/CRBN features from PROTAC-DB AR-V7+CRBN row", + }, + "BRD4CRBN": { + "pocket_graph_file": "brd4_pocket_graph.pt", # BRD4 (BRD4_O60885 + smina) + "target_feature_file": "brd4crbn_target_feature.npy", # PROTAC-DB BRD4+CRBN row + "e3_pocket_source": ("file", "crbn_pocket_graph.pt"), # CRBN (4TZ4 + LVY) + "e3_feature_file": "crbn_e3_feature.npy", # PROTAC-DB CRBN feature + "notes": "BRD4 pocket (BRD4_O60885 + smina-docked warhead); E3=CRBN (4TZ4 + " + "lenalidomide); BRD4/CRBN features from PROTAC-DB BRD4+CRBN row", + }, + "CDK6CRBN": { + "pocket_graph_file": "cdk6crbn_pocket_graph.pt", # CDK6 (5L2I + LQQ palbociclib) + "target_feature_file": "cdk6crbn_target_feature.npy", # PROTAC-DB CDK6+CRBN row + "e3_pocket_source": ("file", "crbn_pocket_graph.pt"), # CRBN (4TZ4 + LVY) + "e3_feature_file": "crbn_e3_feature.npy", # PROTAC-DB CRBN feature + "notes": "CDK6 pocket (5L2I + LQQ palbociclib-class ligand); E3=CRBN (4TZ4 + " + "lenalidomide); CDK6/CRBN features from PROTAC-DB CDK6+CRBN row. " + "Uses cooh (diacid) linker chemistry — same as BRD9-VHL.", + }, + # BRD4VHL — pocket+features are wired (brd4_pocket_graph.pt, + # brd4_target_feature.npy) but the published case-study checkpoint + # saturates at ≥0.99 on 44% of generated linkers (vs 4% on real + # PROTAC-DB BRD4+VHL actives), so DM scores on generated PROTACs + # are OOD-overconfident and not trustworthy. Re-enable here if a + # checkpoint trained on broader data becomes available. + # + # KRASVHL — same: krasvhl_pocket_graph.pt and krasvhl_target_feature.npy + # exist, but the case-study checkpoint collapses to exactly 0.000 on + # all KRAS features (verified across all 18 case_study_2 entries). + # + # ARCRBN (v1) — original, OOD-broken. Use ARCRBNv2 instead. +} + + +def _load_degrademaster(protac_module: str = "ErlotinibVHL"): + """Load DegradeMaster model and per-target pocket data (lazy, cached). + + Returns (model, e3_graph, target_graph, e3_feat, tar_feat, notes) + or None if the target is unsupported / data files are missing. + """ + key = (str(_DM_ROOT), protac_module) + if key in _dm_cache: + return _dm_cache[key] + + if not _DM_ROOT.exists(): + return None + + target_cfg = _DM_TARGETS.get(protac_module) + if target_cfg is None: + # Unsupported module — emit once, cache the negative result. + print(f"[DegradeMaster] no DegradeMaster wiring for protac module " + f"'{protac_module}' — skipping model", file=sys.stderr) + _dm_cache[key] = None + return None + + # Import DegradeMaster model module + dm_model_path = _DM_ROOT / "model.py" + try: + _spec2 = importlib.util.spec_from_file_location("dm_model", dm_model_path) + dm_model_mod = importlib.util.module_from_spec(_spec2) + _spec2.loader.exec_module(dm_model_mod) + except Exception as exc: + print(f"[DegradeMaster] failed to load model.py: {exc} — skipping model", file=sys.stderr) + _dm_cache[key] = None + return None + GraphConv = dm_model_mod.GraphConv + ProtacModel = dm_model_mod.ProtacModel + + # Pocket graphs and features: + # Target pocket — pre-built per-target .pt under data/case_study/ + # Target feature — per-target .npy (30-d), built from DegradeMaster's + # case_study features (one row per target). + # E3 pocket — dispatched per target_cfg["e3_pocket_source"]: + # ("processed_slot", file.pt, idx) → slot from a + # processed/ collated InMemoryDataset + # ("file", filename.pt) → standalone .pt file + # E3 feature vec — per-target file (vhl_e3_feature_egfr.npy is the + # canonical VHL feature; CRBN targets use a different + # .npy extracted from PROTAC-DB). + cs_proc = _DM_ROOT / "data" / "case_study" / "processed" + cs_data = _DM_ROOT / "data" / "case_study" + try: + # E3 pocket dispatch + e3_src = target_cfg["e3_pocket_source"] + if e3_src[0] == "processed_slot": + _, fname, idx = e3_src + lp_data, lp_slices = torch.load(cs_proc / fname, weights_only=False) + e3_graph = _dm_extract_graph(lp_data, lp_slices, idx) + elif e3_src[0] == "file": + _, fname = e3_src + e3_graph = torch.load(cs_data / fname, weights_only=False) + else: + raise ValueError(f"unknown e3_pocket_source kind: {e3_src[0]}") + + target_graph = torch.load(cs_data / target_cfg["pocket_graph_file"], + weights_only=False) + tar_feat = np.load(cs_data / target_cfg["target_feature_file"], allow_pickle=True) + e3_feat = np.load(cs_data / target_cfg["e3_feature_file"], allow_pickle=True) + except FileNotFoundError as exc: + print(f"[DegradeMaster] missing data file for {protac_module}: {exc} " + f"— skipping model", file=sys.stderr) + _dm_cache[key] = None + return None + + # Build GCN model (checkpoint/1000 uses GCN with hidden_size=128) + prot_gcn = GraphConv(num_embeddings=118, graph_dim=167, hidden_size=128) + lig_gcn = GraphConv(num_embeddings=118, graph_dim=30, hidden_size=128) + tar_gcn = GraphConv(num_embeddings=118, graph_dim=30, hidden_size=128) + model = ProtacModel(prot_gcn, lig_gcn, tar_gcn, 128) + + ckpt_path = _DM_ROOT / "checkpoint" / "1000" / "model_for_case_study.pth" + ckpt = torch.load(ckpt_path, map_location="cpu") + model.load_state_dict(ckpt["OurModel"]) + model.eval() + + result = (model, e3_graph, target_graph, e3_feat, tar_feat, target_cfg["notes"]) + _dm_cache[key] = result + return result + + +def _run_degrademaster(protac_smi: str, + protac_module: str = "ErlotinibVHL") -> "float | None": + """Return DegradeMaster degradation probability (0–1) for an assembled + PROTAC SMILES, or None if the model is unavailable or SMILES is invalid.""" + dm = _load_degrademaster(protac_module) + if dm is None: + return None + + from torch_geometric.data import Batch + + model, e3_graph, target_graph, e3_feat, tar_feat, _ = dm + + pg = _dm_smiles_to_graph(protac_smi) + if pg is None: + return None + + maccs = np.array(MACCSkeys.GenMACCSKeys(Chem.MolFromSmiles(protac_smi)), dtype=np.float32) + feat = torch.tensor( + np.concatenate([maccs, tar_feat, e3_feat]).astype(np.float32) + ).unsqueeze(0) # (1, 227) + + with torch.no_grad(): + logits, _ = model( + Batch.from_data_list([pg]), + Batch.from_data_list([e3_graph]), + Batch.from_data_list([target_graph]), + feat, + ) + prob = torch.softmax(logits, dim=-1) + return float(prob[0, 1].item()) + + +# --------------------------------------------------------------------------- +# Property helpers +# --------------------------------------------------------------------------- + +def linker_props(mol: Chem.Mol) -> dict: + return { + "linker_mw": round(Descriptors.MolWt(mol), 1), + "linker_logp": round(Descriptors.MolLogP(mol), 2), + "linker_tpsa": round(Descriptors.TPSA(mol), 1), + "linker_hbd": Descriptors.NumHDonors(mol), + "linker_hba": Descriptors.NumHAcceptors(mol), + "linker_rotbonds": Descriptors.NumRotatableBonds(mol), + "linker_rings": rdMolDescriptors.CalcNumRings(mol), + "linker_hac": mol.GetNumHeavyAtoms(), + "linker_sa": round(sascorer.calculateScore(mol), 2), + "linker_qed": round(QED.qed(mol), 3), + } + + +def protac_props(mol: Chem.Mol) -> dict: + mw = Descriptors.MolWt(mol) + logp = Descriptors.MolLogP(mol) + hbd = Descriptors.NumHDonors(mol) + hba = Descriptors.NumHAcceptors(mol) + return { + "protac_mw": round(mw, 1), + "protac_logp": round(logp, 2), + "protac_tpsa": round(Descriptors.TPSA(mol), 1), + "protac_hbd": hbd, + "protac_hba": hba, + "protac_rotbonds": Descriptors.NumRotatableBonds(mol), + "protac_hac": mol.GetNumHeavyAtoms(), + # Lipinski Ro5 violations (PROTACs routinely violate, but track anyway) + "ro5_violations": int(mw > 500) + int(logp > 5) + int(hbd > 5) + int(hba > 10), + } + + +# --------------------------------------------------------------------------- +# Main evaluation +# --------------------------------------------------------------------------- + +def evaluate(smiles_list: list[str], sa_max: float = 6.0, + verbose: bool = True, + protac_module: str = "ErlotinibVHL", + fix_termini: "str | None" = None) -> pd.DataFrame: + from protac_analyzer import PROTACAnalyzer + import importlib as _il + _pmod = _il.import_module(protac_module) + Builder = _pmod.Builder + warhead_mol = _pmod.WARHEAD_MOL + e3_mol = _pmod.E3_MOL + + # Linker-side attachment SMARTS — must match what the linker exposes after + # generation. Mirrors the dispatch in defog_guided.py. + # cooh : both ends are COOH (or post-assembly CHO) — diacid linker + # amine : both ends are secondary/primary NH — diamine linker + # amino_acid : one end NH2, one end COOH — asymmetric linker + # None / default : cooh (backwards-compat) + if fix_termini == 'amine': + _WH_ATTACH = "[NX3;!H0;!$(NC=O);!$(NS=O);!$(N=O)]" + _E3_ATTACH = "[NX3;!H0;!$(NC=O);!$(NS=O);!$(N=O)]" + elif fix_termini == 'amino_acid': + _WH_ATTACH = "[$([NX3H2]),$([CX3](=[OX1])[OX2H1])]" + _E3_ATTACH = "[$([NX3H2]),$([CX3](=[OX1])[OX2H1])]" + else: # 'cooh' or None + _COOH_OR_CHO = "[$([CX3](=[OX1])[OX2H1]),$([CX3H1](=[OX1]))]" + _WH_ATTACH = _COOH_OR_CHO + _E3_ATTACH = _COOH_OR_CHO + analyzer = PROTACAnalyzer( + warhead=warhead_mol, + e3=e3_mol, + warhead_attachment_smarts=_WH_ATTACH, + e3_attachment_smarts=_E3_ATTACH, + sa_score_max=sa_max, + ) + builder = Builder() + + # Probe DegradeMaster availability once before the loop + dm_loaded = _load_degrademaster(protac_module) + dm_available = dm_loaded is not None + dm_notes = dm_loaded[5] if dm_available else "" + + rows = [] + n_invalid = 0 + + for raw in smiles_list: + smi = raw.split()[-1] if raw.startswith("Valid") else raw.strip() + if not smi: + continue + + mol = Chem.MolFromSmiles(smi) + + # Step 1 — chemical validity + if mol is None: + n_invalid += 1 + continue + + canon = Chem.MolToSmiles(mol) + row = {"smiles": canon} + + # Step 2 — linker properties + row.update(linker_props(mol)) + + # Step 3 — hard constraints + checks = analyzer._run_checks(mol) + row["linker_valid"] = all(checks.values()) + row.update({f"check_{k}": v for k, v in checks.items()}) + + # Step 4 — PROTAC assembly (attempt regardless of linker_valid, + # so we can still score partially valid linkers) + protac_smi = None + protac_mol = None + assembly_ok = False + try: + protac_smi = builder.assemble(mol, return_all=False, amide_only=False) + protac_mol = Chem.MolFromSmiles(protac_smi) + assembly_ok = protac_mol is not None + except Exception: + pass + + row["assembly_ok"] = assembly_ok + row["protac_smiles"] = protac_smi or "" + + # Step 5 — assembled PROTAC PK properties + if assembly_ok: + row.update(protac_props(protac_mol)) + passed, val_checks = builder.validate_protac(protac_mol) + row["protac_valid"] = passed + else: + for k in ["protac_mw", "protac_logp", "protac_tpsa", "protac_hbd", + "protac_hba", "protac_rotbonds", "protac_hac", "ro5_violations"]: + row[k] = None + row["protac_valid"] = False + + # Step 6 — DegradeMaster degradation probability + if assembly_ok and dm_available: + degrad_prob = _run_degrademaster(protac_smi, protac_module) + row["degrad_prob"] = round(degrad_prob, 4) if degrad_prob is not None else None + row["degrad_notes"] = dm_notes if degrad_prob is not None else "DegradeMaster inference failed" + else: + row["degrad_prob"] = None + row["degrad_notes"] = "assembly failed" if not assembly_ok else "model unavailable" + + rows.append(row) + + df = pd.DataFrame(rows) + + if verbose: + _print_summary(df, n_invalid, len(smiles_list), dm_available, dm_notes) + + return df + + +def _print_summary(df: pd.DataFrame, n_invalid: int, n_total: int, + dm_available: bool = True, dm_notes: str = ""): + n_valid_mol = len(df) + n_lk_valid = df["linker_valid"].sum() + n_assembled = df["assembly_ok"].sum() + n_pt_valid = df["protac_valid"].sum() if "protac_valid" in df else 0 + + print("=" * 65) + print("PROTAC LINKER EVALUATION SUMMARY") + print("=" * 65) + print(f" Total input SMILES : {n_total}") + print(f" Chemically invalid (skipped): {n_invalid}") + print(f" Chemically valid linkers : {n_valid_mol} " + f"({n_valid_mol/n_total*100:.1f}%)") + print(f" Pass all hard constraints : {n_lk_valid} " + f"({n_lk_valid/n_total*100:.1f}%)") + print(f" Successfully assembled : {n_assembled} " + f"({n_assembled/n_total*100:.1f}%)") + print(f" Assembled + PROTAC valid : {n_pt_valid} " + f"({n_pt_valid/n_total*100:.1f}%)") + + print() + print("── Linker properties (valid mols only) ──────────────────────") + for col, label in [ + ("linker_sa", "SA score (lower=easier synth)"), + ("linker_qed", "QED (higher=drug-like) "), + ("linker_mw", "Linker MW (Da) "), + ("linker_logp", "Linker logP "), + ("linker_tpsa", "Linker TPSA (Ų) "), + ("linker_rotbonds", "Linker RotBonds "), + ]: + if col in df.columns: + vals = df[col].dropna() + print(f" {label}: mean={vals.mean():.2f} " + f"std={vals.std():.2f} " + f"[{vals.min():.2f}, {vals.max():.2f}]") + + if n_assembled > 0: + assembled = df[df["assembly_ok"]] + print() + print("── Assembled PROTAC PK properties ───────────────────────────") + for col, label, window in [ + ("protac_mw", "PROTAC MW (Da) [700–1200] ", (700, 1200)), + ("protac_logp", "PROTAC logP [−2–7] ", (-2, 7)), + ("protac_tpsa", "PROTAC TPSA (Ų) ", None), + ("protac_hbd", "PROTAC HBD ", None), + ("protac_hba", "PROTAC HBA ", None), + ("protac_rotbonds", "PROTAC RotBonds [10–30] ", (10, 30)), + ("ro5_violations", "Ro5 violations ", None), + ]: + if col in assembled.columns: + vals = assembled[col].dropna() + in_window = "" + if window: + frac = ((vals >= window[0]) & (vals <= window[1])).mean() + in_window = f" {frac*100:.0f}% in window" + print(f" {label}: mean={vals.mean():.2f} " + f"std={vals.std():.2f} " + f"[{vals.min():.2f}, {vals.max():.2f}]{in_window}") + + # DegradeMaster degradation probability + if "degrad_prob" in assembled.columns: + dp = assembled["degrad_prob"].dropna() + if len(dp) > 0: + print() + if dm_available: + print("── DegradeMaster degradation probability (0–1) ──────────────") + print(f" (GCN checkpoint/1000; {dm_notes})") + else: + print("── Degradation probability (model unavailable) ───────────────") + print(f" Scored compounds : {len(dp)}") + print(f" Mean prob : {dp.mean():.3f}") + print(f" Std : {dp.std():.3f}") + print(f" Range : [{dp.min():.3f}, {dp.max():.3f}]") + thresholds = [0.3, 0.5, 0.7] + for t in thresholds: + n_above = (dp >= t).sum() + print(f" ≥ {t:.1f} (likely active) : {n_above} ({n_above/len(dp)*100:.1f}%)") + + print() + print("── Hard constraint breakdown ─────────────────────────────────") + check_cols = [c for c in df.columns if c.startswith("check_")] + for col in check_cols: + name = col.replace("check_", "") + pct = df[col].mean() * 100 + print(f" {name:<30}: {pct:5.1f}% pass") + print("=" * 65) + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + +def main(): + parser = argparse.ArgumentParser(description="Evaluate generated PROTAC linkers") + parser.add_argument("smiles_file", help="Path to generated SMILES file (one per line)") + parser.add_argument("--out", default=None, help="Save results CSV to this path") + parser.add_argument("--sa-max", type=float, default=6.0, + help="Max SA score for linker validity (default 6.0)") + parser.add_argument("--protac-module", dest="protac_module", default="ErlotinibVHL", + help="Name of the protac builder module (e.g. ErlotinibVHL, " + "BRD9VHL, KRASVHL, BRD4VHL, ARCRBN). Selects warhead/E3 and " + "DegradeMaster pocket+features. Default: ErlotinibVHL.") + parser.add_argument("--quiet", action="store_true", help="Suppress printed summary") + args = parser.parse_args() + + path = Path(args.smiles_file) + if not path.exists(): + print(f"Error: file not found: {path}", file=sys.stderr) + sys.exit(1) + + with open(path) as f: + smiles = [line.strip() for line in f if line.strip()] + + print(f"Loaded {len(smiles)} SMILES from {path}\n") + + df = evaluate(smiles, sa_max=args.sa_max, verbose=not args.quiet, + protac_module=args.protac_module) + + if args.out: + df.to_csv(args.out, index=False) + print(f"\nResults saved to {args.out}") + + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/scripts/fetch_third_party.py b/CupOFMoCA-PROTAC/scripts/fetch_third_party.py new file mode 100644 index 0000000000000000000000000000000000000000..a676b9675ef5a1794a01bc4852526964101b6eca --- /dev/null +++ b/CupOFMoCA-PROTAC/scripts/fetch_third_party.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +"""Fetch the third-party components that are not redistributed with this release. + +DegradeMaster and EGFR-PROPK are published without a license, so we cannot ship +copies of them. This script clones each upstream repository at the pinned commit +used for the paper and copies the subset the pipeline imports into the layout +`scripts/evaluate_linkers.py` and `src/classifier/protac_classifier.py` expect. + +Both are optional. Generation (`scripts/generate.py`) runs without either: + - DegradeMaster is needed only for predicted-degradation scoring. + - EGFR-PROPK is needed only for the EGFR-only PK columns of batch_report.py. + +Usage: + python scripts/fetch_third_party.py # fetch both + python scripts/fetch_third_party.py degrademaster +""" +from __future__ import annotations + +import argparse +import shutil +import subprocess +import sys +import tempfile +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] + +COMPONENTS = { + "degrademaster": { + "url": "https://github.com/ABILiLab/DegradeMaster", + "commit": "aa149beaf067a051e070b3b281f7dba43c2f3e90", + "dest": ROOT / "src" / "degrademaster", + "paths": [ + "model.py", "dataset.py", "nn_utils.py", "pdb_utils.py", + "tools.py", "protacloader.py", + "tokenizer", "utils", + "checkpoint/1000/model_for_case_study.pth", + ], + }, + "propk": { + "url": "https://github.com/Zhang-Ran-0119/EGFR-PROPK", + "commit": "6940cb90c449e6c6d0678508fc2f228da796e028", + "dest": ROOT / "src" / "propk", + "paths": [ + "maplight.py", "maplight_gnn.py", + "models/CL_1_finetune.pkl", + "models/t0.5_10_finetune.pkl", + "models/Vss_1_finetune.pkl", + ], + }, +} + + +def fetch(name: str, spec: dict) -> int: + print(f"\n=== {name} ===") + print(f" {spec['url']} @ {spec['commit'][:12]}") + print(" NOTE: published without a license file. Review the upstream terms") + print(" before using or redistributing it.") + + with tempfile.TemporaryDirectory() as tmp: + try: + subprocess.run(["git", "clone", "--quiet", spec["url"], tmp], check=True) + subprocess.run(["git", "-C", tmp, "checkout", "--quiet", spec["commit"]], + check=True) + except subprocess.CalledProcessError as exc: + print(f" FAILED to clone: {exc}") + return 1 + + missing = [] + for rel in spec["paths"]: + src = Path(tmp) / rel + dst = spec["dest"] / rel + if not src.exists(): + missing.append(rel) + continue + dst.parent.mkdir(parents=True, exist_ok=True) + if src.is_dir(): + shutil.copytree(src, dst, dirs_exist_ok=True) + else: + shutil.copy2(src, dst) + print(f" + {rel}") + + if missing: + print(f" {len(missing)} path(s) not found upstream at this commit:") + for rel in missing: + print(f" - {rel}") + print(" The upstream layout may have changed, or the checkpoint may be") + print(" distributed outside git. Fetch those by hand.") + return 1 + return 0 + + +def main() -> int: + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("components", nargs="*", default=[], + choices=list(COMPONENTS), + help="which to fetch (default: all)") + args = ap.parse_args() + + names = args.components or list(COMPONENTS) + failures = sum(fetch(n, COMPONENTS[n]) for n in names) + + print() + if failures: + print(f"{failures} component(s) incomplete.") + else: + print("All components fetched.") + return 1 if failures else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/CupOFMoCA-PROTAC/scripts/generate.py b/CupOFMoCA-PROTAC/scripts/generate.py new file mode 100644 index 0000000000000000000000000000000000000000..3eb7e3192d9f18c3175c608142316deb03dea631 --- /dev/null +++ b/CupOFMoCA-PROTAC/scripts/generate.py @@ -0,0 +1,1484 @@ +""" +DeFoG guided PROTAC linker generation. + +Local-edit loop: each step picks one graph position (atom or edge pair), queries DeFoG +for its predicted clean distribution, tries all candidate types, and commits the best. +Improvements persist: every step builds on all prior commits. + +Objectives (fast_scoring layout): + PROTACClassifier is the hard validity gate (structural checks + alert mask + + >=2 SMARTS sites). It drives the validity mask and is NOT a term in the + weighted sum, so it takes no weight. + + Soft scorers, in --weights positional order (paper Eq. 6): + [0] PROTACLinkerSAScorer — S1 synthetic accessibility in [0,1] w=4.0 + [1] PROTACLinkerChemScorer — S2 logP/MW/RotB/ether chemotype mean w=2.0 + [2] PROTACLinkerConnectivityScorer — S3 connectivity (always runs) w=4.0 + + PreassembledPROTACScorer (Morgan-FP linker quality) was dropped after the + weight ablation (paper Appendix J.5) showed that raising its weight lowered + both assembly validity and DegradeMaster score. + +Aggregated score = Σ weights[k] · soft_scores[k]. Candidates rejected by the +validity gate are masked to zero probability before exponential tilting. +""" + +import argparse +import os +import sys +import json +import csv +from pathlib import Path +import random +from datetime import datetime +from enum import Enum +from typing import Optional, Tuple, Dict + +import numpy as np +import torch +import torch.nn.functional as F +from tqdm import tqdm + +# Add DeFoG to path so its modules resolve at import time. +# DeFoG's internal code does `from flow_matching import ...` (needs src/defog/src) +# and also `from src import utils` (needs src/defog/, so `src` resolves to defog's src). +_DEFOG_ROOT = os.path.normpath( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "../src/defog") +) +_DEFOG_SRC = os.path.join(_DEFOG_ROOT, "src") +for _p in (_DEFOG_ROOT, _DEFOG_SRC): + if _p not in sys.path: + sys.path.insert(0, _p) + +# Add PROTAC sources (classifier + builders) to path. +# _REPO_SRC itself lets us do `from protac. import ...`, +# and the subdirs let us do `from protac_classifier import ...`, etc. +_REPO_SRC = os.path.normpath( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "../src") +) +if _REPO_SRC not in sys.path: + sys.path.insert(0, _REPO_SRC) +for _sub in ("classifier", "protac"): + _p = os.path.join(_REPO_SRC, _sub) + if _p not in sys.path: + sys.path.insert(0, _p) + +from flow_matching import flow_matching_utils # noqa: E402 +from utils import PlaceHolder # noqa: E402 + + +class GuidanceMethod(Enum): + BEST_OF_K = "bok" + EXPONENTIAL_TILT = "exp_tilt" + HYBRID = "hybrid" + + +# --------------------------------------------------------------------------- +# Generation tracker (same structure as cupofmoca_guided.py) +# --------------------------------------------------------------------------- + +class GenerationTracker: + + def __init__(self, output_dir="./generation_logs"): + self.output_dir = output_dir + os.makedirs(output_dir, exist_ok=True) + self.run_id = datetime.now().strftime("%Y%m%d_%H%M%S") + self.step_logs = [] + self.final_results = [] + + def log_step(self, step: int, scores, detailed_results=None, + improved_indices=None, guidance_info: Optional[Dict] = None): + step_info = { + "step": step, + "scores": scores.cpu().numpy().tolist() if isinstance(scores, torch.Tensor) else scores, + "num_valid": int((scores > 0).sum()) if hasattr(scores, "__iter__") else 0, + "improved_indices": improved_indices or [], + "guidance_info": guidance_info or {}, + } + self.step_logs.append(step_info) + + def log_final(self, molecule_list, detailed_results): + for i, (_, result) in enumerate(zip(molecule_list, detailed_results)): + self.final_results.append({ + "sample_idx": i, + "valid": result.get("valid", 0), + "smiles": result.get("smiles", ""), + "checks": result.get("checks", {}), + "properties": result.get("properties", {}), + }) + + def save(self, filename_prefix="defog_guided"): + step_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_steps.json") + with open(step_path, "w") as f: + json.dump(self.step_logs, f, indent=2, default=str) + + final_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_final.json") + with open(final_path, "w") as f: + json.dump(self.final_results, f, indent=2, default=str) + + csv_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_summary.csv") + with open(csv_path, "w", newline="") as f: + writer = csv.writer(f) + writer.writerow(["sample_idx", "valid", "smiles", "mw", "logp", "tpsa", "hbd", "rotbonds"]) + for r in self.final_results: + props = r.get("properties", {}) + writer.writerow([ + r["sample_idx"], r["valid"], r["smiles"], + props.get("mw", ""), props.get("xlogp", ""), + props.get("polararea", ""), props.get("hbonddonor", ""), + props.get("rotbonds", ""), + ]) + + print(f"\n=== Generation logs saved ===") + print(f" Step logs: {step_path}") + print(f" Final: {final_path}") + print(f" CSV: {csv_path}") + return step_path, final_path, csv_path + + def print_step_summary(self, step, scores, detailed_results=None, guidance_method=None): + scores_list = scores.cpu().numpy().tolist() if isinstance(scores, torch.Tensor) else list(scores) + # PROTACClassifier returns binary 0/1: 1 = passed all PROTACAnalyzer structural + # checks + alert mask + has ≥2 SMARTS attachment sites; 0 = failed any. + # Final assembly (actual RDKit reaction trial) is checked separately at the end. + num_valid = sum(1 for s in scores_list if s > 0) + print(f"\n--- Step {step} ({guidance_method or 'unknown'}) --- classifier-valid: {num_valid}/{len(scores_list)}") + + def print_final_summary(self): + total = len(self.final_results) + num_valid = sum(1 for r in self.final_results if r.get("valid", 0) > 0) + print("\n" + "=" * 60) + print("FINAL GENERATION SUMMARY") + print("=" * 60) + print(f"Total molecules: {total} Valid: {num_valid}") + for r in self.final_results: + tag = "✓" if r.get("valid") == 1 else "✗" + smi = r.get("smiles", "") or "INVALID (sanitization failed)" + print(f" [{tag}] {r['sample_idx']:3d} {smi[:60]}") + + +# --------------------------------------------------------------------------- +# DeFoGGenerator (mirrors CupOfMoCAGenerator) +# --------------------------------------------------------------------------- + +class DeFoGGenerator: + """ + DeFoG-based guided PROTAC linker generator. + + Parameters + ---------- + model : GraphDiscreteFlowModel + Pre-loaded DeFoG model (pytorch_lightning module). + atom_decoder : list[str] + Maps atom-type index → element symbol (from dataset_infos.atom_decoder). + objectives : list + Soft scorers used in the weighted sum S(z). Final entry is treated as + the connectivity scorer (always runs even when validity_fn = 0). + validity_fn : callable | None + Binary 0/1 hard gate (PROTACClassifier). Output drives the validity mask + in exp_tilt sampling. Does not contribute to S(z). + args : argparse.Namespace + tracker : GenerationTracker | None + """ + + def __init__(self, model, atom_decoder: list, objectives: list, args, + validity_fn=None, tracker=None): + self.model = model + self.atom_decoder = atom_decoder + self.objectives = objectives + self.validity_fn = validity_fn + self.args = args + self.num_objectives = len(objectives) + self.tracker = tracker + self.device = next(model.parameters()).device + self.guidance_method = GuidanceMethod(args.guidance_method) + + # Vocab mask: restrict to atom types seen in PROTAC linker training data. + # Zeroes out I, P, Cl, Br from pred_X before guidance — model can never pick them. + _LINKER_ATOMS = {"C", "N", "O", "S", "F"} + self.vocab_mask = torch.tensor( + [1.0 if sym in _LINKER_ATOMS else 0.0 for sym in atom_decoder], + device=self.device, + ) # [d_X] + + self.sample_T = getattr(args, 'num_steps', None) or model.sample_T + self.node_dist = model.node_dist + self.time_distorter = model.time_distorter + + + # ------------------------------------------------------------------------- + # Initial graph generation (x0 from prior μ₀) + # ------------------------------------------------------------------------- + + def _generate_single_x0(self) -> Tuple[torch.Tensor, torch.Tensor, + torch.Tensor, torch.Tensor]: + """ + Sample initial noisy graphs from the limit distribution. + Returns X, E, y, node_mask — all on self.device. + """ + batch_size = self.args.num_samples + + # Sample number of nodes per graph + if self.args.num_nodes is not None: + n_nodes = self.args.num_nodes * torch.ones( + batch_size, device=self.device, dtype=torch.int + ) + else: + n_nodes = self.node_dist.sample_n(batch_size, self.device) + # Clamp to PROTAC linker atom range + n_nodes = n_nodes.clamp(self.args.min_nodes, self.args.max_nodes) + + n_max = n_nodes.max().item() + arange = torch.arange(n_max, device=self.device).unsqueeze(0).expand(batch_size, -1) + node_mask = arange < n_nodes.unsqueeze(1) + + z_T = flow_matching_utils.sample_discrete_feature_noise( + limit_dist=self.model.limit_dist, node_mask=node_mask + ) + X, E, y = z_T.X, z_T.E, z_T.y + return X, E, y, node_mask, n_nodes + + # Per-type terminus geometry configs. + # 'cooh' — two COOH groups at nodes 0-5; anchor carbons at 0 and 3 + # 'methyl' — CH3(0)–CH2(1)–[generated]–CH2(2)–CH3(3) scaffold; + # terminal CH3s (0, 3) bond only to their inner CH2 anchor + _TERMINUS_CONFIGS = { + 'cooh': { + 'n_terminus_nodes': 6, + 'min_total_nodes': 8, # 6 terminus + at least 2 free bridge atoms + 'anchor_nodes': (0, 3), + 'fixed_atom_pos': frozenset({0, 1, 2, 3, 4, 5}), + 'fixed_o_nodes': frozenset({1, 2, 4, 5}), + 'fixed_edge_pos': frozenset({ + (0, 1), (1, 0), (0, 2), (2, 0), # terminus-1: C=O, C-OH + (3, 4), (4, 3), (3, 5), (5, 3), # terminus-2: C=O, C-OH + (0, 3), (3, 0), # no direct C–C between anchor atoms + }), + }, + 'methyl': { + 'n_terminus_nodes': 4, + 'min_total_nodes': 6, # 4 terminus + at least 2 free bridge atoms + 'anchor_nodes': (1, 2), + 'fixed_atom_pos': frozenset({0, 1, 2, 3}), + 'fixed_o_nodes': frozenset({0, 3}), # terminal CH3s — bond only to their CH2 + 'fixed_edge_pos': frozenset({ + (0, 1), (1, 0), # CH3–CH2 bond at warhead end + (2, 3), (3, 2), # CH2–CH3 bond at E3 end + (1, 2), (2, 1), # no direct anchor–anchor bond (force bridge through free atoms) + }), + }, + 'amine': { + 'n_terminus_nodes': 2, + 'min_total_nodes': 4, # 2 terminus N + at least 2 free bridge atoms + 'anchor_nodes': (0, 1), + 'fixed_atom_pos': frozenset({0, 1}), + 'fixed_o_nodes': frozenset(), # no O nodes to isolate + 'fixed_edge_pos': frozenset({ + (0, 1), (1, 0), # no direct N–N bond between the two terminus atoms + }), + }, + 'amino_acid': { + # Asymmetric: N(0) on warhead end, COOH(1-3) on VHL end. + # Used for BRD4-VHL and any PROTAC where warhead provides COOH + # and E3 provides NH2 (so linker must bridge with amine + acid termini). + 'n_terminus_nodes': 4, + 'min_total_nodes': 6, # 4 terminus + at least 2 free bridge atoms + 'anchor_nodes': (0, 1), + 'fixed_atom_pos': frozenset({0, 1, 2, 3}), + 'fixed_o_nodes': frozenset({2, 3}), # O atoms of the COOH group + 'fixed_edge_pos': frozenset({ + (1, 2), (2, 1), # C=O (double, carbonyl) + (1, 3), (3, 1), # C-OH (single, hydroxyl) + (0, 1), (1, 0), # no direct N–C(cooh) bond between anchors + }), + }, + } + + @staticmethod + def _limit_anchor_bonds(E: torch.Tensor, i: int, n: int, + anchor: int, blocked: set, max_extra: int = 1) -> None: + """Cap the number of bonds from `anchor` to free atoms (in-place on E). + + For atoms outside the pinned `blocked` set, count bonds and zero out all + but the first `max_extra` of them. Keeps the lowest-indexed bond + (deterministic). Prevents the anchor from being absorbed into a ring + (e.g., the pinned NH2 nitrogen ending up as part of a pyrazole) which + would break the terminal-NH2 / single-bridge-bond invariant required for + amide-bond assembly with the warhead/E3. + """ + free_bonds = [nb for nb in range(n) + if nb != anchor and nb not in blocked + and E[i, anchor, nb].argmax().item() > 0] + if len(free_bonds) > max_extra: + for nb in free_bonds[max_extra:]: + E[i, anchor, nb] = 0.0; E[i, anchor, nb, 0] = 1.0 + E[i, nb, anchor] = 0.0; E[i, nb, anchor, 0] = 1.0 + + def _fix_termini(self, X: torch.Tensor, E: torch.Tensor, + n_nodes: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """Hard-pin terminus groups after every step so guidance can never revert them. + + 'cooh' — nodes 0-5: two COOH groups (carbonyl-C + C=O + C-OH each). + Anchor carbons (0, 3) connect freely to bridge atoms (6+). + 'methyl' — nodes 0-3: CH3(0)–CH2(1) ... CH2(2)–CH3(3) scaffold. + Terminal CH3s (0, 3) locked to only bond their inner CH2 (1 or 2). + 'amine' — nodes 0-1: two N atoms (nucleophiles for warhead/E3 COOH). + 'amino_acid' — nodes 0-3: N(0) warhead-side amine + COOH(1-3) VHL-side acid. + Asymmetric — use for BRD4-VHL where warhead provides COOH and + E3 (VHL) provides NH2, so the linker bridges with NH2 and COOH. + + Each anchor atom is also capped to at most ONE bond to free atoms (via + _limit_anchor_bonds) so the model can't absorb the terminus into a ring. + """ + terminus_type = getattr(self.args, 'fix_termini', None) + if not terminus_type: + return X, E + cfg = self._TERMINUS_CONFIGS[terminus_type] + try: + C_IDX = self.atom_decoder.index("C") + O_IDX = self.atom_decoder.index("O") if terminus_type == 'cooh' else None + except ValueError: + return X, E + + for i in range(X.shape[0]): + n = n_nodes[i].item() + if n < cfg['n_terminus_nodes']: + continue + + if terminus_type == 'cooh': + # Atom types: C, O, O, C, O, O + for pos, atype in [(0, C_IDX), (1, O_IDX), (2, O_IDX), + (3, C_IDX), (4, O_IDX), (5, O_IDX)]: + X[i, pos] = 0.0 + X[i, pos, atype] = 1.0 + # Internal bonds: C=O (double=2), C-OH (single=1) + for r, c, btype in [ + (0, 1, 2), (1, 0, 2), (0, 2, 1), (2, 0, 1), + (3, 4, 2), (4, 3, 2), (3, 5, 1), (5, 3, 1), + ]: + E[i, r, c] = 0.0 + E[i, r, c, btype] = 1.0 + # O atoms bond only to their own carbonyl C + for o_node, c_node in [(1, 0), (2, 0), (4, 3), (5, 3)]: + for other in range(n): + if other == c_node: + continue + E[i, o_node, other] = 0.0; E[i, o_node, other, 0] = 1.0 + E[i, other, o_node] = 0.0; E[i, other, o_node, 0] = 1.0 + # Block direct C–C bond between anchor carbons + E[i, 0, 3] = 0.0; E[i, 0, 3, 0] = 1.0 + E[i, 3, 0] = 0.0; E[i, 3, 0, 0] = 1.0 + # Cap each anchor C to ≤1 bond to free atoms (single bridge attachment). + # Blocked = self + the COOH O atoms + the other anchor C. + self._limit_anchor_bonds(E, i, n, 0, blocked={0, 1, 2, 3}, max_extra=1) + self._limit_anchor_bonds(E, i, n, 3, blocked={0, 3, 4, 5}, max_extra=1) + + elif terminus_type == 'methyl': + # Scaffold: CH3(0)–CH2(1)–[bridge]–CH2(2)–CH3(3) + # Terminal CH3s (0, 3) bond only to their inner CH2 anchor. + for pos in (0, 1, 2, 3): + X[i, pos] = 0.0 + X[i, pos, C_IDX] = 1.0 + # Force CH3–CH2 single bonds at each end + for r, c in [(0, 1), (1, 0), (2, 3), (3, 2)]: + E[i, r, c] = 0.0; E[i, r, c, 1] = 1.0 + # Terminal CH3 atoms (0, 3) bond ONLY to their inner CH2 + for terminal, inner in [(0, 1), (3, 2)]: + for other in range(n): + if other == inner: + continue + E[i, terminal, other] = 0.0; E[i, terminal, other, 0] = 1.0 + E[i, other, terminal] = 0.0; E[i, other, terminal, 0] = 1.0 + # Block direct anchor–anchor bond (force bridge through free atoms) + E[i, 1, 2] = 0.0; E[i, 1, 2, 0] = 1.0 + E[i, 2, 1] = 0.0; E[i, 2, 1, 0] = 1.0 + # Inner CH2 anchors (1, 2) bond ONCE to the bridge; everything else free. + # (The terminal CH3s 0, 3 are already pinned to their inner CH2 only.) + self._limit_anchor_bonds(E, i, n, 1, blocked={0, 1, 2}, max_extra=1) + self._limit_anchor_bonds(E, i, n, 2, blocked={1, 2, 3}, max_extra=1) + + elif terminus_type == 'amine': + # Two terminal N atoms at nodes 0 and 1. + # Each N is the nucleophilic attachment point; warhead/E3 provide COOH. + try: + N_IDX = self.atom_decoder.index("N") + except ValueError: + continue + for pos in (0, 1): + X[i, pos] = 0.0 + X[i, pos, N_IDX] = 1.0 + # Block direct N–N bond between the two terminus atoms + E[i, 0, 1] = 0.0; E[i, 0, 1, 0] = 1.0 + E[i, 1, 0] = 0.0; E[i, 1, 0, 0] = 1.0 + # Cap each anchor N to ≤1 bond to free atoms — keeps it as primary NH2, + # not absorbed into a ring or turned into a secondary amine. + self._limit_anchor_bonds(E, i, n, 0, blocked={0, 1}, max_extra=1) + self._limit_anchor_bonds(E, i, n, 1, blocked={0, 1}, max_extra=1) + + elif terminus_type == 'amino_acid': + # Node 0: N (warhead-side amine — nucleophile for warhead COOH) + # Nodes 1,2,3: COOH group (VHL-side — electrophile for VHL NH2) + # node 1 = carbonyl C, node 2 = C=O oxygen, node 3 = C-OH oxygen + try: + N_IDX = self.atom_decoder.index("N") + O_IDX = self.atom_decoder.index("O") + except ValueError: + continue + X[i, 0] = 0.0; X[i, 0, N_IDX] = 1.0 + X[i, 1] = 0.0; X[i, 1, C_IDX] = 1.0 + for pos in (2, 3): + X[i, pos] = 0.0; X[i, pos, O_IDX] = 1.0 + # COOH internal bonds: C=O (double, type 2) and C-OH (single, type 1) + for r, c, btype in [(1, 2, 2), (2, 1, 2), (1, 3, 1), (3, 1, 1)]: + E[i, r, c] = 0.0; E[i, r, c, btype] = 1.0 + # O atoms bond only to their COOH carbon (node 1) + for o_node in (2, 3): + for other in range(n): + if other == 1: + continue + E[i, o_node, other] = 0.0; E[i, o_node, other, 0] = 1.0 + E[i, other, o_node] = 0.0; E[i, other, o_node, 0] = 1.0 + # Block direct N(0)–C(1) bond between the two anchor atoms + E[i, 0, 1] = 0.0; E[i, 0, 1, 0] = 1.0 + E[i, 1, 0] = 0.0; E[i, 1, 0, 0] = 1.0 + # Cap each anchor to ≤1 bond to free atoms: + # - N(0) capped → primary NH2 (not absorbed into a ring) + # - C(1) capped → carboxyl C with single bridge bond (not 2 substituents) + self._limit_anchor_bonds(E, i, n, 0, blocked={0, 1, 2, 3}, max_extra=1) + self._limit_anchor_bonds(E, i, n, 1, blocked={0, 1, 2, 3}, max_extra=1) + + return X, E + + def _check_validity(self, X: torch.Tensor, E: torch.Tensor, + n_nodes: torch.Tensor) -> torch.Tensor: + """Run the hard validity gate (PROTACClassifier).""" + molecule_list = self._to_molecule_list(X, E, n_nodes) + if self.validity_fn is None: + return torch.ones(len(molecule_list), dtype=torch.bool, device=self.device) + return self.validity_fn(molecule_list) > 0 + + def _to_molecule_list(self, X: torch.Tensor, E: torch.Tensor, + n_nodes: torch.Tensor) -> list: + """Convert batch tensors to DeFoG molecule_list format. + + X may be one-hot [B, N, d_X] or index [B, N]; same for E. + _graph_to_mol expects index tensors, so collapse if needed. + """ + mol_list = [] + for i in range(X.shape[0]): + n = n_nodes[i].item() + atom_types = X[i, :n] + edge_types = E[i, :n, :n] + if atom_types.dim() == 2: # one-hot → indices + atom_types = atom_types.argmax(-1) + if edge_types.dim() == 3: # one-hot → indices + edge_types = edge_types.argmax(-1) + mol_list.append([atom_types.cpu(), edge_types.cpu()]) + return mol_list + + def generate_x0(self) -> Tuple[torch.Tensor, ...]: + """Generate initial graphs, optionally enforcing validity.""" + print("Generating initial graphs (x0 from limit distribution)...") + X, E, y, node_mask, n_nodes = self._generate_single_x0() + + if self.args.require_valid_x0: + print("Enforcing initial validity constraint...") + for retry in range(self.args.max_x0_retries): + validity = self._check_validity(X, E, n_nodes) + num_valid = validity.sum().item() + num_invalid = self.args.num_samples - num_valid + + if num_invalid == 0: + print(f"✓ All {self.args.num_samples} samples valid!") + break + + print(f" Retry {retry + 1}/{self.args.max_x0_retries}: " + f"{num_valid}/{self.args.num_samples} valid") + + invalid_idx = torch.where(~validity)[0] + orig = self.args.num_samples + self.args.num_samples = num_invalid + X_new, E_new, _, nm_new, nn_new = self._generate_single_x0() + self.args.num_samples = orig + + X[invalid_idx] = X_new + E[invalid_idx] = E_new + node_mask[invalid_idx] = nm_new + n_nodes[invalid_idx] = nn_new + + X, E = self._fix_termini(X, E, n_nodes) + return X, E, y, node_mask, n_nodes + + # ------------------------------------------------------------------------- + # Score helpers (identical to cupofmoca_guided) + # ------------------------------------------------------------------------- + + def _get_scores(self, molecule_list: list) -> torch.Tensor: + """S(z) — shape [num_objectives + 1, batch_size]. + + Row layout: + [0] validity_fn (PROTACClassifier) — binary 0/1, hard gate only + (not in the weighted sum; consumed by validity_mask in exp_tilt) + [1:-1] soft scorers — fast / sa / chem (skipped if validity = 0 for all) + [-1] connectivity — always runs even when validity = 0 + + Returning the validity row as row 0 keeps a stable interface with + callers that read `raw[0]` for the validity mask. + """ + from protac_classifier import prebuild_mols as _prebuild + mols = _prebuild(molecule_list, self.atom_decoder) + + batch = len(molecule_list) + if self.validity_fn is not None: + validity = self.validity_fn(molecule_list, mols=mols).to(self.device) + else: + validity = torch.ones(batch, device=self.device) + + if self.num_objectives == 0: + return validity.unsqueeze(0) + + # Connectivity (last) always runs. + conn_scores = self.objectives[-1](molecule_list, mols=mols).to(self.device) + + n_mid = self.num_objectives - 1 # objectives[:-1] + if n_mid > 0 and (validity == 0).all(): + zeros = torch.zeros(n_mid, batch, device=self.device) + return torch.stack([validity, *[zeros[i] for i in range(n_mid)], conn_scores], dim=0) + + mid_scores = [obj(molecule_list, mols=mols).to(self.device) for obj in self.objectives[:-1]] + return torch.stack([validity, *mid_scores, conn_scores], dim=0) + + def _compute_aggregated_score(self, scores: torch.Tensor, + weights: torch.Tensor) -> torch.Tensor: + """ + scores : [1 + num_objectives, batch] (row 0 is validity, ignored here) + weights : [num_objectives, 1] + + Weighted sum over the soft scorers only. The validity row from _get_scores + is consumed separately by the validity_mask in exp_tilt. + """ + soft = scores[1:] + if self.args.score_aggregation == "weighted_min": + return torch.min(weights * soft, dim=0).values + return (weights * soft).sum(dim=0) + + # ------------------------------------------------------------------------- + # Guidance methods — one position per step, same structure as cupofmoca_guided.py + # The position is one atom OR one edge pair; the method selects the type to place there. + # ------------------------------------------------------------------------- + + def _best_of_k_guidance( + self, + x_idx: torch.Tensor, e_idx: torch.Tensor, + ptype: str, pos, + pred_X_i: torch.Tensor, pred_E_i: torch.Tensor, + weights: torch.Tensor, current_score: float, n: int, + ) -> Tuple[torch.Tensor, torch.Tensor, bool]: + """Try every candidate type at one position; commit the highest-scoring one. + + All scorers are run on every candidate (with validity-first short-circuit + inside _get_scores: invalid candidates skip the soft scorers). Commit the + new type only if it strictly improves over the current score. + """ + n_atom = len(self.atom_decoder) + n_bond = pred_E_i.shape[-1] + + if ptype == "A": + if pos >= n: + return x_idx, e_idx, False + best_score = current_score + best_type = x_idx[pos].item() + for atype in range(n_atom): + if self.vocab_mask[atype].item() == 0.0: + continue + trial_x = x_idx.clone(); trial_x[pos] = atype + s = self._compute_aggregated_score( + self._get_scores([[trial_x, e_idx]]), weights + ).item() + if s > best_score: + best_score = s; best_type = atype + if best_type != x_idx[pos].item(): + x_idx = x_idx.clone(); x_idx[pos] = best_type + return x_idx, e_idx, True + + elif ptype == "E": + r, c = pos + if r >= n or c >= n: + return x_idx, e_idx, False + best_score = current_score + best_type = e_idx[r, c].item() + for btype in range(n_bond): + trial_e = e_idx.clone(); trial_e[r, c] = btype; trial_e[c, r] = btype + s = self._compute_aggregated_score( + self._get_scores([[x_idx, trial_e]]), weights + ).item() + if s > best_score: + best_score = s; best_type = btype + if best_type != e_idx[r, c].item(): + e_idx = e_idx.clone(); e_idx[r, c] = best_type; e_idx[c, r] = best_type + return x_idx, e_idx, True + + return x_idx, e_idx, False + + def _exponential_tilt_guidance( + self, + x_idx: torch.Tensor, e_idx: torch.Tensor, + ptype: str, pos, + pred_X_i: torch.Tensor, pred_E_i: torch.Tensor, + beta: float, weights: torch.Tensor, current_score: float, n: int, + ) -> Tuple[torch.Tensor, torch.Tensor, bool]: + """ + Exponential tilting: p'(c) ∝ p(c) · exp(β·S(c)) · 1_valid(c). + + For atoms: tries top_k types from model distribution. + For edges: tries all bond types. + Optional acceptance criterion: reject if selected score < current_score. + """ + n_atom = len(self.atom_decoder) + n_bond = pred_E_i.shape[-1] + + if ptype == "A": + if pos >= n: + return x_idx, e_idx, False + top_k = min(getattr(self.args, 'top_k', n_atom), n_atom) + topk_out = pred_X_i[pos, :n_atom].topk(top_k) + candidates = topk_out.indices.cpu() + base_probs = topk_out.values.cpu() + + agg_list, clf_list = [], [] + for atype in candidates.tolist(): + tx = x_idx.clone(); tx[pos] = atype + raw = self._get_scores([[tx, e_idx]]) + agg_list.append(self._compute_aggregated_score(raw, weights).item()) + clf_list.append(raw[0, 0].item()) + agg_scores = torch.tensor(agg_list) + clf_scores = torch.tensor(clf_list) + current_type = x_idx[pos].item() + + elif ptype == "E": + r, c = pos + if r >= n or c >= n: + return x_idx, e_idx, False + candidates = torch.arange(n_bond) + base_probs = pred_E_i[r, c, :n_bond].cpu() + + agg_list, clf_list = [], [] + for btype in candidates.tolist(): + te = e_idx.clone(); te[r, c] = btype; te[c, r] = btype + raw = self._get_scores([[x_idx, te]]) + agg_list.append(self._compute_aggregated_score(raw, weights).item()) + clf_list.append(raw[0, 0].item()) + agg_scores = torch.tensor(agg_list) + clf_scores = torch.tensor(clf_list) + current_type = e_idx[r, c].item() + + else: + return x_idx, e_idx, False + + # Hard gate: zero out any candidate type whose PROTACClassifier (objectives[0]) + # rejects (liability or insufficient termini). The soft scorers (connectivity, + # SA, ChemScorer, FP-LogReg) provide the climbable gradient for the rest. + validity_mask = (clf_scores > 0).float() + + log_base = torch.log(base_probs.clamp(min=1e-10)) + log_tilted = log_base + beta * agg_scores + torch.log(validity_mask + 1e-10) + log_max = log_tilted.max() + + if torch.isinf(log_max) or torch.isnan(log_max): + final_probs = base_probs.clamp(min=1e-10) + else: + tilted = torch.exp(log_tilted - log_max) + psum = tilted.sum() + final_probs = tilted / psum if psum > 1e-10 else base_probs.clamp(min=1e-10) + + final_probs = final_probs.clamp(min=1e-10) + final_probs = final_probs / final_probs.sum() + + selected_idx = torch.multinomial(final_probs, 1).item() + selected_type = candidates[selected_idx].item() + selected_score = agg_scores[selected_idx].item() + + if self.args.use_acceptance and selected_score < current_score: + return x_idx, e_idx, False + + if selected_type == current_type: + return x_idx, e_idx, False + + # All scorers already ran in the candidate loop above (no two-stage), + # so the selected_score is already the full aggregated score. + if ptype == "A": + x_idx = x_idx.clone(); x_idx[pos] = selected_type + return x_idx, e_idx, True + else: + r, c = pos + e_idx = e_idx.clone(); e_idx[r, c] = selected_type; e_idx[c, r] = selected_type + return x_idx, e_idx, True + + def _hybrid_guidance( + self, + x_idx: torch.Tensor, e_idx: torch.Tensor, + ptype: str, pos, + pred_X_i: torch.Tensor, pred_E_i: torch.Tensor, + beta: float, weights: torch.Tensor, current_score: float, progress: float, n: int, + ) -> Tuple[torch.Tensor, torch.Tensor, bool]: + """BoK (exploration) for steps < 50%; exp_tilt (refinement) for steps ≥ 50%.""" + if progress < 0.5: + return self._best_of_k_guidance( + x_idx, e_idx, ptype, pos, pred_X_i, pred_E_i, weights, current_score, n) + else: + return self._exponential_tilt_guidance( + x_idx, e_idx, ptype, pos, pred_X_i, pred_E_i, beta, weights, current_score, n) + + # ------------------------------------------------------------------------- + # Main generation loop — local-edit style (Algorithm 2 on graphs) + # ------------------------------------------------------------------------- + + def generate(self) -> list: + """ + Run local-edit guided generation. + + Each step picks one position (atom or edge pair, rotating through all positions), + queries the model, tries all candidate types, and commits the best-scoring one. + Improvements persist across all steps. + """ + X, E, y, node_mask, n_nodes = self.generate_x0() + + batch_size = self.args.num_samples + n_atom_types = len(self.atom_decoder) + n_bond_types = E.shape[-1] # read from model tensor, not hardcoded + + # weights apply to the soft scorers only (len = num_objectives). + if self.args.weights is None: + weights = torch.tensor( + [1.0 / self.num_objectives] * self.num_objectives, + device=self.device, + ).view(-1, 1) + else: + raw_w = list(self.args.weights) + # Backward compat: if too many weights are passed (e.g. the + # old 5-vector form with classifier+fast leading), strip the + # extra leading entries so the trailing positions still line up + # with the connectivity scorer. + while len(raw_w) > self.num_objectives: + raw_w = raw_w[1:] + if len(raw_w) != self.num_objectives: + raise ValueError( + f"len(weights)={len(raw_w)} must equal num_objectives=" + f"{self.num_objectives} (got {self.args.weights})" + ) + weights = torch.tensor(raw_w, device=self.device).view(-1, 1) + + # Position schedule: atoms first, then upper-triangle edge pairs. + # Uses n_max so every atom and edge in the largest graph gets visited. + n_max = n_nodes.max().item() + atom_positions = [("A", p) for p in range(n_max)] + edge_positions = [("E", (r, c)) for r in range(n_max) for c in range(r + 1, n_max)] + all_positions = edge_positions + atom_positions + period = len(all_positions) + + warmup_steps = min(getattr(self.args, 'warmup_steps', 0), self.sample_T - 1) + guided_steps = self.sample_T - warmup_steps + + _gm_desc = { + "bok": "exhaustive atom/bond search (model logits unused)", + "exp_tilt": "model-prior × exp(β·score) sampling", + "hybrid": "bok → exp_tilt at 50% progress", + }.get(self.guidance_method.value, "") + print(f"\nGuidance: {self.guidance_method.value} ({_gm_desc})") + print(f"Weights: {weights.squeeze().tolist()}") + print(f"β range: [{self.args.beta_min}, {self.args.beta_max}]") + print(f"Schedule: {n_max} atoms + {n_max*(n_max-1)//2} edges = {period} positions, " + f"{guided_steps} guided steps (positions sampled per-step by model disagreement)") + if warmup_steps: + print(f"Warmup: {warmup_steps} DeFoG denoising steps " + f"(t=0→{warmup_steps/self.sample_T:.2f}) before guidance") + + with torch.no_grad(): + # ------------------------------------------------------------------ + # Warmup: run DeFoG's own denoising kernel to get structured starting + # structures. Guidance operates on noise; warmup replaces those first + # steps with proper flow-matching transitions so guided loop starts from + # real chemistry rather than the limit distribution. + # ------------------------------------------------------------------ + if warmup_steps: + for t_int in tqdm(range(warmup_steps), desc="DeFoG warmup"): + t_array = t_int * torch.ones((batch_size, 1), device=self.device).float() + t_norm = t_array / self.sample_T + if "absorb" in self.model.cfg.model.transition and t_int == 0: + t_norm = t_norm + 1e-6 + s_array = t_array + 1 + s_norm = s_array / self.sample_T + t_norm = self.time_distorter.sample_ft(t_norm, self.model.cfg.sample.time_distortion) + s_norm = self.time_distorter.sample_ft(s_norm, self.model.cfg.sample.time_distortion) + sampled_s, _ = self.model.sample_p_zs_given_zt(t_norm, s_norm, X, E, y, node_mask) + X, E, y = sampled_s.X, sampled_s.E, sampled_s.y + X, E = self._fix_termini(X, E, n_nodes) # re-pin after each warmup step + + warmup_mols = self._to_molecule_list(X, E, n_nodes) + current_scores = self._get_scores(warmup_mols) + warmup_agg = self._compute_aggregated_score(current_scores, weights) + print(f"\nPost-warmup scores: {warmup_agg.cpu().numpy()}") + + for t_int in tqdm(range(warmup_steps, self.sample_T), desc="DeFoG local-edit"): + + progress = t_int / max(self.sample_T - 1, 1) + beta = self.args.beta_min + (self.args.beta_max - self.args.beta_min) * progress + + t_array = t_int * torch.ones((batch_size, 1), device=self.device).float() + t_norm = t_array / self.sample_T + + if "absorb" in self.model.cfg.model.transition and t_norm.min() < 1e-6: + t_norm = t_norm + 1e-6 + + t_norm = self.time_distorter.sample_ft(t_norm, self.model.cfg.sample.time_distortion) + + noisy_data = {"X_t": X, "E_t": E, "y_t": y, "t": t_norm, "node_mask": node_mask} + extra_data = self.model.compute_extra_data(noisy_data) + pred = self.model.forward(noisy_data, extra_data, node_mask) + pred_X = F.softmax(pred.X, dim=-1) # [B, n_max, d_X] + pred_E = F.softmax(pred.E, dim=-1) # [B, n_max, n_max, d_E] + + # Mask disallowed atom types and renormalize + pred_X = pred_X * self.vocab_mask + pred_X = pred_X / pred_X.sum(dim=-1, keepdim=True).clamp(min=1e-10) + + # Score cache: only mols changed in the previous step's commit + # have stale cache entries. Use cached `current_scores` directly + # (initialized post-warmup; refreshed for changed indices below). + current_agg = self._compute_aggregated_score(current_scores, weights) + + if t_int == warmup_steps: + print(f"\nInitial scores: {current_scores.cpu().numpy()}") + + # ── Position sampling weighted by model disagreement ──────────── + # Per-position disagreement = 1 − p_θ(current_type | context). + # High value ⇒ model wants to change this slot ⇒ worth visiting. + # Pinned-terminus positions get weight 0 so they're never sampled. + _ttype = getattr(self.args, 'fix_termini', None) + _cfg = self._TERMINUS_CONFIGS[_ttype] if _ttype else None + + # Atom disagreement: gather p_θ(current_type) per atom slot, average over batch. + _x_cur = X.argmax(-1) # [B, n_max] + _atom_p_cur = pred_X.gather(-1, _x_cur.unsqueeze(-1)).squeeze(-1) # [B, n_max] + _atom_dis = (1.0 - _atom_p_cur).mean(0) # [n_max] + # Edge disagreement: same idea over edge slots. + _e_cur = E.argmax(-1) # [B, n_max, n_max] + _edge_p_cur = pred_E.gather(-1, _e_cur.unsqueeze(-1)).squeeze(-1) # [B, n_max, n_max] + _edge_dis = (1.0 - _edge_p_cur).mean(0) # [n_max, n_max] + + _pos_weights = torch.zeros(period, device=self.device) + for _idx, (_pt, _ps) in enumerate(all_positions): + if _pt == "A": + if _cfg and _ps in _cfg['fixed_atom_pos']: + continue + _pos_weights[_idx] = _atom_dis[_ps] + else: + _r, _c = _ps + if _cfg and (_r in _cfg['fixed_o_nodes'] or _c in _cfg['fixed_o_nodes'] + or _ps in _cfg['fixed_edge_pos']): + continue + _pos_weights[_idx] = _edge_dis[_r, _c] + + # Fall back to uniform if model is fully confident everywhere. + if _pos_weights.sum() < 1e-10: + _pos_weights = torch.ones_like(_pos_weights) + if _cfg: + for _idx, (_pt, _ps) in enumerate(all_positions): + if _pt == "A" and _ps in _cfg['fixed_atom_pos']: + _pos_weights[_idx] = 0.0 + elif _pt == "E": + _r, _c = _ps + if (_r in _cfg['fixed_o_nodes'] or _c in _cfg['fixed_o_nodes'] + or _ps in _cfg['fixed_edge_pos']): + _pos_weights[_idx] = 0.0 + + # Position sampling mode: + # "weighted" (default) — multinomial sample over all eligible positions, + # weighted by disagreement (long-tail exploration) + # "topk" — zero out all but top-K most-disagreed positions + # before sampling (focused exploitation) + _pos_mode = getattr(self.args, 'position_sampling', 'weighted') + if _pos_mode == 'topk': + _k = min(getattr(self.args, 'top_positions', 50), period) + _topk = torch.topk(_pos_weights, _k) + _mask = torch.zeros_like(_pos_weights) + _mask[_topk.indices] = _pos_weights[_topk.indices] + _pos_weights = _mask + if _pos_weights.sum() < 1e-10: + _pos_weights = torch.ones_like(_pos_weights) + + _sampled_idx = torch.multinomial(_pos_weights, 1).item() + ptype, pos = all_positions[_sampled_idx] + + new_X = X.clone() + new_E = E.clone() + + improved_indices = [] + for i in range(batch_size): + n = n_nodes[i].item() + # Current graph as index tensors, clamped to real atom/bond types + x_idx = X[i, :n].argmax(-1).cpu().clamp(0, n_atom_types - 1) + e_idx = E[i, :n, :n].argmax(-1).cpu().clamp(0, n_bond_types - 1) + current_score_i = current_agg[i].item() + + pred_X_i = pred_X[i] + pred_E_i = pred_E[i] + + if self.guidance_method == GuidanceMethod.BEST_OF_K: + x_idx, e_idx, changed = self._best_of_k_guidance( + x_idx, e_idx, ptype, pos, pred_X_i, pred_E_i, + weights, current_score_i, n) + elif self.guidance_method == GuidanceMethod.EXPONENTIAL_TILT: + x_idx, e_idx, changed = self._exponential_tilt_guidance( + x_idx, e_idx, ptype, pos, pred_X_i, pred_E_i, + beta, weights, current_score_i, n) + elif self.guidance_method == GuidanceMethod.HYBRID: + x_idx, e_idx, changed = self._hybrid_guidance( + x_idx, e_idx, ptype, pos, pred_X_i, pred_E_i, + beta, weights, current_score_i, progress, n) + + if changed: + improved_indices.append(i) + + new_X[i, :n] = F.one_hot(x_idx.long().to(self.device), X.shape[-1]).float() + new_E[i, :n, :n] = F.one_hot(e_idx.long().to(self.device), E.shape[-1]).float() + + X, E = new_X, new_E + X, E = self._fix_termini(X, E, n_nodes) # prevent any reversion + + if improved_indices: + # Only re-score the molecules that actually changed. Stitch the + # new column scores into `current_scores` (the cross-step cache). + post_mols = self._to_molecule_list(X, E, n_nodes) + changed_mols = [post_mols[i] for i in improved_indices] + changed_scores = self._get_scores(changed_mols) # [num_obj, len(changed)] + for j, i in enumerate(improved_indices): + current_scores[:, i] = changed_scores[:, j] + new_agg = self._compute_aggregated_score(current_scores, weights) + score_improved = [i for i in improved_indices + if new_agg[i].item() > current_agg[i].item()] + if score_improved: + print(f"\nStep {t_int} [{ptype} pos={pos}]: improved {score_improved}") + for i in score_improved: + print(f" [{i}] {current_agg[i]:.4f} → {new_agg[i]:.4f}") + + if self.tracker: + self.tracker.log_step( + t_int, current_scores[0], improved_indices, + guidance_info={"ptype": ptype, "pos": str(pos)} + ) + if t_int % 10 == 0 or t_int == self.sample_T - 1: + self.tracker.print_step_summary( + t_int, current_scores[0], guidance_method="local_edit" + ) + + # Strip virtual classes, collapse one-hot → indices + X, E, y = self.model.noise_dist.ignore_virtual_classes(X, E, y) + final = PlaceHolder(X=X, E=E, y=y).mask(node_mask, collapse=True) + return self._to_molecule_list(final.X, final.E, n_nodes) + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +def main(args): + # PROTAC source dirs already on sys.path (set at module load above). + # Re-assert here for safety in case main() is called from a wrapper. + _src_root = os.path.normpath( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "../src") + ) + if _src_root not in sys.path: + sys.path.insert(0, _src_root) + for _sub in ("classifier", "protac"): + _p = os.path.join(_src_root, _sub) + if _p not in sys.path: + sys.path.insert(0, _p) + + from rdkit import Chem + from rdkit.Chem import Descriptors, rdMolDescriptors + from protac_analyzer import PROTACAnalyzer + from protac_classifier import PROTACClassifier, EGFRPROPKScorer, PROTACRDKitScorer, PROTACLinkerSAScorer, PROTACLinkerConnectivityScorer, _graph_to_mol + _pmod_name = getattr(args, 'protac_module', 'ErlotinibVHL') + import importlib as _il + _pmod = _il.import_module(f"protac.{_pmod_name}") + WARHEAD_MOL = _pmod.WARHEAD_MOL + E3_MOL = _pmod.E3_MOL + _ProtacBuilder = _pmod.Builder + + class PreassembledPROTACScorer: + """ + Fast linker scorer: no PROTAC assembly at scoring time. + + Pre-computes warhead (EGFR erlotinib) + E3 ligase (VHL) contributions to + Scores linker quality using a logistic regression classifier trained to + distinguish real PROTAC linkers (COOH-terminated, from PROTAC-DB) from + generated junk (N-COOH, N-F, S-F, disconnected, etc.). + + Returns score in [0, 2]: + 0 — disconnected or unsanitizable linker + max_frag/n_atoms — soft connectivity gradient for disconnected mols + 1 + clf_prob — [1, 2] for connected mols; clf_prob ∈ [0,1] is + the classifier's probability of being a real linker + + Use as objectives[0] with sa_scorer as objectives[1]. + Full assembly is still run after generation for final validity reporting. + """ + _ATOM_MIN, _ATOM_MAX = 3, 20 + + # Hard liability filters (same set as ChemScorer / PROTACClassifier). + _LIABILITY_PATS = [ + Chem.MolFromSmarts('[NX3][NX3]'), # hydrazide + Chem.MolFromSmarts('[#7]=[#7]'), # azo + Chem.MolFromSmarts('[N+][O-]'), # N-oxide + Chem.MolFromSmarts('[#7]-[OX2]'), # N-O + Chem.MolFromSmarts('[NX3]C(=[OX1])[OX2H1]'), # carbamate + ] + + def __init__(self, atom_decoder, clf_path): + import pickle + from rdkit.Chem import AllChem as _AllChem + self.atom_decoder = atom_decoder + self._allchem = _AllChem + with open(clf_path, 'rb') as f: + saved = pickle.load(f) + self._clf = saved['clf'] + print(f"[PreassembledPROTACScorer] loaded linker quality classifier " + f"({len(saved['pos_smiles'])} pos / {len(saved['neg_smiles'])} neg)") + + def _fingerprint(self, mol): + import numpy as np + fp = self._allchem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=1024) + return np.array(list(fp), dtype=np.float32).reshape(1, -1) + + def __call__(self, molecule_list, mols=None): + from protac_classifier import prebuild_mols as _prebuild + if mols is None: + mols = _prebuild(molecule_list, self.atom_decoder) + scores = [] + for mol in mols: + if mol is None: + scores.append(0.0) + continue + frag_mols = Chem.GetMolFrags(mol, asMols=True) + if len(frag_mols) > 1: + largest = max(frag_mols, key=lambda f: f.GetNumHeavyAtoms()) + n_heavy_largest = largest.GetNumHeavyAtoms() + if n_heavy_largest < self._ATOM_MIN: + scores.append(0.0) + continue + if any(largest.HasSubstructMatch(p) for p in self._LIABILITY_PATS): + scores.append(0.0) + continue + frac = n_heavy_largest / mol.GetNumHeavyAtoms() + try: + prob = float(self._clf.predict_proba(self._fingerprint(largest))[0, 1]) + except Exception: + prob = 0.0 + scores.append(prob * frac) + continue + n_heavy = mol.GetNumHeavyAtoms() + if not (self._ATOM_MIN <= n_heavy <= self._ATOM_MAX): + scores.append(0.0) + continue + if any(mol.HasSubstructMatch(p) for p in self._LIABILITY_PATS): + scores.append(0.0) + continue + try: + prob = float(self._clf.predict_proba(self._fingerprint(mol))[0, 1]) + except Exception: + prob = 0.0 + scores.append(prob) + return torch.tensor(scores, dtype=torch.float32) + + class PROTACLinkerChemScorer: + """ + Scores linker chemistry toward assembled PROTAC target ranges (no assembly needed). + MW and RotBonds windows are derived by subtracting the fixed warhead+E3 offset from + the assembled PROTAC target ranges [771-1108 Da, 12-25 RotBonds]. + logP scored directly on linker (training mean 0.48±1.37). + Hard zero for MC liabilities: hydrazines (N–N), azo (N=N), N-oxides, + any N–O single bond (hydroxylamines, alkoxyamines, oxime ethers), and + carbamates (N directly bonded to a COOH carbonyl C). + """ + # Assembled PROTAC target ranges (from PROTAC-DB) + _PROTAC_MW_LO, _PROTAC_MW_HI = 771.0, 1108.0 + _PROTAC_ROTB_LO, _PROTAC_ROTB_HI = 12.0, 25.0 + + _ETHER_O = Chem.MolFromSmarts('[OX2;!$(O=C)]') + _HYDRAZINE = Chem.MolFromSmarts('[NX3][NX3]') # N–N single (incl. hydrazine, hydrazide) + _AZO = Chem.MolFromSmarts('[#7]=[#7]') # N=N (azo, diazo) + _NOXIDE = Chem.MolFromSmarts('[N+][O-]') + _NO_BOND = Chem.MolFromSmarts('[#7]-[OX2]') # any N–O single bond (catches hydroxylamines, alkoxyamines, oxime ethers — sp2 + sp3) + _CARBAMATE = Chem.MolFromSmarts('[NX3]C(=[OX1])[OX2H1]') # N directly bonded to COOH carbonyl C — kills cooh-mode "diacid" candidates that grew an N next to a pinned carboxyl. Does NOT match α-amino acids (N–CH–COOH) where N is on the α-C, not the carbonyl. + + @staticmethod + def _trapezoid(v, lo, lo_ramp, hi_ramp, hi): + if v <= lo or v >= hi: return 0.0 + if v <= lo_ramp: return (v - lo) / (lo_ramp - lo) + if v >= hi_ramp: return (hi - v) / (hi - hi_ramp) + return 1.0 + + def __init__(self, atom_decoder, warhead_mol=None, e3_mol=None): + from rdkit.Chem import Descriptors as _Desc + self.atom_decoder = atom_decoder + self._logp = _Desc.MolLogP + self._mw = _Desc.MolWt + self._rotb = _Desc.NumRotatableBonds + + # Compute fixed warhead+E3 offset once (subtract 2×18 Da for two bond formations) + if warhead_mol is not None and e3_mol is not None: + fixed_mw = _Desc.MolWt(warhead_mol) + _Desc.MolWt(e3_mol) - 36.0 + fixed_rotb = float(_Desc.NumRotatableBonds(warhead_mol) + + _Desc.NumRotatableBonds(e3_mol)) + else: + fixed_mw, fixed_rotb = 0.0, 0.0 + + # Derive linker-level target windows; disable guidance if window collapses + _MW_MIN_RANGE = 50.0 # Da — skip MW guidance if flat-top narrower than this + _ROTB_MIN_RANGE = 3.0 # bonds — skip RotB guidance if flat-top narrower than this + mw_lo = max(0.0, self._PROTAC_MW_LO - fixed_mw) + mw_hi = max(0.0, self._PROTAC_MW_HI - fixed_mw) + rotb_lo = max(0.0, self._PROTAC_ROTB_LO - fixed_rotb) + rotb_hi = max(0.0, self._PROTAC_ROTB_HI - fixed_rotb) + shoulder = 0.2 + mw_span = mw_hi - mw_lo + rotb_span = rotb_hi - rotb_lo + self._mw_active = mw_span >= _MW_MIN_RANGE + self._rotb_active = rotb_span >= _ROTB_MIN_RANGE + if self._mw_active: + self._mw_window = (mw_lo - mw_span*shoulder, mw_lo, mw_hi, mw_hi + mw_span*shoulder) + if self._rotb_active: + self._rotb_window = (rotb_lo - rotb_span*shoulder, rotb_lo, rotb_hi, rotb_hi + rotb_span*shoulder) + print(f"[ChemScorer] linker MW target: " + f"[{mw_lo:.0f}, {mw_hi:.0f}] Da (fixed {fixed_mw:.0f} Da)" + f"{'' if self._mw_active else ' ← SKIPPED (window too narrow)'}") + print(f"[ChemScorer] linker RotB target: " + f"[{rotb_lo:.0f}, {rotb_hi:.0f}] (fixed {fixed_rotb:.0f})" + f"{'' if self._rotb_active else ' ← SKIPPED (window too narrow)'}") + + def __call__(self, molecule_list, mols=None): + from protac_classifier import prebuild_mols as _prebuild + if mols is None: + mols = _prebuild(molecule_list, self.atom_decoder) + scores = [] + for mol in mols: + if mol is None: + scores.append(0.0); continue + if len(Chem.GetMolFrags(mol)) > 1: + scores.append(0.0); continue + if (mol.HasSubstructMatch(self._HYDRAZINE) or + mol.HasSubstructMatch(self._AZO) or + mol.HasSubstructMatch(self._NOXIDE) or + mol.HasSubstructMatch(self._NO_BOND) or + mol.HasSubstructMatch(self._CARBAMATE)): + scores.append(0.0); continue + n_ether = len(mol.GetSubstructMatches(self._ETHER_O)) + ether_score = min(n_ether / 4.0, 1.0) + try: + logp = self._logp(mol) + mw = self._mw(mol) + rotb = float(self._rotb(mol)) + except Exception: + logp, mw, rotb = 5.0, 500.0, 20.0 + logp_score = self._trapezoid(logp, -3.0, -1.0, 2.0, 5.0) + mw_score = self._trapezoid(mw, *self._mw_window) if self._mw_active else 1.0 + rotb_score = self._trapezoid(rotb, *self._rotb_window) if self._rotb_active else 1.0 + scores.append(0.25 * ether_score + 0.25 * logp_score + + 0.25 * mw_score + 0.25 * rotb_score) + return torch.tensor(scores, dtype=torch.float32) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Device: {device}") + + # Load DeFoG model + # load_from_checkpoint fails because train_metrics/sampling_metrics are not saved in hparams. + # Instead: pull cfg + dataset_infos directly from the checkpoint, pass dummy metrics. + print(f"Loading DeFoG checkpoint: {args.checkpoint}") + from graph_discrete_flow_model import GraphDiscreteFlowModel + from analysis.visualization import MolecularVisualization + + class _DummyMetrics: + def __call__(self, *a, **kw): return {} + def reset(self): pass + def log_epoch_metrics(self, *a, **kw): pass + + raw_ckpt = torch.load(args.checkpoint, map_location='cpu', weights_only=False) + hp = raw_ckpt['hyper_parameters'] + model = GraphDiscreteFlowModel( + cfg=hp['cfg'], + dataset_infos=hp['dataset_infos'], + train_metrics=_DummyMetrics(), + sampling_metrics=_DummyMetrics(), + visualization_tools=hp.get('visualization_tools') or MolecularVisualization( + remove_h=True, dataset_infos=hp['dataset_infos'] + ), + extra_features=hp.get('extra_features') or _DummyMetrics(), + domain_features=hp.get('domain_features') or _DummyMetrics(), + test_labels=hp.get('test_labels'), + ) + model.load_state_dict(raw_ckpt['state_dict'], strict=False) + model.to(device) + model.eval() + print("Model loaded.") + + _KNOWN_DECODERS = { + "moses": ["C", "N", "S", "O", "F", "Cl", "Br", "H"], + "zinc": ["C", "N", "O", "F", "P", "S", "Cl", "Br", "I"], + "qm9": ["C", "N", "O", "F"], + "guacamol": ["C", "N", "O", "F", "P", "S", "Cl", "Br", "I"], + } + if args.dataset is not None: + atom_decoder = _KNOWN_DECODERS[args.dataset] + print(f"Atom decoder (from --dataset {args.dataset}): {atom_decoder}") + else: + atom_decoder = model.dataset_info.atom_decoder + print(f"Atom decoder (from checkpoint): {atom_decoder}") + + # Build objectives + # Guidance SMARTS: linker must have at least one reactive attachment per end. + # COOH = [CX3](=[OX1])[OX2H1] — direct amide bond + # CHO = [CX3H1](=[OX1]) — post-assembly aldehyde, restored to COOH + # CH3 = [CX4H3] — terminal methyl for C-N alkylation + # When fix_termini=methyl the CH3 groups are pre-pinned so they match immediately, + # making the attachment score trivially 2 and letting other objectives steer. + if args.fix_termini == 'methyl': + _ATTACH_SMARTS = "[$([CX3;!$([CX3][N,n])](=[OX1])[OX2H1]),$([CX3H1](=[OX1])),$([CX4H3])]" + elif args.fix_termini == 'amine': + # Linker presents N termini; warhead/E3 provide COOH electrophiles. + _ATTACH_SMARTS = "[NX3;!H0;!$(NC=O);!$(NS=O);!$(N=O)]" + elif args.fix_termini == 'amino_acid': + # Linker presents one NH2 (warhead end) + one COOH (VHL end). + # Match either — assembly score requires ≥2 hits (one of each type). + _ATTACH_SMARTS = "[$([NX3H2]),$([CX3](=[OX1])[OX2H1])]" + else: + _ATTACH_SMARTS = "[$([CX3;!$([CX3][N,n])](=[OX1])[OX2H1]),$([CX3H1](=[OX1]))]" + analyzer = PROTACAnalyzer(WARHEAD_MOL, E3_MOL, + _ATTACH_SMARTS, _ATTACH_SMARTS, + sa_score_max=5.0) + builder = _ProtacBuilder() + classifier = PROTACClassifier(analyzer, builder, atom_decoder) + classifier.fix_termini = args.fix_termini + conn_scorer = PROTACLinkerConnectivityScorer(atom_decoder) + if args.fix_termini: + # Anchor nodes for the connectivity scorer: halves its soft score when the + # two termini end up in different fragments, so guidance is steered toward + # bridging the warhead/E3 anchors specifically. + conn_scorer.anchor_nodes = DeFoGGenerator._TERMINUS_CONFIGS[args.fix_termini]['anchor_nodes'] + sa_scorer = PROTACLinkerSAScorer(atom_decoder, sa_threshold=4.5) + + if getattr(args, 'fast_scoring', False): + if args.fix_termini not in ('cooh', 'amine', 'amino_acid'): + raise ValueError("--fast_scoring requires --fix_termini cooh, amine, or amino_acid") + # Classifier dispatch priority: + # 1. Explicit --clf_path on CLI (override) + # 2. protac_linker_clf_.pkl (target-specific, if present) + # 3. protac_linker_clf_.pkl (termini-specific fallback) + # 4. protac_linker_clf.pkl (generic default) + _termini_clf = { + 'amine': "protac_linker_clf_amine.pkl", + 'amino_acid': "protac_linker_clf_amino_acid.pkl", + }.get(args.fix_termini, "protac_linker_clf.pkl") + _clf_dir = Path(__file__).resolve().parents[1] / "src" / "classifier" + _module_clf = f"protac_linker_clf_{args.protac_module}.pkl" + _module_path = _clf_dir / _module_clf + if getattr(args, 'clf_path', None): + _clf_path = args.clf_path + elif _module_path.exists(): + _clf_path = str(_module_path) + print(f"[fast_scoring] using target-specific classifier: {_module_clf}") + else: + _clf_path = str(_clf_dir / _termini_clf) + print(f"[fast_scoring] using termini classifier: {_termini_clf}") + chem_scorer = PROTACLinkerChemScorer(atom_decoder, WARHEAD_MOL, E3_MOL) + # classifier is the hard validity gate, not in the soft sum. + # PreassembledPROTACScorer was dropped after the ablation showed it + # hurts AsmVal/DM when weighted up. + OBJECTIVE_FUNCTIONS = [sa_scorer, chem_scorer, conn_scorer] + print("Scoring mode: FAST (SA + linker chem + connectivity; PROTACClassifier as hard gate)") + else: + if not getattr(args, 'model_dir', None): + raise ValueError("--model_dir is required for full scoring mode (or use --fast_scoring)") + pk_scorer = EGFRPROPKScorer(builder, args.model_dir, atom_decoder) + pk_scorer.fix_termini = args.fix_termini + prop_scorer = PROTACRDKitScorer(builder, atom_decoder) + prop_scorer.fix_termini = args.fix_termini + OBJECTIVE_FUNCTIONS = [pk_scorer, prop_scorer, sa_scorer, conn_scorer] + print("Scoring mode: FULL (PROPK + RDKit + SA + connectivity; PROTACClassifier as hard gate)") + + tracker = GenerationTracker(output_dir=args.output_dir) + generator = DeFoGGenerator(model, atom_decoder, OBJECTIVE_FUNCTIONS, args, + validity_fn=classifier, tracker=tracker) + + all_molecules = [] + + for batch_idx in range(args.num_batches): + print(f"\n{'=' * 60}") + print(f"BATCH {batch_idx + 1}/{args.num_batches}") + print(f"{'=' * 60}") + + molecule_list = generator.generate() + final_scores = generator._get_scores(molecule_list) + + print(f"\n--- Batch {batch_idx + 1} Results ---") + print(f"Validity scores: {final_scores[0].cpu().numpy()}") + + all_molecules.extend(molecule_list) + + linker_mols = [] + for atom_types, edge_types in molecule_list: + try: + mol = _graph_to_mol(atom_types, edge_types, atom_decoder) + Chem.SanitizeMol(mol) + linker_mols.append(mol) + except Exception: + linker_mols.append(Chem.RWMol()) + detailed = analyzer.get_detailed_scores(linker_mols) + tracker.log_final(molecule_list, detailed) + + tracker.print_final_summary() + tracker.save(filename_prefix="defog_local_edit") + + # Strict final validation — attempt actual assembly via builder (NH2 or reactive NH) + # This is the ground truth: if builder.assemble() succeeds, the linker is genuinely + # assembly-ready regardless of which N type was used. + strict_pass = 0 + print("\n=== Strict assembly-ready check (actual RDKit assembly attempt) ===") + with open(args.output_file, "w") as f: + for atom_types, edge_types in all_molecules: + try: + mol = _graph_to_mol(atom_types, edge_types, atom_decoder) + Chem.SanitizeMol(mol) + smi = Chem.MolToSmiles(mol) + # Reject disconnected molecules — a valid linker must be a single fragment + if "." in smi: + f.write(f"DISCONNECTED {smi}\n") + continue + try: + amide_only_strict = (args.fix_termini == 'cooh') + builder.assemble(mol, return_all=False, amide_only=amide_only_strict) + tag = "AssemblyReady" + strict_pass += 1 + except Exception: + tag = "Valid" + f.write(f"{tag} {smi}\n") + except Exception: + f.write("INVALID\n") + print(f"Assembly-ready (strict): {strict_pass}/{len(all_molecules)}") + + print(f"\nSMILES saved to: {args.output_file}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="DeFoG Guided PROTAC Linker Generation") + + # Model + parser.add_argument("--checkpoint", type=str, required=True, + help="Path to DeFoG model checkpoint (.ckpt)") + parser.add_argument("--model_dir", type=str, default=None, + help="Path to EGFR-PROPK models directory (required unless --fast_scoring)") + parser.add_argument("--clf_path", type=str, default=None, + help="Path to protac_linker_clf.pkl (default: smiles/protac_linker_clf.pkl)") + parser.add_argument("--protac_module", type=str, default="ErlotinibVHL", + help="Builder module name under MoCA/smiles/protac/ " + "(e.g. ErlotinibVHL, ARCRBN). Must export WARHEAD_MOL, E3_MOL, Builder.") + parser.add_argument("--dataset", type=str, default=None, + choices=["moses", "zinc", "qm9", "guacamol"], + help="Override atom decoder (default: read from checkpoint)") + + # Generation + parser.add_argument("--num_samples", type=int, default=10) + parser.add_argument("--num_batches", type=int, default=5) + parser.add_argument("--num_steps", type=int, default=None, + help="Override denoising steps (default: read from checkpoint, typically 1000)") + parser.add_argument("--num_nodes", type=int, default=None, + help="Fixed number of linker atoms (None = sample from node_dist)") + parser.add_argument("--min_nodes", type=int, default=10) + parser.add_argument("--max_nodes", type=int, default=25) + + # Warmup: run DeFoG's own denoising kernel for this many steps before guided local edits. + # Replaces the first N steps of the loop with proper flow-matching transitions so + # guidance starts from structured molecules rather than pure limit-distribution noise. + # Good default: 40–60% of --num_steps (e.g., 400 for a 1000-step run). + parser.add_argument("--warmup_steps", type=int, default=0, + help="DeFoG denoising steps before guided local-edit loop (default: 0 = disabled)") + + # Guidance — one graph position per step, mirrors CupOfMoca's per-token approach + parser.add_argument("--guidance_method", type=str, default="exp_tilt", + choices=["bok", "exp_tilt", "hybrid"], + help="exp_tilt (default): use DeFoG model logits as base prob, tilt by score (preserves " + "learned chemistry like ring closures). bok: pure score-driven brute force " + "(ignores model — empirically kills ring formation). hybrid: bok→exp_tilt.") + parser.add_argument("--beta_min", type=float, default=1.0, + help="Initial guidance strength β (annealed from beta_min → beta_max)") + parser.add_argument("--beta_max", type=float, default=20.0, + help="Final guidance strength β") + parser.add_argument("--top_k", type=int, default=9, + help="Atom-type candidates for exp_tilt (default: all types)") + parser.add_argument("--use_acceptance", action="store_true", + help="Reject exp_tilt proposals that lower the score (mirrors CupOfMoca)") + parser.add_argument("--score_aggregation", type=str, default="weighted_sum", + choices=["weighted_sum", "weighted_min"]) + parser.add_argument("--weights", type=float, nargs="+", default=None, + help="Objective weights e.g. 1.0 0.5 0.5") + + # Position-sampling strategy for the per-step position pick + parser.add_argument("--position_sampling", type=str, default="weighted", + choices=["weighted", "topk"], + help="weighted (default): multinomial across all eligible positions " + "weighted by 1−p_θ(current_type) — long-tail exploration. " + "topk: zero out all but the top-K most-disagreed positions, then " + "sample within those — focused exploitation.") + parser.add_argument("--top_positions", type=int, default=50, + help="K for --position_sampling topk (default 50). " + "Smaller = more focused; larger = more exploration. " + "Ignored when --position_sampling weighted.") + + # Terminus-conditioned generation + # Pre-pins reactive terminus groups so the attachment score is satisfied immediately + # and guidance concentrates on connecting the bridge and optimising other objectives. + # cooh — two COOH groups at nodes 0-5 (amide-bond assembly path) + # methyl — CH3(0)–CH2(1)…CH2(2)–CH3(3) scaffold (C-N alkylation path, 77% of PROTAC-DB) + parser.add_argument("--fix_termini", default=None, + choices=["cooh", "methyl", "amine", "amino_acid"], + help="Pin terminus groups: 'cooh' (nodes 0-5, amide path), " + "'methyl' (nodes 0-3, C-N alkylation path), " + "'amine' (nodes 0-1, N termini — warhead/E3 provide COOH), " + "'amino_acid' (nodes 0-3, asymmetric N+COOH — for BRD4-VHL)") + + # Initial generation + parser.add_argument("--require_valid_x0", action="store_true") + parser.add_argument("--max_x0_retries", type=int, default=10) + + # Fast scoring: skip full PROTAC assembly at every step. + # Pre-computes warhead + E3 ligase property contributions (MW, logP, RotB) once; + # scores each candidate linker by estimating full PROTAC properties via offsets. + # Eliminates PROTACAnalyzer assembly calls (~10-50x faster per scoring step). + # Only valid with --fix_termini cooh (COOH termini guaranteed, so assembly check is moot). + # Reverts to full assembly for final validity check after generation. + parser.add_argument("--fast_scoring", action="store_true", + help="Use PreassembledPROTACScorer: skip assembly, estimate PROTAC " + "properties via warhead+E3 offsets. Supports cooh, amine, and " + "amino_acid (each has its own dedicated classifier).") + + # Output + parser.add_argument("--output_file", type=str, default="./protac_linkers.smi") + parser.add_argument("--output_dir", type=str, default="./generation_logs") + parser.add_argument("--seed", type=int, default=None) + + args = parser.parse_args() + + if args.fix_termini: + cfg = DeFoGGenerator._TERMINUS_CONFIGS[args.fix_termini] + if args.min_nodes < cfg['min_total_nodes']: + args.min_nodes = cfg['min_total_nodes'] + print(f"--fix_termini {args.fix_termini} requires at least {cfg['min_total_nodes']} nodes; " + f"setting --min_nodes {cfg['min_total_nodes']}") + + if args.seed is not None: + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(args.seed) + + main(args) \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/scripts/runs/ablation_egfr.py b/CupOFMoCA-PROTAC/scripts/runs/ablation_egfr.py new file mode 100644 index 0000000000000000000000000000000000000000..951d7a0b4b78569ee42b66c06215bd0c492c6f54 --- /dev/null +++ b/CupOFMoCA-PROTAC/scripts/runs/ablation_egfr.py @@ -0,0 +1,192 @@ +#!/usr/bin/env python3 +""" +EGFR-VHL ablation driver. Deduplicated layout: + + 3 baseline runs (weights = 1,1,1,1,1, seeds 42-44) + 5 scorers x 2 non-baseline weights {0,2} x 3 seeds = 30 runs + 3 acceptance=on runs (baseline weights, seeds 42-44) + total = 36 runs + +The baseline runs cover every (scorer, w=1) cell at once. The acceptance=off +case is the baseline (same runs reused), so only acceptance=on is run here. + +Each run = 100 samples on EGFR-VHL. Outputs go to +generation_logs/ablation_egfr//. Resumable (skips done .smi files). +""" +import argparse +import itertools +import os +import subprocess +import time +from pathlib import Path + +# ---------------------------------------------------------------------------- +# Config +# ---------------------------------------------------------------------------- +SCRIPT_DIR = Path(__file__).resolve().parent.parent +REPO_ROOT = SCRIPT_DIR.parent +CHECKPOINT = REPO_ROOT / "checkpoints" / "protac_linker_finetune" / "epoch=99-v1.ckpt" +CLF_PATH = REPO_ROOT / "src" / "classifier" / "protac_linker_clf.pkl" +GENERATE = SCRIPT_DIR / "generate.py" +OUT_BASE = REPO_ROOT / "generation_logs" / "ablation_egfr" + +PROTAC_MODULE = "ErlotinibVHL" +N_SAMPLES = 100 +N_STEPS = 1500 +WARMUP_STEPS = 100 +SCORER_NAMES = ["classifier", "fast", "sa", "chem", "conn"] # obj index 0..4 +WEIGHTS = [0.0, 1.0, 2.0, 4.0] # 1.0 is baseline (shared) +NON_BASELINE = [w for w in WEIGHTS if w != 1.0] # [0.0, 2.0, 4.0] +SEEDS = [42, 43, 44] +BASELINE_W = [1.0, 1.0, 1.0, 1.0, 1.0] + + +def weight_vec(scorer_idx: int, w: float) -> list[float]: + """Baseline (1,1,1,1,1) with one slot replaced.""" + v = list(BASELINE_W) + v[scorer_idx] = w + return v + + +def build_runs() -> list[dict]: + """Return list of run configs. Each config has a unique tag.""" + runs = [] + # Shared baseline (weights = 1,1,1,1,1, acceptance off). Reused for all + # (scorer, w=1) cells in the main ablation AND for the acceptance=off cell. + for seed in SEEDS: + runs.append({ + "tag": f"egfr_baseline_seed{seed}", + "weights": list(BASELINE_W), + "seed": seed, + "accept": False, + }) + # Main ablation: per-scorer weight sweep, non-baseline weights only + for idx, name in enumerate(SCORER_NAMES): + for w in NON_BASELINE: + for seed in SEEDS: + runs.append({ + "tag": f"egfr_{name}_w{w:g}_seed{seed}", + "weights": weight_vec(idx, w), + "seed": seed, + "accept": False, + }) + # Acceptance=on (the "off" cell is the baseline above) + for seed in SEEDS: + runs.append({ + "tag": f"egfr_accept_on_seed{seed}", + "weights": list(BASELINE_W), + "seed": seed, + "accept": True, + }) + return runs + + +def run_command(run: dict, gpu: int) -> list[str]: + out_dir = OUT_BASE / run["tag"] + out_dir.mkdir(parents=True, exist_ok=True) + cmd = [ + "python", str(GENERATE), + "--checkpoint", str(CHECKPOINT), + "--protac_module", PROTAC_MODULE, + "--fix_termini", "cooh", + "--fast_scoring", + "--clf_path", str(CLF_PATH), + "--num_samples", str(N_SAMPLES), + "--num_batches", "1", + "--num_steps", str(N_STEPS), + "--warmup_steps", str(WARMUP_STEPS), + "--weights", *[str(w) for w in run["weights"]], + "--seed", str(run["seed"]), + "--output_file", str(out_dir / f"{run['tag']}.smi"), + "--output_dir", str(out_dir), + ] + if run["accept"]: + cmd.append("--use_acceptance") + return cmd + + +def is_done(run: dict) -> bool: + out_dir = OUT_BASE / run["tag"] + smi = out_dir / f"{run['tag']}.smi" + if not smi.exists(): + return False + # crude check: non-empty file means generation reached the "save SMILES" step + return smi.stat().st_size > 0 + + +def launch(cmd: list[str], gpu: int, log_path: Path): + env = os.environ.copy() + env["CUDA_VISIBLE_DEVICES"] = str(gpu) + with open(log_path, "w") as logf: + return subprocess.Popen(cmd, env=env, stdout=logf, stderr=subprocess.STDOUT) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--gpus", default="2,3,4,5,6", + help="comma-separated GPU IDs to use (default: 2,3,4,5,6)") + ap.add_argument("--stagger", type=float, default=2.0, + help="seconds between successive run starts (avoid CSV collisions)") + ap.add_argument("--dry-run", action="store_true", + help="print commands without launching") + args = ap.parse_args() + + gpu_list = [int(g) for g in args.gpus.split(",")] + runs = build_runs() + todo = [r for r in runs if not is_done(r)] + done = [r for r in runs if is_done(r)] + + print(f"Total runs: {len(runs)}") + print(f"Already done: {len(done)}") + print(f"To launch: {len(todo)}") + print(f"GPUs available: {gpu_list}") + + if args.dry_run: + for r in todo[:5]: + cmd = run_command(r, gpu_list[0]) + print("[DRY]", " ".join(cmd)) + if len(todo) > 5: + print(f"[DRY] ... and {len(todo)-5} more") + return + + OUT_BASE.mkdir(parents=True, exist_ok=True) + + active: dict[int, dict] = {} # gpu -> (proc, run, log_path, start_ts) + queue = list(todo) + + def dispatch(gpu: int): + if not queue: + return False + run = queue.pop(0) + cmd = run_command(run, gpu) + log_path = OUT_BASE / run["tag"] / "run.log" + proc = launch(cmd, gpu, log_path) + active[gpu] = {"proc": proc, "run": run, "log": log_path, + "start": time.time()} + print(f"[GPU {gpu}] start {run['tag']} (pid {proc.pid})") + time.sleep(args.stagger) + return True + + # Initial fill + for gpu in gpu_list: + dispatch(gpu) + + # Poll + while active or queue: + for gpu, info in list(active.items()): + ret = info["proc"].poll() + if ret is not None: + elapsed = time.time() - info["start"] + status = "OK" if ret == 0 else f"FAIL({ret})" + print(f"[GPU {gpu}] done {info['run']['tag']} " + f"({status}, {elapsed/60:.1f} min)") + del active[gpu] + if queue: + dispatch(gpu) + time.sleep(10) + + print(f"\nAll runs finished. Outputs under {OUT_BASE}/") + + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/scripts/runs/aggregate_ablation.py b/CupOFMoCA-PROTAC/scripts/runs/aggregate_ablation.py new file mode 100644 index 0000000000000000000000000000000000000000..205a61e0509e26e5aa252d52193b56433710d97e --- /dev/null +++ b/CupOFMoCA-PROTAC/scripts/runs/aggregate_ablation.py @@ -0,0 +1,199 @@ +#!/usr/bin/env python3 +""" +Aggregate the EGFR-VHL ablation outputs into one table. + +Walks generation_logs/ablation_egfr// and for each, parses the summary CSV +into batch_report's compute_stats. Emits a TSV with one row per (tag, seed). +Also prints a per-cell (scorer x weight) and acceptance summary averaged +across seeds. +""" +import argparse +import csv +import importlib.util +import re +import statistics +from pathlib import Path + +import pandas as pd + +# Locate batch_report.py in scripts/ +SCRIPT_DIR = Path(__file__).resolve().parent.parent +spec = importlib.util.spec_from_file_location("batch_report", SCRIPT_DIR / "batch_report.py") +br = importlib.util.module_from_spec(spec) +spec.loader.exec_module(br) + +REPO_ROOT = SCRIPT_DIR.parent +OUT_BASE = REPO_ROOT / "generation_logs" / "ablation_egfr" + + +def parse_tag(tag: str) -> dict: + m = re.match(r"egfr_baseline_seed(?P\d+)$", tag) + if m: + return {"kind": "baseline", "seed": int(m["seed"])} + m = re.match(r"egfr_(?P[a-z]+)_w(?P[\d.]+)_seed(?P\d+)$", tag) + if m: + return {"kind": "main", "scorer": m["sc"], + "weight": float(m["w"]), "seed": int(m["seed"])} + m = re.match(r"egfr_accept_(?Pon|off)_seed(?P\d+)$", tag) + if m: + return {"kind": "accept", "accept": m["acc"], "seed": int(m["seed"])} + return {} + + +def stats_for_csv(csv_path: Path, protac_module: str) -> dict: + br._PROTAC_MODULE = protac_module + br._protac_analyzer_state = None + br._PROTACDB_FP_CACHE = getattr(br, "_PROTACDB_FP_CACHE", {}) + df = br.run_evaluate_full_protac_file(csv_path) if csv_path.suffix == ".txt" \ + else br.run_evaluate(csv_path) + return br.compute_stats(df) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--out", default=str(OUT_BASE / "ablation_results.tsv"), + help="output TSV path") + ap.add_argument("--module", default="ErlotinibVHL") + args = ap.parse_args() + + rows = [] + for run_dir in sorted(OUT_BASE.iterdir()): + if not run_dir.is_dir(): + continue + csvs = list(run_dir.glob("defog_local_edit_*_summary.csv")) + if not csvs: + print(f"[skip] {run_dir.name}: no summary csv") + continue + # In case of multiple (resumed runs), take the newest + csv_path = sorted(csvs, key=lambda p: p.stat().st_mtime)[-1] + try: + s = stats_for_csv(csv_path, args.module) + except Exception as e: + print(f"[fail] {run_dir.name}: {e}") + continue + meta = parse_tag(run_dir.name) + rows.append({ + "tag": run_dir.name, + **meta, + "n_total": s["n_total"], + "std_valid": round(s["std_valid_rate"] * s["n_total"] / 100), + "asm_valid": s["n_valid"], + "asm_pct": round(s["asm_valid_rate"], 1), + "sa_mean": round(s["sa_mean"], 2), + "qed_mean": round(s["qed_mean"], 3), + "dm_mean": round(s["degrad_mean"], 3), + "dm_std": round(s["degrad_std"], 3), + "dm_ge05": round(s["degrad_ge05"], 1), + "mem_ge08": round(s.get("train_sim_pct_ge_0_8", 0), 1), + "logp_in": round(s["logp_in_window_pct"], 1), + "mw_in": round(s["mw_in_window_pct"], 1), + "rotb_in": round(s["rot_in_window_pct"], 1), + }) + print(f"[ok] {run_dir.name}: asm={s['n_valid']}/{s['n_total']}, " + f"dm={s['degrad_mean']:.3f}") + + if not rows: + print("No results to aggregate.") + return + + # Write per-run TSV (union of all keys across baseline / main / accept rows) + fieldnames = [] + for r in rows: + for k in r: + if k not in fieldnames: + fieldnames.append(k) + with open(args.out, "w", newline="") as f: + w = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t", + extrasaction="ignore", restval="") + w.writeheader() + w.writerows(rows) + print(f"\nPer-run TSV: {args.out} ({len(rows)} rows)") + + # Aggregate baseline (shared across all w=1 cells) + base_rows = [r for r in rows if r.get("kind") == "baseline"] + base_acc_off = base_rows # acceptance=off baseline reuses the baseline runs + + def agg(group): + asm = [g["asm_valid"] for g in group] + dm = [g["dm_mean"] for g in group] + mem = [g["mem_ge08"] for g in group] + if not group: + return None + return { + "asm_m": statistics.mean(asm), + "asm_s": statistics.stdev(asm) if len(asm) > 1 else 0, + "dm_m": statistics.mean(dm), + "dm_s": statistics.stdev(dm) if len(dm) > 1 else 0, + "mem_m": statistics.mean(mem), + "mem_s": statistics.stdev(mem) if len(mem) > 1 else 0, + "n": len(group), + } + + main_rows = [r for r in rows if r.get("kind") == "main"] + if main_rows or base_rows: + # collect into (scorer, weight) -> aggregate + by_cell = {} + for r in main_rows: + by_cell.setdefault((r["scorer"], r["weight"]), []).append(r) + scorers = sorted({r["scorer"] for r in main_rows}) + weights = sorted({r["weight"] for r in main_rows} | {1.0}) + + print("\n## Main ablation (EGFR-VHL, 100 samples, mean ± std over 3 seeds)\n") + # Markdown table + header_w = "| scorer | " + " | ".join(f"w={w:g}" for w in weights) + " |" + sep = "|" + "|".join(["---"] * (len(weights) + 1)) + "|" + print("**AsmVal (out of 100)**\n") + print(header_w); print(sep) + base_agg = agg(base_acc_off) + for sc in scorers: + cells = [] + for w in weights: + if w == 1.0: + a = base_agg + else: + a = agg(by_cell.get((sc, w), [])) + cells.append(f"{a['asm_m']:.1f} ± {a['asm_s']:.1f}" if a else "—") + print(f"| {sc} | " + " | ".join(cells) + " |") + + print("\n**DegradeMaster mean**\n") + print(header_w); print(sep) + for sc in scorers: + cells = [] + for w in weights: + if w == 1.0: + a = base_agg + else: + a = agg(by_cell.get((sc, w), [])) + cells.append(f"{a['dm_m']:.3f} ± {a['dm_s']:.3f}" if a else "—") + print(f"| {sc} | " + " | ".join(cells) + " |") + + print("\n**Memorization % (> 0.80 Tanimoto)**\n") + print(header_w); print(sep) + for sc in scorers: + cells = [] + for w in weights: + if w == 1.0: + a = base_agg + else: + a = agg(by_cell.get((sc, w), [])) + cells.append(f"{a['mem_m']:.1f} ± {a['mem_s']:.1f}" if a else "—") + print(f"| {sc} | " + " | ".join(cells) + " |") + + # Acceptance ablation + acc_on = [r for r in rows if r.get("kind") == "accept" and r["accept"] == "on"] + acc_off = base_acc_off # reuse baseline + if acc_on or acc_off: + print("\n## Acceptance ablation (EGFR-VHL, baseline weights, mean ± std over 3 seeds)\n") + print("| accept | AsmVal | DegradeMaster | Mem % |") + print("|---|---|---|---|") + for name, group in (("off", acc_off), ("on", acc_on)): + a = agg(group) + if a is None: + continue + print(f"| {name} | {a['asm_m']:.1f} ± {a['asm_s']:.1f} | " + f"{a['dm_m']:.3f} ± {a['dm_s']:.3f} | " + f"{a['mem_m']:.1f} ± {a['mem_s']:.1f} |") + + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/scripts/train_target_clf.py b/CupOFMoCA-PROTAC/scripts/train_target_clf.py new file mode 100644 index 0000000000000000000000000000000000000000..1f767003e4cfb630224f4c9c029a42ded31bfd8d --- /dev/null +++ b/CupOFMoCA-PROTAC/scripts/train_target_clf.py @@ -0,0 +1,219 @@ +#!/usr/bin/env python3 +"""Train a target-specific PROTAC linker classifier. + +For a given protac_module (e.g. ErlotinibVHL, BRD9VHL, CDK6CRBN): + 1. Load PROTAC-DB entries matching the target + E3 pair + 2. Extract linker SMILES from each (strip warhead + E3 substructures) + 3. Train LogisticRegression on Morgan FP (radius=2, 1024 bits) of + target-specific linkers (positives) vs the existing generic negatives + 4. Save as protac_linker_clf_.pkl + +DeFoG generation can then load this classifier (when --protac_module matches) +and steer linker generation toward the chemistry distribution that's actually +represented in the target's PROTAC-DB active subset. + +Usage: + python train_target_specific_clf.py --protac-module BRD9VHL + python train_target_specific_clf.py --protac-module CDK6CRBN + python train_target_specific_clf.py --protac-module ErlotinibVHL +""" +import argparse +import importlib +import json +import pickle +import sys +from pathlib import Path + +import numpy as np +from rdkit import Chem, RDLogger +from rdkit.Chem import AllChem, DataStructs +from sklearn.linear_model import LogisticRegression + +RDLogger.DisableLog("rdApp.*") + +# Resolve paths relative to the repo layout (scripts/ -> ../src/...). +_REPO_ROOT = Path(__file__).resolve().parent.parent +CLF_DIR = _REPO_ROOT / "src" / "classifier" +PROTACDB_JSON = _REPO_ROOT / "src" / "degrademaster" / "data" / "PROTAC" / "name.json" + +_TARGET_FILTER = { + "ErlotinibVHL": ("EGFR", "VHL"), + "BRD9VHL": ("BRD9", "VHL"), + "BRD4VHL": ("BRD4", "VHL"), + "KRASVHL": ("KRAS", "VHL"), + "ARCRBN": ("AR-V7", "CRBN"), + "ARCRBNv2": ("AR-V7", "CRBN"), + "BRD4CRBN": ("BRD4", "CRBN"), + "CDK6CRBN": ("CDK6", "CRBN"), +} + +_TERMINI_FOR_NEGATIVES = { + # Use the matching termini's existing negatives so the classifier learns + # to distinguish target-specific linker chemistry from generic junk. + "ErlotinibVHL": "protac_linker_clf.pkl", # cooh + "BRD9VHL": "protac_linker_clf.pkl", # cooh + "BRD4VHL": "protac_linker_clf_amino_acid.pkl", + "KRASVHL": "protac_linker_clf.pkl", + "ARCRBN": "protac_linker_clf_amine.pkl", + "ARCRBNv2": "protac_linker_clf.pkl", # methyl uses generic + "BRD4CRBN": "protac_linker_clf_amine.pkl", + "CDK6CRBN": "protac_linker_clf.pkl", +} + + +def _morgan_fp(smi): + mol = Chem.MolFromSmiles(smi) + if mol is None: + return None + fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=1024) + arr = np.zeros(1024, dtype=np.float32) + DataStructs.ConvertToNumpyArray(fp, arr) + return arr + + +def _load_protac_module(module_name): + sys.path.insert(0, str(CLF_DIR / "protac")) + mod = importlib.import_module(module_name) + # Prefer the loose SMARTS (WARHEAD_PAT / E3_PAT) which match the core scaffold + # — full WARHEAD_MOL only matches one specific variant, missing PROTAC-DB + # diversity (e.g., gefitinib vs erlotinib both = "EGFR" but fail strict match). + wh = getattr(mod, "WARHEAD_PAT", None) or mod.WARHEAD_MOL + e3 = getattr(mod, "E3_PAT", None) or mod.E3_MOL + return wh, e3 + + +def _extract_linker_smi(protac_mol, warhead_pat, e3_pat): + """Get all atoms in PROTAC NOT belonging to either warhead-pattern or E3-pattern matches. + Take the largest connected fragment after stripping. Returns None on ambiguity.""" + wh_atoms = set() + for match in protac_mol.GetSubstructMatches(warhead_pat): + wh_atoms.update(match) + e3_atoms = set() + for match in protac_mol.GetSubstructMatches(e3_pat): + e3_atoms.update(match) + if not wh_atoms or not e3_atoms: + return None + lk_atoms = set(range(protac_mol.GetNumAtoms())) - wh_atoms - e3_atoms + if not lk_atoms: + return None + # Bonds at the linker boundary (exactly one endpoint in lk_atoms) + bond_ids = [ + b.GetIdx() for b in protac_mol.GetBonds() + if (b.GetBeginAtomIdx() in lk_atoms) != (b.GetEndAtomIdx() in lk_atoms) + ] + if not bond_ids: + return None + try: + broken = Chem.FragmentOnBonds(protac_mol, bond_ids, addDummies=False) + # Find the fragment containing the linker atoms + frag_atom_lists = Chem.GetMolFrags(broken, asMols=False) + for atom_list in frag_atom_lists: + if set(atom_list).issubset(lk_atoms): + frag_atoms = list(atom_list) + if len(frag_atoms) >= 3: # min linker size + sub_mol = Chem.PathToSubmol(broken, [b.GetIdx() for b in broken.GetBonds() + if b.GetBeginAtomIdx() in frag_atoms + and b.GetEndAtomIdx() in frag_atoms]) + try: + Chem.SanitizeMol(sub_mol) + smi = Chem.MolToSmiles(sub_mol) + if smi and '.' not in smi: + return smi + except Exception: + pass + # Fallback: take all fragments, find the one with exact linker atom count + frags = Chem.GetMolFrags(broken, asMols=True) + for frag in frags: + if frag.GetNumHeavyAtoms() == len(lk_atoms): + Chem.SanitizeMol(frag) + return Chem.MolToSmiles(frag) + except Exception: + return None + return None + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--protac-module", required=True, choices=list(_TARGET_FILTER.keys())) + ap.add_argument("--out", default=None, + help="Output .pkl path (default: protac_linker_clf_.pkl)") + args = ap.parse_args() + + module = args.protac_module + target, e3 = _TARGET_FILTER[module] + out_path = Path(args.out) if args.out else CLF_DIR / f"protac_linker_clf_{module}.pkl" + + print(f"=== Training target-specific classifier for {module} ({target}+{e3}) ===") + warhead_mol, e3_mol = _load_protac_module(module) + + # 1. Load PROTAC-DB target+E3 entries (positives) + with open(PROTACDB_JSON) as f: + name = json.load(f) + target_entries = [n for n in name.values() + if target in n.get("tar_path", "") + and e3 in n.get("e3_ligase_path", "") + and "smiles" in n] + print(f" PROTAC-DB {target}+{e3} entries: {len(target_entries)}") + + # 2. Extract linkers + linker_smis = set() + for n in target_entries: + m = Chem.MolFromSmiles(n["smiles"]) + if m is None: + continue + lk_smi = _extract_linker_smi(m, warhead_mol, e3_mol) + if lk_smi: + linker_smis.add(lk_smi) + pos_smiles = sorted(linker_smis) + print(f" Extracted unique target-specific linkers (positives): {len(pos_smiles)}") + + if len(pos_smiles) < 10: + print(f" ⚠ Only {len(pos_smiles)} positives — classifier may overfit. Consider " + f"augmenting with similar linkers or using termini-default classifier.") + + # 3. Load negatives from existing termini-matched classifier + neg_pkl = CLF_DIR / _TERMINI_FOR_NEGATIVES[module] + with open(neg_pkl, "rb") as f: + existing = pickle.load(f) + neg_smiles = list(existing["neg_smiles"]) + print(f" Negatives (from {neg_pkl.name}): {len(neg_smiles)}") + + # 4. Featurize + train + X_pos = [_morgan_fp(s) for s in pos_smiles] + X_pos = [x for x in X_pos if x is not None] + X_neg = [_morgan_fp(s) for s in neg_smiles] + X_neg = [x for x in X_neg if x is not None] + X = np.vstack(X_pos + X_neg) + y = np.concatenate([np.ones(len(X_pos), dtype=int), + np.zeros(len(X_neg), dtype=int)]) + print(f" Training set: {len(X_pos)} pos / {len(X_neg)} neg, {X.shape[1]}-dim FP") + + # Class-balanced LogReg (since pos is much smaller than neg) + clf = LogisticRegression(class_weight="balanced", max_iter=2000) + clf.fit(X, y) + train_acc = clf.score(X, y) + print(f" Training accuracy: {train_acc*100:.1f}%") + + # Sanity: predict prob on a few positives + negatives + sample_pos = clf.predict_proba(X_pos[:5])[:, 1] + sample_neg = clf.predict_proba(X_neg[:5])[:, 1] + print(f" Sample pos probs: {[f'{p:.3f}' for p in sample_pos]}") + print(f" Sample neg probs: {[f'{p:.3f}' for p in sample_neg]}") + + # 5. Save + with open(out_path, "wb") as f: + pickle.dump({ + "clf": clf, + "pos_smiles": pos_smiles, + "neg_smiles": neg_smiles, + "module": module, + "target": target, + "e3": e3, + "n_pos": len(X_pos), + "n_neg": len(X_neg), + }, f) + print(f" Saved → {out_path}") + + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/scripts/verify_release.py b/CupOFMoCA-PROTAC/scripts/verify_release.py new file mode 100644 index 0000000000000000000000000000000000000000..934baee3e2c07b9911e0c6c392e30caedaf853c7 --- /dev/null +++ b/CupOFMoCA-PROTAC/scripts/verify_release.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +"""Verify bundled PROTAC artifacts and portability-sensitive source files.""" + +from __future__ import annotations + +import hashlib +import json +import sys +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +MANIFEST = ROOT / "docs" / "artifacts.json" + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for block in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def main() -> int: + entries = json.loads(MANIFEST.read_text()) + failures = 0 + for entry in entries: + path = ROOT / entry["path"] + if not path.is_file(): + print(f"MISSING {entry['path']}") + failures += 1 + continue + size_ok = path.stat().st_size == entry["bytes"] + hash_ok = sha256(path) == entry["sha256"] + state = "OK" if size_ok and hash_ok else "FAILED" + print(f"{state:7} {entry['path']}") + failures += int(not (size_ok and hash_ok)) + + private_roots = tuple("/" + stem for stem in ("scratch/", "Users/", "home/")) + for sub in ("src", "scripts", "md"): + for path in (ROOT / sub).rglob("*.py"): + text = path.read_text(errors="replace") + if any(root in text for root in private_roots): + print(f"PRIVATE {path.relative_to(ROOT)}") + failures += 1 + + # Optional components are fetched, not redistributed; flag only if partially present. + for name, probe in (("degrademaster", "src/degrademaster/model.py"), + ("propk", "src/propk/maplight.py")): + if not (ROOT / probe).is_file(): + print(f"ABSENT {name} (optional; run scripts/fetch_third_party.py)") + + print("Release verification passed." if not failures else f"Verification found {failures} problem(s).") + return 1 if failures else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/CupOFMoCA-PROTAC/src/classifier/protac_analyzer.py b/CupOFMoCA-PROTAC/src/classifier/protac_analyzer.py new file mode 100644 index 0000000000000000000000000000000000000000..29042d4727806fbf8d4213cbb4d1384332b0d635 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/classifier/protac_analyzer.py @@ -0,0 +1,401 @@ +""" +PROTAC Analyzer +Analyzes PROTAC linkers for structural validity + +The linker is the generated component; warhead and E3 ligand are fixed inputs. + +Hard constraints (validity = 1 if all pass, 0 if any fails): +1. Attachment site availability: linker has reactive termini for both warhead and E3 ends +2. Linker feasibility: ring-closure constraints, long carbon chains +3. Linker atom count: 3-20 heavy atoms in the linker +4. Assembled PROTAC MW: full PROTAC within 700-1200 Da (requires warhead + E3) +5. Representational validity: RDKit sanitization, valence, round-trip +6. Synthetic accessibility: SA score ≤ threshold (default 6.0) + +All methods accept Chem.Mol objects — the molecular graph produced by DeFoG. +SMILES conversion happens once at the DeFoG graph → RDKit Mol boundary upstream; +this class never calls MolFromSmiles on method inputs. + +Note: Bond-forming chemistry (which atom connects to which) is in protac_builder.py. +""" + +from __future__ import annotations + +import os +import sys +import torch +from rdkit import Chem, RDConfig +from rdkit.Chem import Descriptors, AllChem, rdMolDescriptors +from typing import Optional + +sys.path.insert(0, os.path.join(RDConfig.RDContribDir, "SA_Score")) +import sascorer + + +LINKER_ATOM_MIN = 3 +LINKER_ATOM_MAX = 25 + +PROTAC_MW_MIN = 700.0 +PROTAC_MW_MAX = 1250.0 + +SA_SCORE_MAX = 6.0 # 1 (easy) → 10 (hard); ≤6 keeps synthetically accessible linkers + +_LONG_CHAIN_SMARTS = "[CH2]" + "[CH2]" * 15 # 16 consecutive CH2 + +# Atoms allowed in PROTAC linkers — excludes halogens (I, Br optionally) and P +# which appear as nonsensical bridge atoms when generated from GuacaMol atom decoder. + + +class PROTACAnalyzer: + """ + Validates PROTAC linkers against hard chemical and structural constraints. + + All check_* methods and the batch interface accept Chem.Mol objects only. + The DeFoG graph → Chem.Mol conversion happens once upstream; this class + never parses SMILES strings. + + Parameters + ---------- + warhead : Chem.Mol + Fixed warhead molecule. Required for MW check. + e3 : Chem.Mol + Fixed E3-ligand molecule. Required for MW check. + warhead_attachment_smarts : str + SMARTS for the reactive group the linker must have on the warhead-facing end. + e3_attachment_smarts : str + SMARTS for the reactive group the linker must have on the E3-facing end. + linker_atom_min / linker_atom_max : int + Heavy-atom count bounds for the linker (default 3–20). + protac_mw_min / protac_mw_max : float + Assembled PROTAC MW window in Da (default 700–1200). + check_mw : bool + Enforce the MW window. Requires warhead and e3. + sa_score_max : float + Maximum SA score for the linker (default 6.0). Set to None to disable. + calculate_properties : bool + Compute optional descriptors in get_detailed_scores. + """ + + def __init__( + self, + warhead: Chem.Mol, + e3: Chem.Mol, + warhead_attachment_smarts: str, + e3_attachment_smarts: str, + linker_atom_min: int = LINKER_ATOM_MIN, + linker_atom_max: int = LINKER_ATOM_MAX, + protac_mw_min: float = PROTAC_MW_MIN, + protac_mw_max: float = PROTAC_MW_MAX, + check_mw: bool = True, + sa_score_max: Optional[float] = SA_SCORE_MAX, + calculate_properties: bool = True, + ): + if warhead is None: + raise ValueError("warhead must be a Chem.Mol object") + if e3 is None: + raise ValueError("e3 must be a Chem.Mol object") + + self._warhead_mol = warhead + self._e3_mol = e3 + self.linker_atom_min = linker_atom_min + self.linker_atom_max = linker_atom_max + self.protac_mw_min = protac_mw_min + self.protac_mw_max = protac_mw_max + self.check_mw = check_mw + self.sa_score_max = sa_score_max + self.calculate_properties = calculate_properties + + self._warhead_pat = Chem.MolFromSmarts(warhead_attachment_smarts) + self._e3_pat = Chem.MolFromSmarts(e3_attachment_smarts) + if self._warhead_pat is None: + raise ValueError(f"Invalid warhead_attachment_smarts: {warhead_attachment_smarts!r}") + if self._e3_pat is None: + raise ValueError(f"Invalid e3_attachment_smarts: {e3_attachment_smarts!r}") + + self._long_chain_pat = Chem.MolFromSmarts(_LONG_CHAIN_SMARTS) + + self.property_flags = { + "mf": True, "mw": True, "polararea": True, "xlogp": True, + "heavycnt": True, "hbonddonor": True, "hbondacc": True, "rotbonds": True, + } + + # ------------------------------------------------------------------ + # Property flag helpers + # ------------------------------------------------------------------ + + def set_property_flags(self, **kwargs): + for k, v in kwargs.items(): + if k in self.property_flags: + self.property_flags[k] = bool(v) + else: + raise ValueError(f"Unknown property: {k}. Valid: {list(self.property_flags)}") + + def enable_all_properties(self): + for k in self.property_flags: + self.property_flags[k] = True + + def disable_all_properties(self): + for k in self.property_flags: + self.property_flags[k] = False + + # ------------------------------------------------------------------ + # Hard constraint checks — all accept Chem.Mol + # ------------------------------------------------------------------ + + def check_attachment_sites(self, mol: Chem.Mol) -> bool: + """Constraint 1: linker must have two *distinct* attachment atoms — + one for the warhead end and one for the E3 end. Requiring the same + atom to satisfy both patterns (e.g. a single CHO) is rejected.""" + warhead_atoms = {m[0] for m in mol.GetSubstructMatches(self._warhead_pat)} + e3_atoms = {m[0] for m in mol.GetSubstructMatches(self._e3_pat)} + return any(w != e for w in warhead_atoms for e in e3_atoms) + + def check_linker_atom_count(self, mol: Chem.Mol) -> bool: + """Constraint 3: heavy-atom count within [min, max].""" + n = mol.GetNumHeavyAtoms() + return self.linker_atom_min <= n <= self.linker_atom_max + + def check_protac_mw(self, linker_mol: Chem.Mol) -> bool: + """ + Constraint 4: MW(warhead) + MW(linker) + MW(E3) - 36 Da + (subtracts two amide-bond condensation water losses). + """ + if not self.check_mw: + return True + mw = (Descriptors.MolWt(self._warhead_mol) + + Descriptors.MolWt(linker_mol) + + Descriptors.MolWt(self._e3_mol) + - 36.0) + return self.protac_mw_min <= mw <= self.protac_mw_max + + def _find_linker_rings(self, mol: Chem.Mol): + linker_rings = [] + for ring in mol.GetRingInfo().AtomRings(): + ring_atoms = [mol.GetAtomWithIdx(i) for i in ring] + if all(a.GetIsAromatic() for a in ring_atoms): + continue + size = len(ring) + symbols = [a.GetSymbol() for a in ring_atoms] + if size == 5 and "N" in symbols: + c_count = sum( + 1 for idx in ring + for nb in mol.GetAtomWithIdx(idx).GetNeighbors() + if nb.GetSymbol() == "O" + and nb.GetIdx() not in ring + and mol.GetBondBetweenAtoms(idx, nb.GetIdx()).GetBondType() + == Chem.BondType.DOUBLE + ) + if c_count >= 2: + linker_rings.append(ring) + continue + linker_rings.append(ring) + return linker_rings + + def check_ring_closure_constraints(self, mol: Chem.Mol) -> bool: + """ + Constraint 2a: + - 3-ring with C=C → reject (severe strain) + - 4-ring with >2 sp2 centers → reject + - ring > 12 atoms → reject + """ + for ring in self._find_linker_rings(mol): + size = len(ring) + if size < 3: + return False + if size == 3: + for idx in ring: + atom = mol.GetAtomWithIdx(idx) + if (atom.GetSymbol() == "C" + and atom.GetHybridization() == Chem.HybridizationType.SP2): + for nb in atom.GetNeighbors(): + bond = mol.GetBondBetweenAtoms(idx, nb.GetIdx()) + if bond.GetBondType() == Chem.BondType.DOUBLE and nb.GetSymbol() == "C": + return False + if size == 4: + sp2 = sum(1 for idx in ring + if mol.GetAtomWithIdx(idx).GetHybridization() + == Chem.HybridizationType.SP2) + if sp2 > 2: + return False + if size > 12: + return False + return True + + def check_long_chain(self, mol: Chem.Mol) -> bool: + """Constraint 2b: reject ≥16 consecutive CH2 groups.""" + if self._long_chain_pat is None: + return True + return not mol.HasSubstructMatch(self._long_chain_pat) + + def check_linker_feasibility(self, mol: Chem.Mol) -> bool: + return self.check_ring_closure_constraints(mol) and self.check_long_chain(mol) + + def check_representational_validity(self, mol: Chem.Mol) -> bool: + """Constraint 5: sanitization, round-trip SMILES, valence, 2D coords.""" + try: + Chem.SanitizeMol(mol) + except Exception: + return False + try: + if Chem.MolFromSmiles(Chem.MolToSmiles(mol)) is None: + return False + except Exception: + return False + try: + for prob in Chem.DetectChemistryProblems(mol): + if prob.GetType() in ("AtomValenceException", "KekulizeException"): + return False + except Exception: + return False + try: + AllChem.Compute2DCoords(mol) + except Exception: + return False + return True + + # ------------------------------------------------------------------ + # Validation entry point + # ------------------------------------------------------------------ + + def check_sa_score(self, mol: Chem.Mol) -> bool: + """Constraint 6: SA score ≤ sa_score_max (skipped if sa_score_max is None).""" + if self.sa_score_max is None: + return True + score = sascorer.calculateScore(mol) + if score is None: + return False + return score <= self.sa_score_max + + def _run_checks(self, mol: Chem.Mol) -> dict: + return { + "connected": len(Chem.GetMolFrags(mol)) == 1, + "attachment_sites": self.check_attachment_sites(mol), + "ring_closure": self.check_ring_closure_constraints(mol), + "long_chain": self.check_long_chain(mol), + "linker_atom_count": self.check_linker_atom_count(mol), + "protac_mw": self.check_protac_mw(mol), + "representational": self.check_representational_validity(mol), + "sa_score": self.check_sa_score(mol), + } + + def validate(self, linker_mol: Chem.Mol) -> bool: + """Run all hard constraints. Returns True only if every check passes.""" + return all(self._run_checks(linker_mol).values()) + + # ------------------------------------------------------------------ + # Optional property computation + # ------------------------------------------------------------------ + + def calculate_linker_properties(self, mol: Chem.Mol) -> dict: + if not self.calculate_properties: + return {} + props = {} + if self.property_flags.get("mf"): + props["mf"] = rdMolDescriptors.CalcMolFormula(mol) + if self.property_flags.get("mw"): + props["mw"] = round(Descriptors.MolWt(mol), 2) + if self.property_flags.get("polararea"): + props["polararea"] = round(Descriptors.TPSA(mol), 2) + if self.property_flags.get("xlogp"): + props["xlogp"] = round(Descriptors.MolLogP(mol), 2) + if self.property_flags.get("heavycnt"): + props["heavycnt"] = mol.GetNumHeavyAtoms() + if self.property_flags.get("hbonddonor"): + props["hbonddonor"] = Descriptors.NumHDonors(mol) + if self.property_flags.get("hbondacc"): + props["hbondacc"] = Descriptors.NumHAcceptors(mol) + if self.property_flags.get("rotbonds"): + props["rotbonds"] = Descriptors.NumRotatableBonds(mol) + return props + + def estimate_protac_mw(self, linker_mol: Chem.Mol) -> Optional[float]: + if not (self._warhead_mol and self._e3_mol): + return None + return (Descriptors.MolWt(self._warhead_mol) + + Descriptors.MolWt(linker_mol) + + Descriptors.MolWt(self._e3_mol) + - 36.0) + + # ------------------------------------------------------------------ + # Batch interface — list[Chem.Mol] + # ------------------------------------------------------------------ + + def get_scores(self, linker_mols: list[Chem.Mol]) -> list[int]: + """ + Validate a batch of linker Mol objects. + Returns list of 0/1 scores (1 = all constraints pass). + """ + return [ + 1 if all(self._run_checks(mol).values()) else 0 + for mol in linker_mols + ] + + def get_detailed_scores(self, linker_mols: list[Chem.Mol]) -> list[dict]: + """ + Per-constraint breakdown for each linker Mol. + Keys: valid, smiles (canonical), checks, properties, estimated_protac_mw. + """ + detailed = [] + for mol in linker_mols: + checks = self._run_checks(mol) + entry = { + "valid": 1 if all(checks.values()) else 0, + "smiles": Chem.MolToSmiles(mol), + "checks": checks, + "properties": self.calculate_linker_properties(mol), + } + est_mw = self.estimate_protac_mw(mol) + if est_mw is not None: + entry["estimated_protac_mw"] = round(est_mw, 2) + detailed.append(entry) + return detailed + + # ------------------------------------------------------------------ + # Tensor interface for guidance loop + # ------------------------------------------------------------------ + + def __call__(self, linker_mols: list[Chem.Mol]) -> torch.Tensor: + """Return float tensor of validity scores (0 or 1) for the guidance loop.""" + return torch.tensor(self.get_scores(linker_mols), dtype=torch.float32) + + +# --------------------------------------------------------------------------- +# Self-test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + import sys, os + sys.path.insert(0, os.path.dirname(__file__)) + from protac.ErlotinibVHL import EGFR_WARHEAD_SMILES, VHL_E3_SMILES + + analyzer = PROTACAnalyzer( + warhead=Chem.MolFromSmiles(EGFR_WARHEAD_SMILES), + e3=Chem.MolFromSmiles(VHL_E3_SMILES), + warhead_attachment_smarts="[NX3;H2]", + e3_attachment_smarts="[NX3;H2]", + ) + + test_cases = [ + ("NCCOCCOCCN", "Valid PEG-diamine"), + ("NCCCCN", "Valid butane-1,4-diamine"), + ("NCCCCCCCCCCCCCCCCCN", "Invalid: too long + long chain"), + ("NCC1=CC1N", "Invalid: strained 3-ring"), + ("N", "Invalid: too short"), + ] + + print("=" * 70) + print("PROTAC Linker Analyzer — Self-Test") + print("=" * 70) + + for smiles, desc in test_cases: + mol = Chem.MolFromSmiles(smiles) # SMILES used only here in the test + if mol is None: + print(f"\n[INVALID] {desc} — unparseable SMILES") + continue + result = analyzer.get_detailed_scores([mol])[0] + tag = "VALID" if result["valid"] == 1 else "INVALID" + print(f"\n[{tag}] {desc}") + for k, v in result["checks"].items(): + print(f" {'✓' if v else '✗'} {k}") + if "estimated_protac_mw" in result: + print(f" estimated PROTAC MW: {result['estimated_protac_mw']} Da") \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/classifier/protac_classifier.py b/CupOFMoCA-PROTAC/src/classifier/protac_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..4476ed591f79a464d76574570943518e46809ef4 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/classifier/protac_classifier.py @@ -0,0 +1,543 @@ +""" +PROTAC classifiers and guidance scorers for the DeFoG guidance loop. + +All three classes accept DeFoG's molecule_list format and share _graph_to_mol. +They are called together on the same batch in each guidance loop iteration: + + PROTACClassifier → 0/1 hard validity mask + EGFRPROPKScorer → continuous PK guidance (T½, CL, Vss) + PROTACRDKitScorer → continuous property guidance (QED, logP, TPSA, Fsp3, HBD, rotbonds) + +DeFoG molecule_list format: list of [atom_types, edge_types] + atom_types : 1D int tensor, length n — index into atom_decoder + edge_types : 2D int tensor [n, n] — 0=none 1=single 2=double 3=triple 4=aromatic ≥5=virtual +""" + +from __future__ import annotations + +import os +import pickle +from typing import Optional + +import numpy as np +import torch +from rdkit import Chem, RDLogger +from rdkit.Chem import rdchem + +from protac_analyzer import PROTACAnalyzer + +RDLogger.DisableLog("rdApp.*") + +# --------------------------------------------------------------------------- +# Shared graph → Mol conversion +# --------------------------------------------------------------------------- + +_BOND_TYPES = [ + None, + rdchem.BondType.SINGLE, + rdchem.BondType.DOUBLE, + rdchem.BondType.TRIPLE, + rdchem.BondType.AROMATIC, +] + + +def _graph_to_mol(atom_types: torch.Tensor, edge_types: torch.Tensor, + atom_decoder: list[str]) -> Chem.RWMol: + """Convert DeFoG (atom_types, edge_types) tensors to a Chem.RWMol.""" + mol = Chem.RWMol() + for idx in atom_types: + mol.AddAtom(Chem.Atom(atom_decoder[idx.item()])) + + edge_types = torch.triu(edge_types) + edge_types[edge_types >= 5] = 0 + for i, j in torch.nonzero(edge_types).tolist(): + if i != j: + mol.AddBond(i, j, _BOND_TYPES[edge_types[i, j].item()]) + return mol + + +def prebuild_mols(molecule_list: list, atom_decoder: list[str]) -> list: + """Convert + sanitize each (atom_types, edge_types) once for shared scorer use. + Returns list of sanitized RWMol or None if conversion/sanitization failed.""" + mols = [] + for atom_types, edge_types in molecule_list: + try: + m = _graph_to_mol(atom_types, edge_types, atom_decoder) + Chem.SanitizeMol(m) + mols.append(m) + except Exception: + mols.append(None) + return mols + + +# --------------------------------------------------------------------------- +# Hard validity classifier +# --------------------------------------------------------------------------- + +class PROTACClassifier: + """ + Hard validity scorer for PROTAC linker graphs produced by DeFoG. + Returns 0/1 tensor — 1 only when: + - PROTACAnalyzer structural checks pass (atom count, ring closure, MW, SA, repr.) + - builder.assemble() succeeds (actual RDKit reaction with warhead + E3) + + Using assembly as the attachment check means guidance is steered by real + chemistry — NH2, reactive NH, or any N that actually reacts — rather than + a SMARTS proxy. + + Parameters + ---------- + analyzer : PROTACAnalyzer + Structural checks only (attachment_sites check is bypassed here). + builder : PROTAC_Builder + Used for the assembly-based attachment check. + atom_decoder : list[str] + Maps atom-type index → element symbol. + """ + + def __init__(self, analyzer: PROTACAnalyzer, builder, atom_decoder: list[str]): + self.analyzer = analyzer + self.builder = builder + self.atom_decoder = atom_decoder + # SMARTS for site counting. Requiring an alpha carbon on the carbonyl + # rules out carbamic acids (N-C(=O)OH), which can't form stable amides. + self._cooh_pat = Chem.MolFromSmarts("[#6][CX3](=[OX1])[OX2H1]") + self._cho_pat = Chem.MolFromSmarts("[#6][CX3H1](=[OX1])") + self._methyl_pat = Chem.MolFromSmarts("[CX4H3]") + self._amine_pat = Chem.MolFromSmarts("[NX3;!H0;!$(NC=O);!$(NS=O);!$(N=O)]") + self._nh2_pat = Chem.MolFromSmarts("[NX3H2;D1;!$(NC=O);!$(NS=O);!$(N=O)]") + # Hard rejection patterns. + self._alerts = [ + Chem.MolFromSmarts('[NX3][NX3]'), # hydrazide + Chem.MolFromSmarts('[#7]=[#7]'), # azo + Chem.MolFromSmarts('[N+][O-]'), # N-oxide + Chem.MolFromSmarts('[#7]-[OX2]'), # N-O single bond + Chem.MolFromSmarts('[NX3]C(=[OX1])[OX2H1]'), # carbamate + ] + + def get_scores(self, molecule_list: list, + fix_termini=False, mols=None) -> torch.Tensor: + """Binary validity (0/1) over a list of molecules. + + Returns 1 if the mol passes (a) sanitize + SMILES round-trip, + (b) no liability SMARTS, and (c) >= 2 reactive termini for the chosen + fix_termini mode (cooh / methyl / amine / amino_acid). Returns 0 otherwise. + Pass `mols` to skip graph_to_mol + sanitize. + """ + methyl_mode = (fix_termini == 'methyl') + amine_mode = (fix_termini == 'amine') + amino_acid_mode = (fix_termini == 'amino_acid') + if mols is None: + mols = prebuild_mols(molecule_list, self.atom_decoder) + scores = [] + for mol in mols: + if mol is None: + scores.append(0.0) + continue + try: + # sanitize + SMILES round-trip + try: + Chem.SanitizeMol(mol) + smi = Chem.MolToSmiles(mol) + if not smi or Chem.MolFromSmiles(smi) is None: + scores.append(0.0) + continue + except Exception: + scores.append(0.0) + continue + # liability filter + if any(mol.HasSubstructMatch(p) for p in self._alerts): + scores.append(0.0) + continue + # reactive termini count + if amine_mode: + n_sites = len(mol.GetSubstructMatches(self._nh2_pat)) + elif amino_acid_mode: + n_sites = (len(mol.GetSubstructMatches(self._nh2_pat)) + + len(mol.GetSubstructMatches(self._cooh_pat))) + else: + n_sites = (len(mol.GetSubstructMatches(self._cooh_pat)) + + len(mol.GetSubstructMatches(self._cho_pat))) + if methyl_mode: + n_sites += len(mol.GetSubstructMatches(self._methyl_pat)) + scores.append(1.0 if n_sites >= 2 else 0.0) + except Exception: + scores.append(0.0) + return torch.tensor(scores, dtype=torch.float32) + + def __call__(self, molecule_list: list, mols=None) -> torch.Tensor: + return self.get_scores(molecule_list, fix_termini=getattr(self, 'fix_termini', False), mols=mols) + + +# --------------------------------------------------------------------------- +# EGFR-PROPK soft PK guidance scorer +# --------------------------------------------------------------------------- + +class EGFRPROPKScorer: + """ + Soft PK guidance using fine-tuned CatBoost models from Zhang et al. 2026 + (EGFR-PROPK). Predicts T½, CL, Vss on the assembled PROTAC. + + score = sigmoid(t½_raw) − sigmoid(CL_raw) + + GIN features (300 dims) are zero-padded — DGL not available in this env. + The remaining 2563 structural features still provide useful guidance. + + Parameters + ---------- + builder : PROTAC_Builder + Used to assemble linker + warhead + E3 into a full PROTAC. + model_dir : str + Directory containing t0.5_10_finetune.pkl, CL_1_finetune.pkl, + Vss_1_finetune.pkl. + atom_decoder : list[str] + Maps atom-type index → element symbol. + half_life_weight, clearance_weight : float + Relative weights for the composite score. + """ + + # Feature vector layout matching EGFR-PROPK training (maplight_gnn.py) + _N_MORGAN = 1024 + _N_AVALON = 1024 + _N_ERG = 315 + _N_GIN = 300 # zero-padded — DGL unavailable + _RDKIT_DESC_NAMES = [ + "BalabanJ", "BertzCT", "Chi0", "Chi0n", "Chi0v", "Chi1", + "Chi1n", "Chi1v", "Chi2n", "Chi2v", "Chi3n", "Chi3v", "Chi4n", "Chi4v", + "EState_VSA1", "EState_VSA10", "EState_VSA11", "EState_VSA2", "EState_VSA3", + "EState_VSA4", "EState_VSA5", "EState_VSA6", "EState_VSA7", "EState_VSA8", + "EState_VSA9", "ExactMolWt", "FpDensityMorgan1", "FpDensityMorgan2", + "FpDensityMorgan3", "FractionCSP3", "HallKierAlpha", "HeavyAtomCount", + "HeavyAtomMolWt", "Ipc", "Kappa1", "Kappa2", "Kappa3", "LabuteASA", + "MaxAbsEStateIndex", "MaxAbsPartialCharge", "MaxEStateIndex", "MaxPartialCharge", + "MinAbsEStateIndex", "MinAbsPartialCharge", "MinEStateIndex", "MinPartialCharge", + "MolLogP", "MolMR", "MolWt", "NHOHCount", "NOCount", "NumAliphaticCarbocycles", + "NumAliphaticHeterocycles", "NumAliphaticRings", "NumAromaticCarbocycles", + "NumAromaticHeterocycles", "NumAromaticRings", "NumHAcceptors", "NumHDonors", + "NumHeteroatoms", "NumRadicalElectrons", "NumRotatableBonds", + "NumSaturatedCarbocycles", "NumSaturatedHeterocycles", "NumSaturatedRings", + "NumValenceElectrons", "PEOE_VSA1", "PEOE_VSA10", "PEOE_VSA11", "PEOE_VSA12", + "PEOE_VSA13", "PEOE_VSA14", "PEOE_VSA2", "PEOE_VSA3", "PEOE_VSA4", "PEOE_VSA5", + "PEOE_VSA6", "PEOE_VSA7", "PEOE_VSA8", "PEOE_VSA9", "RingCount", "SMR_VSA1", + "SMR_VSA10", "SMR_VSA2", "SMR_VSA3", "SMR_VSA4", "SMR_VSA5", "SMR_VSA6", + "SMR_VSA7", "SMR_VSA8", "SMR_VSA9", "SlogP_VSA1", "SlogP_VSA10", "SlogP_VSA11", + "SlogP_VSA12", "SlogP_VSA2", "SlogP_VSA3", "SlogP_VSA4", "SlogP_VSA5", + "SlogP_VSA6", "SlogP_VSA7", "SlogP_VSA8", "SlogP_VSA9", "TPSA", + "VSA_EState1", "VSA_EState10", "VSA_EState2", "VSA_EState3", "VSA_EState4", + "VSA_EState5", "VSA_EState6", "VSA_EState7", "VSA_EState8", "VSA_EState9", + "fr_Al_COO", "fr_Al_OH", "fr_Al_OH_noTert", "fr_ArN", "fr_Ar_COO", "fr_Ar_N", + "fr_Ar_NH", "fr_Ar_OH", "fr_COO", "fr_COO2", "fr_C_O", "fr_C_O_noCOO", + "fr_C_S", "fr_HOCCN", "fr_Imine", "fr_NH0", "fr_NH1", "fr_NH2", "fr_N_O", + "fr_Ndealkylation1", "fr_Ndealkylation2", "fr_Nhpyrrole", "fr_SH", + "fr_aldehyde", "fr_alkyl_carbamate", "fr_alkyl_halide", "fr_allylic_oxid", + "fr_amide", "fr_amidine", "fr_aniline", "fr_aryl_methyl", "fr_azide", "fr_azo", + "fr_barbitur", "fr_benzene", "fr_benzodiazepine", "fr_bicyclic", "fr_diazo", + "fr_dihydropyridine", "fr_epoxide", "fr_ester", "fr_ether", "fr_furan", + "fr_guanido", "fr_halogen", "fr_hdrzine", "fr_hdrzone", "fr_imidazole", + "fr_imide", "fr_isocyan", "fr_isothiocyan", "fr_ketone", "fr_ketone_Topliss", + "fr_lactam", "fr_lactone", "fr_methoxy", "fr_morpholine", "fr_nitrile", + "fr_nitro", "fr_nitro_arom", "fr_nitro_arom_nonortho", "fr_nitroso", + "fr_oxazole", "fr_oxime", "fr_para_hydroxylation", "fr_phenol", + "fr_phenol_noOrthoHbond", "fr_phos_acid", "fr_phos_ester", "fr_piperdine", + "fr_piperzine", "fr_priamide", "fr_prisulfonamd", "fr_pyridine", "fr_quatN", + "fr_sulfide", "fr_sulfonamd", "fr_sulfone", "fr_term_acetylene", "fr_tetrazole", + "fr_thiazole", "fr_thiocyan", "fr_thiophene", "fr_unbrch_alkane", "fr_urea", "qed", + ] + + def __init__(self, builder, model_dir: str, atom_decoder: list[str], + half_life_weight: float = 1.0, clearance_weight: float = 1.0): + from rdkit.Chem import DataStructs + from rdkit.Chem.rdMolDescriptors import GetHashedMorganFingerprint + from rdkit.Avalon.pyAvalonTools import GetAvalonCountFP + from rdkit.Chem import rdReducedGraphs + from rdkit.ML.Descriptors.MoleculeDescriptors import MolecularDescriptorCalculator + + self.builder = builder + self.atom_decoder = atom_decoder + self.half_life_weight = half_life_weight + self.clearance_weight = clearance_weight + + self._DataStructs = DataStructs + self._GetHashedMorganFP = GetHashedMorganFingerprint + self._GetAvalonFP = GetAvalonCountFP + self._GetErGFP = rdReducedGraphs.GetErGFingerprint + self._desc_calc = MolecularDescriptorCalculator(self._RDKIT_DESC_NAMES) + + def _load(name): + with open(os.path.join(model_dir, name), "rb") as f: + return pickle.load(f) + + self._model_t_half = _load("t0.5_10_finetune.pkl") + self._model_cl = _load("CL_1_finetune.pkl") + self._model_vss = _load("Vss_1_finetune.pkl") + + def _assemble(self, linker_mol: Chem.Mol) -> Optional[Chem.Mol]: + amide_only = getattr(self, 'fix_termini', None) != 'methyl' + smi = Chem.MolToSmiles(linker_mol) + if smi == getattr(self, '_asm_smi', None): + return self._asm_mol + try: + result = self.builder.assemble(linker_mol, return_all=False, amide_only=amide_only) + smiles = result if isinstance(result, str) else result.get("protac_smiles") + mol = Chem.MolFromSmiles(smiles) if smiles else None + except Exception: + mol = None + self._asm_smi, self._asm_mol = smi, mol + return mol + + def _featurize(self, mol: Chem.Mol) -> np.ndarray: + def _fp_arr(fp): + a = np.zeros((0,), dtype=np.int8) + self._DataStructs.ConvertToNumpyArray(fp, a) + return a + + vec = np.concatenate([ + _fp_arr(self._GetHashedMorganFP(mol, nBits=self._N_MORGAN, radius=2)), + _fp_arr(self._GetAvalonFP(mol, nBits=self._N_AVALON)), + np.array(self._GetErGFP(mol)), + np.array(self._desc_calc.CalcDescriptors(mol)), + np.zeros(self._N_GIN, dtype=np.float32), + ]) + return np.nan_to_num(vec, nan=0.0, posinf=0.0, neginf=0.0).astype(np.float32) + + def _score_protac(self, protac_mol: Chem.Mol) -> float: + X = self._featurize(protac_mol).reshape(1, -1) + t_half = float(self._model_t_half.predict(X)[0]) + cl = float(self._model_cl.predict(X)[0]) + sigmoid = lambda x: 1.0 / (1.0 + np.exp(-x)) + return self.half_life_weight * sigmoid(t_half) - self.clearance_weight * sigmoid(cl) + + def predict_pk(self, protac_mol: Chem.Mol) -> dict: + """Return raw EGFR-PROPK predictions for the assembled PROTAC. + + Keys: t_half (hours, log-scale), cl (clearance), vss (volume of distribution), + plus the composite score (sigmoid(t_half) − sigmoid(cl)). Use this for + post-hoc validation / reporting; use _score_protac for guidance scalars. + """ + X = self._featurize(protac_mol).reshape(1, -1) + t_half = float(self._model_t_half.predict(X)[0]) + cl = float(self._model_cl.predict(X)[0]) + vss = float(self._model_vss.predict(X)[0]) + sigmoid = lambda x: 1.0 / (1.0 + np.exp(-x)) + return { + "t_half": t_half, + "cl": cl, + "vss": vss, + "score": self.half_life_weight * sigmoid(t_half) - self.clearance_weight * sigmoid(cl), + } + + def get_scores(self, molecule_list: list) -> torch.Tensor: + scores = [] + for atom_types, edge_types in molecule_list: + try: + linker = _graph_to_mol(atom_types, edge_types, self.atom_decoder) + Chem.SanitizeMol(linker) + protac = self._assemble(linker) + scores.append(self._score_protac(protac) if protac is not None else 0.0) + except Exception: + scores.append(0.0) + return torch.tensor(scores, dtype=torch.float32) + + def __call__(self, molecule_list: list) -> torch.Tensor: + return self.get_scores(molecule_list) + + +# --------------------------------------------------------------------------- +# RDKit property soft guidance scorer +# --------------------------------------------------------------------------- + +class PROTACRDKitScorer: + """ + Soft property guidance using RDKit descriptors on the assembled PROTAC. + Returns weighted mean of per-property scores, each in [0, 1]. + + All windows are derived from PROTAC-DB (n=6,110 unique assembled PROTACs): + flat top = [mean − 1σ, mean + 1σ] (score = 1) + outer = [mean − 2σ, mean + 2σ] (score ramps 0 → 1) + + Parameters + ---------- + builder : PROTAC_Builder + Used to assemble linker + warhead + E3 into a full PROTAC. + atom_decoder : list[str] + Maps atom-type index → element symbol. + weights : dict[str, float] | None + Per-property weight. Keys: qed, logp, tpsa, fsp3, hbd, rotbonds. + active : set[str] | None + Subset of properties to score. Defaults to all. + """ + + # Trapezoidal: (lo, lo_ramp, hi_ramp, hi) + # Score = 0 outside [lo, hi], linearly ramps to 1 in shoulders, flat 1 in [lo_ramp, hi_ramp]. + _PROPERTY_WINDOWS = { + "qed": (0.001, 0.015, 0.145, 0.210), + "logp": (0.7, 3.1, 7.9, 10.3), + "tpsa": (119.0, 165.0, 259.0, 306.0), + "fsp3": (0.24, 0.33, 0.51, 0.60), + "hbd": (0.7, 2.4, 6.0, 7.8), + "rotbonds": (5.0, 12.0, 25.0, 32.0), + } + + def __init__(self, builder, atom_decoder: list[str], + weights: dict | None = None, active: set | None = None): + from rdkit.Chem import Descriptors, QED, rdMolDescriptors + + self.builder = builder + self.atom_decoder = atom_decoder + self.active = active if active is not None else set(self._PROPERTY_WINDOWS) + self.weights = {k: (weights or {}).get(k, 1.0) for k in self.active} + + self._prop_fns = { + "qed": QED.qed, + "logp": Descriptors.MolLogP, + "tpsa": Descriptors.TPSA, + "fsp3": rdMolDescriptors.CalcFractionCSP3, + "hbd": lambda m: float(Descriptors.NumHDonors(m)), + "rotbonds": lambda m: float(Descriptors.NumRotatableBonds(m)), + } + + @staticmethod + def _trapezoid(v, lo, lo_ramp, hi_ramp, hi) -> float: + if v <= lo or v >= hi: + return 0.0 + if v <= lo_ramp: + return (v - lo) / (lo_ramp - lo) + if v >= hi_ramp: + return (hi - v) / (hi - hi_ramp) + return 1.0 + + def _score_property(self, name: str, mol: Chem.Mol) -> float: + return self._trapezoid(self._prop_fns[name](mol), *self._PROPERTY_WINDOWS[name]) + + def _assemble(self, linker_mol: Chem.Mol) -> Optional[Chem.Mol]: + amide_only = getattr(self, 'fix_termini', None) != 'methyl' + smi = Chem.MolToSmiles(linker_mol) + if smi == getattr(self, '_asm_smi', None): + return self._asm_mol + try: + result = self.builder.assemble(linker_mol, return_all=False, amide_only=amide_only) + smiles = result if isinstance(result, str) else result.get("protac_smiles") + mol = Chem.MolFromSmiles(smiles) if smiles else None + except Exception: + mol = None + self._asm_smi, self._asm_mol = smi, mol + return mol + + def _score_protac(self, protac_mol: Chem.Mol) -> float: + per_prop = {k: self._score_property(k, protac_mol) for k in self.active} + total_w = sum(self.weights[k] for k in per_prop) + return sum(self.weights[k] * v for k, v in per_prop.items()) / total_w if total_w else 0.0 + + def get_scores(self, molecule_list: list) -> torch.Tensor: + scores = [] + for atom_types, edge_types in molecule_list: + try: + linker = _graph_to_mol(atom_types, edge_types, self.atom_decoder) + Chem.SanitizeMol(linker) + protac = self._assemble(linker) + scores.append(self._score_protac(protac) if protac is not None else 0.0) + except Exception: + scores.append(0.0) + return torch.tensor(scores, dtype=torch.float32) + + def __call__(self, molecule_list: list) -> torch.Tensor: + return self.get_scores(molecule_list) + + +# --------------------------------------------------------------------------- +# Connectivity soft guidance scorer +# --------------------------------------------------------------------------- + +class PROTACLinkerConnectivityScorer: + """ + Soft signal pushing the linker toward a single connected molecule with both + anchors in the same fragment. Returns score in [0, 1]: + + 1.0 — connected (single fragment) + frag_frac / num_fragments — disconnected with anchors in same fragment + frag_frac / num_fragments / 2 — disconnected with anchors in different fragments + 0.0 — sanitization fails + + Scaling by 1/num_fragments penalizes "almost-connected" (95%-largest-fragment + + 1 stray atom) much more harshly than the older bare frag_frac, which previously + rewarded such candidates at ~0.95 and gave guidance no incentive to merge the stray. + With this scaling, 2-fragment cases peak at 0.5 even when the largest fragment is + 99% of atoms; only true single-fragment graphs reach 1.0. Anchor halving stacks + on top to favour bridging the warhead/E3 anchors specifically. + """ + + def __init__(self, atom_decoder: list[str]): + self.atom_decoder = atom_decoder + + def get_scores(self, molecule_list: list, mols=None) -> torch.Tensor: + anchor_nodes = getattr(self, 'anchor_nodes', None) + if mols is None: + mols = prebuild_mols(molecule_list, self.atom_decoder) + scores = [] + for mol in mols: + if mol is None: + scores.append(0.0) + continue + try: + frags = Chem.GetMolFrags(mol) + num_frags = len(frags) + if num_frags == 1: + scores.append(1.0) + continue + frag_frac = max(len(f) for f in frags) / mol.GetNumAtoms() + # Harsher: scale by 1/num_fragments so any extra fragment caps the + # score below 0.5, regardless of how small the stray fragment is. + score = frag_frac / num_frags + if anchor_nodes is not None and len(anchor_nodes) >= 2 and mol.GetNumAtoms() > max(anchor_nodes): + anchor_frag = next((f for f in frags if anchor_nodes[0] in f), None) + if anchor_frag is None or any(a not in anchor_frag for a in anchor_nodes[1:]): + score *= 0.5 + scores.append(score) + except Exception: + scores.append(0.0) + return torch.tensor(scores, dtype=torch.float32) + + def __call__(self, molecule_list: list, mols=None) -> torch.Tensor: + return self.get_scores(molecule_list, mols=mols) + + +# --------------------------------------------------------------------------- +# SA score soft guidance scorer (scores the linker, not the assembled PROTAC) +# --------------------------------------------------------------------------- + +class PROTACLinkerSAScorer: + """ + Soft guidance scorer based on synthetic accessibility of the linker itself. + + Maps SA score [1, sa_threshold] → [1.0, 0.0] linearly (soft gradient). + SA > sa_threshold hard-zeros to 0.0, acting as both a guidance penalty and + a hard validity gate. Scoring the linker (not the assembled PROTAC) keeps + the signal focused on what the generator actually controls. + """ + + def __init__(self, atom_decoder: list[str], sa_threshold: float = 6.0): + import sys + from rdkit.Chem import RDConfig + sys.path.insert(0, os.path.join(RDConfig.RDContribDir, "SA_Score")) + import sascorer as _sascorer + self._sascorer = _sascorer + self.atom_decoder = atom_decoder + self.sa_threshold = sa_threshold + + def get_scores(self, molecule_list: list, mols=None) -> torch.Tensor: + if mols is None: + mols = prebuild_mols(molecule_list, self.atom_decoder) + scores = [] + for mol in mols: + if mol is None: + scores.append(0.0) + continue + try: + sa = self._sascorer.calculateScore(mol) + scores.append(max(0.0, (self.sa_threshold - sa) / (self.sa_threshold - 1.0))) + except Exception: + scores.append(0.0) + return torch.tensor(scores, dtype=torch.float32) + + def __call__(self, molecule_list: list, mols=None) -> torch.Tensor: + return 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b/CupOFMoCA-PROTAC/src/defog/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..ace1bddf9ef8d73e8c7d13a2fbad51854b5e717f --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/LICENSE @@ -0,0 +1,20 @@ +Copyright (c) 2012-2025 Manuel Madeira, Yiming Qin + +Permission is hereby granted, free of charge, to any person obtaining +a copy of this software and associated documentation files (the +"Software"), to deal in the Software without restriction, including +without limitation the rights to use, copy, modify, merge, publish, +distribute, sublicense, and/or sell copies of the Software, and to +permit persons to whom the Software is furnished to do so, subject to +the following conditions: + +The above copyright notice and this permission notice shall be +included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE +LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION +OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/CupOFMoCA-PROTAC/src/defog/configs/__init__.py b/CupOFMoCA-PROTAC/src/defog/configs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/CupOFMoCA-PROTAC/src/defog/configs/config.yaml b/CupOFMoCA-PROTAC/src/defog/configs/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..29ac795c445f20716d03fd9631686167a0254fc3 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/config.yaml @@ -0,0 +1,14 @@ +defaults: + - _self_ + - general : general_default + - model : discrete + - train : train_default + - dataset : qm9 + - sample : sample_default + +hydra: + job: + chdir: True + run: + dir: ../outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}-${dataset.name}-${general.name} + diff --git a/CupOFMoCA-PROTAC/src/defog/configs/dataset/comm20.yaml b/CupOFMoCA-PROTAC/src/defog/configs/dataset/comm20.yaml new file mode 100644 index 0000000000000000000000000000000000000000..417035db29e29ef5f9f6bc4791a16aa5cc2c79d6 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/dataset/comm20.yaml @@ -0,0 +1,3 @@ +name: comm20 +remove_h: null +datadir: 'data/comm20/' \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/configs/dataset/guacamol.yaml b/CupOFMoCA-PROTAC/src/defog/configs/dataset/guacamol.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aced0281555368ed1cbb6898a5010b13b0bd068f --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/dataset/guacamol.yaml @@ -0,0 +1,5 @@ +name: 'guacamol' +datadir: 'data/guacamol/guacamol_pyg/' # Relative to the guacamol_dataset.py file +remove_h: null +filter: True # Use the filtered version or the raw guacamol file +compute_fcd: False # Compute FCD scores \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/configs/dataset/moses.yaml b/CupOFMoCA-PROTAC/src/defog/configs/dataset/moses.yaml new file mode 100644 index 0000000000000000000000000000000000000000..faa344f13b4da5f223dfb0d230a46a35cee5751c --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/dataset/moses.yaml @@ -0,0 +1,5 @@ +name: 'moses' +datadir: 'data/moses/moses_pyg/' # Relative to the moses_dataset.py file +remove_h: null +filter: False +compute_fcd: False # Compute FCD scores \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/configs/dataset/planar.yaml b/CupOFMoCA-PROTAC/src/defog/configs/dataset/planar.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8ac4d1e1e4b073fd2e0bdd50d782b9698bc2fdcd --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/dataset/planar.yaml @@ -0,0 +1,3 @@ +name: planar +remove_h: null +datadir: 'data/planar/' \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/configs/dataset/protac_linker.yaml b/CupOFMoCA-PROTAC/src/defog/configs/dataset/protac_linker.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4b84ce5b9844efe0777cd921ded70a092d354383 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/dataset/protac_linker.yaml @@ -0,0 +1,7 @@ +name: 'protac_linker' +datadir: 'protacs_data/' +csv_path: null # null = auto-resolve to protacs_data/linker.csv +remove_h: True +pin_memory: False +compute_fcd: False # FCD requires a reference set; disable until one is built +filter: False \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/configs/dataset/qm9.yaml b/CupOFMoCA-PROTAC/src/defog/configs/dataset/qm9.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fa76c336d8353100b2ddfb3aafb9761782b2b1ce --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/dataset/qm9.yaml @@ -0,0 +1,7 @@ +name: 'qm9' # qm9, qm9_positional +datadir: 'data/qm9/qm9_pyg/' +remove_h: True +random_subset: null +pin_memory: False +compute_fcd: True # Compute FCD scores +aromatic: True \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/configs/dataset/sbm.yaml b/CupOFMoCA-PROTAC/src/defog/configs/dataset/sbm.yaml new file mode 100644 index 0000000000000000000000000000000000000000..16b0dfdb89bf21f5df2d35d144b31d06082014a3 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/dataset/sbm.yaml @@ -0,0 +1,3 @@ +name: sbm +remove_h: null +datadir: 'data/sbm/' \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/configs/dataset/tls.yaml b/CupOFMoCA-PROTAC/src/defog/configs/dataset/tls.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a7fe3f0bcba0bf66a1d818ed6d97a3f02ebe615f --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/dataset/tls.yaml @@ -0,0 +1,2 @@ +name: tls +datadir: 'data/tls' \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/configs/dataset/tree.yaml b/CupOFMoCA-PROTAC/src/defog/configs/dataset/tree.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e8cf599a002b8f9ba75c5db34eca63fa7a3728b4 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/dataset/tree.yaml @@ -0,0 +1,3 @@ +name: tree +remove_h: null +datadir: 'data/tree/' \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/configs/dataset/zinc.yaml b/CupOFMoCA-PROTAC/src/defog/configs/dataset/zinc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e580dbf96d11274084716b4df6d6f58931867b8b --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/dataset/zinc.yaml @@ -0,0 +1,7 @@ +name: 'zinc' # qm9, qm9_positional +datadir: 'data/zinc/' +remove_h: True +random_subset: null +pin_memory: False +compute_fcd: True # Compute FCD scores +aromatic: False \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/configs/experiment/comm20.yaml b/CupOFMoCA-PROTAC/src/defog/configs/experiment/comm20.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b395ceb17f72d3486ca813bdfd3312d21409b50a --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/experiment/comm20.yaml @@ -0,0 +1,24 @@ +# @package _global_ +general: + name : 'comm20' + gpus : 1 + wandb: 'online' + resume: null # If resume, path to ckpt file from outputs directory in main directory + test_only: null + # test_only: /home/yqin/coding/graph_dfm/outputs/2024-04-25/18-10-09-max_64Temb/checkpoints/max_64Temb/epoch=287999.ckpt + check_val_every_n_epochs: 1000 + sample_every_val: 10 + samples_to_generate: 20 + samples_to_save: 20 + chains_to_save: 1 + log_every_steps: 50 + number_chain_steps: 50 # Number of frames in each gif + final_model_samples_to_generate: 20 + final_model_samples_to_save: 10 + final_model_chains_to_save: 10 +train: + n_epochs: 1000000 + batch_size: 256 + save_model: True +model: + n_layers: 8 \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/configs/experiment/debug.yaml b/CupOFMoCA-PROTAC/src/defog/configs/experiment/debug.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8abccb3b19332dfd449fb4acdcd314cda58540be --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/experiment/debug.yaml @@ -0,0 +1,22 @@ +# @package _global_ +general: + name : 'debug' + gpus : 0 + wandb: 'disabled' + sample_every_val: 1 + samples_to_generate: 4 + samples_to_save: 2 + chains_to_save: 1 + remove_h: True + number_chain_steps: 10 # Number of frames in each gif + check_val_every_n_epochs: 1 +train: + batch_size: 4 + save_model: False + n_epochs: 2 +model: + n_layers: 2 + hidden_mlp_dims: {'X': 17, 'E': 18, 'y': 19 } + hidden_dims: {'dx': 20, 'de': 21, 'dy': 22, 'n_head': 5, 'dim_ffX': 23, 'dim_ffE': 24, 'dim_ffy': 25} +sample: + sample_steps: 10 diff --git a/CupOFMoCA-PROTAC/src/defog/configs/experiment/guacamol.yaml b/CupOFMoCA-PROTAC/src/defog/configs/experiment/guacamol.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0f71b8dbcc2bb01b4dc9ea00f23c8eddc8965b4b --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/experiment/guacamol.yaml @@ -0,0 +1,41 @@ +# @package _global_ +general: + name : 'guacamol' + gpus : 1 + wandb: 'online' + resume: null + test_only: null + check_val_every_n_epochs: 2 + val_check_interval: null + sample_every_val: 2 + samples_to_generate: 500 + samples_to_save: 20 + chains_to_save: 5 + log_every_steps: 50 + final_model_samples_to_generate: 18000 + final_model_samples_to_save: 10 + final_model_chains_to_save: 5 +train: + optimizer: adam + n_epochs: 1000 + batch_size: 64 + save_model: True + lr: 2e-4 + time_distortion: 'polydec' +sample: + time_distortion: 'polydec' + omega: 0.1 + eta: 300 +model: + n_layers: 12 + type: 'discrete' + transition: 'marginal' # uniform or marginal + model: 'graph_tf' + + # Do not set hidden_mlp_E, dim_ffE too high, computing large tensors on the edges is costly + # At the moment (03/08), y contains quite little information + hidden_mlp_dims: {'X': 256, 'E': 128, 'y': 256} + rrwp_steps: 20 + + # The dimensions should satisfy dx % n_head == 0 + hidden_dims: {'dx': 256, 'de': 64, 'dy': 128, 'n_head': 8, 'dim_ffX': 256, 'dim_ffE': 128, 'dim_ffy': 256} diff --git a/CupOFMoCA-PROTAC/src/defog/configs/experiment/moses.yaml b/CupOFMoCA-PROTAC/src/defog/configs/experiment/moses.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d37e2bfbb95ad4474939fa1e67af99c39e232817 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/experiment/moses.yaml @@ -0,0 +1,45 @@ +# @package _global_ +general: + name : 'moses' + gpus : 1 + wandb: 'online' + remove_h: True + resume: null + test_only: null + check_val_every_n_epochs: 1 + val_check_interval: null + sample_every_val: 4 + samples_to_generate: 256 + samples_to_save: 20 + chains_to_save: 5 + log_every_steps: 50 + + final_model_samples_to_generate: 25000 + final_model_samples_to_save: 50 + final_model_chains_to_save: 20 + +train: + optimizer: adamw + n_epochs: 300 + batch_size: 256 + save_model: True + lr: 2e-4 + num_workers: 4 + time_distortion: 'polydec' +sample: + time_distortion: 'polydec' + omega: 0.5 + eta: 200 +model: + n_layers: 12 + type: 'discrete' + transition: 'marginal' # uniform or marginal + model: 'graph_tf' + + # Do not set hidden_mlp_E, dim_ffE too high, computing large tensors on the edges is costly + # At the moment (03/08), y contains quite little information + hidden_mlp_dims: { 'X': 256, 'E': 128, 'y': 256} + rrwp_steps: 20 + + # The dimensions should satisfy dx % n_head == 0 + hidden_dims: { 'dx': 256, 'de': 64, 'dy': 128, 'n_head': 8, 'dim_ffX': 256, 'dim_ffE': 128, 'dim_ffy': 256} diff --git a/CupOFMoCA-PROTAC/src/defog/configs/experiment/planar.yaml b/CupOFMoCA-PROTAC/src/defog/configs/experiment/planar.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d3362416f3d6f81ebf3deaa2de6a9c943994ee2e --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/experiment/planar.yaml @@ -0,0 +1,33 @@ +# @package _global_ +general: + name : 'planar' + gpus : 1 + wandb: 'online' + resume: null # If resume, path to ckpt file from outputs directory in main directory + test_only: null + check_val_every_n_epochs: 2000 + sample_every_val: 1 + samples_to_generate: 40 + samples_to_save: 9 + chains_to_save: 1 + final_model_samples_to_generate: 40 + final_model_samples_to_save: 30 + final_model_chains_to_save: 20 + sample_steps: 1000 +train: + n_epochs: 100000 + batch_size: 64 + save_model: True +sample: + time_distortion: 'polydec' + omega: 0.05 + eta: 50 +model: + n_layers: 10 + + # Do not set hidden_mlp_E, dim_ffE too high, computing large tensors on the edges is costly + # At the moment (03/08), y contains quite little information + hidden_mlp_dims: { 'X': 128, 'E': 64, 'y': 128 } + + # The dimensions should satisfy dx % n_head == 0 + hidden_dims: { 'dx': 256, 'de': 64, 'dy': 64, 'n_head': 8, 'dim_ffX': 256, 'dim_ffE': 64, 'dim_ffy': 256 } diff --git a/CupOFMoCA-PROTAC/src/defog/configs/experiment/protac_linker.yaml b/CupOFMoCA-PROTAC/src/defog/configs/experiment/protac_linker.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a13c95507c4c2faba2c0a3f09208ccf973d4b4d9 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/experiment/protac_linker.yaml @@ -0,0 +1,43 @@ +# @package _global_ +general: + name: 'protac_linker_finetune' + gpus: 1 + wandb: 'online' + wandb_project: 'CupOfMoCA_DeFoG' + resume: null # use this to resume an interrupted fine-tuning run + finetune_from: null # use this to load weights from a different-dataset checkpoint (e.g. zinc.ckpt) + test_only: null + check_val_every_n_epochs: 10 + sample_every_val: 5 + samples_to_generate: 256 + samples_to_save: 20 + chains_to_save: 5 + log_every_steps: 50 + number_chain_steps: 50 + final_model_samples_to_generate: 2000 + final_model_samples_to_save: 50 + final_model_chains_to_save: 20 + +train: + optimizer: adamw + n_epochs: 100 + batch_size: 128 # smaller dataset, smaller batch + save_model: True + lr: 5e-5 # lower LR for fine-tuning + num_workers: 4 + time_distortion: 'polydec' + +sample: + time_distortion: 'polydec' + omega: 0.1 + eta: 300 + sample_steps: 300 + +model: + n_layers: 12 + type: 'discrete' + transition: 'marginal' + model: 'graph_tf' + hidden_mlp_dims: {'X': 256, 'E': 128, 'y': 256} + rrwp_steps: 20 + hidden_dims: {'dx': 256, 'de': 64, 'dy': 128, 'n_head': 8, 'dim_ffX': 256, 'dim_ffE': 128, 'dim_ffy': 256} \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/configs/experiment/qm9_no_h.yaml b/CupOFMoCA-PROTAC/src/defog/configs/experiment/qm9_no_h.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7009bbc9aa4d75b54c69d941c2e24c35edd40266 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/experiment/qm9_no_h.yaml @@ -0,0 +1,26 @@ +# @package _global_ +general: + name : 'qm9_no_h' + gpus : 1 + wandb: 'online' + test_only: null + evaluate_all_checkpoints: False + # debug + check_val_every_n_epochs: 50 + sample_every_val: 1 +train: + n_epochs: 1000 + batch_size: 1024 + save_model: True +sample: + sample_steps: 500 + time_distortion: 'polydec' + omega: 0 + eta: 0 +model: + n_layers: 9 + transition: marginal +dataset: + remove_h: True + pin_memory: True + num_workers: 16 diff --git a/CupOFMoCA-PROTAC/src/defog/configs/experiment/qm9_with_h.yaml b/CupOFMoCA-PROTAC/src/defog/configs/experiment/qm9_with_h.yaml new file mode 100644 index 0000000000000000000000000000000000000000..86d5a4f8b931f703b33ba7a097de805550f7647e --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/experiment/qm9_with_h.yaml @@ -0,0 +1,20 @@ +# @package _global_ +general: + name : 'qm9h' + gpus : 1 + wandb: 'online' + evaluate_all_checkpoints: False + test_only: null +train: + n_epochs: 2000 + batch_size: 512 + save_model: True +sample: + sample_steps: 500 + time_distortion: 'polydec' + omega: 0.05 + eta: 0 +model: + n_layers: 7 +dataset: + remove_h: False diff --git a/CupOFMoCA-PROTAC/src/defog/configs/experiment/sbm.yaml b/CupOFMoCA-PROTAC/src/defog/configs/experiment/sbm.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a6eef832ac2e1a254d12f0a6bbfabd11653556da --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/experiment/sbm.yaml @@ -0,0 +1,35 @@ +# @package _global_ +general: + name : 'sbm' + gpus : 1 + wandb: 'online' + resume: null # If resume, path to ckpt file from outputs directory in main directory + test_only: null + check_val_every_n_epochs: 2000 + sample_every_val: 1 + samples_to_generate: 40 + samples_to_save: 9 + chains_to_save: 1 + final_model_samples_to_generate: 40 + final_model_samples_to_save: 30 + final_model_chains_to_save: 20 + sample_steps: 1000 +train: + n_epochs: 50000 + batch_size: 32 + save_model: True +sample: + time_distortion: 'identity' + omega: 0 + eta: 0 +model: + transition: 'absorbfirst' + n_layers: 8 + rrwp_steps: 20 + + # Do not set hidden_mlp_E, dim_ffE too high, computing large tensors on the edges is costly + # At the moment (03/08), y contains quite little information + hidden_mlp_dims: { 'X': 128, 'E': 64, 'y': 128 } + + # The dimensions should satisfy dx % n_head == 0 + hidden_dims: { 'dx': 256, 'de': 64, 'dy': 64, 'n_head': 8, 'dim_ffX': 256, 'dim_ffE': 64, 'dim_ffy': 256 } \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/configs/experiment/test.yaml b/CupOFMoCA-PROTAC/src/defog/configs/experiment/test.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0fbe0fe0c85a7ecac8af078f7aa408af89ed2dd1 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/experiment/test.yaml @@ -0,0 +1,15 @@ +# @package _global_ +general: + name : 'test' + gpus : 1 + wandb: 'disabled' + sample_every_val: 1 + samples_to_generate: 10 + samples_to_save: 5 + chains_to_save: 1 + test_only: False +train: + ema_decay : 0 + batch_size: 3 + save_model: False + n_epochs : 3 diff --git a/CupOFMoCA-PROTAC/src/defog/configs/experiment/tls.yaml b/CupOFMoCA-PROTAC/src/defog/configs/experiment/tls.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bbcd200ea016b1c5c85a26ac557f1b9dc4c3f80b --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/experiment/tls.yaml @@ -0,0 +1,37 @@ +# @package _global_ +general: + name : 'tls' + gpus : 1 + wandb: 'online' + resume: null # If resume, path to ckpt file from outputs directory in main directory + test_only: null + check_val_every_n_epochs: 2000 + sample_every_val: 1 + samples_to_generate: 40 + samples_to_save: 9 + chains_to_save: 1 + final_model_samples_to_generate: 80 # same as test set + final_model_samples_to_save: 30 + final_model_chains_to_save: 20 + sample_steps: 1000 + conditional: True + target: 'k2' + guidance_weight: 2.0 +train: + n_epochs: 100000 + batch_size: 64 + save_model: True +sample: + time_distortion: 'polydec' + omega: 0.05 + eta: 0 +model: + n_layers: 10 + rrwp_steps: 20 + + # Do not set hidden_mlp_E, dim_ffE too high, computing large tensors on the edges is costly + # At the moment (03/08), y contains quite little information + hidden_mlp_dims: { 'X': 128, 'E': 64, 'y': 128 } + + # The dimensions should satisfy dx % n_head == 0 + hidden_dims: { 'dx': 256, 'de': 64, 'dy': 64, 'n_head': 8, 'dim_ffX': 256, 'dim_ffE': 64, 'dim_ffy': 256 } diff --git a/CupOFMoCA-PROTAC/src/defog/configs/experiment/tree.yaml b/CupOFMoCA-PROTAC/src/defog/configs/experiment/tree.yaml new file mode 100644 index 0000000000000000000000000000000000000000..98497fcc96a7919b2fa16f0e2a4d435a9d6c348e --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/experiment/tree.yaml @@ -0,0 +1,34 @@ +# @package _global_ +general: + name : 'tree' + gpus : 1 + wandb: 'online' + resume: null # If resume, path to ckpt file from outputs directory in main directory + test_only: null + check_val_every_n_epochs: 2000 + sample_every_val: 1 + samples_to_generate: 40 + samples_to_save: 9 + chains_to_save: 1 + final_model_samples_to_generate: 40 + final_model_samples_to_save: 30 + final_model_chains_to_save: 20 + sample_steps: 1000 +train: + n_epochs: 100000 + batch_size: 64 + save_model: True + time_distortion: 'polydec' +sample: + time_distortion: 'polydec' + omega: 0 + eta: 0 +model: + n_layers: 10 + + # Do not set hidden_mlp_E, dim_ffE too high, computing large tensors on the edges is costly + # At the moment (03/08), y contains quite little information + hidden_mlp_dims: { 'X': 128, 'E': 64, 'y': 128 } + + # The dimensions should satisfy dx % n_head == 0 + hidden_dims: { 'dx': 256, 'de': 64, 'dy': 64, 'n_head': 8, 'dim_ffX': 256, 'dim_ffE': 64, 'dim_ffy': 256 } diff --git a/CupOFMoCA-PROTAC/src/defog/configs/experiment/zinc.yaml b/CupOFMoCA-PROTAC/src/defog/configs/experiment/zinc.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7055d348622b2011733255a725b7209e01bc062d --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/experiment/zinc.yaml @@ -0,0 +1,44 @@ +# @package _global_ +general: + name : 'zinc' + gpus : 1 + wandb: 'online' + remove_h: True + resume: null + test_only: null + check_val_every_n_epochs: 4 + val_check_interval: null + sample_every_val: 2 + samples_to_generate: 256 + samples_to_save: 20 + chains_to_save: 5 + log_every_steps: 50 + + final_model_samples_to_generate: 10000 + final_model_samples_to_save: 50 + final_model_chains_to_save: 20 +train: + optimizer: adamw + n_epochs: 300 + batch_size: 256 + save_model: True + lr: 2e-4 + num_workers: 4 + time_distortion: 'polydec' +sample: + time_distortion: 'polydec' + omega: 0.1 + eta: 300 +model: + n_layers: 12 + type: 'discrete' + transition: 'marginal' # uniform or marginal + model: 'graph_tf' + + # Do not set hidden_mlp_E, dim_ffE too high, computing large tensors on the edges is costly + # At the moment (03/08), y contains quite little information + hidden_mlp_dims: { 'X': 256, 'E': 128, 'y': 256} + rrwp_steps: 20 + + # The dimensions should satisfy dx % n_head == 0 + hidden_dims: { 'dx': 256, 'de': 64, 'dy': 128, 'n_head': 8, 'dim_ffX': 256, 'dim_ffE': 128, 'dim_ffy': 256} diff --git a/CupOFMoCA-PROTAC/src/defog/configs/general/general_default.yaml b/CupOFMoCA-PROTAC/src/defog/configs/general/general_default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d3d78b8c7f655fee91e376f3e7f3f6ab883640ae --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/general/general_default.yaml @@ -0,0 +1,31 @@ +# General settings +name: 'graph-tf-model' # Warning: 'debug' and 'test' are reserved name that have a special behavior + +wandb: 'online' # online | offline | disabled +gpus: 1 # Multi-gpu is not implemented on this branch + +resume: null # If resume, path to ckpt file from outputs directory in main directory +test_only: null # Use absolute path + +check_val_every_n_epochs: 5 +sample_every_val: 4 +val_check_interval: null +samples_to_generate: 512 # We advise to set it to 2 x batch_size maximum +samples_to_save: 20 +chains_to_save: 1 +log_every_steps: 50 +number_chain_steps: 50 # Number of frames in each gif + +# Test +generated_path: null +final_model_samples_to_generate: 10000 +final_model_samples_to_save: 30 +final_model_chains_to_save: 20 +num_sample_fold: 1 +evaluate_all_checkpoints: False +save_samples: True # Save samples at the final test step or not, normally only used at the last epoch or during inference + +# Conditional Generation +conditional: False +target: 'k2' +guidance_weight: 2.0 diff --git a/CupOFMoCA-PROTAC/src/defog/configs/model/discrete.yaml b/CupOFMoCA-PROTAC/src/defog/configs/model/discrete.yaml new file mode 100644 index 0000000000000000000000000000000000000000..78a4b2b3c642b5cad690bf959e6f665b00ec93fe --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/model/discrete.yaml @@ -0,0 +1,16 @@ +# Model settings +transition: 'marginal' # uniform, marginal, argmax, absorbfirst, absorbing +model: 'graph_tf' +n_layers: 5 + +extra_features: 'rrwp' # 'all', 'cycles', 'eigenvalues', 'rrwp', 'rrwp_comp' or null +rrwp_steps: 12 + +# Do not set hidden_mlp_E, dim_ffE too high, computing large tensors on the edges is costly +hidden_mlp_dims: {'X': 256, 'E': 128, 'y': 128} + +# The dimensions should satisfy dx % n_head == 0 +hidden_dims : {'dx': 256, 'de': 64, 'dy': 64, 'n_head': 8, 'dim_ffX': 256, 'dim_ffE': 128, 'dim_ffy': 128} + +# training weight for edges, y, and nodes +lambda_train: [5, 0] # X=1, E = lambda[0], y = lambda[1] diff --git a/CupOFMoCA-PROTAC/src/defog/configs/sample/sample_default.yaml b/CupOFMoCA-PROTAC/src/defog/configs/sample/sample_default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7573276f59aeb6ab6cf793c4831100907c87e93d --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/sample/sample_default.yaml @@ -0,0 +1,13 @@ +# balanced rate matrice settings +eta: 0. +omega: 0. + +# generation settings +sample_steps: 1000 +time_distortion: "identity" # 'identity', 'cosine', polyinc, polydec +search: False # 'all' | 'target_guidance' | 'distortion' | 'stochasticity' | False + +# fixed +rdb: 'general' # general | column | entry +rdb_crit: dummy # max_marginal | x_t | p_x1_g_xt | x_1 | p_xt_g_x1 | p_xtdt_g_xt | x_0 | xhat_t + # abs_state diff --git a/CupOFMoCA-PROTAC/src/defog/configs/train/train_default.yaml b/CupOFMoCA-PROTAC/src/defog/configs/train/train_default.yaml new file mode 100644 index 0000000000000000000000000000000000000000..31c2550f33e6fbfc12a9d318eda04b391565b320 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/configs/train/train_default.yaml @@ -0,0 +1,14 @@ +# Training settings +n_epochs: 1000 +batch_size: 512 +lr: 0.0002 +clip_grad: null # float, null to disable +save_model: True +num_workers: 0 +ema_decay: 0 # 'Amount of EMA decay, 0 means off. A reasonable value is 0.999.' +progress_bar: false +weight_decay: 1e-12 +optimizer: adamw # adamw,nadamw,nadam => nadamw for large batches, see http://arxiv.org/abs/2102.06356 for the use of nesterov momentum with large batches +seed: 0 + +time_distortion: "identity" # 'identity', 'cosine', polyinc, polydec diff --git a/CupOFMoCA-PROTAC/src/defog/protacs_data/assemblable_linkers.txt b/CupOFMoCA-PROTAC/src/defog/protacs_data/assemblable_linkers.txt new file mode 100644 index 0000000000000000000000000000000000000000..ff5dd8c7cab602396cfcc1db98c154aafa3fbec6 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/protacs_data/assemblable_linkers.txt @@ -0,0 +1,97 @@ +O=CCCC(=O)NCC=O +O=CCCC(=O)NCCOCCOCCOCC=O +O=CCCCC(=O)NCC=O +O=CCCCC=O +O=CCCCCC(=O)N(CC=O)CCS +O=CCCCCC=O +O=CCCCCCC=O +O=CCCCCCCC(=O)N(CC=O)CCS +O=CCCCCCCC=O +O=CCCCCCCCC=O +O=CCCCCCCCCC(=O)N(CC=O)CCS +O=CCCCCCCCCC=O +O=CCCCCCCCCCC=O +O=CCCCCCCCCCCC(=O)N(CC=O)CCS +O=CCCCCCCCCCCC=O +O=CCCCCCCCCCCCC=O +O=CCCCCCCCCCCCCC=O +O=CCCCCCCCCCCCCCCC=O +O=CCCCCCCCCCCCCNCC=O +O=CCCCCCCCCCCCNCC=O +O=CCCCCCCCCCCNCC=O +O=CCCCCCCCCCNCC=O +O=CCCCCCCCCNCC=O +O=CCCCCCCCNC(=O)CCC=O +O=CCCCCCCCNCC=O +O=CCCCCCCNC(=O)CCC=O +O=CCCCCCCNCC=O +O=CCCCCCNC(=O)CCC(=O)NCCCC=O +O=CCCCCCNC(=O)CCC=O +O=CCCCCCNCC=O +O=CCCCCCOCCc1cn(CCCC=O)nn1 +O=CCCCCCOCCc1cn(CCCCC=O)nn1 +O=CCCCCCOCCc1cn(CCCCCC=O)nn1 +O=CCCCCCn1cc(CCOCCCC=O)nn1 +O=CCCCCCn1cc(CCOCCCCC=O)nn1 +O=CCCCCNC(=O)CCC=O +O=CCCCCNC(=O)CCCC=O +O=CCCCCNCC=O +O=CCCCCOCCc1cn(CCCC=O)nn1 +O=CCCCCOCCc1cn(CCCCC=O)nn1 +O=CCCCCn1cc(CCOCCCC=O)nn1 +O=CCCCNC(=O)CCC=O +O=CCCCNCC=O +O=CCCCOCCc1cn(CCCC=O)nn1 +O=CCCCn1cc(COCCOCCOCC=O)nn1 +O=CCCNCC=O +O=CCN1CCC(CNC=O)CC1 +O=CCNCC=O +O=CCOCCNC=O +O=CCOCCOCC=O +O=CCOCCOCCNC=O +O=CCOCCOCCOCC=O +O=CCOCCOCCOCCNC=O +O=CCOCCOCCOCCOCC=O +O=CCOCCOCCOCCOCCNC=O +O=CCOCCOCCOCCOCCOCCNC=O +O=CCOCCOCCOCCOCCn1cc(CNC=O)nn1 +O=CCOCCOCCOCCn1cc(CNC=O)nn1 +O=CCOCCOCCn1cc(CNC=O)nn1 +O=CCOCCn1cc(CNC=O)nn1 +O=CNCCOCCOCCNC=O +O=COCCCCCCCCCCCCOC=O +O=COCCCCCCCCCCCOC=O +O=COCCCCCCCCCCOC=O +O=COCCCCCCCCCCOCCOC=O +O=COCCCCCCCCCCOCCOCCOC=O +O=COCCCCCCCCCCOCCOCCOCCOC=O +O=COCCCCCCCCCOC=O +O=COCCCCCCCCOC=O +O=COCCCCCCCCOCCOC=O +O=COCCCCCCCCOCCOCCOC=O +O=COCCCCCCCCOCCOCCOCCOC=O +O=COCCCCCCCOC=O +O=COCCCCCCOC=O +O=COCCCCCCOCCOC=O +O=COCCCCCCOCCOCCOC=O +O=COCCCCCCOCCOCCOCCOC=O +O=COCCCCCOC=O +O=COCCCCOC=O +O=COCCCCOCCCCOC=O +O=COCCCCOCCCOC=O +O=COCCCCOCCOC=O +O=COCCCCOCCOCCOC=O +O=COCCCCOCCOCCOCCOC=O +O=COCCCOC=O +O=COCCCOCCCOC=O +O=COCCCOCCOC=O +O=COCCOC=O +O=COCCOCCOC=O +O=COCCOCCOCCOC=O +O=COCCOCCOCCOCCOC=O +O=COCCOCCOCCOCCOCCOC=O +O=COCCOCCOCCOCCOCCOCCOC=O +O=COCCOCCOCCOCCOCCOCCOCCOC=O +O=COCCOCCOCCOCCOCCOCCOCCOCCOC=O +O=COCCOCCOCCOCCOCCOCCOCCOCCOCCOC=O +O=COCOC=O \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/protacs_data/linker.csv b/CupOFMoCA-PROTAC/src/defog/protacs_data/linker.csv new file mode 100644 index 0000000000000000000000000000000000000000..ae1c44183d39495b5ff7efa03d53d43af9ec446d --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/protacs_data/linker.csv @@ -0,0 +1,2845 @@ +Compound ID,Smiles,Smiles_R,Molecular Weight,Exact Mass,XLogP3,Heavy Atom Count,Ring Count,Hydrogen Bond Acceptor Count,Hydrogen Bond Donor Count,Rotatable Bond Count,Topological Polar Surface Area,Molecular Formula,InChI,InChI Key +"1","CCOCCOC","[R1]CCOCCOC[R2]","104.149","104.083729624","0.3","7","0","2","0","4","18.46","C5H12O2","InChI=1S/C5H12O2/c1-3-7-5-4-6-2/h3-5H2,1-2H3","CAQYAZNFWDDMIT-UHFFFAOYSA-N" +"2","CCOCCOCCOCCOC","[R1]CCOCCOCCOCCOC[R2]","192.255","192.13615912","0","13","0","4","0","10","36.92","C9H20O4","InChI=1S/C9H20O4/c1-3-11-6-7-13-9-8-12-5-4-10-2/h3-9H2,1-2H3","JRRDISHSXWGFRF-UHFFFAOYSA-N" +"3","CCNC(=O)COCCOCC","[R1]CCOCCOCC(=O)NCC[R2]","175.228","175.120843404","0.01","12","0","3","1","7","47.56","C8H17NO3","InChI=1S/C8H17NO3/c1-3-9-8(10)7-12-6-5-11-4-2/h3-7H2,1-2H3,(H,9,10)","QETLNMPRYFLOQP-UHFFFAOYSA-N" 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0000000000000000000000000000000000000000..03d42d470fa19c81b2b68d8d58ce05702bfcaaca --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/protacs_data/raw/smiles_val.txt @@ -0,0 +1,258 @@ +CCCCN +CCCCOc1ccc(CCNC(C)=O)cc1 +C#CCCCN1CCCCC1 +CCCCCCOCCCCCCOC +CC(=O)NCCOCCOCCOCCNC(=O)CO +COCCOCCNC(=O)c1ccc(=O)n(CCOCCOC)c1 +NCC(=O)NCCCCO +CCOCCOCCOCCOCCOCCC(=O)N1CCC(C)CC1 +CC(=O)NCCN(C)CC=O +CCCCCCN1CC(C)C1 +C#CCCCCCC(=O)NCCC +COCCOCCOCCOCCOCCOCCNC(=O)c1ccccc1 +CCOCCn1cc(CCCO)nn1 +O=COCCCCOC=O +CCOCCOCCOc1ccccc1 +COCCOCCOCCOCCOCCOCCOc1ccc(/C=N/NC=O)cc1 +NCCCCCCCN1CCN(c2ccc(C=O)cc2)CC1 +CCOCCOCCOCCNC(C)=O +O=CCCCCCOCCc1cn(CCCCC=O)nn1 +CCCOCCCCn1cc(CCCO)nn1 +O=C(CN1CCNCC1)NCCOCCOCCO +C#CC1CCNCC1 +CCOc1cc(N)cc(CCOC)c1 +NC(=O)Cn1cc(CCC=O)nn1 +C#CCCCCCNCC +NCCCO +Cc1cn(CCOCCOCC=O)nn1 +NCCNC(=O)c1ccc(CO)cc1 +CCCCCC(=O)NCc1ccccc1 +CCCCCCCCCCCCN1CCN(C(=O)CO)CC1 +OCc1ccccc1 +CCCCCN +O=C(c1ccccc1)N1CCN(C2CNC2)CC1 +O=C(CCOCCOCCO)N1CCC(O)CC1 +O=CCCCCCNC(=O)CCC(=O)NCCCC=O +NCCCCCO +Cn1cc(COCCC=O)nn1 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+C#CCNC(=O)COCCOCCOCCOCCOCCOCCOc1ccc(/C=N/NC=O)cc1 +NCCCCCCCCCCCCOCC=O +C1CN(C2CCNCC2)C1 +CCCCCNC(=O)CCCNC(=O)CO +O=CCCOCCOCCOCCOCCOCCOCCOCCOCCOCCC(=O)NCCCO +CCCCCCN +CCOCCOCCOCCn1cc(CCCCO)nn1 +CCCCc1cn(CCC=O)nn1 +C#CCCCCCCC +CCCCCCCC(=O)NCCCO +O=COCCCCCCCCOCCOC=O +CCCc1ccc(NC(C)=O)cc1 +CC(=O)NCCCNC(=O)CCC(=O)N1CCNCC1 +CCCCCCCCCCCCCCC +CCc1cn(CCCCCCCCO)nn1 +CCC(=O)NCc1ccc(C2=NNC=C3C4CC(C(=O)NCCCC=O)C(C4)C32)cc1 +CC(=O)NCCOCCOCCOCCOCCC=O +CCNC(=O)CN(CCS)C(=O)CCCCCCC=O +CCCc1cn(CCC=O)nn1 +CCCCCCCCC(=O)N1CCN(C)CC1 +CCC(=O)CO +COc1cc(N=N)cc(OC)c1OCC(=O)NCCCCN +CCOCCOCCN1CC(C)C1 +Cc1ccc(C)cc1 +O=COCCOCCOCCOCCOCCOCCOCCOCCOCCOC=O +O=COCCCCOCCCCOC=O +CCNC(=O)COCCOCCOCCOC +C#CCN1CCN(CC#CCNC(=O)CCC=O)CC1 +CCCCNC(=O)CN1CCN(C=O)CC1 +C#CCCCCCCCC=O +CCCNCCCN +CCCCNC(=O)CCC=O +CCCCc1cn(CCCC(=O)NCCOCCOCC)nn1 +CCOCCOCCOCCOCCNC(=O)CCN +CCOCCOCCOCCOCCn1cc(C=O)nn1 +CC(=O)NCCOCCOCCOCCOCCNC(=O)CN1CCNCC1 +CCCCCCCN1CC(C)C1 +CCCCCCCCc1cn(CCCCCC=O)nn1 +O=CCCCCOCCc1cn(CCCC=O)nn1 +NCCOCCOCCNC(=O)c1ccc(=O)n(CCOCCOCCN)c1 +NCCn1cc(CO)nn1 +CCCCCCCCC(=O)NCCC +CCCCCCCCC(=O)Nc1ccccc1 +Nc1ccc(C(=O)NC2CCNCC2)cc1 +CCCC(=O)NCCCCCCCC(=O)NCC +c1ccccc1 +CCOCCOCCn1cc(COCC)nn1 +CCCNC(=O)c1ccc(N)cc1 +CCCCCCCCCCC(=O)NCCC +CCCCCCCNC(=O)CC +CCOCCOCCOCCCCC(=O)NC +C#CCNC(=O)COCCOCCOCCOCCOCCOc1ccc(/C=N/NC=O)cc1 +C#CCCCCCCN1CCNCC1 +CC(=O)NCCOCCOCCN(C)C(C)C +CCCCCCNC(=O)CO +CCC(=O)NCCCCCCCN +C#CCCCCO +O=C(NCCC(=O)N1CCNCC1)c1ccccc1 +CCCOCCOC=O +CCCNC(=O)COCC(N)=O +O=CCCCn1cc(COCCOCCOCC=O)nn1 +O=C(CO)NCCOCCOCCC(=O)N1CCC(O)CC1 +CCCCCCCCOC=O +CCCCCCCc1cn(CCCCCCCO)nn1 +CCOCCOCCOCCCCC=O +CCCC(=O)NCCOCC +CCNC(=O)CCC(N)=O +CCCC(=O)N1CCC(C=O)CC1 +CCOCCOCCOC=O +C1CC(CN2CCOC3(CCNCC3)CC2)CCN1 +CCCCCCCn1cc(CCCCCO)nn1 +CC(=O)NCCCCNC(=O)CCC(=O)N1CCNCC1 +CC(=O)NCCOCCOCCNC(=O)CN1CCNCC1 +Cn1cc(COCCOCCOCCOCCOCCO)nn1 +CCCNCCCC(=O)NCCOCCOCC +CCNC(=O)COCCOCCOCC +CCCCCCCCCCOc1ccccc1 +CCCOCCOCCOCCOCCOCCOCCC +CCC(=O)NCCOCCOCCOCCNC(=O)CO +NCC(=O)NCCCCCCC(=O)NCCCCCCC=O +CCOc1ccccc1OCCO +CCOCCCC(=O)N(C)CC1(C)CC1 +C#CCN1CCN(CC#CCNC(=O)[C@H]2CC[C@H](C=O)CC2)CC1 +O=COCCOCCOCCOC=O +NCCOCCNC(=O)CO +O=CCCCCC=O +O=C(CO)NCCOCCOCCOCCO +CC(=O)NCCCCCCNC(=O)CO +CCOCCOCCOCCOCCOCCOCCO +O=C(CO)NCCOCCOCCCN1CC(O)C1 +CC(=O)NCCCCCCC=O +CCOCCOCCOCCC(N)=O diff --git a/CupOFMoCA-PROTAC/src/defog/protacs_data/ref_metrics_no_h.pkl b/CupOFMoCA-PROTAC/src/defog/protacs_data/ref_metrics_no_h.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a9b721efe2478e35734c7c647e515b5257ed0405 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/protacs_data/ref_metrics_no_h.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85c54bc4d487ba8858975ddc663b82081b572e7a7c41fa80a628bd8a354fafd0 +size 2016 diff --git a/CupOFMoCA-PROTAC/src/defog/setup.py b/CupOFMoCA-PROTAC/src/defog/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..2216ade3099b5365a1a94e2f748c73a5675adb85 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/setup.py @@ -0,0 +1,11 @@ +from setuptools import setup, find_packages + +setup( + name="defog", + version="1.0.0", + url=None, + author="Yiming Qin, Manuel Madeira et al.", + author_email="", + description="DeFoG: Discrete flow matching for graph generation", + packages=find_packages(), +) diff --git a/CupOFMoCA-PROTAC/src/defog/src/__init__.py b/CupOFMoCA-PROTAC/src/defog/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/CupOFMoCA-PROTAC/src/defog/src/analysis/__init__.py b/CupOFMoCA-PROTAC/src/defog/src/analysis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/CupOFMoCA-PROTAC/src/defog/src/analysis/dist_helper.py b/CupOFMoCA-PROTAC/src/defog/src/analysis/dist_helper.py new file mode 100644 index 0000000000000000000000000000000000000000..ace88d73af88493362b4734b0cf12a5573d3bbdb --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/analysis/dist_helper.py @@ -0,0 +1,167 @@ +############################################################################### +# +# Adapted from https://github.com/lrjconan/GRAN/ which in turn is adapted from https://github.com/JiaxuanYou/graph-generation +# +############################################################################### +import pyemd +import numpy as np +import concurrent.futures +from functools import partial +from scipy.linalg import toeplitz + + +def emd(x, y, distance_scaling=1.0): + support_size = max(len(x), len(y)) + d_mat = toeplitz(range(support_size)).astype(float) + distance_mat = d_mat / distance_scaling + + # convert histogram values x and y to float, and make them equal len + x = x.astype(float) + y = y.astype(float) + if len(x) < len(y): + x = np.hstack((x, [0.0] * (support_size - len(x)))) + elif len(y) < len(x): + y = np.hstack((y, [0.0] * (support_size - len(y)))) + + emd = pyemd.emd(x, y, distance_mat) + return emd + + +def l2(x, y): + dist = np.linalg.norm(x - y, 2) + return dist + + +def emd(x, y, sigma=1.0, distance_scaling=1.0): + """EMD + Args: + x, y: 1D pmf of two distributions with the same support + sigma: standard deviation + """ + support_size = max(len(x), len(y)) + d_mat = toeplitz(range(support_size)).astype(float) + distance_mat = d_mat / distance_scaling + + # convert histogram values x and y to float, and make them equal len + x = x.astype(float) + y = y.astype(float) + if len(x) < len(y): + x = np.hstack((x, [0.0] * (support_size - len(x)))) + elif len(y) < len(x): + y = np.hstack((y, [0.0] * (support_size - len(y)))) + + return np.abs(pyemd.emd(x, y, distance_mat)) + + +def gaussian_emd(x, y, sigma=1.0, distance_scaling=1.0): + """Gaussian kernel with squared distance in exponential term replaced by EMD + Args: + x, y: 1D pmf of two distributions with the same support + sigma: standard deviation + """ + support_size = max(len(x), len(y)) + d_mat = toeplitz(range(support_size)).astype(float) + distance_mat = d_mat / distance_scaling + + # convert histogram values x and y to float, and make them equal len + x = x.astype(float) + y = y.astype(float) + if len(x) < len(y): + x = np.hstack((x, [0.0] * (support_size - len(x)))) + elif len(y) < len(x): + y = np.hstack((y, [0.0] * (support_size - len(y)))) + + emd = pyemd.emd(x, y, distance_mat) + return np.exp(-emd * emd / (2 * sigma * sigma)) + + +def gaussian(x, y, sigma=1.0): + support_size = max(len(x), len(y)) + # convert histogram values x and y to float, and make them equal len + x = x.astype(float) + y = y.astype(float) + if len(x) < len(y): + x = np.hstack((x, [0.0] * (support_size - len(x)))) + elif len(y) < len(x): + y = np.hstack((y, [0.0] * (support_size - len(y)))) + + dist = np.linalg.norm(x - y, 2) + return np.exp(-dist * dist / (2 * sigma * sigma)) + + +def gaussian_tv(x, y, sigma=1.0): + support_size = max(len(x), len(y)) + # convert histogram values x and y to float, and make them equal len + x = x.astype(float) + y = y.astype(float) + if len(x) < len(y): + x = np.hstack((x, [0.0] * (support_size - len(x)))) + elif len(y) < len(x): + y = np.hstack((y, [0.0] * (support_size - len(y)))) + + dist = np.abs(x - y).sum() / 2.0 + return np.exp(-dist * dist / (2 * sigma * sigma)) + + +def kernel_parallel_unpacked(x, samples2, kernel): + d = 0 + for s2 in samples2: + d += kernel(x, s2) + return d + + +def kernel_parallel_worker(t): + return kernel_parallel_unpacked(*t) + + +def disc(samples1, samples2, kernel, is_parallel=True, *args, **kwargs): + """Discrepancy between 2 samples""" + d = 0 + + if not is_parallel: + for s1 in samples1: + for s2 in samples2: + d += kernel(s1, s2, *args, **kwargs) + else: + with concurrent.futures.ThreadPoolExecutor() as executor: + for dist in executor.map( + kernel_parallel_worker, + [(s1, samples2, partial(kernel, *args, **kwargs)) for s1 in samples1], + ): + d += dist + if len(samples1) * len(samples2) > 0: + d /= len(samples1) * len(samples2) + else: + d = 1e6 + return d + + +def compute_mmd(samples1, samples2, kernel, is_hist=True, *args, **kwargs): + """MMD between two samples""" + # normalize histograms into pmf + if is_hist: + samples1 = [s1 / (np.sum(s1) + 1e-6) for s1 in samples1] + samples2 = [s2 / (np.sum(s2) + 1e-6) for s2 in samples2] + mmd = ( + disc(samples1, samples1, kernel, *args, **kwargs) + + disc(samples2, samples2, kernel, *args, **kwargs) + - 2 * disc(samples1, samples2, kernel, *args, **kwargs) + ) + + mmd = np.abs(mmd) + + if mmd < 0: + import pdb + + pdb.set_trace() + + return mmd + + +def compute_emd(samples1, samples2, kernel, is_hist=True, *args, **kwargs): + """EMD between average of two samples""" + # normalize histograms into pmf + if is_hist: + samples1 = [np.mean(samples1)] + samples2 = [np.mean(samples2)] + return disc(samples1, samples2, kernel, *args, **kwargs), [samples1[0], samples2[0]] diff --git a/CupOFMoCA-PROTAC/src/defog/src/analysis/rdkit_functions.py b/CupOFMoCA-PROTAC/src/defog/src/analysis/rdkit_functions.py new file mode 100644 index 0000000000000000000000000000000000000000..86b579daffca34cd0af7557dd48baefa22455c3d --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/analysis/rdkit_functions.py @@ -0,0 +1,666 @@ +import math +import numpy as np +import torch +import re +import wandb +import pandas as pd +from tqdm import tqdm +from torchmetrics import MeanSquaredError, MeanAbsoluteError + +try: + from rdkit import Chem + from rdkit.Chem.rdDistGeom import ETKDGv3, EmbedMolecule + from rdkit.Chem.rdForceFieldHelpers import ( + MMFFHasAllMoleculeParams, + MMFFOptimizeMolecule, + ) + + print("Found rdkit, all good") +except ModuleNotFoundError as e: + use_rdkit = False + from warnings import warn + + warn("Didn't find rdkit, this will fail") + assert use_rdkit, "Didn't find rdkit" + +try: + import psi4 +except ModuleNotFoundError: + print("PSI4 not found") + +allowed_bonds = { + "H": 1, + "C": 4, + "N": 3, + "O": 2, + "F": 1, + "B": 3, + "Al": 3, + "Si": 4, + "P": [3, 5], + "S": 4, + "Cl": 1, + "As": 3, + "Br": 1, + "I": 1, + "Hg": [1, 2], + "Bi": [3, 5], + "Se": [2, 4, 6], +} +bond_dict = [ + None, + Chem.rdchem.BondType.SINGLE, + Chem.rdchem.BondType.DOUBLE, + Chem.rdchem.BondType.TRIPLE, + Chem.rdchem.BondType.AROMATIC, +] +ATOM_VALENCY = {6: 4, 7: 3, 8: 2, 9: 1, 15: 3, 16: 2, 17: 1, 35: 1, 53: 1} + + +class BasicMolecularMetrics(object): + def __init__(self, dataset_info, train_smiles=None, args=None): + self.atom_decoder = dataset_info.atom_decoder + self.dataset_info = dataset_info + self.args = args + + # Retrieve dataset smiles only for qm9 currently. + self.dataset_smiles_list = train_smiles + self.cond_val = MeanAbsoluteError() + + def compute_validity(self, generated): + """generated: list of couples (positions, atom_types)""" + valid = [] + num_components = [] + all_smiles = [] + all_smiles_without_test = [] + + for graph in tqdm( + generated, desc="Generated molecules validity check progress" + ): + atom_types, edge_types = graph + mol = build_molecule(atom_types, edge_types, self.dataset_info.atom_decoder) + smiles = mol2smiles(mol) + all_smiles_without_test.append(mol2smilesWithNoSanitize(mol)) + try: + mol_frags = Chem.rdmolops.GetMolFrags( + mol, asMols=True, sanitizeFrags=True + ) + num_components.append(len(mol_frags)) + except: + pass + if smiles is not None: + try: + mol_frags = Chem.rdmolops.GetMolFrags( + mol, asMols=True, sanitizeFrags=True + ) + largest_mol = max( + mol_frags, default=mol, key=lambda m: m.GetNumAtoms() + ) + smiles = mol2smiles(largest_mol) + valid.append(smiles) + all_smiles.append(smiles) + except Chem.rdchem.AtomValenceException: + print("Valence error in GetmolFrags") + all_smiles.append(None) + except Chem.rdchem.KekulizeException: + print("Can't kekulize molecule") + all_smiles.append(None) + else: + all_smiles.append(None) + + with open(r"final_smiles_all.txt", "w") as fp: + for smiles in all_smiles_without_test: + # write each item on a new line + fp.write("%s\n" % smiles) + print("All smiles saved") + + print(all_smiles_without_test) + df = pd.DataFrame(all_smiles_without_test, columns=["SMILES"]) + df.to_csv("final_smiles_all.csv", index=False) + print("All SMILES saved to CSV") + + return valid, len(valid) / len(generated), np.array(num_components), all_smiles + + def compute_uniqueness(self, valid): + """valid: list of SMILES strings.""" + return list(set(valid)), len(set(valid)) / len(valid) + + def compute_novelty(self, unique): + num_novel = 0 + novel = [] + if self.dataset_smiles_list is None: + print("Dataset smiles is None, novelty computation skipped") + return 1, 1 + for smiles in tqdm(unique, desc="Unique molecules novelty check progress"): + if smiles not in self.dataset_smiles_list: + novel.append(smiles) + num_novel += 1 + return novel, num_novel / len(unique) + + def compute_relaxed_validity(self, generated): + valid = [] + for graph in tqdm( + generated, desc="Generated molecules relaxed validity check progress" + ): + atom_types, edge_types = graph + mol = build_molecule_with_partial_charges( + atom_types, edge_types, self.dataset_info.atom_decoder + ) + smiles = mol2smiles(mol) + if smiles is not None: + try: + mol_frags = Chem.rdmolops.GetMolFrags( + mol, asMols=True, sanitizeFrags=True + ) + largest_mol = max( + mol_frags, default=mol, key=lambda m: m.GetNumAtoms() + ) + smiles = mol2smiles(largest_mol) + valid.append(smiles) + except Chem.rdchem.AtomValenceException: + print("Valence error in GetmolFrags") + except Chem.rdchem.KekulizeException: + print("Can't kekulize molecule") + return valid, len(valid) / len(generated) + + def cond_sample_metric(self, samples, input_properties): + mols_dipoles = [] + mols_homo = [] + self.num_valid_molecules = 0 + self.num_total = 0 + + # Hardware side settings (CPU thread number and memory settings used for calculation) + psi4.set_num_threads(nthread=4) + psi4.set_memory("5GB") + psi4.core.set_output_file("psi4_output.dat", False) + # input_properties = torch.cat(input_properties).unsqueeze(-1) + # print(input_properties) + true_properties = [] + + for i, sample in enumerate(samples): + atom_types, edge_types = sample + mol = build_molecule_with_partial_charges( + atom_types, edge_types, self.dataset_info.atom_decoder + ) + try: + Chem.SanitizeMol(mol) + except: + print("invalid chemistry") + continue + + # Coarse 3D structure optimization by generating 3D structure from SMILES + mol = Chem.AddHs(mol) + params = ETKDGv3() + params.randomSeed = 1 + try: + EmbedMolecule(mol, params) + except Chem.rdchem.AtomValenceException: + print("invalid chemistry") + continue + + # Structural optimization with MMFF (Merck Molecular Force Field) + try: + s = MMFFOptimizeMolecule(mol) + print(s) + except: + print("Bad conformer ID") + continue + + try: + conf = mol.GetConformer() + except: + print("Can not get conformer") + continue + + # Convert to a format that can be input to Psi4. + # Set charge and spin multiplicity (below is charge 0, spin multiplicity 1) + + # Get the formal charge + fc = "FormalCharge" + mol_FormalCharge = ( + int(mol.GetProp(fc)) if mol.HasProp(fc) else Chem.GetFormalCharge(mol) + ) + + sm = "SpinMultiplicity" + if mol.HasProp(sm): + mol_spin_multiplicity = int(mol.GetProp(sm)) + else: + # Calculate spin multiplicity using Hund's rule of maximum multiplicity... + NumRadicalElectrons = 0 + for Atom in mol.GetAtoms(): + NumRadicalElectrons += Atom.GetNumRadicalElectrons() + TotalElectronicSpin = NumRadicalElectrons / 2 + SpinMultiplicity = 2 * TotalElectronicSpin + 1 + mol_spin_multiplicity = int(SpinMultiplicity) + + mol_input = "%s %s" % (mol_FormalCharge, mol_spin_multiplicity) + print(mol_input) + # mol_input = "0 1" + + # Describe the coordinates of each atom in XYZ format + for atom in mol.GetAtoms(): + mol_input += ( + "\n " + + atom.GetSymbol() + + " " + + str(conf.GetAtomPosition(atom.GetIdx()).x) + + " " + + str(conf.GetAtomPosition(atom.GetIdx()).y) + + " " + + str(conf.GetAtomPosition(atom.GetIdx()).z) + ) + + try: + molecule = psi4.geometry(mol_input) + except: + print("Can not calculate psi4 geometry") + continue + + # Convert to a format that can be input to pyscf + # Set calculation method (functional) and basis set + level = "b3lyp/6-31G*" + + # Calculation method (functional), example of basis set + # theory = ['hf', 'b3lyp'] + # basis_set = ['sto-3g', '3-21G', '6-31G(d)', '6-31+G(d,p)', '6-311++G(2d,p)'] + + # Perform structural optimization calculations + print("Psi4 calculation starts!!!") + print("input properties", input_properties[i]) + # energy, wave_function = psi4.optimize(level, molecule=molecule, return_wfn=True) + try: + energy, wave_function = psi4.energy( + level, molecule=molecule, return_wfn=True + ) + true_properties.append(input_properties[i]) + except psi4.driver.SCFConvergenceError: + print("Psi4 did not converge") + continue + + print("Chemistry information check!!!") + + if self.args.general.target in ["mu", "both"]: + dip_x, dip_y, dip_z = ( + wave_function.variable("SCF DIPOLE")[0], + wave_function.variable("SCF DIPOLE")[1], + wave_function.variable("SCF DIPOLE")[2], + ) + dipole_moment = math.sqrt(dip_x**2 + dip_y**2 + dip_z**2) * 2.5417464519 + print("Dipole moment", dipole_moment) + mols_dipoles.append(dipole_moment) + + if self.args.general.target in ["homo", "both"]: + # Compute HOMO (Unit: au= Hartree) + LUMO_idx = wave_function.nalpha() + HOMO_idx = LUMO_idx - 1 + homo = wave_function.epsilon_a_subset("AO", "ALL").np[HOMO_idx] + + # convert unit from a.u. to ev + # homo = homo * 27.211324570273 + print("HOMO", homo) + mols_homo.append(homo) + + true_properties = torch.cat(true_properties).unsqueeze(-1) + + num_valid_molecules = max(len(mols_dipoles), len(mols_homo)) + print("Number of valid samples", num_valid_molecules) + self.num_valid_molecules += num_valid_molecules + self.num_total += len(samples) + + mols_dipoles = torch.FloatTensor(mols_dipoles) + mols_homo = torch.FloatTensor(mols_homo) + + if self.args.general.target == "mu": + mae = self.cond_val( + mols_dipoles.unsqueeze(1), + # input_properties.repeat(len(mols_dipoles), 1).cpu(), + true_properties, + ) + + elif self.args.general.target == "homo": + mae = self.cond_val( + mols_homo.unsqueeze(1), + # input_properties.repeat(len(mols_homo), 1).cpu() + true_properties, + ) + + elif self.args.general.target == "both": + properties = torch.hstack( + (mols_dipoles.unsqueeze(1), mols_homo.unsqueeze(1)) + ) + mae = self.cond_val( + properties, + # input_properties.repeat(len(mols_dipoles), 1).cpu() + true_properties, + ) + + print("Conditional generation metric:") + print(f"MAE: {mae}") + # wandb.log( + # { + # "val_epoch/conditional generation mae": mae, + # "Valid molecules": num_valid_molecules, + # } + # ) + + return mae, self.num_valid_molecules / self.num_total + + def evaluate(self, generated, input_properties=None, test=False): + """generated: list of pairs (positions: n x 3, atom_types: n [int]) + the positions and atom types should already be masked.""" + valid, validity, num_components, all_smiles = self.compute_validity(generated) + + if test: + with open(r"final_smiles.txt", "w") as fp: + for smiles in all_smiles: + # write each item on a new line + fp.write("%s\n" % smiles) + print("All smiles saved") + + print(all_smiles) + df = pd.DataFrame(all_smiles, columns=["SMILES"]) + df.to_csv("final_smiles.csv", index=False) + print("All SMILES saved to CSV") + + nc_mu = num_components.mean() if len(num_components) > 0 else 0 + nc_min = num_components.min() if len(num_components) > 0 else 0 + nc_max = num_components.max() if len(num_components) > 0 else 0 + print(f"Validity over {len(generated)} molecules: {validity * 100 :.2f}%") + print( + f"Number of connected components of {len(generated)} molecules: min:{nc_min:.2f} mean:{nc_mu:.2f} max:{nc_max:.2f}" + ) + + relaxed_valid, relaxed_validity = self.compute_relaxed_validity(generated) + print( + f"Relaxed validity over {len(generated)} molecules: {relaxed_validity * 100 :.2f}%" + ) + + cond_mae = cond_val = -1.0 + if input_properties is not None: + cond_mae, cond_val = self.cond_sample_metric(generated, input_properties) + + if relaxed_validity > 0: + unique, uniqueness = self.compute_uniqueness(relaxed_valid) + print( + f"Uniqueness over {len(relaxed_valid)} valid molecules: {uniqueness * 100 :.2f}%" + ) + + if self.dataset_smiles_list is not None: + _, novelty = self.compute_novelty(unique) + print( + f"Novelty over {len(unique)} unique valid molecules: {novelty * 100 :.2f}%" + ) + else: + novelty = -1.0 + else: + novelty = -1.0 + uniqueness = 0.0 + unique = [] + return ( + [validity, relaxed_validity, uniqueness, novelty], + unique, + dict(nc_min=nc_min, nc_max=nc_max, nc_mu=nc_mu), + all_smiles, + [cond_mae, cond_val], + ) + + +def mol2smiles(mol): + try: + Chem.SanitizeMol(mol) + except ValueError: + return None + return Chem.MolToSmiles(mol) + + +def mol2smilesWithNoSanitize(mol): + return Chem.MolToSmiles(mol) + + +def build_molecule(atom_types, edge_types, atom_decoder, verbose=False): + if verbose: + print("building new molecule") + + mol = Chem.RWMol() + for atom in atom_types: + a = Chem.Atom(atom_decoder[atom.item()]) + mol.AddAtom(a) + if verbose: + print("Atom added: ", atom.item(), atom_decoder[atom.item()]) + + edge_types = torch.triu(edge_types) + edge_types[edge_types >= 5] = 0 # set edges in virtual state to non-bonded + all_bonds = torch.nonzero(edge_types) + for i, bond in enumerate(all_bonds): + if bond[0].item() != bond[1].item(): + mol.AddBond( + bond[0].item(), + bond[1].item(), + bond_dict[edge_types[bond[0], bond[1]].item()], + ) + if verbose: + print( + "bond added:", + bond[0].item(), + bond[1].item(), + edge_types[bond[0], bond[1]].item(), + bond_dict[edge_types[bond[0], bond[1]].item()], + ) + return mol + + +def build_molecule_with_partial_charges( + atom_types, edge_types, atom_decoder, verbose=False +): + if verbose: + print("\nbuilding new molecule") + + mol = Chem.RWMol() + for atom in atom_types: + a = Chem.Atom(atom_decoder[atom.item()]) + mol.AddAtom(a) + if verbose: + print("Atom added: ", atom.item(), atom_decoder[atom.item()]) + edge_types = torch.triu(edge_types) + edge_types[edge_types >= 5] = 0 # set edges in virtual state to non-bonded + all_bonds = torch.nonzero(edge_types) + + for i, bond in enumerate(all_bonds): + if bond[0].item() != bond[1].item(): + mol.AddBond( + bond[0].item(), + bond[1].item(), + bond_dict[edge_types[bond[0], bond[1]].item()], + ) + if verbose: + print( + "bond added:", + bond[0].item(), + bond[1].item(), + edge_types[bond[0], bond[1]].item(), + bond_dict[edge_types[bond[0], bond[1]].item()], + ) + # add formal charge to atom: e.g. [O+], [N+], [S+] + # not support [O-], [N-], [S-], [NH+] etc. + flag, atomid_valence = check_valency(mol) + if verbose: + print("flag, valence", flag, atomid_valence) + if flag: + continue + else: + assert len(atomid_valence) == 2 + idx = atomid_valence[0] + v = atomid_valence[1] + an = mol.GetAtomWithIdx(idx).GetAtomicNum() + if verbose: + print("atomic num of atom with a large valence", an) + if an in (7, 8, 16) and (v - ATOM_VALENCY[an]) == 1: + mol.GetAtomWithIdx(idx).SetFormalCharge(1) + # print("Formal charge added") + return mol + + +# Functions from GDSS +def check_valency(mol): + try: + Chem.SanitizeMol(mol, sanitizeOps=Chem.SanitizeFlags.SANITIZE_PROPERTIES) + return True, None + except ValueError as e: + e = str(e) + p = e.find("#") + e_sub = e[p:] + atomid_valence = list(map(int, re.findall(r"\d+", e_sub))) + return False, atomid_valence + + +def correct_mol(m): + # xsm = Chem.MolToSmiles(x, isomericSmiles=True) + mol = m + + ##### + no_correct = False + flag, _ = check_valency(mol) + if flag: + no_correct = True + + while True: + flag, atomid_valence = check_valency(mol) + if flag: + break + else: + assert len(atomid_valence) == 2 + idx = atomid_valence[0] + v = atomid_valence[1] + queue = [] + check_idx = 0 + for b in mol.GetAtomWithIdx(idx).GetBonds(): + type = int(b.GetBondType()) + queue.append((b.GetIdx(), type, b.GetBeginAtomIdx(), b.GetEndAtomIdx())) + if type == 12: + check_idx += 1 + queue.sort(key=lambda tup: tup[1], reverse=True) + + if queue[-1][1] == 12: + return None, no_correct + elif len(queue) > 0: + start = queue[check_idx][2] + end = queue[check_idx][3] + t = queue[check_idx][1] - 1 + mol.RemoveBond(start, end) + if t >= 1: + mol.AddBond(start, end, bond_dict[t]) + return mol, no_correct + + +def valid_mol_can_with_seg(m, largest_connected_comp=True): + if m is None: + return None + sm = Chem.MolToSmiles(m, isomericSmiles=True) + if largest_connected_comp and "." in sm: + vsm = [ + (s, len(s)) for s in sm.split(".") + ] # 'C.CC.CCc1ccc(N)cc1CCC=O'.split('.') + vsm.sort(key=lambda tup: tup[1], reverse=True) + mol = Chem.MolFromSmiles(vsm[0][0]) + else: + mol = Chem.MolFromSmiles(sm) + return mol + + +if __name__ == "__main__": + smiles_mol = "C1CCC1" + print("Smiles mol %s" % smiles_mol) + chem_mol = Chem.MolFromSmiles(smiles_mol) + block_mol = Chem.MolToMolBlock(chem_mol) + print("Block mol:") + print(block_mol) + +use_rdkit = True + + +def check_stability( + atom_types, edge_types, dataset_info, debug=False, atom_decoder=None +): + if atom_decoder is None: + atom_decoder = dataset_info.atom_decoder + + n_bonds = np.zeros(len(atom_types), dtype="int") + + for i in range(len(atom_types)): + for j in range(i + 1, len(atom_types)): + n_bonds[i] += abs((edge_types[i, j] + edge_types[j, i]) / 2) + n_bonds[j] += abs((edge_types[i, j] + edge_types[j, i]) / 2) + n_stable_bonds = 0 + for atom_type, atom_n_bond in zip(atom_types, n_bonds): + possible_bonds = allowed_bonds[atom_decoder[atom_type]] + if type(possible_bonds) == int: + is_stable = possible_bonds == atom_n_bond + else: + is_stable = atom_n_bond in possible_bonds + if not is_stable and debug: + print( + "Invalid bonds for molecule %s with %d bonds" + % (atom_decoder[atom_type], atom_n_bond) + ) + n_stable_bonds += int(is_stable) + + molecule_stable = n_stable_bonds == len(atom_types) + return molecule_stable, n_stable_bonds, len(atom_types) + + +def compute_molecular_metrics( + molecule_list, train_smiles, dataset_info, labels, args=None, test=False +): + """molecule_list: (dict)""" + + if not dataset_info.remove_h: + print(f"Analyzing molecule stability...") + + molecule_stable = 0 + nr_stable_bonds = 0 + n_atoms = 0 + n_molecules = len(molecule_list) + + for i, mol in tqdm( + enumerate(molecule_list), desc="Stability computation progress" + ): + atom_types, edge_types = mol + + validity_results = check_stability(atom_types, edge_types, dataset_info) + + molecule_stable += int(validity_results[0]) + nr_stable_bonds += int(validity_results[1]) + n_atoms += int(validity_results[2]) + + # Validity + fraction_mol_stable = molecule_stable / float(n_molecules) + fraction_atm_stable = nr_stable_bonds / float(n_atoms) + validity_dict = { + "mol_stable": fraction_mol_stable, + "atm_stable": fraction_atm_stable, + } + if wandb.run: + wandb.log(validity_dict) + else: + validity_dict = {"mol_stable": -1, "atm_stable": -1} + + metrics = BasicMolecularMetrics(dataset_info, train_smiles, args) + rdkit_metrics = metrics.evaluate(molecule_list, labels, test) + all_smiles = rdkit_metrics[-2] + + nc = rdkit_metrics[-3] + dic = { + "Validity": rdkit_metrics[0][0], + "Relaxed Validity": rdkit_metrics[0][1], + "Uniqueness": rdkit_metrics[0][2], + "Novelty": rdkit_metrics[0][3], + "nc_max": nc["nc_max"], + "nc_mu": nc["nc_mu"], + "cond_mae": rdkit_metrics[-1][0], + "cond_val": rdkit_metrics[-1][1], + } + if wandb.run: + dic = dic + wandb.log(dic) + + return validity_dict, rdkit_metrics, all_smiles, dic diff --git a/CupOFMoCA-PROTAC/src/defog/src/analysis/spectre_utils.py b/CupOFMoCA-PROTAC/src/defog/src/analysis/spectre_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d01c6f9a24eb277cc3d7417384cfe7f8f0b91e62 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/analysis/spectre_utils.py @@ -0,0 +1,1195 @@ +############################################################################### +# +# Adapted from https://github.com/lrjconan/GRAN/ which in turn is adapted from https://github.com/JiaxuanYou/graph-generation +# +############################################################################### +import graph_tool.all as gt + +##Navigate to the ./util/orca directory and compile orca.cpp +# g++ -O2 -std=c++11 -o orca orca.cpp +import os +import sys +import copy +import signal +import torch +import torch.nn as nn +import numpy as np +import networkx as nx +import subprocess as sp +import concurrent.futures + +import pygsp as pg +import secrets +from string import ascii_uppercase, digits +from datetime import datetime +from scipy.linalg import eigvalsh +from scipy.stats import chi2 +from analysis.dist_helper import ( + compute_mmd, + gaussian_emd, + gaussian, + emd, + gaussian_tv, + disc, +) +from torch_geometric.utils import to_networkx +import wandb + +from metrics.abstract_metrics import compute_ratios + +PRINT_TIME = False +__all__ = [ + "degree_stats", + "clustering_stats", + "orbit_stats_all", + "spectral_stats", + "eval_acc_lobster_graph", +] + + +# Define a timeout handler +def handler(signum, frame): + raise TimeoutError + + +# Set the signal handler for the alarm +signal.signal(signal.SIGALRM, handler) + + +def degree_worker(G): + return np.array(nx.degree_histogram(G)) + + +def degree_stats(graph_ref_list, graph_pred_list, is_parallel=True, compute_emd=False): + """Compute the distance between the degree distributions of two unordered sets of graphs. + Args: + graph_ref_list, graph_target_list: two lists of networkx graphs to be evaluated + """ + sample_ref = [] + sample_pred = [] + # in case an empty graph is generated + graph_pred_list_remove_empty = [ + G for G in graph_pred_list if not G.number_of_nodes() == 0 + ] + + prev = datetime.now() + if is_parallel: + with concurrent.futures.ThreadPoolExecutor() as executor: + for deg_hist in executor.map(degree_worker, graph_ref_list): + sample_ref.append(deg_hist) + with concurrent.futures.ThreadPoolExecutor() as executor: + for deg_hist in executor.map(degree_worker, graph_pred_list_remove_empty): + sample_pred.append(deg_hist) + else: + for i in range(len(graph_ref_list)): + degree_temp = np.array(nx.degree_histogram(graph_ref_list[i])) + sample_ref.append(degree_temp) + for i in range(len(graph_pred_list_remove_empty)): + degree_temp = np.array(nx.degree_histogram(graph_pred_list_remove_empty[i])) + sample_pred.append(degree_temp) + + # mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=gaussian_emd) + # mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=emd) + if compute_emd: + # EMD option uses the same computation as GraphRNN, the alternative is MMD as computed by GRAN + # mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=emd) + mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=gaussian_emd) + else: + mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=gaussian_tv) + # mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=gaussian) + + elapsed = datetime.now() - prev + if PRINT_TIME: + print("Time computing degree mmd: ", elapsed) + return mmd_dist + + +############################################################################### + + +def spectral_worker(G, n_eigvals=-1): + # eigs = nx.laplacian_spectrum(G) + try: + eigs = eigvalsh(nx.normalized_laplacian_matrix(G).todense()) + except: + eigs = np.zeros(G.number_of_nodes()) + if n_eigvals > 0: + eigs = eigs[1 : n_eigvals + 1] + spectral_pmf, _ = np.histogram(eigs, bins=200, range=(-1e-5, 2), density=False) + spectral_pmf = spectral_pmf / spectral_pmf.sum() + return spectral_pmf + + +def get_spectral_pmf(eigs, max_eig): + spectral_pmf, _ = np.histogram( + np.clip(eigs, 0, max_eig), bins=200, range=(-1e-5, max_eig), density=False + ) + spectral_pmf = spectral_pmf / spectral_pmf.sum() + return spectral_pmf + + +def eigval_stats( + eig_ref_list, eig_pred_list, max_eig=20, is_parallel=True, compute_emd=False +): + """Compute the distance between the degree distributions of two unordered sets of graphs. + Args: + graph_ref_list, graph_target_list: two lists of networkx graphs to be evaluated + """ + sample_ref = [] + sample_pred = [] + + prev = datetime.now() + if is_parallel: + with concurrent.futures.ThreadPoolExecutor() as executor: + for spectral_density in executor.map( + get_spectral_pmf, + eig_ref_list, + [max_eig for i in range(len(eig_ref_list))], + ): + sample_ref.append(spectral_density) + with concurrent.futures.ThreadPoolExecutor() as executor: + for spectral_density in executor.map( + get_spectral_pmf, + eig_pred_list, + [max_eig for i in range(len(eig_ref_list))], + ): + sample_pred.append(spectral_density) + else: + for i in range(len(eig_ref_list)): + spectral_temp = get_spectral_pmf(eig_ref_list[i]) + sample_ref.append(spectral_temp) + for i in range(len(eig_pred_list)): + spectral_temp = get_spectral_pmf(eig_pred_list[i]) + sample_pred.append(spectral_temp) + + # mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=gaussian_emd) + if compute_emd: + mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=emd) + else: + mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=gaussian_tv) + # mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=gaussian) + + elapsed = datetime.now() - prev + if PRINT_TIME: + print("Time computing eig mmd: ", elapsed) + return mmd_dist + + +def eigh_worker(G): + L = nx.normalized_laplacian_matrix(G).todense() + try: + eigvals, eigvecs = np.linalg.eigh(L) + except: + eigvals = np.zeros(L[0, :].shape) + eigvecs = np.zeros(L.shape) + return (eigvals, eigvecs) + + +def compute_list_eigh(graph_list, is_parallel=False): + eigval_list = [] + eigvec_list = [] + if is_parallel: + with concurrent.futures.ThreadPoolExecutor() as executor: + for e_U in executor.map(eigh_worker, graph_list): + eigval_list.append(e_U[0]) + eigvec_list.append(e_U[1]) + else: + for i in range(len(graph_list)): + e_U = eigh_worker(graph_list[i]) + eigval_list.append(e_U[0]) + eigvec_list.append(e_U[1]) + return eigval_list, eigvec_list + + +def get_spectral_filter_worker(eigvec, eigval, filters, bound=1.4): + ges = filters.evaluate(eigval) + linop = [] + for ge in ges: + linop.append(eigvec @ np.diag(ge) @ eigvec.T) + linop = np.array(linop) + norm_filt = np.sum(linop**2, axis=2) + hist_range = [0, bound] + hist = np.array( + [np.histogram(x, range=hist_range, bins=100)[0] for x in norm_filt] + ) # NOTE: change number of bins + return hist.flatten() + + +def spectral_filter_stats( + eigvec_ref_list, + eigval_ref_list, + eigvec_pred_list, + eigval_pred_list, + is_parallel=False, + compute_emd=False, +): + """Compute the distance between the eigvector sets. + Args: + graph_ref_list, graph_target_list: two lists of networkx graphs to be evaluated + """ + prev = datetime.now() + + class DMG(object): + """Dummy Normalized Graph""" + + lmax = 2 + + n_filters = 12 + filters = pg.filters.Abspline(DMG, n_filters) + bound = np.max(filters.evaluate(np.arange(0, 2, 0.01))) + sample_ref = [] + sample_pred = [] + if is_parallel: + with concurrent.futures.ThreadPoolExecutor() as executor: + for spectral_density in executor.map( + get_spectral_filter_worker, + eigvec_ref_list, + eigval_ref_list, + [filters for i in range(len(eigval_ref_list))], + [bound for i in range(len(eigval_ref_list))], + ): + sample_ref.append(spectral_density) + with concurrent.futures.ThreadPoolExecutor() as executor: + for spectral_density in executor.map( + get_spectral_filter_worker, + eigvec_pred_list, + eigval_pred_list, + [filters for i in range(len(eigval_pred_list))], + [bound for i in range(len(eigval_pred_list))], + ): + sample_pred.append(spectral_density) + else: + for i in range(len(eigval_ref_list)): + try: + spectral_temp = get_spectral_filter_worker( + eigvec_ref_list[i], eigval_ref_list[i], filters, bound + ) + sample_ref.append(spectral_temp) + except: + pass + for i in range(len(eigval_pred_list)): + try: + spectral_temp = get_spectral_filter_worker( + eigvec_pred_list[i], eigval_pred_list[i], filters, bound + ) + sample_pred.append(spectral_temp) + except: + pass + + if compute_emd: + # EMD option uses the same computation as GraphRNN, the alternative is MMD as computed by GRAN + # mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=emd) + mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=gaussian_emd) + else: + mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=gaussian_tv) + + elapsed = datetime.now() - prev + if PRINT_TIME: + print("Time computing spectral filter stats: ", elapsed) + return mmd_dist + + +def spectral_stats( + graph_ref_list, graph_pred_list, is_parallel=True, n_eigvals=-1, compute_emd=False +): + """Compute the distance between the degree distributions of two unordered sets of graphs. + Args: + graph_ref_list, graph_target_list: two lists of networkx graphs to be evaluated + """ + sample_ref = [] + sample_pred = [] + # in case an empty graph is generated + graph_pred_list_remove_empty = [ + G for G in graph_pred_list if not G.number_of_nodes() == 0 + ] + + prev = datetime.now() + if is_parallel: + with concurrent.futures.ThreadPoolExecutor() as executor: + for spectral_density in executor.map( + spectral_worker, graph_ref_list, [n_eigvals for i in graph_ref_list] + ): + sample_ref.append(spectral_density) + with concurrent.futures.ThreadPoolExecutor() as executor: + for spectral_density in executor.map( + spectral_worker, + graph_pred_list_remove_empty, + [n_eigvals for i in graph_pred_list_remove_empty], + ): + sample_pred.append(spectral_density) + else: + for i in range(len(graph_ref_list)): + spectral_temp = spectral_worker(graph_ref_list[i], n_eigvals) + sample_ref.append(spectral_temp) + for i in range(len(graph_pred_list_remove_empty)): + spectral_temp = spectral_worker(graph_pred_list_remove_empty[i], n_eigvals) + sample_pred.append(spectral_temp) + + # mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=gaussian_emd) + # mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=emd) + if compute_emd: + # EMD option uses the same computation as GraphRNN, the alternative is MMD as computed by GRAN + # mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=emd) + mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=gaussian_emd) + else: + mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=gaussian_tv) + # mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=gaussian) + + elapsed = datetime.now() - prev + if PRINT_TIME: + print("Time computing degree mmd: ", elapsed) + return mmd_dist + + +############################################################################### + + +def clustering_worker(param): + G, bins = param + clustering_coeffs_list = list(nx.clustering(G).values()) + hist, _ = np.histogram( + clustering_coeffs_list, bins=bins, range=(0.0, 1.0), density=False + ) + return hist + + +def clustering_stats( + graph_ref_list, graph_pred_list, bins=100, is_parallel=True, compute_emd=False +): + sample_ref = [] + sample_pred = [] + graph_pred_list_remove_empty = [ + G for G in graph_pred_list if not G.number_of_nodes() == 0 + ] + + prev = datetime.now() + if is_parallel: + with concurrent.futures.ThreadPoolExecutor() as executor: + for clustering_hist in executor.map( + clustering_worker, [(G, bins) for G in graph_ref_list] + ): + sample_ref.append(clustering_hist) + with concurrent.futures.ThreadPoolExecutor() as executor: + for clustering_hist in executor.map( + clustering_worker, [(G, bins) for G in graph_pred_list_remove_empty] + ): + sample_pred.append(clustering_hist) + + # check non-zero elements in hist + # total = 0 + # for i in range(len(sample_pred)): + # nz = np.nonzero(sample_pred[i])[0].shape[0] + # total += nz + # print(total) + else: + for i in range(len(graph_ref_list)): + clustering_coeffs_list = list(nx.clustering(graph_ref_list[i]).values()) + hist, _ = np.histogram( + clustering_coeffs_list, bins=bins, range=(0.0, 1.0), density=False + ) + sample_ref.append(hist) + + for i in range(len(graph_pred_list_remove_empty)): + clustering_coeffs_list = list( + nx.clustering(graph_pred_list_remove_empty[i]).values() + ) + hist, _ = np.histogram( + clustering_coeffs_list, bins=bins, range=(0.0, 1.0), density=False + ) + sample_pred.append(hist) + + if compute_emd: + # EMD option uses the same computation as GraphRNN, the alternative is MMD as computed by GRAN + # mmd_dist = compute_mmd(sample_ref, sample_pred, kernel=emd, sigma=1.0 / 10) + mmd_dist = compute_mmd( + sample_ref, + sample_pred, + kernel=gaussian_emd, + sigma=1.0 / 10, + distance_scaling=bins, + ) + else: + mmd_dist = compute_mmd( + sample_ref, sample_pred, kernel=gaussian_tv, sigma=1.0 / 10 + ) + + elapsed = datetime.now() - prev + if PRINT_TIME: + print("Time computing clustering mmd: ", elapsed) + return mmd_dist + + +# maps motif/orbit name string to its corresponding list of indices from orca output +motif_to_indices = { + "3path": [1, 2], + "4cycle": [8], +} +COUNT_START_STR = "orbit counts:" + + +def edge_list_reindexed(G): + idx = 0 + id2idx = dict() + for u in G.nodes(): + id2idx[str(u)] = idx + idx += 1 + + edges = [] + for u, v in G.edges(): + edges.append((id2idx[str(u)], id2idx[str(v)])) + return edges + + +def orca(graph): + # tmp_fname = f'analysis/orca/tmp_{"".join(secrets.choice(ascii_uppercase + digits) for i in range(8))}.txt' + tmp_fname = f'orca/tmp_{"".join(secrets.choice(ascii_uppercase + digits) for i in range(8))}.txt' + tmp_fname = os.path.join(os.path.dirname(os.path.realpath(__file__)), tmp_fname) + # print(tmp_fname, flush=True) + f = open(tmp_fname, "w") + f.write(str(graph.number_of_nodes()) + " " + str(graph.number_of_edges()) + "\n") + for u, v in edge_list_reindexed(graph): + f.write(str(u) + " " + str(v) + "\n") + f.close() + output = sp.check_output( + [ + str(os.path.join(os.path.dirname(os.path.realpath(__file__)), "orca/orca")), + "node", + "4", + tmp_fname, + "std", + ] + ) + output = output.decode("utf8").strip() + idx = output.find(COUNT_START_STR) + len(COUNT_START_STR) + 2 + output = output[idx:] + node_orbit_counts = np.array( + [ + list(map(int, node_cnts.strip().split(" "))) + for node_cnts in output.strip("\n").split("\n") + ] + ) + + try: + os.remove(tmp_fname) + except OSError: + pass + + return node_orbit_counts + + +def motif_stats( + graph_ref_list, + graph_pred_list, + motif_type="4cycle", + ground_truth_match=None, + bins=100, + compute_emd=False, +): + # graph motif counts (int for each graph) + # normalized by graph size + total_counts_ref = [] + total_counts_pred = [] + + num_matches_ref = [] + num_matches_pred = [] + + graph_pred_list_remove_empty = [ + G for G in graph_pred_list if not G.number_of_nodes() == 0 + ] + indices = motif_to_indices[motif_type] + + for G in graph_ref_list: + orbit_counts = orca(G) + motif_counts = np.sum(orbit_counts[:, indices], axis=1) + + if ground_truth_match is not None: + match_cnt = 0 + for elem in motif_counts: + if elem == ground_truth_match: + match_cnt += 1 + num_matches_ref.append(match_cnt / G.number_of_nodes()) + + # hist, _ = np.histogram( + # motif_counts, bins=bins, density=False) + motif_temp = np.sum(motif_counts) / G.number_of_nodes() + total_counts_ref.append(motif_temp) + + for G in graph_pred_list_remove_empty: + orbit_counts = orca(G) + motif_counts = np.sum(orbit_counts[:, indices], axis=1) + + if ground_truth_match is not None: + match_cnt = 0 + for elem in motif_counts: + if elem == ground_truth_match: + match_cnt += 1 + num_matches_pred.append(match_cnt / G.number_of_nodes()) + + motif_temp = np.sum(motif_counts) / G.number_of_nodes() + total_counts_pred.append(motif_temp) + + total_counts_ref = np.array(total_counts_ref)[:, None] + total_counts_pred = np.array(total_counts_pred)[:, None] + + if compute_emd: + # EMD option uses the same computation as GraphRNN, the alternative is MMD as computed by GRAN + # mmd_dist = compute_mmd(total_counts_ref, total_counts_pred, kernel=emd, is_hist=False) + mmd_dist = compute_mmd( + total_counts_ref, total_counts_pred, kernel=gaussian, is_hist=False + ) + else: + mmd_dist = compute_mmd( + total_counts_ref, total_counts_pred, kernel=gaussian, is_hist=False + ) + return mmd_dist + + +def orbit_stats_all(graph_ref_list, graph_pred_list, compute_emd=False): + total_counts_ref = [] + total_counts_pred = [] + + graph_pred_list_remove_empty = [ + G for G in graph_pred_list if not G.number_of_nodes() == 0 + ] + + for G in graph_ref_list: + orbit_counts = orca(G) + orbit_counts_graph = np.sum(orbit_counts, axis=0) / G.number_of_nodes() + total_counts_ref.append(orbit_counts_graph) + + for G in graph_pred_list: + orbit_counts = orca(G) + orbit_counts_graph = np.sum(orbit_counts, axis=0) / G.number_of_nodes() + total_counts_pred.append(orbit_counts_graph) + + total_counts_ref = np.array(total_counts_ref) + total_counts_pred = np.array(total_counts_pred) + + # mmd_dist = compute_mmd( + # total_counts_ref, + # total_counts_pred, + # kernel=gaussian, + # is_hist=False, + # sigma=30.0) + + # mmd_dist = compute_mmd( + # total_counts_ref, + # total_counts_pred, + # kernel=gaussian_tv, + # is_hist=False, + # sigma=30.0) + + if compute_emd: + # mmd_dist = compute_mmd(total_counts_ref, total_counts_pred, kernel=emd, sigma=30.0) + # EMD option uses the same computation as GraphRNN, the alternative is MMD as computed by GRAN + mmd_dist = compute_mmd( + total_counts_ref, + total_counts_pred, + kernel=gaussian, + is_hist=False, + sigma=30.0, + ) + else: + mmd_dist = compute_mmd( + total_counts_ref, + total_counts_pred, + kernel=gaussian_tv, + is_hist=False, + sigma=30.0, + ) + return mmd_dist + + +def eval_acc_lobster_graph(G_list): + G_list = [copy.deepcopy(gg) for gg in G_list] + count = 0 + for gg in G_list: + if is_lobster_graph(gg): + count += 1 + return count / float(len(G_list)) + + +def eval_acc_tree_graph(G_list): + count = 0 + for gg in G_list: + if nx.is_tree(gg): + count += 1 + return count / float(len(G_list)) + + +def eval_acc_grid_graph(G_list, grid_start=10, grid_end=20): + count = 0 + for gg in G_list: + if is_grid_graph(gg): + count += 1 + return count / float(len(G_list)) + + +def eval_acc_sbm_graph( + G_list, + p_intra=0.3, + p_inter=0.005, + strict=True, + refinement_steps=100, + is_parallel=True, +): + count = 0.0 + if is_parallel: + with concurrent.futures.ThreadPoolExecutor() as executor: + for prob in executor.map( + is_sbm_graph, + [gg for gg in G_list], + [p_intra for i in range(len(G_list))], + [p_inter for i in range(len(G_list))], + [strict for i in range(len(G_list))], + [refinement_steps for i in range(len(G_list))], + ): + count += prob + else: + for gg in G_list: + count += is_sbm_graph( + gg, + p_intra=p_intra, + p_inter=p_inter, + strict=strict, + refinement_steps=refinement_steps, + ) + return count / float(len(G_list)) + + +def eval_acc_planar_graph(G_list): + count = 0 + for gg in G_list: + if is_planar_graph(gg): + count += 1 + return count / float(len(G_list)) + + +def is_planar_graph(G): + return nx.is_connected(G) and nx.check_planarity(G)[0] + + +def is_lobster_graph(G): + """ + Check a given graph is a lobster graph or not + + Removing leaf nodes twice: + + lobster -> caterpillar -> path + + """ + ### Check if G is a tree + if nx.is_tree(G): + G = G.copy() + ### Check if G is a path after removing leaves twice + leaves = [n for n, d in G.degree() if d == 1] + G.remove_nodes_from(leaves) + + leaves = [n for n, d in G.degree() if d == 1] + G.remove_nodes_from(leaves) + + num_nodes = len(G.nodes()) + num_degree_one = [d for n, d in G.degree() if d == 1] + num_degree_two = [d for n, d in G.degree() if d == 2] + + if sum(num_degree_one) == 2 and sum(num_degree_two) == 2 * (num_nodes - 2): + return True + elif sum(num_degree_one) == 0 and sum(num_degree_two) == 0: + return True + else: + return False + else: + return False + + +def is_grid_graph(G): + """ + Check if the graph is grid, by comparing with all the real grids with the same node count + """ + all_grid_file = f"data/all_grids.pt" + if os.path.isfile(all_grid_file): + all_grids = torch.load(all_grid_file) + else: + all_grids = {} + for i in range(2, 20): + for j in range(2, 20): + G_grid = nx.grid_2d_graph(i, j) + n_nodes = f"{len(G_grid.nodes())}" + all_grids[n_nodes] = all_grids.get(n_nodes, []) + [G_grid] + torch.save(all_grids, all_grid_file) + + n_nodes = f"{len(G.nodes())}" + if n_nodes in all_grids: + for G_grid in all_grids[n_nodes]: + if nx.faster_could_be_isomorphic(G, G_grid): + if nx.is_isomorphic(G, G_grid): + return True + return False + else: + return False + + +def is_sbm_graph(G, p_intra=0.3, p_inter=0.005, strict=True, refinement_steps=100): + """ + Check if how closely given graph matches a SBM with given probabilites by computing mean probability of Wald test statistic for each recovered parameter + """ + + adj = nx.adjacency_matrix(G).toarray() + idx = adj.nonzero() + g = gt.Graph() + g.add_edge_list(np.transpose(idx)) + try: + state = gt.minimize_blockmodel_dl(g) + except ValueError: + if strict: + return False + else: + return 0.0 + + # Refine using merge-split MCMC + for i in range(refinement_steps): + state.multiflip_mcmc_sweep(beta=np.inf, niter=10) + + b = state.get_blocks() + b = gt.contiguous_map(state.get_blocks()) + state = state.copy(b=b) + e = state.get_matrix() + n_blocks = state.get_nonempty_B() + node_counts = state.get_nr().get_array()[:n_blocks] + edge_counts = e.todense()[:n_blocks, :n_blocks] + if strict: + if ( + (node_counts > 40).sum() > 0 + or (node_counts < 20).sum() > 0 + or n_blocks > 5 + or n_blocks < 2 + ): + return False + + max_intra_edges = node_counts * (node_counts - 1) + est_p_intra = np.diagonal(edge_counts) / (max_intra_edges + 1e-6) + + max_inter_edges = node_counts.reshape((-1, 1)) @ node_counts.reshape((1, -1)) + np.fill_diagonal(edge_counts, 0) + est_p_inter = edge_counts / (max_inter_edges + 1e-6) + + W_p_intra = (est_p_intra - p_intra) ** 2 / (est_p_intra * (1 - est_p_intra) + 1e-6) + W_p_inter = (est_p_inter - p_inter) ** 2 / (est_p_inter * (1 - est_p_inter) + 1e-6) + + W = W_p_inter.copy() + np.fill_diagonal(W, W_p_intra) + p = 1 - chi2.cdf(abs(W), 1) + p = p.mean() + if strict: + return p > 0.9 # p value < 10 % + else: + return p + + +def eval_fraction_isomorphic(fake_graphs, train_graphs): + count = 0 + for fake_g in fake_graphs: + for train_g in train_graphs: + if nx.faster_could_be_isomorphic(fake_g, train_g): + if nx.is_isomorphic(fake_g, train_g): + count += 1 + break + return count / float(len(fake_graphs)) + + +def eval_fraction_unique(fake_graphs, precise=False): + count_non_unique = 0 + fake_evaluated = [] + for fake_g in fake_graphs: + unique = True + if not fake_g.number_of_nodes() == 0: + for fake_old in fake_evaluated: + if precise: + if nx.faster_could_be_isomorphic(fake_g, fake_old): + if nx.is_isomorphic(fake_g, fake_old): + count_non_unique += 1 + unique = False + break + else: + if nx.faster_could_be_isomorphic(fake_g, fake_old): + if nx.could_be_isomorphic(fake_g, fake_old): + count_non_unique += 1 + unique = False + break + if unique: + fake_evaluated.append(fake_g) + + frac_unique = (float(len(fake_graphs)) - count_non_unique) / float( + len(fake_graphs) + ) # Fraction of distinct isomorphism classes in the fake graphs + + return frac_unique + + +def eval_fraction_unique_non_isomorphic_valid( + fake_graphs, train_graphs, validity_func=(lambda x: True) +): + count_valid = 0 + count_isomorphic = 0 + count_non_unique = 0 + fake_evaluated = [] + for fake_g in fake_graphs: + unique = True + + for fake_old in fake_evaluated: + try: + # Set the alarm for 60 seconds + signal.alarm(60) + if nx.is_isomorphic(fake_g, fake_old): + count_non_unique += 1 + unique = False + break + except TimeoutError: + print("Timeout: Skipping this iteration") + continue + finally: + # Disable the alarm + signal.alarm(0) + if unique: + fake_evaluated.append(fake_g) + non_isomorphic = True + for train_g in train_graphs: + if nx.faster_could_be_isomorphic(fake_g, train_g): + if nx.is_isomorphic(fake_g, train_g): + count_isomorphic += 1 + non_isomorphic = False + break + if non_isomorphic: + if validity_func(fake_g): + count_valid += 1 + + frac_unique = (float(len(fake_graphs)) - count_non_unique) / float( + len(fake_graphs) + ) # Fraction of distinct isomorphism classes in the fake graphs + frac_unique_non_isomorphic = ( + float(len(fake_graphs)) - count_non_unique - count_isomorphic + ) / float( + len(fake_graphs) + ) # Fraction of distinct isomorphism classes in the fake graphs that are not in the training set + frac_unique_non_isomorphic_valid = count_valid / float( + len(fake_graphs) + ) # Fraction of distinct isomorphism classes in the fake graphs that are not in the training set and are valid + return frac_unique, frac_unique_non_isomorphic, frac_unique_non_isomorphic_valid + + +class SpectreSamplingMetrics(nn.Module): + def __init__(self, datamodule, compute_emd, metrics_list): + super().__init__() + + self.train_graphs = self.loader_to_nx(datamodule.train_dataloader()) + self.val_graphs = self.loader_to_nx(datamodule.val_dataloader()) + self.test_graphs = self.loader_to_nx(datamodule.test_dataloader()) + self.num_graphs_test = len(self.test_graphs) + self.num_graphs_val = len(self.val_graphs) + self.compute_emd = compute_emd + self.metrics_list = metrics_list + + # Store for wavelet computaiton + self.val_ref_eigvals, self.val_ref_eigvecs = compute_list_eigh(self.val_graphs) + self.test_ref_eigvals, self.test_ref_eigvecs = compute_list_eigh( + self.test_graphs + ) + + def loader_to_nx(self, loader): + networkx_graphs = [] + for i, batch in enumerate(loader): + data_list = batch.to_data_list() + for j, data in enumerate(data_list): + networkx_graphs.append( + to_networkx( + data, + node_attrs=None, + edge_attrs=None, + to_undirected=True, + remove_self_loops=True, + ) + ) + return networkx_graphs + + def forward( + self, + generated_graphs: list, + ref_metrics, + name, + current_epoch, + val_counter, + local_rank, + test=False, + labels=None, + ): + reference_graphs = self.test_graphs if test else self.val_graphs + if local_rank == 0: + print( + f"Computing sampling metrics between {len(generated_graphs)} generated graphs and {len(reference_graphs)}" + f" test graphs -- emd computation: {self.compute_emd}" + ) + networkx_graphs = [] + adjacency_matrices = [] + if local_rank == 0: + print("Building networkx graphs...") + for graph in generated_graphs: + node_types, edge_types = graph + A = edge_types.bool().cpu().numpy() + adjacency_matrices.append(A) + + nx_graph = nx.from_numpy_array(A) + networkx_graphs.append(nx_graph) + + to_log = {} + np.savez("generated_adjs.npz", *adjacency_matrices) + + if "degree" in self.metrics_list: + if local_rank == 0: + print("Computing degree stats..") + degree = degree_stats( + reference_graphs, + networkx_graphs, + is_parallel=True, + compute_emd=self.compute_emd, + ) + if wandb.run: + wandb.run.summary["degree"] = degree + to_log["degree"] = degree + + # val_eigvals = [graph["eigval"][1:self.k + 1].cpu().detach().numpy() for graph in self.val] + # train_eigvals = [graph["eigval"][1:self.k + 1].cpu().detach().numpy() for graph in self.train] + + # eigval_stats(eig_ref_list, eig_pred_list, max_eig=20, is_parallel=True, compute_emd=False) + # spectral_filter_stats(eigvec_ref_list, eigval_ref_list, eigvec_pred_list, eigval_pred_list, is_parallel=False, + # compute_emd=False) # This is the one called wavelet + + if "wavelet" in self.metrics_list: + if local_rank == 0: + print("Computing wavelet stats...") + + ref_eigvecs = self.test_ref_eigvecs if test else self.val_ref_eigvecs + ref_eigvals = self.test_ref_eigvals if test else self.val_ref_eigvals + + pred_graph_eigvals, pred_graph_eigvecs = compute_list_eigh(networkx_graphs) + wavelet = spectral_filter_stats( + eigvec_ref_list=ref_eigvecs, + eigval_ref_list=ref_eigvals, + eigvec_pred_list=pred_graph_eigvecs, + eigval_pred_list=pred_graph_eigvals, + is_parallel=False, + compute_emd=self.compute_emd, + ) + to_log["wavelet"] = wavelet + if wandb.run: + wandb.run.summary["wavelet"] = wavelet + + if "spectre" in self.metrics_list: + if local_rank == 0: + print("Computing spectre stats...") + spectre = spectral_stats( + reference_graphs, + networkx_graphs, + is_parallel=True, + n_eigvals=-1, + compute_emd=self.compute_emd, + ) + + to_log["spectre"] = spectre + if wandb.run: + wandb.run.summary["spectre"] = spectre + + if "clustering" in self.metrics_list: + if local_rank == 0: + print("Computing clustering stats...") + clustering = clustering_stats( + reference_graphs, + networkx_graphs, + bins=100, + is_parallel=True, + compute_emd=self.compute_emd, + ) + to_log["clustering"] = clustering + if wandb.run: + wandb.run.summary["clustering"] = clustering + + if "motif" in self.metrics_list: + if local_rank == 0: + print("Computing motif stats") + motif = motif_stats( + reference_graphs, + networkx_graphs, + motif_type="4cycle", + ground_truth_match=None, + bins=100, + compute_emd=self.compute_emd, + ) + to_log["motif"] = motif + if wandb.run: + wandb.run.summary["motif"] = motif + + if "orbit" in self.metrics_list: + if local_rank == 0: + print("Computing orbit stats...") + orbit = orbit_stats_all( + reference_graphs, networkx_graphs, compute_emd=self.compute_emd + ) + to_log["orbit"] = orbit + if wandb.run: + wandb.run.summary["orbit"] = orbit + + if "sbm" in self.metrics_list: + if local_rank == 0: + print("Computing accuracy...") + sbm_acc = eval_acc_sbm_graph( + networkx_graphs, refinement_steps=100, strict=True + ) + to_log["sbm_acc"] = sbm_acc + if wandb.run: + wandb.run.summary["sbm_acc"] = sbm_acc + + if "planar" in self.metrics_list: + if local_rank == 0: + print("Computing planar accuracy...") + planar_acc = eval_acc_planar_graph(networkx_graphs) + to_log["planar_acc"] = planar_acc + if wandb.run: + wandb.run.summary["planar_acc"] = planar_acc + + if "tree" in self.metrics_list: + if local_rank == 0: + print("Computing tree accuracy...") + tree_acc = eval_acc_tree_graph(networkx_graphs) + to_log["tree_acc"] = tree_acc + if wandb.run: + wandb.run.summary["tree_acc"] = tree_acc + + if ( + "sbm" in self.metrics_list + or "planar" in self.metrics_list + or "tree" in self.metrics_list + ): + if local_rank == 0: + print("Computing all fractions...") + if "sbm" in self.metrics_list: + validity_func = is_sbm_graph + elif "planar" in self.metrics_list: + validity_func = is_planar_graph + elif "tree" in self.metrics_list: + validity_func = nx.is_tree + else: + validity_func = None + ( + frac_unique, + frac_unique_non_isomorphic, + fraction_unique_non_isomorphic_valid, + ) = eval_fraction_unique_non_isomorphic_valid( + networkx_graphs, + self.train_graphs, + validity_func, + ) + frac_non_isomorphic = 1.0 - eval_fraction_isomorphic( + networkx_graphs, self.train_graphs + ) + to_log.update( + { + "sampling/frac_unique": frac_unique, + "sampling/frac_unique_non_iso": frac_unique_non_isomorphic, + "sampling/frac_unic_non_iso_valid": fraction_unique_non_isomorphic_valid, + "sampling/frac_non_iso": frac_non_isomorphic, + } + ) + + ratios = compute_ratios( + gen_metrics=to_log, + ref_metrics=ref_metrics["test"] if test else ref_metrics["val"], + metrics_keys=["degree", "clustering", "orbit", "spectre", "wavelet"], + ) + to_log.update(ratios) + + if local_rank == 0: + print("Sampling statistics", to_log) + if wandb.run: + wandb.log(to_log, commit=False) + + return to_log + + def reset(self): + pass + + +class Comm20SamplingMetrics(SpectreSamplingMetrics): + + def __init__(self, datamodule): + super().__init__( + datamodule=datamodule, + compute_emd=True, + metrics_list=["degree", "clustering", "orbit", "spectre", "wavelet"], + ) + + +class PlanarSamplingMetrics(SpectreSamplingMetrics): + def __init__(self, datamodule): + super().__init__( + datamodule=datamodule, + compute_emd=False, + metrics_list=[ + "degree", + "clustering", + "orbit", + "spectre", + "wavelet", + "planar", + ], + ) + + +class SBMSamplingMetrics(SpectreSamplingMetrics): + def __init__(self, datamodule): + super().__init__( + datamodule=datamodule, + compute_emd=False, + metrics_list=["degree", "clustering", "orbit", "spectre", "wavelet", "sbm"], + ) + + +class TreeSamplingMetrics(SpectreSamplingMetrics): + def __init__(self, datamodule): + super().__init__( + datamodule=datamodule, + compute_emd=False, + metrics_list=[ + "degree", + "clustering", + "orbit", + "spectre", + "wavelet", + "tree", + ], + ) + + +class EgoSamplingMetrics(SpectreSamplingMetrics): + def __init__(self, datamodule): + super().__init__( + datamodule=datamodule, + compute_emd=False, + metrics_list=["degree", "clustering", "orbit", "spectre", "wavelet"], + ) + + +class ProteinSamplingMetrics(SpectreSamplingMetrics): + def __init__(self, datamodule): + super().__init__( + datamodule=datamodule, + compute_emd=False, + metrics_list=["degree", "clustering", "orbit", "spectre", "wavelet"], + ) + + +class IMDBSamplingMetrics(SpectreSamplingMetrics): + def __init__(self, datamodule): + super().__init__( + datamodule=datamodule, + compute_emd=False, + metrics_list=["degree", "clustering", "orbit", "spectre", "wavelet"], + ) diff --git a/CupOFMoCA-PROTAC/src/defog/src/analysis/visualization.py b/CupOFMoCA-PROTAC/src/defog/src/analysis/visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..475b62087bad9bf9392b9f33190a82054689cbd3 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/analysis/visualization.py @@ -0,0 +1,306 @@ +import os + +from rdkit import Chem +from rdkit.Chem import Draw, AllChem +from rdkit.Geometry import Point3D +from rdkit import RDLogger +import imageio +import networkx as nx +import numpy as np +import rdkit.Chem +import wandb +import matplotlib.pyplot as plt + + +from datasets.tls_dataset import CellGraph + + +class MolecularVisualization: + def __init__(self, remove_h, dataset_infos): + self.remove_h = remove_h + self.dataset_infos = dataset_infos + + def mol_from_graphs(self, node_list, adjacency_matrix): + """ + Convert graphs to rdkit molecules + node_list: the nodes of a batch of nodes (bs x n) + adjacency_matrix: the adjacency_matrix of the molecule (bs x n x n) + """ + # dictionary to map integer value to the char of atom + atom_decoder = self.dataset_infos.atom_decoder + + # create empty editable mol object + mol = Chem.RWMol() + + # add atoms to mol and keep track of index + node_to_idx = {} + for i in range(len(node_list)): + if node_list[i] == -1: + continue + a = Chem.Atom(atom_decoder[int(node_list[i])]) + molIdx = mol.AddAtom(a) + node_to_idx[i] = molIdx + + for ix, row in enumerate(adjacency_matrix): + for iy, bond in enumerate(row): + # only traverse half the symmetric matrix + if iy <= ix: + continue + if bond == 1: + bond_type = Chem.rdchem.BondType.SINGLE + elif bond == 2: + bond_type = Chem.rdchem.BondType.DOUBLE + elif bond == 3: + bond_type = Chem.rdchem.BondType.TRIPLE + elif bond == 4: + bond_type = Chem.rdchem.BondType.AROMATIC + else: + continue + mol.AddBond(node_to_idx[ix], node_to_idx[iy], bond_type) + + try: + mol = mol.GetMol() + except rdkit.Chem.KekulizeException: + print("Can't kekulize molecule") + mol = None + return mol + + def visualize( + self, path: str, molecules: list, num_molecules_to_visualize: int, log="graph" + ): + # define path to save figures + if not os.path.exists(path): + os.makedirs(path) + + # visualize the final molecules + print(f"Visualizing {num_molecules_to_visualize} of {len(molecules)}") + if num_molecules_to_visualize > len(molecules): + print(f"Shortening to {len(molecules)}") + num_molecules_to_visualize = len(molecules) + + for i in range(num_molecules_to_visualize): + file_path = os.path.join(path, "molecule_{}.png".format(i)) + mol = self.mol_from_graphs(molecules[i][0].numpy(), molecules[i][1].numpy()) + try: + Draw.MolToFile(mol, file_path) + if wandb.run and log is not None: + print(f"Saving {file_path} to wandb") + wandb.log({log: wandb.Image(file_path)}, commit=True) + except rdkit.Chem.KekulizeException: + print("Can't kekulize molecule") + + def visualize_chain(self, path, nodes_list, adjacency_matrix, times, trainer=None): + RDLogger.DisableLog("rdApp.*") + # convert graphs to the rdkit molecules + mols = [ + self.mol_from_graphs(nodes_list[i], adjacency_matrix[i]) + for i in range(nodes_list.shape[0]) + ] + + # find the coordinates of atoms in the final molecule + final_molecule = mols[-1] + AllChem.Compute2DCoords(final_molecule) + + coords = [] + for i, atom in enumerate(final_molecule.GetAtoms()): + positions = final_molecule.GetConformer().GetAtomPosition(i) + coords.append((positions.x, positions.y, positions.z)) + + # align all the molecules + for i, mol in enumerate(mols): + AllChem.Compute2DCoords(mol) + conf = mol.GetConformer() + for j, atom in enumerate(mol.GetAtoms()): + x, y, z = coords[j] + conf.SetAtomPosition(j, Point3D(x, y, z)) + + # draw gif + save_paths = [] + num_frams = nodes_list.shape[0] + + for frame in range(num_frams): + file_name = os.path.join(path, "fram_{}.png".format(frame)) + Draw.MolToFile( + mols[frame], + file_name, + size=(300, 300), + legend=f"t = {times[frame]:.2f}", + ) + save_paths.append(file_name) + + imgs = [imageio.imread(fn) for fn in save_paths] + gif_path = os.path.join( + os.path.dirname(path), "{}.gif".format(path.split("/")[-1]) + ) + imgs.extend([imgs[-1]] * 10) + imageio.mimsave(gif_path, imgs, subrectangles=True, duration=200) + + if wandb.run: + print(f"Saving {gif_path} to wandb") + wandb.log( + {"chain": wandb.Video(gif_path, fps=5, format="gif")}, commit=True + ) + + # draw grid image + try: + img = Draw.MolsToGridImage(mols, molsPerRow=10, subImgSize=(200, 200)) + img.save( + os.path.join(path, "{}_grid_image.png".format(path.split("/")[-1])) + ) + except Chem.rdchem.KekulizeException: + print("Can't kekulize molecule") + return mols + + +class NonMolecularVisualization: + + def __init__(self, dataset_name): + self.is_tls = "tls" in dataset_name + + def to_networkx(self, node_list, adjacency_matrix): + """ + Convert graphs to networkx graphs + node_list: the nodes of a batch of nodes (bs x n) + adjacency_matrix: the adjacency_matrix of the molecule (bs x n x n) + """ + graph = nx.Graph() + + for i in range(len(node_list)): + if node_list[i] == -1: + continue + graph.add_node(i, number=i, symbol=node_list[i], color_val=node_list[i]) + + rows, cols = np.where(adjacency_matrix >= 1) + edges = zip(rows.tolist(), cols.tolist()) + for edge in edges: + edge_type = adjacency_matrix[edge[0]][edge[1]] + graph.add_edge( + edge[0], edge[1], color=float(edge_type), weight=3 * edge_type + ) + + return graph + + def visualize_non_molecule( + self, + graph, + pos, + path, + iterations=100, + node_size=100, + largest_component=False, + time=None, + ): + if largest_component: + CGs = [graph.subgraph(c) for c in nx.connected_components(graph)] + CGs = sorted(CGs, key=lambda x: x.number_of_nodes(), reverse=True) + graph = CGs[0] + + # Plot the graph structure with colors + if pos is None: + pos = nx.spring_layout(graph, iterations=iterations) + + # Set node colors based on the eigenvectors + w, U = np.linalg.eigh(nx.normalized_laplacian_matrix(graph).toarray()) + vmin, vmax = np.min(U[:, 1]), np.max(U[:, 1]) + m = max(np.abs(vmin), vmax) + vmin, vmax = -m, m + + plt.figure() + nx.draw( + graph, + pos, + font_size=5, + node_size=node_size, + with_labels=False, + node_color=U[:, 1], + cmap=plt.cm.coolwarm, + vmin=vmin, + vmax=vmax, + edge_color="grey", + ) + if time is not None: + plt.text( + 0.5, + 0.05, # place below the graph + f"t = {time:.2f}", + ha="center", + va="center", + transform=plt.gcf().transFigure, + fontsize=16, + ) + + plt.tight_layout() + plt.savefig(path) + plt.close("all") + + def visualize( + self, path: str, graphs: list, num_graphs_to_visualize: int, log="graph" + ): + # define path to save figures + if not os.path.exists(path): + os.makedirs(path) + + # visualize the final molecules + for i in range(num_graphs_to_visualize): + file_path = os.path.join(path, "graph_{}.png".format(i)) + + if self.is_tls: + cg = CellGraph.from_dense_graph(graphs[i]) + cg.plot_graph(save_path=file_path, has_legend=True) + else: + graph = self.to_networkx(graphs[i][0].numpy(), graphs[i][1].numpy()) + self.visualize_non_molecule(graph=graph, pos=None, path=file_path) + + im = plt.imread(file_path) + if wandb.run and log is not None: + wandb.log({log: [wandb.Image(im, caption=file_path)]}) + + def visualize_chain(self, path, nodes_list, adjacency_matrix, times): + + graphs = [] + for i in range(nodes_list.shape[0]): + if self.is_tls: + graphs.append( + CellGraph.from_dense_graph((nodes_list[i], adjacency_matrix[i])) + ) + else: + graphs.append(self.to_networkx(nodes_list[i], adjacency_matrix[i])) + + # find the coordinates of atoms in the final molecule + final_graph = graphs[-1] + final_pos = nx.spring_layout(final_graph, seed=0) + + # draw gif + save_paths = [] + num_frams = nodes_list.shape[0] + + for frame in range(num_frams): + file_name = os.path.join(path, "fram_{}.png".format(frame)) + if self.is_tls: + if not graphs[frame].get_pos(): # The last one already has a pos + graphs[frame].set_pos(pos=final_pos) + graphs[frame].plot_graph( + save_path=file_name, + has_legend=False, + verbose=False, + time=times[frame], + ) + else: + self.visualize_non_molecule( + graph=graphs[frame], + pos=final_pos, + path=file_name, + time=times[frame], + ) + save_paths.append(file_name) + + imgs = [imageio.imread(fn) for fn in save_paths] + gif_path = os.path.join( + os.path.dirname(path), "{}.gif".format(path.split("/")[-1]) + ) + imgs.extend([imgs[-1]] * 10) + imageio.mimsave(gif_path, imgs, subrectangles=True, duration=200) + if wandb.run: + wandb.log( + {"chain": [wandb.Video(gif_path, caption=gif_path, format="gif")]} + ) diff --git a/CupOFMoCA-PROTAC/src/defog/src/datasets/__init__.py b/CupOFMoCA-PROTAC/src/defog/src/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/CupOFMoCA-PROTAC/src/defog/src/datasets/abstract_dataset.py b/CupOFMoCA-PROTAC/src/defog/src/datasets/abstract_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..f07215cb924c204965931d2494001855e2ea23b2 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/datasets/abstract_dataset.py @@ -0,0 +1,225 @@ +import copy +import os + +import torch +import wandb +import pytorch_lightning as pl +from torch_geometric.loader import DataLoader +from torch_geometric.data.lightning import LightningDataset +from tqdm import tqdm + +import utils as utils +from datasets.dataset_utils import DistributionNodes, load_pickle, save_pickle + + +class AbstractDataModule(LightningDataset): + def __init__(self, cfg, datasets): + super().__init__( + train_dataset=datasets["train"], + val_dataset=datasets["val"], + test_dataset=datasets["test"], + batch_size=cfg.train.batch_size if "debug" not in cfg.general.name else 2, + num_workers=cfg.train.num_workers, + pin_memory=getattr(cfg.dataset, "pin_memory", False), + ) + self.cfg = cfg + self.input_dims = None + self.output_dims = None + print( + f'This dataset contains {len(datasets["train"])} training graphs, {len(datasets["val"])} validation graphs, {len(datasets["test"])} test graphs.' + ) + + def __getitem__(self, idx): + return self.train_dataset[idx] + + def node_counts(self, max_nodes_possible=1000): + all_counts = torch.zeros(max_nodes_possible) + for loader in [self.train_dataloader(), self.val_dataloader()]: + for data in loader: + unique, counts = torch.unique(data.batch, return_counts=True) + for count in counts: + all_counts[count] += 1 + max_index = max(all_counts.nonzero()) + all_counts = all_counts[: max_index + 1] + all_counts = all_counts / all_counts.sum() + return all_counts + + def node_types(self): + num_classes = None + + for data in self.train_dataloader(): + num_classes = data.x.shape[1] + break + + counts = torch.zeros(num_classes) + + for i, data in enumerate(self.train_dataloader()): + counts += data.x.sum(dim=0) + + counts = counts / counts.sum() + return counts + + def edge_counts(self): + num_classes = None + for data in self.train_dataloader(): + num_classes = data.edge_attr.shape[1] + break + + d = torch.zeros(num_classes, dtype=torch.float) + + for i, data in enumerate(self.train_dataloader()): + unique, counts = torch.unique(data.batch, return_counts=True) + + all_pairs = 0 + for count in counts: + all_pairs += count * (count - 1) + + num_edges = data.edge_index.shape[1] + num_non_edges = all_pairs - num_edges + + edge_types = data.edge_attr.sum(dim=0) + assert num_non_edges >= 0 + d[0] += num_non_edges + d[1:] += edge_types[1:] + + d = d / d.sum() + return d + + +class MolecularDataModule(AbstractDataModule): + def valency_count(self, max_n_nodes, zinc=False): + valencies = torch.zeros( + 3 * max_n_nodes - 2 + ) # Max valency possible if everything is connected + + # No bond, single bond, double bond, triple bond, aromatic bond + multiplier = torch.tensor([0, 1, 2, 3, 1.5]) + if zinc: + multiplier = torch.tensor([0, 1, 2, 3]) # zinc250 + + for data in self.train_dataloader(): + n = data.x.shape[0] + + for atom in range(n): + edges = data.edge_attr[data.edge_index[0] == atom] + edges_total = edges.sum(dim=0) + valency = (edges_total * multiplier).sum() + valencies[valency.long().item()] += 1 + valencies = valencies / valencies.sum() + return valencies + + +class AbstractDatasetInfos: + def complete_infos(self, n_nodes, node_types): + self.input_dims = None + self.output_dims = None + self.num_classes = len(node_types) + self.max_n_nodes = len(n_nodes) - 1 + self.nodes_dist = DistributionNodes(n_nodes) + + def compute_input_output_dims(self, datamodule, extra_features, domain_features): + example_batch = next(iter(datamodule.train_dataloader())) + ex_dense, node_mask = utils.to_dense( + example_batch.x, + example_batch.edge_index, + example_batch.edge_attr, + example_batch.batch, + ) + + # ex_dense.E = ex_dense.E[..., :-1] # debug + + example_data = { + "X_t": ex_dense.X, + "E_t": ex_dense.E, + "y_t": example_batch["y"], + "node_mask": node_mask, + } + self.input_dims = { + "X": example_batch["x"].size(1), + "E": example_batch["edge_attr"].size(1), + "y": example_batch["y"].size(1) + + 1, # this part take into account the conditioning + } # + 1 due to time conditioning + ex_extra_feat = extra_features(example_data) + self.input_dims["X"] += ex_extra_feat.X.size(-1) + self.input_dims["E"] += ex_extra_feat.E.size(-1) + self.input_dims["y"] += ex_extra_feat.y.size(-1) + + ex_extra_molecular_feat = domain_features(example_data) + self.input_dims["X"] += ex_extra_molecular_feat.X.size(-1) + self.input_dims["E"] += ex_extra_molecular_feat.E.size(-1) + self.input_dims["y"] += ex_extra_molecular_feat.y.size(-1) + + self.output_dims = { + "X": example_batch["x"].size(1), + "E": example_batch["edge_attr"].size(1), + "y": 0, + } + + def compute_reference_metrics(self, datamodule, sampling_metrics): + + ref_metrics_path = os.path.join( + datamodule.train_dataloader().dataset.root, f"ref_metrics.pkl" + ) + if hasattr(datamodule, "remove_h"): + if datamodule.remove_h: + ref_metrics_path = ref_metrics_path.replace(".pkl", "_no_h.pkl") + else: + ref_metrics_path = ref_metrics_path.replace(".pkl", "_h.pkl") + + # Only compute the reference metrics if they haven't been computed already + if not os.path.exists(ref_metrics_path): + + print("Reference metrics not found. Computing them now.") + # Transform the training dataset into a list of graphs in the appropriate format + training_graphs = [] + print("Converting training dataset to format required by sampling metrics.") + for data_batch in tqdm(datamodule.train_dataloader()): + dense_data, node_mask = utils.to_dense( + data_batch.x, + data_batch.edge_index, + data_batch.edge_attr, + data_batch.batch, + ) + dense_data = dense_data.mask(node_mask, collapse=True).split(node_mask) + for graph in dense_data: + training_graphs.append([graph.X, graph.E]) + + # defining dummy arguments + dummy_kwargs = { + "name": "ref_metrics", + "current_epoch": 0, + "val_counter": 0, + "local_rank": 0, + "ref_metrics": {"val": None, "test": None}, + } + + print("Computing validation reference metrics.") + # do not have to worry about wandb because it was not init yet + val_sampling_metrics = copy.deepcopy(sampling_metrics) + + val_ref_metrics = val_sampling_metrics.forward( + training_graphs, + test=False, + **dummy_kwargs, + ) + + print("Computing test reference metrics.") + test_sampling_metrics = copy.deepcopy(sampling_metrics) + test_ref_metrics = test_sampling_metrics.forward( + training_graphs, + test=True, + **dummy_kwargs, + ) + + print("Saving reference metrics.") + # print(f"deg: {test_reference_metrics['degree']} | clus: {test_reference_metrics['clustering']} | orbit: {test_reference_metrics['orbit']}") + # breakpoint() + save_pickle( + {"val": val_ref_metrics, "test": test_ref_metrics}, ref_metrics_path + ) + + print("Loading reference metrics.") + self.ref_metrics = load_pickle(ref_metrics_path) + print("Validation reference metrics:", self.ref_metrics["val"]) + print("Test reference metrics:", self.ref_metrics["test"]) diff --git a/CupOFMoCA-PROTAC/src/defog/src/datasets/dataset_utils.py b/CupOFMoCA-PROTAC/src/defog/src/datasets/dataset_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..31b964653d7ab92f65832395cfe1390093f7cc5e --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/datasets/dataset_utils.py @@ -0,0 +1,119 @@ +import os.path as osp +import pickle +from typing import Any, Sequence + +from rdkit import Chem +import torch +from torch_geometric.data import Data +from torch_geometric.utils import subgraph + + +def mol_to_torch_geometric(mol, atom_encoder, smiles): + adj = torch.from_numpy(Chem.rdmolops.GetAdjacencyMatrix(mol, useBO=True)) + edge_index = adj.nonzero().contiguous().T + bond_types = adj[edge_index[0], edge_index[1]] + bond_types[bond_types == 1.5] = 4 + edge_attr = bond_types.long() + + node_types = [] + all_charge = [] + for atom in mol.GetAtoms(): + node_types.append(atom_encoder[atom.GetSymbol()]) + all_charge.append(atom.GetFormalCharge()) + + node_types = torch.Tensor(node_types).long() + all_charge = torch.Tensor(all_charge).long() + + data = Data( + x=node_types, + edge_index=edge_index, + edge_attr=edge_attr, + charge=all_charge, + smiles=smiles, + ) + return data + + +def remove_hydrogens(data: Data): + to_keep = data.x > 0 + new_edge_index, new_edge_attr = subgraph( + to_keep, + data.edge_index, + data.edge_attr, + relabel_nodes=True, + num_nodes=len(to_keep), + ) + return Data( + x=data.x[to_keep] - 1, # Shift onehot encoding to match atom decoder + charge=data.charge[to_keep], + edge_index=new_edge_index, + edge_attr=new_edge_attr, + ) + + +def save_pickle(array, path): + with open(path, "wb") as f: + pickle.dump(array, f) + + +def load_pickle(path): + with open(path, "rb") as f: + return pickle.load(f) + + +def files_exist(files) -> bool: + return len(files) != 0 and all([osp.exists(f) for f in files]) + + +def to_list(value: Any) -> Sequence: + if isinstance(value, Sequence) and not isinstance(value, str): + return value + else: + return [value] + + +class Statistics: + def __init__( + self, num_nodes, node_types, bond_types, charge_types=None, valencies=None + ): + self.num_nodes = num_nodes + self.node_types = node_types + self.bond_types = bond_types + self.charge_types = charge_types + self.valencies = valencies + + +class RemoveYTransform: + def __call__(self, data): + data.y = torch.zeros((1, 0), dtype=torch.float) + return data + + +class DistributionNodes: + def __init__(self, histogram): + """Compute the distribution of the number of nodes in the dataset, and sample from this distribution. + historgram: dict. The keys are num_nodes, the values are counts + """ + + if type(histogram) == dict: + max_n_nodes = max(histogram.keys()) + prob = torch.zeros(max_n_nodes + 1) + for num_nodes, count in histogram.items(): + prob[num_nodes] = count + else: + prob = histogram + + self.prob = prob / prob.sum() + self.m = torch.distributions.Categorical(prob) + + def sample_n(self, n_samples, device): + idx = self.m.sample((n_samples,)) + return idx.to(device) + + def log_prob(self, batch_n_nodes): + assert len(batch_n_nodes.size()) == 1 + p = self.prob.to(batch_n_nodes.device) + + probas = p[batch_n_nodes] + log_p = torch.log(probas + 1e-30) + return log_p diff --git a/CupOFMoCA-PROTAC/src/defog/src/datasets/guacamol_dataset.py b/CupOFMoCA-PROTAC/src/defog/src/datasets/guacamol_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..05b5533b4ff220586b720528614e66551bff0c91 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/datasets/guacamol_dataset.py @@ -0,0 +1,491 @@ +from rdkit import Chem, RDLogger +from rdkit.Chem.rdchem import BondType as BT + +import os +import os.path as osp +import pathlib +import hashlib +from typing import Any, Sequence + +import torch +import torch.nn.functional as F +from tqdm import tqdm +import numpy as np +from torch_geometric.data import Data, InMemoryDataset, download_url + +from src import utils +from analysis.rdkit_functions import ( + mol2smiles, + build_molecule_with_partial_charges, + compute_molecular_metrics, +) +from datasets.abstract_dataset import AbstractDatasetInfos, MolecularDataModule + + +TRAIN_HASH = "05ad85d871958a05c02ab51a4fde8530" +VALID_HASH = "e53db4bff7dc4784123ae6df72e3b1f0" +TEST_HASH = "677b757ccec4809febd83850b43e1616" + + +def files_exist(files) -> bool: + # NOTE: We return `False` in case `files` is empty, leading to a + # re-processing of files on every instantiation. + return len(files) != 0 and all([osp.exists(f) for f in files]) + + +def to_list(value: Any) -> Sequence: + if isinstance(value, Sequence) and not isinstance(value, str): + return value + else: + return [value] + + +def compare_hash(output_file: str, correct_hash: str) -> bool: + """ + Computes the md5 hash of a SMILES file and check it against a given one + Returns false if hashes are different + """ + output_hash = hashlib.md5(open(output_file, "rb").read()).hexdigest() + if output_hash != correct_hash: + print( + f"{output_file} file has different hash, {output_hash}, than expected, {correct_hash}!" + ) + return False + + return True + + +class GuacamolDataset(InMemoryDataset): + train_url = "https://figshare.com/ndownloader/files/13612760" + test_url = "https://figshare.com/ndownloader/files/13612757" + valid_url = "https://figshare.com/ndownloader/files/13612766" + all_url = "https://figshare.com/ndownloader/files/13612745" + + def __init__( + self, + stage, + root, + filter_dataset: bool, + transform=None, + pre_transform=None, + pre_filter=None, + ): + self.stage = stage + self.filter_dataset = filter_dataset + if self.stage == "train": + self.file_idx = 0 + elif self.stage == "val": + self.file_idx = 1 + else: + self.file_idx = 2 + super().__init__(root, transform, pre_transform, pre_filter) + self.data, self.slices = torch.load(self.processed_paths[self.file_idx]) + + @property + def raw_file_names(self): + return [ + "guacamol_v1_train.smiles", + "guacamol_v1_valid.smiles", + "guacamol_v1_test.smiles", + ] + + @property + def split_file_name(self): + return [ + "guacamol_v1_train.smiles", + "guacamol_v1_valid.smiles", + "guacamol_v1_test.smiles", + ] + + @property + def split_paths(self): + r"""The absolute filepaths that must be present in order to skip + splitting.""" + files = to_list(self.split_file_name) + return [osp.join(self.raw_dir, f) for f in files] + + @property + def processed_file_names(self): + if self.filter_dataset: + return ["new_proc_tr.pt", "new_proc_val.pt", "new_proc_test.pt"] + else: + return ["old_proc_tr.pt", "old_proc_val.pt", "old_proc_test.pt"] + + def download(self): + import rdkit # noqa + + train_path = download_url(self.train_url, self.raw_dir) + os.rename(train_path, osp.join(self.raw_dir, "guacamol_v1_train.smiles")) + train_path = osp.join(self.raw_dir, "guacamol_v1_train.smiles") + + test_path = download_url(self.test_url, self.raw_dir) + os.rename(test_path, osp.join(self.raw_dir, "guacamol_v1_test.smiles")) + test_path = osp.join(self.raw_dir, "guacamol_v1_test.smiles") + + valid_path = download_url(self.valid_url, self.raw_dir) + os.rename(valid_path, osp.join(self.raw_dir, "guacamol_v1_valid.smiles")) + valid_path = osp.join(self.raw_dir, "guacamol_v1_valid.smiles") + + # check the hashes + # Check whether the md5-hashes of the generated smiles files match + # the precomputed hashes, this ensures everyone works with the same splits. + valid_hashes = [ + compare_hash(train_path, TRAIN_HASH), + compare_hash(valid_path, VALID_HASH), + compare_hash(test_path, TEST_HASH), + ] + + if not all(valid_hashes): + raise SystemExit("Invalid hashes for the dataset files") + + print("Dataset download successful. Hashes are correct.") + + if files_exist(self.split_paths): + return + + def process(self): + RDLogger.DisableLog("rdApp.*") + types = { + "C": 0, + "N": 1, + "O": 2, + "F": 3, + "B": 4, + "Br": 5, + "Cl": 6, + "I": 7, + "P": 8, + "S": 9, + "Se": 10, + "Si": 11, + } + bonds = {BT.SINGLE: 0, BT.DOUBLE: 1, BT.TRIPLE: 2, BT.AROMATIC: 3} + + smile_list = open(self.split_paths[self.file_idx]).readlines() + + data_list = [] + smiles_kept = [] + for i, smile in enumerate(tqdm(smile_list)): + mol = Chem.MolFromSmiles(smile) + N = mol.GetNumAtoms() + + type_idx = [] + for atom in mol.GetAtoms(): + type_idx.append(types[atom.GetSymbol()]) + + row, col, edge_type = [], [], [] + for bond in mol.GetBonds(): + start, end = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx() + row += [start, end] + col += [end, start] + edge_type += 2 * [bonds[bond.GetBondType()] + 1] + + if len(row) == 0: + continue + + edge_index = torch.tensor([row, col], dtype=torch.long) + edge_type = torch.tensor(edge_type, dtype=torch.long) + edge_attr = F.one_hot(edge_type, num_classes=len(bonds) + 1).to(torch.float) + + perm = (edge_index[0] * N + edge_index[1]).argsort() + edge_index = edge_index[:, perm] + edge_attr = edge_attr[perm] + + x = F.one_hot(torch.tensor(type_idx), num_classes=len(types)).float() + y = torch.zeros(size=(1, 0), dtype=torch.float) + + data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i) + + if self.filter_dataset: + # Try to build the molecule again from the graph. If it fails, do not add it to the training set + dense_data, node_mask = utils.to_dense( + data.x, data.edge_index, data.edge_attr, data.batch + ) + dense_data = dense_data.mask(node_mask, collapse=True) + X, E = dense_data.X, dense_data.E + + assert X.size(0) == 1 + atom_types = X[0] + edge_types = E[0] + atom_decoder = [ + "C", + "N", + "O", + "F", + "B", + "Br", + "Cl", + "I", + "P", + "S", + "Se", + "Si", + ] + mol = build_molecule_with_partial_charges( + atom_types, edge_types, atom_decoder + ) + smiles = mol2smiles(mol) + if smiles is not None: + try: + mol_frags = Chem.rdmolops.GetMolFrags( + mol, asMols=True, sanitizeFrags=True + ) + if len(mol_frags) == 1: + data_list.append(data) + smiles_kept.append(smiles) + + except Chem.rdchem.AtomValenceException: + print("Valence error in GetmolFrags") + except Chem.rdchem.KekulizeException: + print("Can't kekulize molecule") + else: + if self.pre_filter is not None and not self.pre_filter(data): + continue + if self.pre_transform is not None: + data = self.pre_transform(data) + data_list.append(data) + + torch.save(self.collate(data_list), self.processed_paths[self.file_idx]) + if self.filter_dataset: + smiles_save_path = osp.join( + pathlib.Path(self.raw_paths[0]).parent, f"new_{self.stage}.smiles" + ) + print(smiles_save_path) + with open(smiles_save_path, "w") as f: + f.writelines("%s\n" % s for s in smiles_kept) + print(f"Number of molecules kept: {len(smiles_kept)} / {len(smile_list)}") + + +class GuacamolDataModule(MolecularDataModule): + def __init__(self, cfg): + + self.remove_h = True + self.datadir = cfg.dataset.datadir + self.filter = cfg.dataset.filter + self.train_smiles = [] + base_path = pathlib.Path(os.path.realpath(__file__)).parents[2] + root_path = os.path.join(base_path, self.datadir) + datasets = { + "train": GuacamolDataset( + stage="train", root=root_path, filter_dataset=self.filter + ), + "val": GuacamolDataset( + stage="val", root=root_path, filter_dataset=self.filter + ), + "test": GuacamolDataset( + stage="test", root=root_path, filter_dataset=self.filter + ), + } + super().__init__(cfg, datasets) + + +class Guacamolinfos(AbstractDatasetInfos): + atom_encoder = { + "C": 0, + "N": 1, + "O": 2, + "F": 3, + "B": 4, + "Br": 5, + "Cl": 6, + "I": 7, + "P": 8, + "S": 9, + "Se": 10, + "Si": 11, + } + atom_decoder = ["C", "N", "O", "F", "B", "Br", "Cl", "I", "P", "S", "Se", "Si"] + + def __init__(self, datamodule, cfg, recompute_statistics=False): + self.name = "Guacamol" + self.input_dims = None + self.output_dims = None + self.remove_h = True + self.compute_fcd = cfg.dataset.compute_fcd + self.num_atom_types = 12 + self.max_weight = 1000 + + self.valencies = [4, 3, 2, 1, 3, 1, 1, 1, 3, 2, 2, 4] + + self.atom_weights = { + 1: 12, + 2: 14, + 3: 16, + 4: 19, + 5: 10.81, + 6: 79.9, + 7: 35.45, + 8: 126.9, + 9: 30.97, + 10: 30.07, + 11: 78.97, + 12: 28.09, + } + + self.node_types = torch.tensor( + [ + 7.4090e-01, + 1.0693e-01, + 1.1220e-01, + 1.4213e-02, + 6.0579e-05, + 1.7171e-03, + 8.4113e-03, + 2.2902e-04, + 5.6947e-04, + 1.4673e-02, + 4.1532e-05, + 5.3416e-05, + ] + ) + + self.edge_types = torch.tensor( + [9.2526e-01, 3.6241e-02, 4.8489e-03, 1.6513e-04, 3.3489e-02] + ) + + self.n_nodes = torch.tensor( + [ + 0, + 0, + 3.5760e-06, + 2.7893e-05, + 6.9374e-05, + 1.6020e-04, + 2.8036e-04, + 4.3484e-04, + 7.3022e-04, + 1.1722e-03, + 1.7830e-03, + 2.8129e-03, + 4.0981e-03, + 5.5421e-03, + 7.9645e-03, + 1.0824e-02, + 1.4459e-02, + 1.8818e-02, + 2.3961e-02, + 2.9558e-02, + 3.6324e-02, + 4.1931e-02, + 4.8105e-02, + 5.2316e-02, + 5.6601e-02, + 5.7483e-02, + 5.6685e-02, + 5.2317e-02, + 5.2107e-02, + 4.9651e-02, + 4.8100e-02, + 4.4363e-02, + 4.0704e-02, + 3.5719e-02, + 3.1685e-02, + 2.6821e-02, + 2.2542e-02, + 1.8591e-02, + 1.6114e-02, + 1.3399e-02, + 1.1543e-02, + 9.6116e-03, + 8.4744e-03, + 6.9532e-03, + 6.2001e-03, + 4.9921e-03, + 4.4378e-03, + 3.5803e-03, + 3.3078e-03, + 2.7085e-03, + 2.6784e-03, + 2.2050e-03, + 2.0533e-03, + 1.5598e-03, + 1.5177e-03, + 9.8626e-04, + 8.6396e-04, + 5.6429e-04, + 5.0422e-04, + 2.9323e-04, + 2.2243e-04, + 9.8697e-05, + 9.9413e-05, + 6.0077e-05, + 6.9374e-05, + 3.0754e-05, + 3.5045e-05, + 1.6450e-05, + 2.1456e-05, + 1.2874e-05, + 1.2158e-05, + 5.7216e-06, + 7.1520e-06, + 2.8608e-06, + 2.8608e-06, + 7.1520e-07, + 2.8608e-06, + 1.4304e-06, + 7.1520e-07, + 0.0000e00, + 0.0000e00, + 0.0000e00, + 7.1520e-07, + 0.0000e00, + 1.4304e-06, + 7.1520e-07, + 7.1520e-07, + 0.0000e00, + 1.4304e-06, + ] + ) + + self.complete_infos(n_nodes=self.n_nodes, node_types=self.node_types) + self.valency_distribution = torch.zeros(self.max_n_nodes * 3 - 2) + self.valency_distribution[0:7] = torch.tensor( + [0.0000, 0.1105, 0.2645, 0.3599, 0.2552, 0.0046, 0.0053] + ) + + self.complete_infos(n_nodes=self.n_nodes, node_types=self.node_types) + + if recompute_statistics: + self.n_nodes = datamodule.node_counts() + print("Distribution of number of nodes", self.n_nodes) + np.savetxt("n_counts.txt", self.n_nodes.numpy()) + self.node_types = datamodule.node_types() # There are no node types + print("Distribution of node types", self.node_types) + np.savetxt("atom_types.txt", self.node_types.numpy()) + + self.edge_types = datamodule.edge_counts() + print("Distribution of edge types", self.edge_types) + np.savetxt("edge_types.txt", self.edge_types.numpy()) + + valencies = datamodule.valency_count() + print("Distribution of the valencies", valencies) + np.savetxt("valencies.txt", valencies.numpy()) + self.valency_distribution = valencies + + +def get_smiles(raw_dir, filter_dataset): + + if filter_dataset: + smiles_save_paths = { + "train": osp.join(raw_dir, "new_train.smiles"), + "val": osp.join(raw_dir, "new_val.smiles"), + "test": osp.join(raw_dir, "new_test.smiles"), + } + else: + smiles_save_paths = { + "train": osp.join(raw_dir, "guacamol_v1_test.smiles"), + "val": osp.join(raw_dir, "guacamol_v1_valid.smiles"), + "test": osp.join(raw_dir, "guacamol_v1_test.smiles"), + } + + return { + "train": extract_smiles_from_file(smiles_save_paths["train"]), + "val": extract_smiles_from_file(smiles_save_paths["val"]), + "test": extract_smiles_from_file(smiles_save_paths["test"]), + } + + +def extract_smiles_from_file(file_path): + with open(file_path, "r") as f: + lines = [line.strip() for line in f.readlines()] + return lines diff --git a/CupOFMoCA-PROTAC/src/defog/src/datasets/moses_dataset.py b/CupOFMoCA-PROTAC/src/defog/src/datasets/moses_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..aae1fed2eb5223bf5be3db12a2c8466a18226bbd --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/datasets/moses_dataset.py @@ -0,0 +1,353 @@ +from rdkit import Chem, RDLogger +from rdkit.Chem.rdchem import BondType as BT + +import os +import os.path as osp +import pathlib +from typing import Any, Sequence + +import torch +import torch.nn.functional as F +from tqdm import tqdm +import numpy as np +from torch_geometric.data import Data, InMemoryDataset, download_url +import pandas as pd + +from src import utils +from analysis.rdkit_functions import ( + mol2smiles, + build_molecule_with_partial_charges, + compute_molecular_metrics, +) +from datasets.abstract_dataset import AbstractDatasetInfos, MolecularDataModule + + +def to_list(value: Any) -> Sequence: + if isinstance(value, Sequence) and not isinstance(value, str): + return value + else: + return [value] + + +atom_decoder = ["C", "N", "S", "O", "F", "Cl", "Br", "H"] + + +class MOSESDataset(InMemoryDataset): + train_url = "https://media.githubusercontent.com/media/molecularsets/moses/master/data/train.csv" + val_url = "https://media.githubusercontent.com/media/molecularsets/moses/master/data/test.csv" + test_url = "https://media.githubusercontent.com/media/molecularsets/moses/master/data/test_scaffolds.csv" + + def __init__( + self, + stage, + root, + filter_dataset: bool, + transform=None, + pre_transform=None, + pre_filter=None, + ): + self.stage = stage + self.atom_decoder = atom_decoder + self.filter_dataset = filter_dataset + if self.stage == "train": + self.file_idx = 0 + elif self.stage == "val": + self.file_idx = 1 + else: + self.file_idx = 2 + super().__init__(root, transform, pre_transform, pre_filter) + self.data, self.slices = torch.load(self.processed_paths[self.file_idx]) + + @property + def raw_file_names(self): + return ["train_moses.csv", "val_moses.csv", "test_moses.csv"] + + @property + def split_file_name(self): + return ["train_moses.csv", "val_moses.csv", "test_moses.csv"] + + @property + def split_paths(self): + r"""The absolute filepaths that must be present in order to skip + splitting.""" + files = to_list(self.split_file_name) + return [osp.join(self.raw_dir, f) for f in files] + + @property + def processed_file_names(self): + if self.filter_dataset: + return [ + "train_filtered.pt", + "test_filtered.pt", + "test_scaffold_filtered.pt", + ] + else: + return ["train.pt", "test.pt", "test_scaffold.pt"] + + def download(self): + import rdkit # noqa + + train_path = download_url(self.train_url, self.raw_dir) + os.rename(train_path, osp.join(self.raw_dir, "train_moses.csv")) + + test_path = download_url(self.test_url, self.raw_dir) + os.rename(test_path, osp.join(self.raw_dir, "val_moses.csv")) + + valid_path = download_url(self.val_url, self.raw_dir) + os.rename(valid_path, osp.join(self.raw_dir, "test_moses.csv")) + + def process(self): + RDLogger.DisableLog("rdApp.*") + types = {atom: i for i, atom in enumerate(self.atom_decoder)} + + bonds = {BT.SINGLE: 0, BT.DOUBLE: 1, BT.TRIPLE: 2, BT.AROMATIC: 3} + + path = self.split_paths[self.file_idx] + smiles_list = pd.read_csv(path)["SMILES"].values + + data_list = [] + smiles_kept = [] + + for i, smile in enumerate(tqdm(smiles_list)): + mol = Chem.MolFromSmiles(smile) + N = mol.GetNumAtoms() + + type_idx = [] + for atom in mol.GetAtoms(): + type_idx.append(types[atom.GetSymbol()]) + + row, col, edge_type = [], [], [] + for bond in mol.GetBonds(): + start, end = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx() + row += [start, end] + col += [end, start] + edge_type += 2 * [bonds[bond.GetBondType()] + 1] + + if len(row) == 0: + continue + + edge_index = torch.tensor([row, col], dtype=torch.long) + edge_type = torch.tensor(edge_type, dtype=torch.long) + edge_attr = F.one_hot(edge_type, num_classes=len(bonds) + 1).to(torch.float) + + perm = (edge_index[0] * N + edge_index[1]).argsort() + edge_index = edge_index[:, perm] + edge_attr = edge_attr[perm] + + x = F.one_hot(torch.tensor(type_idx), num_classes=len(types)).float() + y = torch.zeros(size=(1, 0), dtype=torch.float) + + data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i) + + if self.filter_dataset: + # Try to build the molecule again from the graph. If it fails, do not add it to the training set + dense_data, node_mask = utils.to_dense( + data.x, data.edge_index, data.edge_attr, data.batch + ) + dense_data = dense_data.mask(node_mask, collapse=True) + X, E = dense_data.X, dense_data.E + + assert X.size(0) == 1 + atom_types = X[0] + edge_types = E[0] + mol = build_molecule_with_partial_charges( + atom_types, edge_types, atom_decoder + ) + smiles = mol2smiles(mol) + if smiles is not None: + try: + mol_frags = Chem.rdmolops.GetMolFrags( + mol, asMols=True, sanitizeFrags=True + ) + if len(mol_frags) == 1: + data_list.append(data) + smiles_kept.append(smiles) + + except Chem.rdchem.AtomValenceException: + print("Valence error in GetmolFrags") + except Chem.rdchem.KekulizeException: + print("Can't kekulize molecule") + else: + if self.pre_filter is not None and not self.pre_filter(data): + continue + if self.pre_transform is not None: + data = self.pre_transform(data) + data_list.append(data) + + torch.save(self.collate(data_list), self.processed_paths[self.file_idx]) + + if self.filter_dataset: + smiles_save_path = osp.join( + pathlib.Path(self.raw_paths[0]).parent, f"new_{self.stage}.smiles" + ) + print(smiles_save_path) + with open(smiles_save_path, "w") as f: + f.writelines("%s\n" % s for s in smiles_kept) + print(f"Number of molecules kept: {len(smiles_kept)} / {len(smiles_list)}") + + +class MosesDataModule(MolecularDataModule): + def __init__(self, cfg): + self.remove_h = False + self.datadir = cfg.dataset.datadir + self.filter_dataset = cfg.dataset.filter + self.train_smiles = [] + base_path = pathlib.Path(os.path.realpath(__file__)).parents[2] + root_path = os.path.join(base_path, self.datadir) + datasets = { + "train": MOSESDataset( + stage="train", root=root_path, filter_dataset=self.filter_dataset + ), + "val": MOSESDataset( + stage="val", root=root_path, filter_dataset=self.filter_dataset + ), + "test": MOSESDataset( + stage="test", root=root_path, filter_dataset=self.filter_dataset + ), + } + super().__init__(cfg, datasets) + + +class MOSESinfos(AbstractDatasetInfos): + def __init__(self, datamodule, cfg, recompute_statistics=False, meta=None): + self.name = "MOSES" + self.input_dims = None + self.output_dims = None + self.remove_h = False + self.compute_fcd = cfg.dataset.compute_fcd + + self.atom_decoder = atom_decoder + self.atom_encoder = {atom: i for i, atom in enumerate(self.atom_decoder)} + self.atom_weights = {0: 12, 1: 14, 2: 32, 3: 16, 4: 19, 5: 35.4, 6: 79.9, 7: 1} + self.valencies = [4, 3, 4, 2, 1, 1, 1, 1] + self.num_atom_types = len(self.atom_decoder) + self.max_weight = 350 + + meta_files = dict( + n_nodes=f"{self.name}_n_counts.txt", + node_types=f"{self.name}_atom_types.txt", + edge_types=f"{self.name}_edge_types.txt", + valency_distribution=f"{self.name}_valencies.txt", + ) + + self.n_nodes = torch.tensor( + [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 3.097634362347889692e-06, + 1.858580617408733815e-05, + 5.007842264603823423e-05, + 5.678996240021660924e-05, + 1.244216400664299726e-04, + 4.486406978685408831e-04, + 2.253012731671333313e-03, + 3.231865121051669121e-03, + 6.709992419928312302e-03, + 2.289564721286296844e-02, + 5.411050841212272644e-02, + 1.099515631794929504e-01, + 1.223291903734207153e-01, + 1.280680745840072632e-01, + 1.445975750684738159e-01, + 1.505961418151855469e-01, + 1.436946094036102295e-01, + 9.265746921300888062e-02, + 1.820066757500171661e-02, + 2.065089574898593128e-06, + ] + ) + self.max_n_nodes = len(self.n_nodes) - 1 if self.n_nodes is not None else None + self.node_types = torch.tensor( + [0.722338, 0.13661, 0.163655, 0.103549, 0.1421803, 0.005411, 0.00150, 0.0] + ) + self.edge_types = torch.tensor( + [0.89740, 0.0472947, 0.062670, 0.0003524, 0.0486] + ) + self.valency_distribution = torch.zeros(3 * self.max_n_nodes - 2) + self.valency_distribution[:7] = torch.tensor( + [0.0, 0.1055, 0.2728, 0.3613, 0.2499, 0.00544, 0.00485] + ) + + if meta is None: + meta = dict( + n_nodes=None, + node_types=None, + edge_types=None, + valency_distribution=None, + ) + assert set(meta.keys()) == set(meta_files.keys()) + for k, v in meta_files.items(): + if (k not in meta or meta[k] is None) and os.path.exists(v): + meta[k] = np.loadtxt(v) + setattr(self, k, meta[k]) + if recompute_statistics or self.n_nodes is None: + self.n_nodes = datamodule.node_counts() + print("Distribution of number of nodes", self.n_nodes) + np.savetxt(meta_files["n_nodes"], self.n_nodes.numpy()) + self.max_n_nodes = len(self.n_nodes) - 1 + if recompute_statistics or self.node_types is None: + self.node_types = datamodule.node_types() # There are no node types + print("Distribution of node types", self.node_types) + np.savetxt(meta_files["node_types"], self.node_types.numpy()) + + if recompute_statistics or self.edge_types is None: + self.edge_types = datamodule.edge_counts() + print("Distribution of edge types", self.edge_types) + np.savetxt(meta_files["edge_types"], self.edge_types.numpy()) + if recompute_statistics or self.valency_distribution is None: + valencies = datamodule.valency_count(self.max_n_nodes) + print("Distribution of the valencies", valencies) + np.savetxt(meta_files["valency_distribution"], valencies.numpy()) + self.valency_distribution = valencies + # after we can be sure we have the data, complete infos + self.complete_infos(n_nodes=self.n_nodes, node_types=self.node_types) + + +def get_smiles(raw_dir, filter_dataset): + + if filter_dataset: + smiles_save_paths = { + "train": osp.join(raw_dir, "new_train.smiles"), + "val": osp.join(raw_dir, "new_val.smiles"), + "test": osp.join(raw_dir, "new_test.smiles"), + } + train_smiles = open(smiles_save_paths["train"]).readlines() + val_smiles = open(smiles_save_paths["val"]).readlines() + test_smiles = open(smiles_save_paths["test"]).readlines() + + else: + smiles_save_paths = { + "train": osp.join(raw_dir, "train_moses.csv"), + "val": osp.join(raw_dir, "val_moses.csv"), + "test": osp.join(raw_dir, "test_moses.csv"), + } + train_smiles = extract_smiles_from_csv(smiles_save_paths["train"]) + val_smiles = extract_smiles_from_csv(smiles_save_paths["val"]) + test_smiles = extract_smiles_from_csv(smiles_save_paths["test"]) + + return { + "train": train_smiles, + "val": val_smiles, + "test": test_smiles, + } + + +def extract_smiles_from_csv(csv_path): + return pd.read_csv(csv_path)["SMILES"].to_list() + + +if __name__ == "__main__": + ds = [ + MOSESDataset( + s, + os.path.join(os.path.abspath(__file__), "../../../data/moses"), + preprocess=True, + ) + for s in ["train", "val", "test"] + ] diff --git a/CupOFMoCA-PROTAC/src/defog/src/datasets/protac_dataset.py b/CupOFMoCA-PROTAC/src/defog/src/datasets/protac_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..d6af21dc05290522d8e64735b1ad03745d5f165d --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/datasets/protac_dataset.py @@ -0,0 +1,258 @@ +import os +import os.path as osp +import pathlib +from typing import Any, Sequence + +import torch +import torch.nn.functional as F +from tqdm import tqdm +import numpy as np +import pandas as pd +from rdkit import Chem, RDLogger +from rdkit.Chem.rdchem import BondType as BT +from torch_geometric.data import Data, InMemoryDataset + +from datasets.abstract_dataset import AbstractDatasetInfos, MolecularDataModule + + +def to_list(value: Any) -> Sequence: + if isinstance(value, Sequence) and not isinstance(value, str): + return value + return [value] + + +atom_decoder = ['C', 'N', 'O', 'F', 'P', 'S', 'Cl', 'Br', 'I'] + + +def get_smiles(raw_dir): + result = {} + for split in ['train', 'val', 'test']: + path = osp.join(raw_dir, f'smiles_{split}.txt') + with open(path) as f: + result[split] = [s.strip() for s in f if s.strip()] + return result + + +class ProtacLinkerDataset(InMemoryDataset): + """ + 2D linker graph dataset from PROTACDB. + Encoding is identical to DeFoG's ZINC dataset: Kekulized bonds, + 3 bond types (SINGLE/DOUBLE/TRIPLE, no AROMATIC), d_E=4. + """ + + def __init__(self, stage, root, csv_path, transform=None, + pre_transform=None, pre_filter=None): + self.stage = stage + self.csv_path = csv_path + self.atom_decoder = atom_decoder + assert stage in ['train', 'val', 'test'] + self.file_idx = ['train', 'val', 'test'].index(stage) + super().__init__(root, transform, pre_transform, pre_filter) + self.data, self.slices = torch.load(self.processed_paths[self.file_idx], + weights_only=False) + + @property + def raw_file_names(self): + return ['smiles_train.txt', 'smiles_val.txt', 'smiles_test.txt'] + + @property + def processed_file_names(self): + return ['train.pt', 'val.pt', 'test.pt'] + + def download(self): + """Filter PROTACDB linker SMILES and write per-split text files.""" + smiles = self._load_and_filter() + + rng = np.random.default_rng(42) + idx = rng.permutation(len(smiles)) + n = len(smiles) + n_train = int(0.8 * n) + n_val = int(0.1 * n) + + splits = { + 'train': [smiles[i] for i in idx[:n_train]], + 'val': [smiles[i] for i in idx[n_train:n_train + n_val]], + 'test': [smiles[i] for i in idx[n_train + n_val:]], + } + + os.makedirs(self.raw_dir, exist_ok=True) + for split, smi_list in splits.items(): + path = osp.join(self.raw_dir, f'smiles_{split}.txt') + with open(path, 'w') as f: + f.writelines(f'{s}\n' for s in smi_list) + + print(f'Saved splits — ' + f'train: {len(splits["train"])} ' + f'val: {len(splits["val"])} ' + f'test: {len(splits["test"])}') + + def _load_and_filter(self): + RDLogger.DisableLog('rdApp.*') + valid_atoms = set(atom_decoder) + df = pd.read_csv(self.csv_path) + seen, valid = set(), [] + for smi in df['Smiles'].dropna(): + mol = Chem.MolFromSmiles(smi) + if mol is None: + continue + if len(Chem.rdmolops.GetMolFrags(mol)) > 1: + continue + if not {a.GetSymbol() for a in mol.GetAtoms()}.issubset(valid_atoms): + continue + canon = Chem.MolToSmiles(mol) + if canon in seen: + continue + seen.add(canon) + valid.append(canon) + print(f'Filtered to {len(valid)} unique valid linker SMILES') + return valid + + def process(self): + RDLogger.DisableLog('rdApp.*') + types = {a: i for i, a in enumerate(atom_decoder)} + bonds = {BT.SINGLE: 0, BT.DOUBLE: 1, BT.TRIPLE: 2} + + with open(self.raw_paths[self.file_idx]) as f: + smiles_list = [s.strip() for s in f if s.strip()] + + data_list = [] + for i, smi in enumerate(tqdm(smiles_list, desc=f'Processing {self.stage}')): + mol = Chem.MolFromSmiles(smi, sanitize=False) + if mol is None: + continue + mol = Chem.RemoveHs(mol) + try: + Chem.Kekulize(mol, clearAromaticFlags=True) + except Exception: + continue + + N = mol.GetNumAtoms() + if N == 0: + continue + + type_idx = [types[a.GetSymbol()] for a in mol.GetAtoms()] + + row, col, edge_type = [], [], [] + for bond in mol.GetBonds(): + s, e = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx() + bt = bonds.get(bond.GetBondType()) + if bt is None: + continue + row += [s, e] + col += [e, s] + edge_type += 2 * [bt + 1] + + if len(row) == 0: + continue + + edge_index = torch.tensor([row, col], dtype=torch.long) + edge_type_t = torch.tensor(edge_type, dtype=torch.long) + edge_attr = F.one_hot(edge_type_t, num_classes=len(bonds) + 1).float() + + perm = (edge_index[0] * N + edge_index[1]).argsort() + edge_index = edge_index[:, perm] + edge_attr = edge_attr[perm] + + x = F.one_hot(torch.tensor(type_idx), num_classes=len(types)).float() + y = torch.zeros((1, 0), dtype=torch.float) + + data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i) + + if self.pre_filter is not None and not self.pre_filter(data): + continue + if self.pre_transform is not None: + data = self.pre_transform(data) + + data_list.append(data) + + torch.save(self.collate(data_list), self.processed_paths[self.file_idx]) + print(f'{self.stage}: saved {len(data_list)} graphs') + + +class ProtacLinkerDataModule(MolecularDataModule): + def __init__(self, cfg): + self.remove_h = True + self.datadir = cfg.dataset.datadir + self.train_smiles = [] + + base_path = pathlib.Path(os.path.realpath(__file__)).parents[2] + root_path = osp.join(base_path, self.datadir) + + # CSV path: configurable, defaults to protacs_data/ next to DeFoG root + csv_path = getattr(cfg.dataset, 'csv_path', None) + if csv_path is None: + csv_path = osp.join(base_path, 'protacs_data', 'linker.csv') + + datasets = { + split: ProtacLinkerDataset(stage=split, root=root_path, csv_path=csv_path) + for split in ['train', 'val', 'test'] + } + super().__init__(cfg, datasets) + + +class ProtacLinkerInfos(AbstractDatasetInfos): + def __init__(self, datamodule, cfg, recompute_statistics=False, meta=None): + self.name = 'protac_linker' + self.input_dims = None + self.output_dims = None + self.remove_h = True + self.compute_fcd = cfg.dataset.compute_fcd + + self.atom_decoder = atom_decoder + self.atom_encoder = {a: i for i, a in enumerate(atom_decoder)} + self.atom_weights = {0: 12, 1: 14, 2: 16, 3: 19, 4: 30, + 5: 32, 6: 35.5, 7: 79.9, 8: 127} + self.valencies = [4, 3, 2, 1, 5, 6, 1, 1, 1] + self.num_atom_types = len(atom_decoder) + self.max_weight = 500 + + # Save stats next to the data so they persist across runs + _stats_dir = osp.join( + pathlib.Path(os.path.realpath(__file__)).parents[2], + 'protacs_data' + ) + meta_files = dict( + n_nodes=osp.join(_stats_dir, 'protac_n_counts.txt'), + node_types=osp.join(_stats_dir, 'protac_atom_types.txt'), + edge_types=osp.join(_stats_dir, 'protac_edge_types.txt'), + valency_distribution=osp.join(_stats_dir, 'protac_valencies.txt'), + ) + + self.n_nodes = None + self.node_types = None + self.edge_types = None + self.valency_distribution = None + + if meta is None: + meta = {k: None for k in meta_files} + assert set(meta.keys()) == set(meta_files.keys()) + + for k, fname in meta_files.items(): + if meta[k] is None and os.path.exists(fname): + val = np.loadtxt(fname) + setattr(self, k, torch.tensor(val, dtype=torch.float)) + + if recompute_statistics or self.n_nodes is None: + self.n_nodes = datamodule.node_counts() + print('n_nodes:', self.n_nodes) + np.savetxt(meta_files['n_nodes'], self.n_nodes.numpy()) + + if recompute_statistics or self.node_types is None: + self.node_types = datamodule.node_types() + print('node_types:', self.node_types) + np.savetxt(meta_files['node_types'], self.node_types.numpy()) + + if recompute_statistics or self.edge_types is None: + self.edge_types = datamodule.edge_counts() + print('edge_types:', self.edge_types) + np.savetxt(meta_files['edge_types'], self.edge_types.numpy()) + + if recompute_statistics or self.valency_distribution is None: + self.max_n_nodes = len(self.n_nodes) - 1 + valencies = datamodule.valency_count(self.max_n_nodes, zinc=True) + print('valency_distribution:', valencies) + np.savetxt(meta_files['valency_distribution'], valencies.numpy()) + self.valency_distribution = valencies + + self.max_n_nodes = len(self.n_nodes) - 1 if self.n_nodes is not None else 36 + self.complete_infos(n_nodes=self.n_nodes, node_types=self.node_types) \ No newline at end of file diff --git a/CupOFMoCA-PROTAC/src/defog/src/datasets/qm9_dataset.py b/CupOFMoCA-PROTAC/src/defog/src/datasets/qm9_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..94e7e864ae7272b80809eff3ac39ed374dbbbc64 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/datasets/qm9_dataset.py @@ -0,0 +1,563 @@ +import os +import os.path as osp +import pathlib +from typing import Any, Sequence + +import torch +import torch.nn.functional as F +from rdkit import Chem, RDLogger +from rdkit.Chem.rdchem import BondType as BT +from tqdm import tqdm +import numpy as np +import pandas as pd +from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip +from torch_geometric.utils import subgraph + +import utils as utils +from datasets.abstract_dataset import MolecularDataModule, AbstractDatasetInfos +from analysis.rdkit_functions import mol2smiles, build_molecule_with_partial_charges +from analysis.rdkit_functions import compute_molecular_metrics + + +def files_exist(files) -> bool: + # NOTE: We return `False` in case `files` is empty, leading to a + # re-processing of files on every instantiation. + return len(files) != 0 and all([osp.exists(f) for f in files]) + + +def to_list(value: Any) -> Sequence: + if isinstance(value, Sequence) and not isinstance(value, str): + return value + else: + return [value] + + +class RemoveYTransform: + def __call__(self, data, return_y=False): + if return_y: + return torch.zeros((1, 0), dtype=torch.float) + data.y = torch.zeros((1, 0), dtype=torch.float) + return data + + +class SelectMuTransform: + def __call__(self, data, return_y=False): + if return_y: + return data.y[..., 3].unsqueeze(1) + data.y = data.y[..., 3].unsqueeze(1) + return data + + +class SelectHOMOTransform: + def __call__(self, data, return_y=False): + if return_y: + return data.y[..., 5].unsqueeze(1) + data.y = data.y[..., 5].unsqueeze(1) + return data + + +class SelectBothTransform: + def __call__(self, data, return_y=False): + if return_y: + return torch.hstack([data.y[..., 3], data.y[..., 5]]).unsqueeze(0) + data.y = torch.hstack([data.y[..., 3], data.y[..., 5]]).unsqueeze(0) + return data + + +class QM9Dataset(InMemoryDataset): + raw_url = ( + "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/" + "molnet_publish/qm9.zip" + ) + raw_url2 = "https://ndownloader.figshare.com/files/3195404" + processed_url = "https://data.pyg.org/datasets/qm9_v3.zip" + + def __init__( + self, + stage, + root, + remove_h: bool, + aromatic: bool, + target_prop=None, + transform=None, + pre_transform=None, + pre_filter=None, + ): + self.target_prop = target_prop + self.stage = stage + self.aromatic = aromatic + if self.stage == "train": + self.file_idx = 0 + elif self.stage == "val": + self.file_idx = 1 + else: + self.file_idx = 2 + self.remove_h = remove_h + super().__init__(root, transform, pre_transform, pre_filter) + self.data, self.slices = torch.load(self.processed_paths[self.file_idx]) + + @property + def raw_file_names(self): + return ["gdb9.sdf", "gdb9.sdf.csv", "uncharacterized.txt"] + + @property + def split_file_name(self): + return ["train.csv", "val.csv", "test.csv"] + + @property + def split_paths(self): + r"""The absolute filepaths that must be present in order to skip + splitting.""" + files = to_list(self.split_file_name) + return [osp.join(self.raw_dir, f) for f in files] + + @property + def processed_file_names(self): + if self.remove_h: + return ["proc_tr_no_h.pt", "proc_val_no_h.pt", "proc_test_no_h.pt"] + else: + return ["proc_tr_h.pt", "proc_val_h.pt", "proc_test_h.pt"] + + def download(self): + """ + Download raw qm9 files. Taken from PyG QM9 class + """ + try: + import rdkit # noqa + + file_path = download_url(self.raw_url, self.raw_dir) + extract_zip(file_path, self.raw_dir) + os.unlink(file_path) + + file_path = download_url(self.raw_url2, self.raw_dir) + os.rename( + osp.join(self.raw_dir, "3195404"), + osp.join(self.raw_dir, "uncharacterized.txt"), + ) + except ImportError: + path = download_url(self.processed_url, self.raw_dir) + extract_zip(path, self.raw_dir) + os.unlink(path) + + if files_exist(self.split_paths): + return + + dataset = pd.read_csv(self.raw_paths[1]) + + n_samples = len(dataset) + n_train = 100000 + n_test = int(0.1 * n_samples) + n_val = n_samples - (n_train + n_test) + + # Shuffle dataset with df.sample, then split + train, val, test = np.split( + dataset.sample(frac=1, random_state=42), [n_train, n_val + n_train] + ) + + train.to_csv(os.path.join(self.raw_dir, "train.csv")) + val.to_csv(os.path.join(self.raw_dir, "val.csv")) + test.to_csv(os.path.join(self.raw_dir, "test.csv")) + + def process(self): + RDLogger.DisableLog("rdApp.*") + + types = {"H": 0, "C": 1, "N": 2, "O": 3, "F": 4} + if self.aromatic: + bonds = {BT.SINGLE: 0, BT.DOUBLE: 1, BT.TRIPLE: 2, BT.AROMATIC: 3} + else: + bonds = {BT.SINGLE: 0, BT.DOUBLE: 1, BT.TRIPLE: 2} # debug + + target_df = pd.read_csv(self.split_paths[self.file_idx], index_col=0) + target_df.drop(columns=["mol_id"], inplace=True) + + with open(self.raw_paths[-1], "r") as f: + skip = [int(x.split()[0]) - 1 for x in f.read().split("\n")[9:-2]] + + suppl = Chem.SDMolSupplier(self.raw_paths[0], removeHs=False, sanitize=False) + + data_list = [] + for i, mol in enumerate(tqdm(suppl)): + if i in skip or i not in target_df.index: + continue + + N = mol.GetNumAtoms() + + type_idx = [] + for atom in mol.GetAtoms(): + type_idx.append(types[atom.GetSymbol()]) + + row, col, edge_type = [], [], [] + for bond in mol.GetBonds(): + start, end = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx() + row += [start, end] + col += [end, start] + edge_type += 2 * [bonds[bond.GetBondType()] + 1] + + edge_index = torch.tensor([row, col], dtype=torch.long) + edge_type = torch.tensor(edge_type, dtype=torch.long) + edge_attr = F.one_hot(edge_type, num_classes=len(bonds) + 1).to(torch.float) + + perm = (edge_index[0] * N + edge_index[1]).argsort() + edge_index = edge_index[:, perm] + edge_attr = edge_attr[perm] + + x = F.one_hot(torch.tensor(type_idx), num_classes=len(types)).float() + y = torch.tensor([target_df.loc[i]]) + # y = torch.zeros((1, 0), dtype=torch.float) + # y = mol.GetProp(self.target_prop) + + if self.remove_h: + type_idx = torch.tensor(type_idx).long() + to_keep = type_idx > 0 + edge_index, edge_attr = subgraph( + to_keep, + edge_index, + edge_attr, + relabel_nodes=True, + num_nodes=len(to_keep), + ) + x = x[to_keep] + # Shift onehot encoding to match atom decoder + x = x[:, 1:] + + data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i) + + if self.pre_filter is not None and not self.pre_filter(data): + continue + if self.pre_transform is not None: + data = self.pre_transform(data) + + data_list.append(data) + + torch.save(self.collate(data_list), self.processed_paths[self.file_idx]) + + +class QM9DataModule(MolecularDataModule): + def __init__(self, cfg): + self.datadir = cfg.dataset.datadir + self.remove_h = cfg.dataset.remove_h + self.aromatic = cfg.dataset.aromatic + + target = getattr(cfg.general, "target", None) + regressor = getattr(cfg.general, "conditional", None) + if regressor and target == "mu": + transform = SelectMuTransform() + elif regressor and target == "homo": + transform = SelectHOMOTransform() + elif regressor and target == "both": + transform = SelectBothTransform() + else: + transform = RemoveYTransform() + + base_path = pathlib.Path(os.path.realpath(__file__)).parents[2] + root_path = os.path.join(base_path, self.datadir) + datasets = { + "train": QM9Dataset( + stage="train", + root=root_path, + remove_h=cfg.dataset.remove_h, + aromatic=cfg.dataset.aromatic, + target_prop=target, + transform=transform, + ), + "val": QM9Dataset( + stage="val", + root=root_path, + remove_h=cfg.dataset.remove_h, + aromatic=cfg.dataset.aromatic, + target_prop=target, + transform=transform, + ), + "test": QM9Dataset( + stage="test", + root=root_path, + remove_h=cfg.dataset.remove_h, + aromatic=cfg.dataset.aromatic, + target_prop=target, + transform=transform, + ), + } + self.test_labels = transform(datasets["test"].data, return_y=True) + + train_len = len(datasets["train"].data.idx) + val_len = len(datasets["val"].data.idx) + test_len = len(datasets["test"].data.idx) + print(f"Dataset sizes: train {train_len}, val {val_len}, test {test_len}") + super().__init__(cfg, datasets) + + +class QM9infos(AbstractDatasetInfos): + def __init__(self, datamodule, cfg, recompute_statistics=False): + self.remove_h = cfg.dataset.remove_h + self.aromatic = cfg.dataset.aromatic + self.need_to_strip = ( + False # to indicate whether we need to ignore one output from the model + ) + self.compute_fcd = cfg.dataset.compute_fcd + + # if cfg.general.conditional: + # self.test_labels = datasets["test"].data.y + + self.name = "qm9" + if self.remove_h: + self.atom_encoder = {"C": 0, "N": 1, "O": 2, "F": 3} + self.atom_decoder = ["C", "N", "O", "F"] + self.num_atom_types = 4 + self.valencies = [4, 3, 2, 1] + self.atom_weights = {0: 12, 1: 14, 2: 16, 3: 19} + self.max_n_nodes = 9 + self.max_weight = 150 + self.n_nodes = torch.tensor( + [ + 0, + 2.2930e-05, + 3.8217e-05, + 6.8791e-05, + 2.3695e-04, + 9.7072e-04, + 0.0046472, + 0.023985, + 0.13666, + 0.83337, + ] + ) + self.node_types = torch.tensor([0.7230, 0.1151, 0.1593, 0.0026]) + if self.aromatic: + self.edge_types = torch.tensor([0.7261, 0.2384, 0.0274, 0.0081, 0.0]) + else: + self.edge_types = torch.tensor( + [0.7261, 0.2384, 0.0274, 0.0081] + ) # debug + + super().complete_infos(n_nodes=self.n_nodes, node_types=self.node_types) + self.valency_distribution = torch.zeros(3 * self.max_n_nodes - 2) + self.valency_distribution[0:6] = torch.tensor( + [2.6071e-06, 0.163, 0.352, 0.320, 0.16313, 0.00073] + ) + else: + self.atom_encoder = {"H": 0, "C": 1, "N": 2, "O": 3, "F": 4} + self.atom_decoder = ["H", "C", "N", "O", "F"] + self.valencies = [1, 4, 3, 2, 1] + self.num_atom_types = 5 + self.max_n_nodes = 29 + self.max_weight = 390 + self.atom_weights = {0: 1, 1: 12, 2: 14, 3: 16, 4: 19} + self.n_nodes = torch.tensor( + [ + 0, + 0, + 0, + 1.5287e-05, + 3.0574e-05, + 3.8217e-05, + 9.1721e-05, + 1.5287e-04, + 4.9682e-04, + 1.3147e-03, + 3.6918e-03, + 8.0486e-03, + 1.6732e-02, + 3.0780e-02, + 5.1654e-02, + 7.8085e-02, + 1.0566e-01, + 1.2970e-01, + 1.3332e-01, + 1.3870e-01, + 9.4802e-02, + 1.0063e-01, + 3.3845e-02, + 4.8628e-02, + 5.4421e-03, + 1.4698e-02, + 4.5096e-04, + 2.7211e-03, + 0.0000e00, + 2.6752e-04, + ] + ) + + self.node_types = torch.tensor([0.5122, 0.3526, 0.0562, 0.0777, 0.0013]) + self.edge_types = torch.tensor( + [0.88162, 0.11062, 5.9875e-03, 1.7758e-03, 0] + ) + + if self.aromatic: + self.edge_types = torch.tensor( + [0.88162, 0.11062, 5.9875e-03, 1.7758e-03, 0] + ) + else: + self.edge_types = torch.tensor( + [0.88162, 0.11062, 5.9875e-03, 1.7758e-03] + ) + # self.edge_types = torch.tensor([0.88162, 0.11062, 5.9875e-03, 1.7758e-03]) # debug + + super().complete_infos(n_nodes=self.n_nodes, node_types=self.node_types) + self.valency_distribution = torch.zeros(3 * self.max_n_nodes - 2) + self.valency_distribution[0:6] = torch.tensor( + [0, 0.5136, 0.0840, 0.0554, 0.3456, 0.0012] + ) + + if recompute_statistics: + np.set_printoptions(suppress=True, precision=5) + self.n_nodes = datamodule.node_counts() + print("Distribution of number of nodes", self.n_nodes) + np.savetxt("n_counts.txt", self.n_nodes.numpy()) + self.node_types = datamodule.node_types() # There are no node types + print("Distribution of node types", self.node_types) + np.savetxt("atom_types.txt", self.node_types.numpy()) + + self.edge_types = datamodule.edge_counts() + print("Distribution of edge types", self.edge_types) + np.savetxt("edge_types.txt", self.edge_types.numpy()) + + valencies = datamodule.valency_count(self.max_n_nodes) + print("Distribution of the valencies", valencies) + np.savetxt("valencies.txt", valencies.numpy()) + self.valency_distribution = valencies + assert False + + +def get_smiles(cfg, datamodule, dataset_infos, evaluate_datasets=False): + + return { + "train": get_loader_smiles( + cfg, + datamodule.train_dataloader(), + dataset_infos, + "train", + evaluate_dataset=evaluate_datasets, + ), + "val": get_loader_smiles( + cfg, + datamodule.val_dataloader(), + dataset_infos, + "val", + evaluate_dataset=evaluate_datasets, + ), + "test": get_loader_smiles( + cfg, + datamodule.test_dataloader(), + dataset_infos, + "test", + evaluate_dataset=evaluate_datasets, + ), + } + + +def get_loader_smiles( + cfg, + dataloader, + dataset_infos, + split_key, + evaluate_dataset=False, +): + datadir = cfg.dataset.datadir + remove_h = cfg.dataset.remove_h + atom_decoder = dataset_infos.atom_decoder + root_dir = pathlib.Path(os.path.realpath(__file__)).parents[2] + smiles_file_name = ( + f"{split_key}_smiles_no_h.npy" if remove_h else f"{split_key}_smiles_h.npy" + ) + smiles_path = os.path.join(root_dir, datadir, smiles_file_name) + if os.path.exists(smiles_path): + print(f"Dataset {split_key} smiles were found.") + smiles = np.load(smiles_path).tolist() + else: + print(f"Computing dataset {split_key} smiles...") + smiles = compute_qm9_smiles(atom_decoder, dataloader, remove_h) + np.save(smiles_path, np.array(smiles)) + + if evaluate_dataset: + # Convert loader to molecules + assert ( + dataset_infos is not None + ), "If wanting to evaluate dataset, need to pass dataset_infos" + all_molecules = [] + for i, data in enumerate(dataloader): + dense_data, node_mask = utils.to_dense( + data.x, data.edge_index, data.edge_attr, data.batch + ) + dense_data = dense_data.mask(node_mask, collapse=True) + X, E = dense_data.X, dense_data.E + + for k in range(X.size(0)): + n = int(torch.sum((X != -1)[k, :])) + atom_types = X[k, :n].cpu() + edge_types = E[k, :n, :n].cpu() + all_molecules.append([atom_types, edge_types]) + + print( + "Evaluating the dataset -- number of molecules to evaluate", + len(all_molecules), + ) + # load train smiles + train_smiles_file_name = ( + f"train_smiles_no_h.npy" if remove_h else f"train_smiles_h.npy" + ) + train_smiles_path = os.path.join(root_dir, datadir, train_smiles_file_name) + train_smiles = np.load(train_smiles_path) + # get evaluation and output + metrics = compute_molecular_metrics( + molecule_list=all_molecules, + train_smiles=train_smiles, + dataset_info=dataset_infos, + ) + print(metrics[0]) + + return smiles + + +def compute_qm9_smiles(atom_decoder, train_dataloader, remove_h): + """ + :param dataset_name: qm9 or qm9_second_half + :return: + """ + print(f"\tConverting QM9 dataset to SMILES for remove_h={remove_h}...") + + mols_smiles = [] + len_train = len(train_dataloader) + invalid = 0 + disconnected = 0 + for i, data in enumerate(train_dataloader): + dense_data, node_mask = utils.to_dense( + data.x, data.edge_index, data.edge_attr, data.batch + ) + dense_data = dense_data.mask(node_mask, collapse=True) + X, E = dense_data.X, dense_data.E + + n_nodes = [int(torch.sum((X != -1)[j, :])) for j in range(X.size(0))] + + molecule_list = [] + for k in range(X.size(0)): + n = n_nodes[k] + atom_types = X[k, :n].cpu() + edge_types = E[k, :n, :n].cpu() + molecule_list.append([atom_types, edge_types]) + + for l, molecule in enumerate(molecule_list): + mol = build_molecule_with_partial_charges( + molecule[0], molecule[1], atom_decoder + ) + smile = mol2smiles(mol) + if smile is not None: + mols_smiles.append(smile) + mol_frags = Chem.rdmolops.GetMolFrags( + mol, asMols=True, sanitizeFrags=True + ) + if len(mol_frags) > 1: + print("Disconnected molecule", mol, mol_frags) + disconnected += 1 + else: + print("Invalid molecule obtained.") + invalid += 1 + + if i % 1000 == 0: + print( + "\tConverting QM9 dataset to SMILES {0:.2%}".format( + float(i) / len_train + ) + ) + print("Number of invalid molecules", invalid) + print("Number of disconnected molecules", disconnected) + return mols_smiles diff --git a/CupOFMoCA-PROTAC/src/defog/src/datasets/spectre_dataset.py b/CupOFMoCA-PROTAC/src/defog/src/datasets/spectre_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..5e32aea144a47a574a0497344ab207818089b845 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/datasets/spectre_dataset.py @@ -0,0 +1,254 @@ +import os +import pathlib +import pickle as pkl +import zipfile + +from networkx import to_numpy_array + +import torch +from torch.utils.data import random_split +import torch_geometric.utils +from torch_geometric.data import InMemoryDataset, download_url + +from datasets.abstract_dataset import AbstractDataModule, AbstractDatasetInfos + + +class SpectreGraphDataset(InMemoryDataset): + def __init__( + self, + dataset_name, + split, + root, + transform=None, + pre_transform=None, + pre_filter=None, + ): + self.sbm_file = "sbm_200.pt" + self.planar_file = "planar_64_200.pt" + self.comm20_file = "community_12_21_100.pt" + self.dataset_name = dataset_name + self.split = split + super().__init__(root, transform, pre_transform, pre_filter) + self.data, self.slices = torch.load(self.processed_paths[0]) + self.num_graphs = len(self.data.n_nodes) + + @property + def raw_file_names(self): + return ["train.pt", "val.pt", "test.pt"] + + @property + def processed_file_names(self): + return [self.split + ".pt"] + + def download(self): + """ + Download raw qm9 files. Taken from PyG QM9 class + """ + if self.dataset_name == "sbm": + raw_url = "https://raw.githubusercontent.com/AndreasBergmeister/graph-generation/main/data/sbm.pkl" + elif self.dataset_name == "planar": + raw_url = "https://raw.githubusercontent.com/AndreasBergmeister/graph-generation/main/data/planar.pkl" + elif self.dataset_name == "tree": + raw_url = "https://raw.githubusercontent.com/AndreasBergmeister/graph-generation/main/data/tree.pkl" + elif self.dataset_name == "comm20": + raw_url = "https://raw.githubusercontent.com/KarolisMart/SPECTRE/main/data/community_12_21_100.pt" + elif self.dataset_name == "ego": + raw_url = "https://raw.githubusercontent.com/tufts-ml/graph-generation-EDGE/main/graphs/Ego.pkl" + elif self.dataset_name == "imdb": + raw_url = "https://www.chrsmrrs.com/graphkerneldatasets/IMDB-BINARY.zip" + else: + raise ValueError(f"Unknown dataset {self.dataset_name}") + file_path = download_url(raw_url, self.raw_dir) + + if self.dataset_name in ["tree", "sbm", "planar"]: + with open(file_path, "rb") as f: + dataset = pkl.load(f) + train_data = dataset["train"] + val_data = dataset["val"] + test_data = dataset["test"] + + train_data = [ + torch.Tensor(to_numpy_array(graph)).fill_diagonal_(0) + for graph in train_data + ] + val_data = [ + torch.Tensor(to_numpy_array(graph)).fill_diagonal_(0) + for graph in val_data + ] + test_data = [ + torch.Tensor(to_numpy_array(graph)).fill_diagonal_(0) + for graph in test_data + ] + else: + if self.dataset_name == "ego": + networks = pkl.load(open(file_path, "rb")) + adjs = [ + torch.Tensor(to_numpy_array(network)).fill_diagonal_(0) + for network in networks + ] + elif self.dataset_name == "imdb": + with zipfile.ZipFile(file_path, "r") as zip_ref: + zip_ref.extractall(os.path.dirname(file_path)) + + # Step 1: Read edge_index from file + index_path = os.path.join( + os.path.dirname(file_path), "IMDB-BINARY", "IMDB-BINARY_A.txt" + ) + edge_index = [] + with open(index_path, "r") as file: + for line in file: + int1, int2 = map(int, line.strip().split(",")) + edge_index.append([int1, int2]) + edge_index = torch.tensor(edge_index).t().contiguous() - 1 + + # Step 2: Read graph_indicator from file + index_path = os.path.join( + os.path.dirname(file_path), + "IMDB-BINARY", + "IMDB-BINARY_graph_indicator.txt", + ) + graph_indicator = [] + with open(index_path, "r") as file: + for line in file: + num = int(line.strip()) + graph_indicator.append(num) + graph_indicator = torch.tensor(graph_indicator) - 1 + + # Step 3: Create individual graphs based on graph_indicator + num_graphs = graph_indicator.max().item() + 1 + adjs = [] + for i in range(num_graphs): + node_mask = graph_indicator == i + n_node = node_mask.sum().item() + edge_mask = node_mask[edge_index[0]] & node_mask[edge_index[1]] + edges = edge_index[:, edge_mask] + ptr = torch.where(node_mask)[0][0] + edges -= ptr + adj = torch.zeros(n_node, n_node) + adj[edges[0], edges[1]] = 1 + adj[edges[1], edges[0]] = 1 + adjs.append(adj) + else: + ( + adjs, + eigvals, + eigvecs, + n_nodes, + max_eigval, + min_eigval, + same_sample, + n_max, + ) = torch.load(file_path) + + g_cpu = torch.Generator().manual_seed(1234) + self.num_graphs = 200 + if self.dataset_name in ["ego", "protein"]: + self.num_graphs = len(adjs) + elif self.dataset_name == "imdb": + self.num_graphs = graph_indicator.max().item() + 1 + + if self.dataset_name == "ego": + test_len = int(round(self.num_graphs * 0.2)) + train_len = int(round(self.num_graphs * 0.8)) + val_len = int(round(self.num_graphs * 0.2)) + indices = torch.randperm(self.num_graphs, generator=g_cpu) + print( + f"Dataset sizes: train {train_len}, val {val_len}, test {test_len}" + ) + train_indices = indices[:train_len] + val_indices = indices[:val_len] + test_indices = indices[train_len:] + else: + test_len = int(round(self.num_graphs * 0.2)) + train_len = int(round((self.num_graphs - test_len) * 0.8)) + val_len = self.num_graphs - train_len - test_len + indices = torch.randperm(self.num_graphs, generator=g_cpu) + print( + f"Dataset sizes: train {train_len}, val {val_len}, test {test_len}" + ) + train_indices = indices[:train_len] + val_indices = indices[train_len : train_len + val_len] + test_indices = indices[train_len + val_len :] + + train_data = [] + val_data = [] + test_data = [] + + for i, adj in enumerate(adjs): + if i in train_indices: + train_data.append(adj) + if i in val_indices: + val_data.append(adj) + if i in test_indices: + test_data.append(adj) + + torch.save(train_data, self.raw_paths[0]) + torch.save(val_data, self.raw_paths[1]) + torch.save(test_data, self.raw_paths[2]) + + def process(self): + file_idx = {"train": 0, "val": 1, "test": 2} + raw_dataset = torch.load(self.raw_paths[file_idx[self.split]]) + + data_list = [] + for adj in raw_dataset: + n = adj.shape[-1] + X = torch.ones(n, 1, dtype=torch.float) + y = torch.zeros([1, 0]).float() + edge_index, _ = torch_geometric.utils.dense_to_sparse(adj) + edge_attr = torch.zeros(edge_index.shape[-1], 2, dtype=torch.float) + edge_attr[:, 1] = 1 + num_nodes = n * torch.ones(1, dtype=torch.long) + data = torch_geometric.data.Data( + x=X, edge_index=edge_index, edge_attr=edge_attr, y=y, n_nodes=num_nodes + ) + + if self.pre_filter is not None and not self.pre_filter(data): + continue + if self.pre_transform is not None: + data = self.pre_transform(data) + + data_list.append(data) + + torch.save(self.collate(data_list), self.processed_paths[0]) + + +class SpectreGraphDataModule(AbstractDataModule): + def __init__(self, cfg, n_graphs=200): + self.cfg = cfg + self.datadir = cfg.dataset.datadir + base_path = pathlib.Path(os.path.realpath(__file__)).parents[2] + root_path = os.path.join(base_path, self.datadir) + + datasets = { + "train": SpectreGraphDataset( + dataset_name=self.cfg.dataset.name, split="train", root=root_path + ), + "val": SpectreGraphDataset( + dataset_name=self.cfg.dataset.name, split="val", root=root_path + ), + "test": SpectreGraphDataset( + dataset_name=self.cfg.dataset.name, split="test", root=root_path + ), + } + + train_len = len(datasets["train"].data.n_nodes) + val_len = len(datasets["val"].data.n_nodes) + test_len = len(datasets["test"].data.n_nodes) + print(f"Dataset sizes: train {train_len}, val {val_len}, test {test_len}") + + super().__init__(cfg, datasets) + self.inner = self.train_dataset + + def __getitem__(self, item): + return self.inner[item] + + +class SpectreDatasetInfos(AbstractDatasetInfos): + def __init__(self, datamodule, dataset_config): + # self.datamodule = datamodule + self.dataset_name = datamodule.inner.dataset_name + self.n_nodes = datamodule.node_counts() + self.node_types = datamodule.node_types() + self.edge_types = datamodule.edge_counts() + super().complete_infos(self.n_nodes, self.node_types) diff --git a/CupOFMoCA-PROTAC/src/defog/src/datasets/tls_dataset.py b/CupOFMoCA-PROTAC/src/defog/src/datasets/tls_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..17f8526836e28d4b5a7fdc3a2cd9a608f93d8b2c --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/datasets/tls_dataset.py @@ -0,0 +1,753 @@ +import copy +import os +import os.path as osp +import pathlib +from typing import Any, Sequence + +import torch +import torch.nn.functional as F +from rdkit import Chem, RDLogger +from rdkit.Chem.rdchem import BondType as BT +from tqdm import tqdm +import numpy as np +import pandas as pd +import torch_geometric +from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip +from torch_geometric.utils import subgraph +import networkx as nx +import matplotlib.pyplot as plt +from matplotlib import rc +from concurrent.futures import ThreadPoolExecutor, TimeoutError + + +from datasets.dataset_utils import load_pickle, save_pickle, to_list +from datasets.abstract_dataset import AbstractDataModule, AbstractDatasetInfos + +import multiprocessing +import time +import threading + + +class TLSGraphDataset(InMemoryDataset): + def __init__( + self, + stage, + root, + target_prop=None, + transform=None, + pre_transform=None, + pre_filter=None, + ): + + self.stage = stage + # self.file_idx is used by the init of super class + if self.stage == "train": + self.file_idx = 0 + elif self.stage == "val": + self.file_idx = 1 + else: + self.file_idx = 2 + + super().__init__(root, transform, pre_transform, pre_filter) + self.data, self.slices = torch.load(self.processed_paths[self.file_idx]) + + @property + def raw_file_names(self): + return ["train.pkl", "val.pkl", "test.pkl"] + + @property + def split_file_name(self): + return ["train.pkl", "val.pkl", "test.pkl"] + + @property + def split_paths(self): + r"""The absolute filepaths that must be present in order to skip splitting.""" + files = to_list(self.split_file_name) + return [osp.join(self.raw_dir, f) for f in files] + + @property + def processed_file_names(self): + return ["train.pt", "val.pt", "test.pt"] + + def download(self): + low_tls_urls = { + "train": "https://github.com/manuelmlmadeira/ConStruct/raw/main/data/low_tls_200/raw/train.pkl", + "val": "https://github.com/manuelmlmadeira/ConStruct/raw/main/data/low_tls_200/raw/val.pkl", + "test": "https://github.com/manuelmlmadeira/ConStruct/raw/main/data/low_tls_200/raw/test.pkl", + } + high_tls_urls = { + "train": "https://github.com/manuelmlmadeira/ConStruct/raw/main/data/high_tls_200/raw/train.pkl", + "val": "https://github.com/manuelmlmadeira/ConStruct/raw/main/data/high_tls_200/raw/val.pkl", + "test": "https://github.com/manuelmlmadeira/ConStruct/raw/main/data/high_tls_200/raw/test.pkl", + } + + for i, key in enumerate(["train", "val", "test"]): + low_tls_file_path = download_url( + low_tls_urls[key], osp.join(self.raw_dir, f"low_tls") + ) + low_tls_data = load_pickle(low_tls_file_path) + high_tls_file_path = download_url( + high_tls_urls[key], osp.join(self.raw_dir, f"high_tls") + ) + high_tls_data = load_pickle(high_tls_file_path) + # Merge datasets + all_data = low_tls_data + high_tls_data + save_pickle(all_data, self.raw_paths[i]) + print(f"Created {key} dataset (len: {len(all_data)})") + + def process(self): + raw_dataset = load_pickle(os.path.join(self.raw_dir, f"{self.stage}.pkl")) + + data_list = [] + for idx, graph in enumerate(raw_dataset): + cell_graph = CellGraph(graph) + + # Check that all cell graphs are either low or high TLS (no ambiguous cell graphs) + if not (cell_graph.has_low_TLS() or cell_graph.has_high_TLS()): + raise ValueError( + f"Cell graph {idx} is ambiguous in TLS content: does not have low (k1 < 0.05) or high TLS (k2 > 0.05)" + ) + + data = cell_graph.to_torch_geometric() + if self.pre_filter is not None and not self.pre_filter(data): + continue + if self.pre_transform is not None: + data = self.pre_transform(data) + + data.x = F.one_hot(data.x, num_classes=9).float() + data.edge_attr = F.one_hot(data.edge_attr, num_classes=2).float() + data.y = torch.tensor( + [cell_graph.tls_features[f"k_{a}"] for a in range(6)] + ).unsqueeze(0) + data.idx = idx + data_list.append(data) + + torch.save(self.collate(data_list), self.processed_paths[self.file_idx]) + + +class SelectK2Transform: + def __call__(self, data): + data.y = (data.y[..., 2] > 0.05).float().unsqueeze(0) + return data + + +class TLSDataModule(AbstractDataModule): + def __init__(self, cfg): + self.cfg = cfg + self.datadir = cfg.dataset.datadir + + # For conditional generation + is_conditional = cfg.general.conditional + target = cfg.general.target + if is_conditional: + if target == "k2": + transform = SelectK2Transform() + else: + raise ValueError(f"Unknown target {target}") + + base_path = pathlib.Path(os.path.realpath(__file__)).parents[2] + root_path = os.path.join(base_path, self.datadir) + datasets = { + "train": TLSGraphDataset( + transform=transform, + stage="train", + root=root_path, + ), + "val": TLSGraphDataset( + transform=transform, + stage="val", + root=root_path, + ), + "test": TLSGraphDataset( + transform=transform, + stage="test", + root=root_path, + ), + } + + train_len = len(datasets["train"].data.idx) + val_len = len(datasets["val"].data.idx) + test_len = len(datasets["test"].data.idx) + print(f"Dataset sizes: train {train_len}, val {val_len}, test {test_len}") + super().__init__(cfg, datasets) + + +class TLSInfos(AbstractDatasetInfos): + def __init__(self, datamodule): + # self.datamodule = datamodule + self.n_nodes = datamodule.node_counts() + self.node_types = datamodule.node_types() + self.edge_types = datamodule.edge_counts() + super().complete_infos(self.n_nodes, self.node_types) + + +PHENOTYPE_DECODER = [ + "B", + "T", + "Epithelial", + "Fibroblast", + "Myofibroblast", + "CD38+ Lymphocyte", + "Macrophages/Granulocytes", + "Marker", + "Endothelial", +] + +PHENOTYPE_ENCODER = {v: k for k, v in enumerate(PHENOTYPE_DECODER)} + + +class CellGraph(nx.Graph): + def __init__(self, graph): + # nx specific + super().__init__() + self.add_nodes_from(graph.nodes(data=True)) + self.add_edges_from(graph.edges(data=True)) + + self.tls_features = self.compute_tls_features() + + def has_low_TLS(self): + return self.tls_features["k_1"] < 0.05 + + def has_high_TLS(self): + return 0.05 < self.tls_features["k_2"] + + def to_label(self): + if self.has_low_TLS(): + return 0 + elif self.has_high_TLS(): + return 1 + else: + return -1 + + def to_torch_geometric(self): + n = self.number_of_nodes() + n_nodes = n * torch.ones(1, dtype=torch.long) + phenotypes = [self.nodes[node].get("phenotype") for node in self.nodes()] + encoded_phenotypes = [PHENOTYPE_ENCODER[phenotype] for phenotype in phenotypes] + X = torch.tensor(encoded_phenotypes, dtype=torch.long) + torch_adj = torch.Tensor( + nx.to_numpy_array(self) + ) # by default follows same order as G.nodes() + edge_index, _ = torch_geometric.utils.dense_to_sparse(torch_adj) + edge_attr = torch.ones(edge_index.shape[-1], dtype=torch.long) # no edge types + + data = torch_geometric.data.Data( + x=X, + edge_index=edge_index, + edge_attr=edge_attr, + n_nodes=n_nodes, + ) + return data + + @classmethod + def from_torch_geometric(cls, data): + nx_graph = torch_geometric.utils.to_networkx( + data, node_attrs=["x"], to_undirected=True + ) + + # collapse the one-hot encoding of the phenotypes + for node in nx_graph.nodes(): + ohe_encoded_phenotype = nx_graph.nodes[node].pop( + "x" + ) # also deletes the key "x" + enc_pheno = torch.argmax(torch.tensor(ohe_encoded_phenotype)).item() + nx_graph.nodes[node]["phenotype"] = PHENOTYPE_DECODER[enc_pheno] + + cell_graph = cls(nx_graph) + return cell_graph + + @classmethod + def from_dense_graph(cls, dense_graph): + + node_features = ( + dense_graph[0].astype(int) + if isinstance(dense_graph[0], np.ndarray) + else dense_graph[0].cpu().numpy().astype(int) + ) + adj = ( + dense_graph[1].astype(int) + if isinstance(dense_graph[1], np.ndarray) + else dense_graph[1].cpu().numpy().astype(int) + ) + n_nodes = np.sum(node_features != -1) + node_features = node_features[:n_nodes] + adj = adj[:n_nodes, :n_nodes] + + nx_graph = nx.from_numpy_array(adj) + # Delete weight on edges + for _, _, data in nx_graph.edges(data=True): + del data["weight"] + + for node_idx in nx_graph.nodes(): + encoded_phenotype = node_features[node_idx] + phenotype = PHENOTYPE_DECODER[encoded_phenotype] + nx_graph.nodes[node_idx]["phenotype"] = phenotype + + cell_graph = cls(nx_graph) + return cell_graph + + # def to_placeholder(self, dataset_infos): + # tg_graph = self.to_torch_geometric() + # placeholder = to_dense(tg_graph, dataset_infos) + # placeholder = placeholder.collapse(collapse_charges=None) + # print("is placeholder implemented in flow matching??") + # breakpoint() + # return placeholder + + # def is_isomorphic(self, other_cg): + # return nx.is_isomorphic( + # self, + # other_cg, + # node_match=lambda x, y: x["phenotype"] == y["phenotype"], + # ) + + def is_isomorphic(self, other_cg): + import networkx.algorithms.isomorphism as iso + + # Create a copy of the graphs with numerical phenotypes + self_numerical = CellGraph(self) # Create a copy of the graph + other_numerical = CellGraph(other_cg) # Create a copy of the other graph + + # Manually relabel the "phenotype" node features + for node, data in self_numerical.nodes(data=True): + if data["phenotype"] in PHENOTYPE_ENCODER: + self_numerical.nodes[node]["phenotype"] = PHENOTYPE_ENCODER[ + data["phenotype"] + ] + + for node, data in other_numerical.nodes(data=True): + if data["phenotype"] in PHENOTYPE_ENCODER: + other_numerical.nodes[node]["phenotype"] = PHENOTYPE_ENCODER[ + data["phenotype"] + ] + node_matching = iso.categorical_node_match("phenotype", None) + + return iso.is_isomorphic( + self_numerical, other_numerical, node_match=node_matching + ) + + # def is_isomorphic(self, other_cg, timeout=1): + # def check_isomorphism(): + # print("start check_isomorphism") + # time.sleep(10) # Simulate a long-running task + # return nx.is_isomorphic( + # self, + # other_cg, + # node_match=lambda x, y: x["phenotype"] == y["phenotype"], + # ) + + # def check_isomorphism_numerical(): + # import networkx.algorithms.isomorphism as iso + + # # Create a copy of the graphs with numerical phenotypes + # self_numerical = CellGraph(self) # Create a copy of the graph + # other_numerical = CellGraph(other_cg) # Create a copy of the other graph + + # # Manually relabel the "phenotype" node features + # for node, data in self_numerical.nodes(data=True): + # if data["phenotype"] in PHENOTYPE_ENCODER: + # self_numerical.nodes[node]["phenotype"] = PHENOTYPE_ENCODER[data["phenotype"]] + + # for node, data in other_numerical.nodes(data=True): + # if data["phenotype"] in PHENOTYPE_ENCODER: + # other_numerical.nodes[node]["phenotype"] = PHENOTYPE_ENCODER[data["phenotype"]] + # time.sleep(10) # Simulate a long-running task + # node_matching = iso.categorical_node_match("phenotype", None) + # return iso.is_isomorphic(self_numerical, other_numerical, node_match=node_matching) + + # def run_with_timeout(func, timeout): + # result = [None] # Use a list to allow access in the inner function + # stop_event = threading.Event() # Create an event for signaling thread termination + + # def target(): + # # Check periodically if the stop_event has been set + # while not stop_event.is_set(): + # try: + # result[0] = func() # Set the result in the list + # break # Exit the loop if function completes + # except Exception as e: + # # You can handle any exceptions or set stop_event here if needed + # result[0] = None + # break + + # thread = threading.Thread(target=target) + # thread.start() + # thread.join(timeout) + + # if thread.is_alive(): + # print("Timeout exceeded, signaling the thread to stop.") + # stop_event.set() # Signal the thread to stop + + # # Optionally, wait a bit for it to clean up + # thread.join(5) # Allow some grace time for the thread to finish after signaling + + # if thread.is_alive(): + # print("Thread did not stop after signaling.") + # return None + + # return result[0] + + # # First try the original isomorphism check + # result = run_with_timeout(check_isomorphism, timeout) + # if result is not None: + # return result + + # # If timeout happens, try numerical version + # print(f"Timeout after {timeout} seconds in original isomorphism test. Trying numerical version...") + # result_numerical = run_with_timeout(check_isomorphism_numerical, timeout) + # if result_numerical is not None: + # return result_numerical + + # print(f"Timeout again in numerical isomorphism test. Returning True as fallback.") + # return True + + # with ThreadPoolExecutor() as executor: + # # First try the original isomorphism check + # future = executor.submit(check_isomorphism) + # try: + # result = future.result(timeout=timeout) + # return result # Return result if successful + # except TimeoutError: + # print( + # f"Timeout after {timeout} seconds in original isomorphism test. Trying numerical version..." + # ) + + # # Now try the numerical isomorphism check + # future_numerical = executor.submit(check_isomorphism_numerical) + # try: + # result_numerical = future_numerical.result(timeout=timeout) + # return result_numerical # Return result if successful + # except TimeoutError: + # print( + # f"Timeout again after {timeout} seconds in numerical isomorphism test. Returning False as fallback." + # ) + # return True # Return True if both checks fail, get a lower bound on actual performance... + + # def get_phenotypes_list(self): + # return [self.nodes[node]["phenotype"] for node in self.nodes()] + + # def get_cell_phenotype_from_idx(self, idx): + # return self.nodes[idx]["phenotype"] + + @property + def map_phenotype_to_color(self): + return { + "B": "c", + "T": "b", + "Epithelial": "k", + "Fibroblast": "#C4A484", # light brown + "Myofibroblast": "g", + "CD38+ Lymphocyte": "#FEE12B", # yellow (towards gold) + "Macrophages/Granulocytes": "C3", # "r", + "Marker": "0.9", # grey + "Endothelial": "0.75", # grey + } + + def set_pos(self, pos=None): + if self.get_pos(): + raise ValueError("Positions already set") + elif pos is None: + positions = nx.spring_layout(self) + else: + positions = pos + nx.set_node_attributes(self, positions, "pos") + + def get_pos(self): + return nx.get_node_attributes(self, "pos") + + def plot_graph( + self, + has_legend=True, + node_size=50, + save_path=None, + black_border=True, + no_edges=False, + fontsize=12, + verbose=True, + time=None, + ): + node_colors = [ + self.map_phenotype_to_color[phenotype] + for phenotype in nx.get_node_attributes(self, "phenotype").values() + ] + + # Positons + if not self.get_pos(): + self.set_pos() + positions = self.get_pos() + + # Plot graph + plt.figure() + nx.draw_networkx_nodes( + self, + # with_labels=False, + pos=positions, + node_size=node_size, + node_color=node_colors, + # font_weight=font_weight, + edgecolors="k" if black_border else None, + ) + if not no_edges: + nx.draw_networkx_edges( + self, + pos=positions, + edge_color="k", + width=1, + ) + + # Create a legend + if has_legend: + legend_labels = list(self.map_phenotype_to_color.keys()) + legend_handles = [ + plt.Line2D( + [0], + [0], + marker="o", + color="w", + markerfacecolor=color, + markersize=10, + label=label, + ) + for label, color in self.map_phenotype_to_color.items() + ] + plt.legend( + handles=legend_handles, + labels=legend_labels, + loc="upper center", + bbox_to_anchor=(0.5, 1.20), + ncol=len(legend_labels) / 3, + ) + + if time is not None: + plt.text( + 0.5, + 0.05, # place below the graph + f"t = {time:.2f}", + ha="center", + va="center", + transform=plt.gcf().transFigure, + fontsize=16, + ) + + plt.axis("off") # delete black square around + + if save_path is not None: + plt.savefig(save_path, bbox_inches="tight") + if verbose: + print(f"Saved graph plot to {save_path}") + plt.close() + else: + plt.show() + + def plot_graph_with_tls_edges( + self, + save_path=None, + has_legend=True, + fontsize=12, + ): + blur_color = "0.9" + node_colors = { + node_idx: ( + self.map_phenotype_to_color[phenotype] + if phenotype in ["B", "T"] + else "w" + ) + for node_idx, phenotype in nx.get_node_attributes(self, "phenotype").items() + } + + nodes_positions = nx.get_node_attributes(self, "pos") + if not nodes_positions: + nodes_positions = nx.spring_layout( + self, seed=0 + ) # needed for reproducibility + for color in set(node_colors.values()): + nodes_with_color = [ + node_idx + for node_idx, node_color in node_colors.items() + if node_color == color + ] + nx.draw_networkx_nodes( + self, + pos=nodes_positions, + nodelist=nodes_with_color, + node_color=color, + edgecolors=blur_color if color == "w" else "k", + node_size=100, + ) + + edge_types = [] + map_edge_type_to_color = { + "ignore": blur_color, + "alpha": "k", + "gamma_0": "#1f77b4", + "gamma_1": "#ff7f0e", + "gamma_2": "#2ca02c", + "gamma_3": "#d62728", + "gamma_4": "#9467bd", + "gamma_5": "#8c564b", + } + + for edge in self.edges: + edge_types.append(self.classify_TLS_edge(edge)) + edge_colors = [map_edge_type_to_color[edge_type] for edge_type in edge_types] + edge_widths = [2 if edge_type != "ignore" else 0.5 for edge_type in edge_types] + h_edges = nx.draw_networkx_edges( + self, + pos=nodes_positions, + edge_color=edge_colors, + width=edge_widths, + ) + + # # Legend + from matplotlib.lines import Line2D + + def make_line(clr, **kwargs): + return Line2D([], [], color=clr, linewidth=2, **kwargs) + + labels = [] + proxies = [] + for edge_type in [ + "alpha", + "gamma_0", + "gamma_1", + "gamma_2", + "gamma_3", + "gamma_4", + "gamma_5", + ]: + # get edge with that edge type + try: + edge_idx = edge_types.index(edge_type) + edge_color = edge_colors[edge_idx] + labels.append("$\\" + edge_type + "$") + proxies.append(make_line(edge_color)) + except: + continue # that type does not exists in this graph + + plt.rcParams.update( + { + "text.latex.preamble": r"\renewcommand{\seriesdefault}{b}\boldmath", # bold text and math in latex + "text.usetex": True, + "font.family": "Computer Modern Roman", + "font.size": fontsize, + } + ) + + plt.legend( + handles=proxies, + labels=labels, + loc="upper center", + bbox_to_anchor=(0.5, 1.20), + ncol=4, + ) + + plt.axis("off") # delete square around + + plt.savefig(save_path, bbox_inches="tight") + print(f"Saved graph plot to {save_path}") + plt.close() + + def classify_TLS_edge(self, edge): + allowed_cell_types = ["B", "T"] + + start_node, end_node = edge + start_phenotype = self.nodes[start_node]["phenotype"] + end_phenotype = self.nodes[end_node]["phenotype"] + + if ( + start_phenotype not in allowed_cell_types + or end_phenotype not in allowed_cell_types + ): + edge_type = "ignore" + + elif start_phenotype == end_phenotype: + edge_type = "alpha" + else: + b_cell = start_node if start_phenotype == "B" else end_node + num_of_b_neighbors = len( + [ + node + for node in self.neighbors(b_cell) + if self.nodes[node]["phenotype"] == "B" + ] + ) + edge_type = f"gamma_{num_of_b_neighbors}" + + return edge_type + + def compute_tls_features( + self, + a_max: int = 5, + min_num_gamma_edges: int = 0, + verbose: bool = True, + ): + """Compute TLS feature metric from https://arxiv.org/pdf/2310.06661.pdf. + + Args: + graph (nx.Graph): Graph to compute the TLS features. Should have a "phenotype" attribute with some nodes labeled as "B" and "T". + a_max (int, optional): Maximum a of k(a) to consider. Defaults to 5. + min_num_gamma_edges (int, optional): Minimum number of gamma edges to consider the + + Returns: + dict: Dictionary with the TLO features. + """ + + if self.is_directed(): + raise ValueError("Graph should be undirected.") + + graph_phenotypes = nx.get_node_attributes(self, "phenotype") + nodes_to_remove = [ + node + for node, phenotype in graph_phenotypes.items() + if phenotype != "B" and phenotype != "T" + ] + bt_subgraph = copy.deepcopy(self) + bt_subgraph.remove_nodes_from(nodes_to_remove) + total_num_edges = bt_subgraph.number_of_edges() + + # Get alpha and gamma edges count + num_edge_types_idxs = self.get_edges_idxs_by_tlo_type(a_max) + + # Compute TLO features + denominator = total_num_edges - num_edge_types_idxs["alpha"] + # If the number of gamma edges are too few, then the feature estimation + # is unreliable. + if denominator < min_num_gamma_edges: + tlo_dict = {f"k_{a}": None for a in range(a_max + 1)} + if verbose: + print("WARNING: too few gamma edges. TLO feature set to -1.") + else: + tlo_dict = {} + if denominator == 0: + tlo_dict.update({f"k_{a}": 0.0 for a in range(a_max + 1)}) + else: + k = 1.0 + for a in range(a_max + 1): + k -= num_edge_types_idxs[f"gamma_{a}"] / denominator + # Due to precision errors, sometimes the k computed is negative. To avoid + # this, we clip it to 0 in those cases. The rounding is performed to 4 + # decimal cases, as there is no meaning on the remaining decimal cases. + tlo_dict.update({f"k_{a}": max(0.0, round(k, 4))}) + + return tlo_dict + + def get_edges_idxs_by_tlo_type(self, a_max: int): + """Provided a graph, count + + Args: + graph (nx.Graph): _description_ + a_max (int): _description_ + + Returns: + _type_: _description_ + """ + + num_edges_by_type = {f"gamma_{a}": 0 for a in range(a_max + 1)} + num_edges_by_type["alpha"] = 0 + + for edge in self.edges: + edge_type = self.classify_TLS_edge(edge) + if edge_type in num_edges_by_type: + num_edges_by_type[edge_type] += 1 + + return num_edges_by_type diff --git a/CupOFMoCA-PROTAC/src/defog/src/datasets/tu_dataset.py b/CupOFMoCA-PROTAC/src/defog/src/datasets/tu_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..68f66dc231677a41358fe0821a9a4a417a567c44 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/datasets/tu_dataset.py @@ -0,0 +1,276 @@ +import os +import pathlib +import os.path as osp +from typing import Callable, List, Optional + +import numpy as np +import torch.nn.functional as F +import torch +from torch.utils.data import random_split +import torch_geometric.utils +from torch_geometric.io import fs, read_tu_data +from torch_geometric.utils import remove_self_loops +from torch_geometric.data import Data, InMemoryDataset, download_url +from hydra.utils import get_original_cwd + +from utils import PlaceHolder +from datasets.abstract_dataset import ( + AbstractDataModule, + AbstractDatasetInfos, +) +from datasets.dataset_utils import ( + load_pickle, + save_pickle, + Statistics, + to_list, + RemoveYTransform, +) + + +class ProteinDataset(InMemoryDataset): + """ + Implementation based on https://github.com/KarolisMart/SPECTRE/blob/main/data.py + """ + + def __init__( + self, + split, + root, + transform=None, + pre_transform=None, + pre_filter=None, + ): + self.dataset_name = "protein" + root = root + + self.split = split + if self.split == "train": + self.file_idx = 0 + elif self.split == "val": + self.file_idx = 1 + else: + self.file_idx = 2 + + super().__init__(root, transform, pre_transform, pre_filter) + self.data, self.slices = torch.load(self.processed_paths[0]) + + @property + def raw_file_names(self): + return ["train_indices.pt", "val_indices.pt", "test_indices.pt"] + + @property + def split_file_name(self): + return ["train.pt", "val.pt", "test.pt"] + + @property + def split_paths(self): + r"""The absolute filepaths that must be present in order to skip + splitting.""" + files = to_list(self.split_file_name) + return [osp.join(self.raw_dir, f) for f in files] + + @property + def processed_file_names(self): + if self.split == "train": + return [ + f"train.pt", + f"train_n.pickle", + f"train_node_types.npy", + f"train_bond_types.npy", + ] + elif self.split == "val": + return [ + f"val.pt", + f"val_n.pickle", + f"val_node_types.npy", + f"val_bond_types.npy", + ] + else: + return [ + f"test.pt", + f"test_n.pickle", + f"test_node_types.npy", + f"test_bond_types.npy", + ] + + def download(self): + """ + Download raw files. + """ + raw_url = "https://raw.githubusercontent.com/KarolisMart/SPECTRE/main/data/DD" + for name in [ + "DD_A.txt", + "DD_graph_indicator.txt", + "DD_graph_labels.txt", + "DD_node_labels.txt", + ]: + download_url(f"{raw_url}/{name}", self.raw_dir) + + # read + path = os.path.join(self.root, "raw") + data_graph_indicator = np.loadtxt( + os.path.join(path, "DD_graph_indicator.txt"), delimiter="," + ).astype(int) + + # split data + g_cpu = torch.Generator() + g_cpu.manual_seed(1234) + + min_num_nodes = 100 + max_num_nodes = 500 + available_graphs = [] + for idx in np.arange(1, data_graph_indicator.max() + 1): + node_idx = data_graph_indicator == idx + if node_idx.sum() >= min_num_nodes and node_idx.sum() <= max_num_nodes: + available_graphs.append(idx) + available_graphs = torch.Tensor(available_graphs) + + self.num_graphs = len(available_graphs) + test_len = int(round(self.num_graphs * 0.2)) + train_len = int(round((self.num_graphs - test_len) * 0.8)) + val_len = self.num_graphs - train_len - test_len + + train_indices, val_indices, test_indices = random_split( + available_graphs, + [train_len, val_len, test_len], + generator=torch.Generator().manual_seed(1234), + ) + print(f"Dataset sizes: train {train_len}, val {val_len}, test {test_len}") + + print(f"Train indices: {train_indices}") + print(f"Val indices: {val_indices}") + print(f"Test indices: {test_indices}") + + torch.save(train_indices, self.raw_paths[0]) + torch.save(val_indices, self.raw_paths[1]) + torch.save(test_indices, self.raw_paths[2]) + + def process(self): + indices = torch.load( + os.path.join(self.raw_dir, "{}_indices.pt".format(self.split)) + ) + data_adj = ( + torch.Tensor( + np.loadtxt(os.path.join(self.raw_dir, "DD_A.txt"), delimiter=",") + ).long() + - 1 + ) + data_node_label = ( + torch.Tensor( + np.loadtxt( + os.path.join(self.raw_dir, "DD_node_labels.txt"), delimiter="," + ) + ).long() + - 1 + ) + data_graph_indicator = torch.Tensor( + np.loadtxt( + os.path.join(self.raw_dir, "DD_graph_indicator.txt"), delimiter="," + ) + ).long() + data_graph_types = ( + torch.Tensor( + np.loadtxt( + os.path.join(self.raw_dir, "DD_graph_labels.txt"), delimiter="," + ) + ).long() + - 1 + ) + data_list = [] + + # get information + self.num_node_type = data_node_label.max() + 1 + self.num_edge_type = 2 + self.num_graph_type = data_graph_types.max() + 1 + print(f"Number of node types: {self.num_node_type}") + print(f"Number of edge types: {self.num_edge_type}") + print(f"Number of graph types: {self.num_graph_type}") + + for idx in indices: + offset = torch.where(data_graph_indicator == idx)[0].min() + node_idx = data_graph_indicator == idx + # perm = torch.randperm(node_idx.sum()).long() + # reverse_perm = torch.sort(perm)[1] + # nodes = data_node_label[node_idx][perm].long() + # edge_idx = node_idx[data_adj[:, 0]] + # edge_index = data_adj[edge_idx] - offset + # edge_index[:, 0] = reverse_perm[edge_index[:, 0]] + # edge_index[:, 1] = reverse_perm[edge_index[:, 1]] + # nodes = data_node_label[node_idx].float() + nodes = torch.ones(len(data_node_label[node_idx]), 1).float() + edge_idx = node_idx[data_adj[:, 0]] + edge_index = data_adj[edge_idx] - offset + edge_attr = torch.ones_like(edge_index[:, 0]).float() + edge_index, edge_attr = remove_self_loops(edge_index.T, edge_attr) + data = torch_geometric.data.Data( + x=nodes, + edge_index=edge_index, + edge_attr=edge_attr.unsqueeze(-1), + n_nodes=nodes.shape[0], + ) + + if self.pre_filter is not None and not self.pre_filter(data): + continue + if self.pre_transform is not None: + data = self.pre_transform(data) + + data_list.append(data) + + torch.save(self.collate(data_list), self.processed_paths[0]) + + +class ProteinDataModule(AbstractDataModule): + def __init__(self, cfg): + self.cfg = cfg + self.dataset_name = self.cfg.dataset.name + self.datadir = cfg.dataset.datadir + base_path = pathlib.Path(get_original_cwd()).parents[0] + root_path = os.path.join(base_path, cfg.dataset.datadir) + transform = RemoveYTransform() + + datasets = { + "train": ProteinDataset( + root=root_path, + transform=transform, + split="train", + ), + "val": ProteinDataset( + root=root_path, + transform=transform, + split="val", + ), + "test": ProteinDataset( + root=root_path, + transform=transform, + split="test", + ), + } + + super().__init__(cfg, datasets) + self.inner = self.train_dataset + + +class ProteinInfos(AbstractDatasetInfos): + def __init__(self, datamodule): + self.is_molecular = False + self.spectre = True + self.use_charge = False + # self.datamodule = datamodule + self.dataset_name = datamodule.inner.dataset_name + self.n_nodes = datamodule.node_counts() + self.node_types = datamodule.node_types() + self.edge_types = datamodule.edge_counts() + super().complete_infos(self.n_nodes, self.node_types) + + def to_one_hot(self, data): + """ + call in the beginning of data + get the one_hot encoding for a charge beginning from -1 + """ + data.charge = data.x.new_zeros((*data.x.shape[:-1], 0)) + data.x = F.one_hot(data.x, num_classes=self.num_node_types).float() + data.edge_attr = F.one_hot( + data.edge_attr, num_classes=self.num_edge_types + ).float() + + return data diff --git a/CupOFMoCA-PROTAC/src/defog/src/datasets/tu_dataset_origin.py b/CupOFMoCA-PROTAC/src/defog/src/datasets/tu_dataset_origin.py new file mode 100644 index 0000000000000000000000000000000000000000..07df7d7b930106ff281e6115f9c790cfe206a984 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/datasets/tu_dataset_origin.py @@ -0,0 +1,249 @@ +import os +import pathlib +import os.path as osp +from typing import Callable, List, Optional + +import numpy as np +import torch.nn.functional as F +import torch +from torch.utils.data import random_split +import torch_geometric.utils +from torch_geometric.io import fs, read_tu_data +from torch_geometric.utils import remove_self_loops +from torch_geometric.data import Data, InMemoryDataset, download_url +from hydra.utils import get_original_cwd + +from utils import PlaceHolder +from datasets.abstract_dataset import ( + AbstractDataModule, + AbstractDatasetInfos, +) +from datasets.dataset_utils import ( + load_pickle, + save_pickle, + Statistics, + to_list, + RemoveYTransform, +) + + +class TUDataset(InMemoryDataset): + r"""A variety of graph kernel benchmark datasets, *.e.g.*, + :obj:`"IMDB-BINARY"`, :obj:`"REDDIT-BINARY"` or :obj:`"PROTEINS"`, + collected from the `TU Dortmund University + `_. + In addition, this dataset wrapper provides `cleaned dataset versions + `_ as motivated by the + `"Understanding Isomorphism Bias in Graph Data Sets" + `_ paper, containing only non-isomorphic + graphs. + + .. note:: + Some datasets may not come with any node labels. + You can then either make use of the argument :obj:`use_node_attr` + to load additional continuous node attributes (if present) or provide + synthetic node features using transforms such as + :class:`torch_geometric.transforms.Constant` or + :class:`torch_geometric.transforms.OneHotDegree`. + + Args: + root (str): Root directory where the dataset should be saved. + name (str): The `name + `_ of the + dataset. + transform (callable, optional): A function/transform that takes in an + :obj:`torch_geometric.data.Data` object and returns a transformed + version. The data object will be transformed before every access. + (default: :obj:`None`) + pre_transform (callable, optional): A function/transform that takes in + an :obj:`torch_geometric.data.Data` object and returns a + transformed version. The data object will be transformed before + being saved to disk. (default: :obj:`None`) + pre_filter (callable, optional): A function that takes in an + :obj:`torch_geometric.data.Data` object and returns a boolean + value, indicating whether the data object should be included in the + final dataset. (default: :obj:`None`) + force_reload (bool, optional): Whether to re-process the dataset. + (default: :obj:`False`) + use_node_attr (bool, optional): If :obj:`True`, the dataset will + contain additional continuous node attributes (if present). + (default: :obj:`False`) + use_edge_attr (bool, optional): If :obj:`True`, the dataset will + contain additional continuous edge attributes (if present). + (default: :obj:`False`) + cleaned (bool, optional): If :obj:`True`, the dataset will + contain only non-isomorphic graphs. (default: :obj:`False`) + + **STATS:** + + .. list-table:: + :widths: 20 10 10 10 10 10 + :header-rows: 1 + + * - Name + - #graphs + - #nodes + - #edges + - #features + - #classes + * - MUTAG + - 188 + - ~17.9 + - ~39.6 + - 7 + - 2 + * - ENZYMES + - 600 + - ~32.6 + - ~124.3 + - 3 + - 6 + * - PROTEINS + - 1,113 + - ~39.1 + - ~145.6 + - 3 + - 2 + * - COLLAB + - 5,000 + - ~74.5 + - ~4914.4 + - 0 + - 3 + * - IMDB-BINARY + - 1,000 + - ~19.8 + - ~193.1 + - 0 + - 2 + * - REDDIT-BINARY + - 2,000 + - ~429.6 + - ~995.5 + - 0 + - 2 + * - ... + - + - + - + - + - + """ + + url = "https://www.chrsmrrs.com/graphkerneldatasets" + cleaned_url = ( + "https://raw.githubusercontent.com/nd7141/" "graph_datasets/master/datasets" + ) + + def __init__( + self, + root: str, + name: str, + transform: Optional[Callable] = None, + pre_transform: Optional[Callable] = None, + pre_filter: Optional[Callable] = None, + force_reload: bool = False, + use_node_attr: bool = False, + use_edge_attr: bool = False, + cleaned: bool = False, + ) -> None: + self.name = name + self.cleaned = cleaned + super().__init__( + root, transform, pre_transform, pre_filter, force_reload=force_reload + ) + + out = fs.torch_load(self.processed_paths[0]) + if not isinstance(out, tuple) or len(out) < 3: + raise RuntimeError( + "The 'data' object was created by an older version of PyG. " + "If this error occurred while loading an already existing " + "dataset, remove the 'processed/' directory in the dataset's " + "root folder and try again." + ) + assert len(out) == 3 or len(out) == 4 + + if len(out) == 3: # Backward compatibility. + data, self.slices, self.sizes = out + data_cls = Data + else: + data, self.slices, self.sizes, data_cls = out + + if not isinstance(data, dict): # Backward compatibility. + self.data = data + else: + self.data = data_cls.from_dict(data) + + assert isinstance(self._data, Data) + if self._data.x is not None and not use_node_attr: + num_node_attributes = self.num_node_attributes + self._data.x = self._data.x[:, num_node_attributes:] + if self._data.edge_attr is not None and not use_edge_attr: + num_edge_attrs = self.num_edge_attributes + self._data.edge_attr = self._data.edge_attr[:, num_edge_attrs:] + + @property + def raw_dir(self) -> str: + name = f'raw{"_cleaned" if self.cleaned else ""}' + return osp.join(self.root, self.name, name) + + @property + def processed_dir(self) -> str: + name = f'processed{"_cleaned" if self.cleaned else ""}' + return osp.join(self.root, self.name, name) + + @property + def num_node_labels(self) -> int: + return self.sizes["num_node_labels"] + + @property + def num_node_attributes(self) -> int: + return self.sizes["num_node_attributes"] + + @property + def num_edge_labels(self) -> int: + return self.sizes["num_edge_labels"] + + @property + def num_edge_attributes(self) -> int: + return self.sizes["num_edge_attributes"] + + @property + def raw_file_names(self) -> List[str]: + names = ["A", "graph_indicator"] + return [f"{self.name}_{name}.txt" for name in names] + + @property + def processed_file_names(self) -> str: + return "data.pt" + + def download(self) -> None: + url = self.cleaned_url if self.cleaned else self.url + fs.cp(f"{url}/{self.name}.zip", self.raw_dir, extract=True) + for filename in fs.ls(osp.join(self.raw_dir, self.name)): + fs.mv(filename, osp.join(self.raw_dir, osp.basename(filename))) + fs.rm(osp.join(self.raw_dir, self.name)) + + def process(self) -> None: + self.data, self.slices, sizes = read_tu_data(self.raw_dir, self.name) + + if self.pre_filter is not None or self.pre_transform is not None: + data_list = [self.get(idx) for idx in range(len(self))] + + if self.pre_filter is not None: + data_list = [d for d in data_list if self.pre_filter(d)] + + if self.pre_transform is not None: + data_list = [self.pre_transform(d) for d in data_list] + + self.data, self.slices = self.collate(data_list) + self._data_list = None # Reset cache. + + assert isinstance(self._data, Data) + fs.torch_save( + (self._data.to_dict(), self.slices, sizes, self._data.__class__), + self.processed_paths[0], + ) + + def __repr__(self) -> str: + return f"{self.name}({len(self)})" diff --git a/CupOFMoCA-PROTAC/src/defog/src/datasets/zinc_dataset.py b/CupOFMoCA-PROTAC/src/defog/src/datasets/zinc_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..8c4310ea34eaaa2dda6eefb9992ab7a38394c58c --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/datasets/zinc_dataset.py @@ -0,0 +1,519 @@ +import os +import os.path as osp +import pathlib +from typing import Any, Sequence, Callable, List, Optional + +import torch +import torch.nn.functional as F +# from torch_geometric.io import fs + +import pickle +from rdkit import Chem, RDLogger +from rdkit.Chem.rdchem import BondType as BT +from tqdm import tqdm +import numpy as np +import pandas as pd +from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip +from torch_geometric.utils import subgraph + +import src.utils as utils +from src.datasets.abstract_dataset import MolecularDataModule, AbstractDatasetInfos +from src.analysis.rdkit_functions import mol2smiles, build_molecule_with_partial_charges +from src.analysis.rdkit_functions import compute_molecular_metrics + + +def files_exist(files) -> bool: + # NOTE: We return `False` in case `files` is empty, leading to a + # re-processing of files on every instantiation. + return len(files) != 0 and all([osp.exists(f) for f in files]) + + +def to_list(value: Any) -> Sequence: + if isinstance(value, Sequence) and not isinstance(value, str): + return value + else: + return [value] + + +class RemoveYTransform: + def __call__(self, data, return_y=False): + if return_y: + return torch.zeros((1, 0), dtype=torch.float) + data.y = torch.zeros((1, 0), dtype=torch.float) + return data + + +class SelectMuTransform: + def __call__(self, data, return_y=False): + if return_y: + return data.y[..., 3].unsqueeze(1) + data.y = data.y[..., 3].unsqueeze(1) + return data + + +class SelectHOMOTransform: + def __call__(self, data, return_y=False): + if return_y: + return data.y[..., 5].unsqueeze(1) + data.y = data.y[..., 5].unsqueeze(1) + return data + + +class SelectBothTransform: + def __call__(self, data, return_y=False): + if return_y: + return torch.hstack([data.y[..., 3], data.y[..., 5]]).unsqueeze(0) + data.y = torch.hstack([data.y[..., 3], data.y[..., 5]]).unsqueeze(0) + return data + + +class ZINCDataset(InMemoryDataset): + csv_url = "https://raw.githubusercontent.com/harryjo97/GruM/master/GruM_2D/data/zinc250k.csv" + val_index_url = "https://raw.githubusercontent.com/harryjo97/GruM/master/GruM_2D/data/valid_idx_zinc250k.json" + + url = 'https://www.dropbox.com/s/feo9qle74kg48gy/molecules.zip?dl=1' + split_url = ('https://raw.githubusercontent.com/graphdeeplearning/' + 'benchmarking-gnns/master/data/molecules/{}.index') + + def __init__( + self, + root: str, + subset: bool = False, + split: str = 'train', + remove_h: bool = True, + aromatic: bool = False, + transform=None, + pre_transform=None, + pre_filter=None, + force_reload: bool = False, + ) -> None: + self.subset = subset + assert split in ['train', 'val', 'test'] + self.remove_h = remove_h + super().__init__(root, transform, pre_transform, pre_filter) + path = osp.join(self.processed_dir, f'{split}.pt') + + if split == "train": + self.file_idx = 0 + elif split == "val": + self.file_idx = 1 + else: + self.file_idx = 2 + self.data, self.slices = torch.load(self.processed_paths[self.file_idx]) + + @property + def raw_file_names(self): + return [ + 'train.pickle', 'val.pickle', 'test.pickle', 'train.index', + 'val.index', 'test.index' + ] + + @property + def processed_dir(self): + name = 'subset' if self.subset else 'full' + return osp.join(self.root, name, 'processed') + + @property + def processed_file_names(self): + return ['train.pt', 'val.pt', 'test.pt'] + + def download(self): + download_url(self.csv_url, self.root) + download_url(self.val_index_url, self.root) + + path = download_url(self.url, self.root) + extract_zip(path, self.root) + os.rename(osp.join(self.root, 'molecules'), self.raw_dir) + os.unlink(path) + + for split in ['train', 'val', 'test']: + download_url(self.split_url.format(split), self.raw_dir) + + def save_smiles(self): + # Load the full dataset from CSV. + smiles_df = pd.read_csv(osp.join(self.root, 'zinc250k.csv')) + + # Load the valid indices from JSON. + valid_indices = pd.read_json(osp.join(self.root, 'valid_idx_zinc250k.json'))[0].tolist() + + # Loop over the splits: train, val, and test. + for split in ['train', 'val', 'test']: + # For 'val' and 'test', use the valid indices. + if split in ['val', 'test']: + indices = valid_indices + else: + # For training, use all indices that are not in valid_indices. + indices = [i for i in range(len(smiles_df)) if i not in valid_indices] + + # Extract SMILES strings for the given indices. + split_smiles = [smiles_df.iloc[idx]['smiles'] for idx in range(len(indices))] + + # Build the output CSV file name and path. + smiles_file_name = f"{split}_smiles_origin.csv" + smiles_path = osp.join(self.root, smiles_file_name) # or use a specific directory if needed + + # save the SMILES strings to a CSV file. + pd.DataFrame({'smiles': split_smiles}).to_csv(smiles_path, index=False) + + def process(self): + smiles = pd.read_csv(osp.join(self.root, 'zinc250k.csv')) + types = {'C': 0, 'N': 1, 'O': 2, 'F': 3, 'P': 4, 'S': 5, 'Cl': 6, 'Br': 7, 'I': 8} + bonds = {BT.SINGLE: 0, BT.DOUBLE: 1, BT.TRIPLE: 2} + + for split in ['train', 'val', 'test']: + val_indices = pd.read_json(osp.join(self.root, 'valid_idx_zinc250k.json'))[0].tolist() + if split == 'val' or split == 'test': + indices = val_indices + else: + indices = [] + for i in range(len(smiles)): + if i not in val_indices: + indices.append(i) + + print(f'Processing {split} dataset with {len(indices)} molecules') + + pbar = tqdm(total=len(indices)) + pbar.set_description(f'Processing {split} dataset') + + data_list = [] + for idx in indices: + smile = smiles.iloc[idx]['smiles'] + + mol = Chem.MolFromSmiles(smile, sanitize=False) + + # Remove hydrogen atoms if they are present + mol = Chem.RemoveHs(mol) + + # Kekulize the molecule: convert aromatic bonds to alternating single and double bonds + # The 'clearAromaticFlags=True' option ensures that aromatic flags are removed after kekulization + Chem.Kekulize(mol, clearAromaticFlags=True) + + N = mol.GetNumAtoms() + + type_idx = [] + for atom in mol.GetAtoms(): + type_idx.append(types[atom.GetSymbol()]) + + row, col, edge_type = [], [], [] + for bond in mol.GetBonds(): + start, end = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx() + row += [start, end] + col += [end, start] + edge_type += 2 * [bonds[bond.GetBondType()] + 1] + + edge_index = torch.tensor([row, col], dtype=torch.long) + edge_type = torch.tensor(edge_type, dtype=torch.long) + edge_attr = F.one_hot(edge_type, num_classes=len(bonds) + 1).to(torch.float) + + perm = (edge_index[0] * N + edge_index[1]).argsort() + edge_index = edge_index[:, perm] + edge_attr = edge_attr[perm] + + x = F.one_hot(torch.tensor(type_idx), num_classes=len(types)).float() + y = torch.zeros(size=(1, 0), dtype=torch.float) + + data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i) + + if self.pre_filter is not None and not self.pre_filter(data): + continue + if self.pre_transform is not None: + data = self.pre_transform(data) + + data_list.append(data) + pbar.update(1) + + pbar.close() + + torch.save(self.collate(data_list), osp.join(self.processed_dir, f'{split}.pt')) + + +class ZINCDataModule(MolecularDataModule): + def __init__(self, cfg): + self.datadir = cfg.dataset.datadir + self.remove_h = cfg.dataset.remove_h + self.aromatic = cfg.dataset.aromatic + + target = getattr(cfg.general, "target", None) + regressor = getattr(cfg.general, "conditional", None) + if regressor and target == "mu": + transform = SelectMuTransform() + elif regressor and target == "homo": + transform = SelectHOMOTransform() + elif regressor and target == "both": + transform = SelectBothTransform() + else: + transform = RemoveYTransform() + + base_path = pathlib.Path(os.path.realpath(__file__)).parents[2] + root_path = os.path.join(base_path, self.datadir) + datasets = { + "train": ZINCDataset( + split="train", + root=root_path, + remove_h=cfg.dataset.remove_h, + aromatic=cfg.dataset.aromatic, + transform=transform, + ), + "val": ZINCDataset( + split="val", + root=root_path, + remove_h=cfg.dataset.remove_h, + aromatic=cfg.dataset.aromatic, + transform=transform, + ), + "test": ZINCDataset( + split="test", + root=root_path, + remove_h=cfg.dataset.remove_h, + aromatic=cfg.dataset.aromatic, + transform=transform, + ), + } + self.test_labels = transform(datasets["test"].data, return_y=True) + + + train_len = len(datasets["train"].data.idx) + val_len = len(datasets["val"].data.idx) + test_len = len(datasets["test"].data.idx) + print(f"Dataset sizes: train {train_len}, val {val_len}, test {test_len}") + super().__init__(cfg, datasets) + + +class ZINCinfos(AbstractDatasetInfos): + def __init__(self, datamodule, cfg, recompute_statistics=False): + self.remove_h = cfg.dataset.remove_h + self.aromatic = cfg.dataset.aromatic + self.need_to_strip = ( + False # to indicate whether we need to ignore one output from the model + ) + self.compute_fcd = cfg.dataset.compute_fcd + + self.name = "zinc" + if self.remove_h: + # Atom encoder/decoder + self.atom_encoder = {'C': 0, 'N': 1, 'O': 2, 'F': 3, 'P': 4, 'S': 5, 'Cl': 6, 'Br': 7, 'I': 8} + self.atom_decoder = ['C', 'N', 'O', 'F', 'P', 'S', 'Cl', 'Br', 'I'] + + self.num_atom_types = 9 + + # Valencies based on chemistry rules for ZINC atoms + self.valencies = [4, 3, 2, 1, 5, 6, 1, 1, 1] + + # Approximate atomic weights (standard values) + self.atom_weights = { + 0: 12, # C + 1: 14, # N + 2: 16, # O + 3: 19, # F + 4: 30, # P + 5: 32., # S + 6: 35.5, # Cl + 7: 78, # Br + 8: 127 # I + } + + self.max_n_nodes = 38 # typical upper bound from ZINC250k + self.max_weight = 500 # standard upper bound for ZINC molecules + + self.n_nodes = torch.tensor( + [0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 1.3359e-05, 2.2265e-05, 5.7889e-05, 2.9835e-04, 7.9263e-04, 2.9123e-03, + 4.6890e-03, 7.1515e-03, 1.1275e-02, 1.7117e-02, 2.5360e-02, 3.5014e-02, + 4.6707e-02, 5.8178e-02, 7.0829e-02, 8.1472e-02, 7.4922e-02, 8.4384e-02, + 9.3099e-02, 9.1451e-02, 7.7175e-02, 6.3397e-02, 4.0331e-02, 3.1131e-02, + 2.4394e-02, 1.9237e-02, 1.5029e-02, 1.0362e-02, 6.9155e-03, 4.1190e-03, + 1.5942e-03, 5.6108e-04, 8.9060e-06 + ] + ) + self.node_types = torch.tensor([ + 7.3678e-01, 1.2211e-01, 9.9746e-02, + 1.3745e-02, 2.4428e-05, 1.7806e-02, + 7.4231e-03, 2.2057e-03, 1.5522e-04]) + self.edge_types = torch.tensor([9.0658e-01, 6.9411e-02, 2.3771e-02, 2.3480e-04]) + self.valency_distribution = torch.tensor( + [0.0000e+00, 1.1364e-01, 3.0431e-01, 3.5063e-01, 2.2655e-01, 2.2697e-05, + 4.8356e-03, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, + 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00] + ) + + super().complete_infos(n_nodes=self.n_nodes, node_types=self.node_types) + + if recompute_statistics: + np.set_printoptions(suppress=True, precision=5) + self.n_nodes = datamodule.node_counts() + print("Distribution of number of nodes", self.n_nodes) + np.savetxt("n_counts.txt", self.n_nodes.numpy()) + self.node_types = datamodule.node_types() # There are no node types + print("Distribution of node types", self.node_types) + np.savetxt("atom_types.txt", self.node_types.numpy()) + + self.edge_types = datamodule.edge_counts() + print("Distribution of edge types", self.edge_types) + np.savetxt("edge_types.txt", self.edge_types.numpy()) + + valencies = datamodule.valency_count(self.max_n_nodes, zinc=True) + print("Distribution of the valencies", valencies) + np.savetxt("valencies.txt", valencies.numpy()) + self.valency_distribution = valencies + assert False + + +def get_smiles(cfg, datamodule, dataset_infos, evaluate_datasets=False): + + return { + "train": get_loader_smiles( + cfg, + datamodule.train_dataloader(), + dataset_infos, + "train", + evaluate_dataset=evaluate_datasets, + ), + "val": get_loader_smiles( + cfg, + datamodule.val_dataloader(), + dataset_infos, + "val", + evaluate_dataset=evaluate_datasets, + ), + "test": get_loader_smiles( + cfg, + datamodule.test_dataloader(), + dataset_infos, + "test", + evaluate_dataset=evaluate_datasets, + ), + } + + +def get_loader_smiles( + cfg, + dataloader, + dataset_infos, + split_key, + evaluate_dataset=False, +): + datadir = cfg.dataset.datadir + remove_h = cfg.dataset.remove_h + atom_decoder = dataset_infos.atom_decoder + root_dir = pathlib.Path(os.path.realpath(__file__)).parents[2] + smiles_file_name = ( + f"{split_key}_smiles_no_h.npy" if remove_h else f"{split_key}_smiles_h.npy" + ) + smiles_path = os.path.join(root_dir, datadir, smiles_file_name) + if os.path.exists(smiles_path): + print(f"Dataset {split_key} smiles were found.") + smiles = np.load(smiles_path).tolist() + else: + print(f"Computing dataset {split_key} smiles...") + smiles = compute_zinc_smiles(atom_decoder, dataloader, remove_h) + np.save(smiles_path, np.array(smiles)) + + if evaluate_dataset: + # Convert loader to molecules + assert ( + dataset_infos is not None + ), "If wanting to evaluate dataset, need to pass dataset_infos" + all_molecules = [] + for i, data in enumerate(dataloader): + dense_data, node_mask = utils.to_dense( + data.x, data.edge_index, data.edge_attr, data.batch + ) + dense_data = dense_data.mask(node_mask, collapse=True) + X, E = dense_data.X, dense_data.E + + for k in range(X.size(0)): + n = int(torch.sum((X != -1)[k, :])) + atom_types = X[k, :n].cpu() + edge_types = E[k, :n, :n].cpu() + all_molecules.append([atom_types, edge_types]) + + print( + "Evaluating the dataset -- number of molecules to evaluate", + len(all_molecules), + ) + # load train smiles + train_smiles_file_name = ( + f"train_smiles_no_h.npy" if remove_h else f"train_smiles_h.npy" + ) + train_smiles_path = os.path.join(root_dir, datadir, train_smiles_file_name) + train_smiles = np.load(train_smiles_path) + # get evaluation and output + metrics = compute_molecular_metrics( + molecule_list=all_molecules, + train_smiles=train_smiles, + dataset_info=dataset_infos, + ) + print(metrics[0]) + + return smiles + + +def compute_zinc_smiles(atom_decoder, train_dataloader, remove_h): + """ + :param dataset_name: zinc or zinc_second_half + :return: + """ + print(f"\tConverting ZINC dataset to SMILES for remove_h={remove_h}...") + + mols_smiles = [] + len_train = len(train_dataloader) + invalid = 0 + disconnected = 0 + for i, data in enumerate(train_dataloader): + dense_data, node_mask = utils.to_dense( + data.x, data.edge_index, data.edge_attr, data.batch + ) + dense_data = dense_data.mask(node_mask, collapse=True) + X, E = dense_data.X, dense_data.E + + n_nodes = [int(torch.sum((X != -1)[j, :])) for j in range(X.size(0))] + + molecule_list = [] + for k in range(X.size(0)): + n = n_nodes[k] + atom_types = X[k, :n].cpu() + edge_types = E[k, :n, :n].cpu() + molecule_list.append([atom_types, edge_types]) + + for l, molecule in enumerate(molecule_list): + mol = build_molecule_with_partial_charges( + molecule[0], molecule[1], atom_decoder + ) + smile = mol2smiles(mol) + if smile is not None: + mols_smiles.append(smile) + mol_frags = Chem.rdmolops.GetMolFrags( + mol, asMols=True, sanitizeFrags=True + ) + if len(mol_frags) > 1: + print("Disconnected molecule", mol, mol_frags) + disconnected += 1 + else: + print("Invalid molecule obtained.") + invalid += 1 + + if i % 1000 == 0: + print( + "\tConverting ZINC dataset to SMILES {0:.2%}".format( + float(i) / len_train + ) + ) + print("Number of invalid molecules", invalid) + print("Number of disconnected molecules", disconnected) + return mols_smiles diff --git a/CupOFMoCA-PROTAC/src/defog/src/flow_matching/flow_matching_utils.py b/CupOFMoCA-PROTAC/src/defog/src/flow_matching/flow_matching_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..75e7fa947b55ed87ec79ae2dc2fc1c3290750c34 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/flow_matching/flow_matching_utils.py @@ -0,0 +1,94 @@ +import torch +from torch.nn import functional as F +from torch.distributions.categorical import Categorical +import numpy as np +import math + +from utils import PlaceHolder + + +def assert_correctly_masked(variable, node_mask): + assert ( + variable * (1 - node_mask.long()) + ).abs().max().item() < 1e-4, "Variables not masked properly." + + +def sample_discrete_feature_noise(limit_dist, node_mask): + """Sample from the limit distribution of the diffusion process""" + bs, n_max = node_mask.shape + x_limit = limit_dist.X[None, None, :].expand(bs, n_max, -1) + e_limit = limit_dist.E[None, None, None, :].expand(bs, n_max, n_max, -1) + y_limit = limit_dist.y[None, :].expand(bs, -1) + U_X = ( + x_limit.flatten(end_dim=-2).multinomial(1, replacement=True).reshape(bs, n_max) + ) + U_E = ( + e_limit.flatten(end_dim=-2) + .multinomial(1, replacement=True) + .reshape(bs, n_max, n_max) + ) + U_y = torch.empty((bs, 0)) + + long_mask = node_mask.long() + U_X = U_X.type_as(long_mask) + U_E = U_E.type_as(long_mask) + U_y = U_y.type_as(long_mask) + + U_X = F.one_hot(U_X, num_classes=x_limit.shape[-1]).float() + U_E = F.one_hot(U_E, num_classes=e_limit.shape[-1]).float() + + # Get upper triangular part of edge noise, without main diagonal + upper_triangular_mask = torch.zeros_like(U_E) + indices = torch.triu_indices(row=U_E.size(1), col=U_E.size(2), offset=1) + upper_triangular_mask[:, indices[0], indices[1], :] = 1 + + U_E = U_E * upper_triangular_mask + U_E = U_E + torch.transpose(U_E, 1, 2) + + assert (U_E == torch.transpose(U_E, 1, 2)).all() + + return PlaceHolder(X=U_X, E=U_E, y=U_y).mask(node_mask) + + +def sample_discrete_features(probX, probE, node_mask, mask=False): + """Sample features from multinomial distribution with given probabilities (probX, probE, proby) + :param probX: bs, n, dx_out node features + :param probE: bs, n, n, de_out edge features + :param proby: bs, dy_out global features. + """ + bs, n, _ = probX.shape + # Noise X + # The masked rows should define probability distributions as well + probX[~node_mask] = 1 / probX.shape[-1] + + # Flatten the probability tensor to sample with multinomial + probX = probX.reshape(bs * n, -1) # (bs * n, dx_out) + + # Sample X + X_t = probX.multinomial(1, replacement=True) # (bs * n, 1) + # X_t = Categorical(probs=probX).sample() # (bs * n, 1) + X_t = X_t.reshape(bs, n) # (bs, n) + + # Noise E + # The masked rows should define probability distributions as well + inverse_edge_mask = ~(node_mask.unsqueeze(1) * node_mask.unsqueeze(2)) + diag_mask = torch.eye(n).unsqueeze(0).expand(bs, -1, -1) + + probE[inverse_edge_mask] = 1 / probE.shape[-1] + probE[diag_mask.bool()] = 1 / probE.shape[-1] + + probE = probE.reshape(bs * n * n, -1) # (bs * n * n, de_out) + + # Sample E + E_t = probE.multinomial(1, replacement=True).reshape(bs, n, n) # (bs, n, n) + # E_t = Categorical(probs=probE).sample().reshape(bs, n, n) # (bs, n, n) + E_t = torch.triu(E_t, diagonal=1) + E_t = E_t + torch.transpose(E_t, 1, 2) + + if mask: + X_t = X_t * node_mask + E_t = E_t * node_mask.unsqueeze(1) * node_mask.unsqueeze(2) + + return PlaceHolder(X=X_t, E=E_t, y=torch.zeros(bs, 0).type_as(X_t)) + + diff --git a/CupOFMoCA-PROTAC/src/defog/src/flow_matching/init.py b/CupOFMoCA-PROTAC/src/defog/src/flow_matching/init.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/CupOFMoCA-PROTAC/src/defog/src/flow_matching/noise_distribution.py b/CupOFMoCA-PROTAC/src/defog/src/flow_matching/noise_distribution.py new file mode 100644 index 0000000000000000000000000000000000000000..b106ebd4fa3d68ab49245ec535b4b3b1ce09dd11 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/flow_matching/noise_distribution.py @@ -0,0 +1,129 @@ +import torch + +from src import utils + + +class NoiseDistribution: + + def __init__(self, model_transition, dataset_infos): + + self.x_num_classes = dataset_infos.output_dims["X"] + self.e_num_classes = dataset_infos.output_dims["E"] + self.y_num_classes = dataset_infos.output_dims["y"] + self.x_added_classes = 0 + self.e_added_classes = 0 + self.y_added_classes = 0 + self.transition = model_transition + + if model_transition == "uniform": + x_limit = torch.ones(self.x_num_classes) / self.x_num_classes + e_limit = torch.ones(self.e_num_classes) / self.e_num_classes + + elif model_transition == "absorbfirst": + x_limit = torch.zeros(self.x_num_classes) + x_limit[0] = 1 + e_limit = torch.zeros(self.e_num_classes) + e_limit[0] = 1 + + elif model_transition == "argmax": + node_types = dataset_infos.node_types.float() + x_marginals = node_types / torch.sum(node_types) + + edge_types = dataset_infos.edge_types.float() + e_marginals = edge_types / torch.sum(edge_types) + + x_max_dim = torch.argmax(x_marginals) + e_max_dim = torch.argmax(e_marginals) + x_limit = torch.zeros(self.x_num_classes) + x_limit[x_max_dim] = 1 + e_limit = torch.zeros(self.e_num_classes) + e_limit[e_max_dim] = 1 + + elif model_transition == "absorbing": + # only add virtual classes when there are several + if self.x_num_classes > 1: + # if self.x_num_classes >= 1: + self.x_num_classes += 1 + self.x_added_classes = 1 + if self.e_num_classes > 1: + self.e_num_classes += 1 + self.e_added_classes = 1 + + x_limit = torch.zeros(self.x_num_classes) + x_limit[-1] = 1 + e_limit = torch.zeros(self.e_num_classes) + e_limit[-1] = 1 + + elif model_transition == "marginal": + + node_types = dataset_infos.node_types.float() + x_limit = node_types / torch.sum(node_types) + + edge_types = dataset_infos.edge_types.float() + e_limit = edge_types / torch.sum(edge_types) + + elif model_transition == "edge_marginal": + x_limit = torch.ones(self.x_num_classes) / self.x_num_classes + + edge_types = dataset_infos.edge_types.float() + e_limit = edge_types / torch.sum(edge_types) + + elif model_transition == "node_marginal": + e_limit = torch.ones(self.e_num_classes) / self.e_num_classes + + node_types = dataset_infos.node_types.float() + x_limit = node_types / torch.sum(node_types) + + else: + raise ValueError(f"Unknown transition model: {model_transition}") + + y_limit = torch.ones(self.y_num_classes) / self.y_num_classes # typically dummy + print( + f"Limit distribution of the classes | Nodes: {x_limit} | Edges: {e_limit}" + ) + self.limit_dist = utils.PlaceHolder(X=x_limit, E=e_limit, y=y_limit) + + def update_input_output_dims(self, input_dims): + input_dims["X"] += self.x_added_classes + input_dims["E"] += self.e_added_classes + input_dims["y"] += self.y_added_classes + + def update_dataset_infos(self, dataset_infos): + if hasattr(dataset_infos, "atom_decoder"): + dataset_infos.atom_decoder = ( + dataset_infos.atom_decoder + ["Y"] * self.x_added_classes + ) + + def get_limit_dist(self): + return self.limit_dist + + def get_noise_dims(self): + return { + "X": len(self.limit_dist.X), + "E": len(self.limit_dist.E), + "y": len(self.limit_dist.E), + } + + def ignore_virtual_classes(self, X, E, y=None): + if self.transition == "absorbing": + new_X = X[..., : -self.x_added_classes] + new_E = E[..., : -self.e_added_classes] + new_y = y[..., : -self.y_added_classes] if y is not None else None + return new_X, new_E, new_y + else: + return X, E, y + + def add_virtual_classes(self, X, E, y=None): + x_virtual = torch.zeros_like(X[..., :1]).repeat(1, 1, self.x_added_classes) + new_X = torch.cat([X, x_virtual], dim=-1) + + e_virtual = torch.zeros_like(E[..., :1]).repeat(1, 1, 1, self.e_added_classes) + new_E = torch.cat([E, e_virtual], dim=-1) + + if y is not None: + y_virtual = torch.zeros_like(y[..., :1]).repeat(1, self.y_added_classes) + new_y = torch.cat([y, y_virtual], dim=-1) + else: + new_y = None + + return new_X, new_E, new_y diff --git a/CupOFMoCA-PROTAC/src/defog/src/flow_matching/rate_matrix.py b/CupOFMoCA-PROTAC/src/defog/src/flow_matching/rate_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..4e3ef606a90a913a608cd339c399331787285be0 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/flow_matching/rate_matrix.py @@ -0,0 +1,338 @@ +import torch +import torch.nn.functional as F + +from flow_matching import flow_matching_utils +from flow_matching.utils import dt_p_xt_g_x1, p_xt_g_x1 + + +class RateMatrixDesigner: + + def __init__(self, rdb, rdb_crit, eta, omega, limit_dist): + + self.omega = omega # target guidance + self.eta = eta # stochasticity + # Different designs of R^db + self.rdb = rdb + self.rdb_crit = rdb_crit + self.limit_dist = limit_dist + self.num_classes_X = len(self.limit_dist.X) + self.num_classes_E = len(self.limit_dist.E) + + print( + f"RateMatrixDesigner: rdb={rdb}, rdb_crit={rdb_crit}, eta={eta}, omega={omega}" + ) + + def compute_graph_rate_matrix(self, t, node_mask, G_t, G_1_pred): + + X_t, E_t = G_t + X_1_pred, E_1_pred = G_1_pred + + X_t_label = X_t.argmax(-1, keepdim=True) + E_t_label = E_t.argmax(-1, keepdim=True) + sampled_G_1 = flow_matching_utils.sample_discrete_features( + X_1_pred, + E_1_pred, + node_mask=node_mask, + ) + X_1_sampled = sampled_G_1.X + E_1_sampled = sampled_G_1.E + + dfm_variables = self.compute_dfm_variables( + t, X_t_label, E_t_label, X_1_sampled, E_1_sampled + ) + + Rstar_t_X, Rstar_t_E = self.compute_Rstar(dfm_variables) + + Rdb_t_X, Rdb_t_E = self.compute_RDB( + X_t_label, + E_t_label, + X_1_pred, + E_1_pred, + X_1_sampled, + E_1_sampled, + node_mask, + t, + dfm_variables, + ) + + Rtg_t_X, Rtg_t_E = self.compute_R_tg( + X_1_sampled, + E_1_sampled, + X_t_label, + E_t_label, + dfm_variables, + ) + + # sum to get the final R_t_X and R_t_E + R_t_X = Rstar_t_X + Rdb_t_X + Rtg_t_X + R_t_E = Rstar_t_E + Rdb_t_E + Rtg_t_E + + # Stabilize rate matrices + R_t_X, R_t_E = self.stabilize_rate_matrix(R_t_X, R_t_E, dfm_variables) + + return R_t_X, R_t_E + + def compute_dfm_variables(self, t, X_t_label, E_t_label, X_1_sampled, E_1_sampled): + + dt_p_vals_X, dt_p_vals_E = dt_p_xt_g_x1( + X_1_sampled, + E_1_sampled, + self.limit_dist, + ) # (bs, n, dx), (bs, n, n, de) + + dt_p_vals_at_Xt = dt_p_vals_X.gather(-1, X_t_label).squeeze(-1) # (bs, n, ) + dt_p_vals_at_Et = dt_p_vals_E.gather(-1, E_t_label).squeeze(-1) # (bs, n, n, ) + + pt_vals_X, pt_vals_E = p_xt_g_x1( + X_1_sampled, + E_1_sampled, + t, + self.limit_dist, + ) # (bs, n, dx), (bs, n, n, de) + + pt_vals_at_Xt = pt_vals_X.gather(-1, X_t_label).squeeze(-1) # (bs, n, ) + pt_vals_at_Et = pt_vals_E.gather(-1, E_t_label).squeeze(-1) # (bs, n, n, ) + + Z_t_X = torch.count_nonzero(pt_vals_X, dim=-1) # (bs, n) + Z_t_E = torch.count_nonzero(pt_vals_E, dim=-1) # (bs, n, n) + + dfm_variables = { + "pt_vals_X": pt_vals_X, + "pt_vals_E": pt_vals_E, + "pt_vals_at_Xt": pt_vals_at_Xt, + "pt_vals_at_Et": pt_vals_at_Et, + "dt_p_vals_X": dt_p_vals_X, + "dt_p_vals_E": dt_p_vals_E, + "dt_p_vals_at_Xt": dt_p_vals_at_Xt, + "dt_p_vals_at_Et": dt_p_vals_at_Et, + "Z_t_X": Z_t_X, + "Z_t_E": Z_t_E, + } + + return dfm_variables + + def compute_Rstar(self, dfm_variables): + + # Unpack needed variables + dt_p_vals_X = dfm_variables["dt_p_vals_X"] + dt_p_vals_E = dfm_variables["dt_p_vals_E"] + dt_p_vals_at_Xt = dfm_variables["dt_p_vals_at_Xt"] + dt_p_vals_at_Et = dfm_variables["dt_p_vals_at_Et"] + pt_vals_at_Xt = dfm_variables["pt_vals_at_Xt"] + pt_vals_at_Et = dfm_variables["pt_vals_at_Et"] + Z_t_X = dfm_variables["Z_t_X"] + Z_t_E = dfm_variables["Z_t_E"] + + # Numerator of R_t^* + inner_X = dt_p_vals_X - dt_p_vals_at_Xt[:, :, None] + inner_E = dt_p_vals_E - dt_p_vals_at_Et[:, :, :, None] + Rstar_t_numer_X = F.relu(inner_X) # (bs, n, dx) + Rstar_t_numer_E = F.relu(inner_E) # (bs, n, n, de) + + # Denominator + Rstar_t_denom_X = Z_t_X * pt_vals_at_Xt # (bs, n) + Rstar_t_denom_E = Z_t_E * pt_vals_at_Et # (bs, n, n) + + # Final R^\star + Rstar_t_X = Rstar_t_numer_X / Rstar_t_denom_X[:, :, None] # (bs, n, dx) + Rstar_t_E = Rstar_t_numer_E / Rstar_t_denom_E[:, :, :, None] # (B, n, n, de) + + return Rstar_t_X, Rstar_t_E + + def compute_RDB( + self, + X_t_label, + E_t_label, + X_1_pred, + E_1_pred, + X_1_sampled, + E_1_sampled, + node_mask, + t, + dfm_variables, + ): + # unpack needed variables + pt_vals_X = dfm_variables["pt_vals_X"] + pt_vals_E = dfm_variables["pt_vals_E"] + + # dimensions + dx = pt_vals_X.shape[-1] + de = pt_vals_E.shape[-1] + + # build mask for Rdb + if self.rdb == "general": + x_mask = torch.ones_like(pt_vals_X) + e_mask = torch.ones_like(pt_vals_E) + + elif self.rdb == "marginal": + x_limit = self.limit_dist.X + e_limit = self.limit_dist.E + + Xt_marginal = x_limit[X_t_label] + Et_marginal = e_limit[E_t_label] + + x_mask = x_limit.repeat(X_t_label.shape[0], X_t_label.shape[1], 1) + e_mask = e_limit.repeat( + E_t_label.shape[0], E_t_label.shape[1], E_t_label.shape[2], 1 + ) + + x_mask = x_mask > Xt_marginal + e_mask = e_mask > Et_marginal + + elif self.rdb == "column": + # Get column idx to pick + if self.rdb_crit == "max_marginal": + x_column_idxs = self.limit_dist.X.argmax(keepdim=True).expand( + X_t_label.shape + ) + e_column_idxs = self.limit_dist.E.argmax(keepdim=True).expand( + E_t_label.shape + ) + elif self.rdb_crit == "x_t": + x_column_idxs = X_t_label + e_column_idxs = E_t_label + elif self.rdb_crit == "abs_state": + x_column_idxs = torch.ones_like(X_t_label) * (dx - 1) + e_column_idxs = torch.ones_like(E_t_label) * (de - 1) + elif self.rdb_crit == "p_x1_g_xt": + x_column_idxs = X_1_pred.argmax(dim=-1, keepdim=True) + e_column_idxs = E_1_pred.argmax(dim=-1, keepdim=True) + elif self.rdb_crit == "x_1": # as in paper, uniform + x_column_idxs = X_1_sampled.unsqueeze(-1) + e_column_idxs = E_1_sampled.unsqueeze(-1) + elif self.rdb_crit == "p_xt_g_x1": + x_column_idxs = pt_vals_X.argmax(dim=-1, keepdim=True) + e_column_idxs = pt_vals_E.argmax(dim=-1, keepdim=True) + elif self.rdb_crit == "xhat_t": + sampled_1_hat = flow_matching_utils.sample_discrete_features( + pt_vals_X, + pt_vals_E, + node_mask=node_mask, + ) + x_column_idxs = sampled_1_hat.X.unsqueeze(-1) + e_column_idxs = sampled_1_hat.E.unsqueeze(-1) + else: + raise NotImplementedError + + # create mask based on columns picked + x_mask = F.one_hot(x_column_idxs.squeeze(-1), num_classes=dx) + x_mask[(x_column_idxs == X_t_label).squeeze(-1)] = 1.0 + e_mask = F.one_hot(e_column_idxs.squeeze(-1), num_classes=de) + e_mask[(e_column_idxs == E_t_label).squeeze(-1)] = 1.0 + + elif self.rdb == "entry": + if self.rdb_crit == "abs_state": + # select last index + x_masked_idx = torch.tensor( + dx + - 1 # delete -1 for the last index + # dx - 1 + ).to( + self.device + ) # leaving this for now, can change later if we want to explore it a bit more + e_masked_idx = torch.tensor(de - 1).to(self.device) + + x1_idxs = X_1_sampled.unsqueeze(-1) # (bs, n, 1) + e1_idxs = E_1_sampled.unsqueeze(-1) # (bs, n, n, 1) + if self.rdb_crit == "first": # here in all datasets it's the argmax + # select last index + x_masked_idx = torch.tensor(0).to( + self.device + ) # leaving this for now, can change later if we want to explore it a bit more + e_masked_idx = torch.tensor(0).to(self.device) + + x1_idxs = X_1_sampled.unsqueeze(-1) # (bs, n, 1) + e1_idxs = E_1_sampled.unsqueeze(-1) # (bs, n, n, 1) + else: + raise NotImplementedError + + # create mask based on columns picked + # bs, n, _ = X_t_label.shape + # x_mask = torch.zeros((bs, n, dx), device=self.device) # (bs, n, dx) + x_mask = torch.zeros_like(pt_vals_X) # (bs, n, dx) + xt_in_x1 = (X_t_label == x1_idxs).squeeze(-1) # (bs, n, 1) + x_mask[xt_in_x1] = F.one_hot(x_masked_idx, num_classes=dx).float() + xt_in_masked = (X_t_label == x_masked_idx).squeeze(-1) + x_mask[xt_in_masked] = F.one_hot( + x1_idxs.squeeze(-1), num_classes=dx + ).float()[xt_in_masked] + + # e_mask = torch.zeros((bs, n, n, de), device=self.device) # (bs, n, dx) + e_mask = torch.zeros_like(pt_vals_E) + et_in_e1 = (E_t_label == e1_idxs).squeeze(-1) + e_mask[et_in_e1] = F.one_hot(e_masked_idx, num_classes=de).float() + et_in_masked = (E_t_label == e_masked_idx).squeeze(-1) + e_mask[et_in_masked] = F.one_hot( + e1_idxs.squeeze(-1), num_classes=de + ).float()[et_in_masked] + + else: + raise NotImplementedError(f"Not implemented rdb type: {self.rdb}") + + # stochastic rate matrix + Rdb_t_X = pt_vals_X * x_mask * self.eta + Rdb_t_E = pt_vals_E * e_mask * self.eta + + return Rdb_t_X, Rdb_t_E + + def compute_R_tg( + self, + X_1_sampled, + E_1_sampled, + X_t_label, + E_t_label, + dfm_variables, + ): + """Target guidance rate matrix""" + + # Unpack needed variables + pt_vals_at_Xt = dfm_variables["pt_vals_at_Xt"] + pt_vals_at_Et = dfm_variables["pt_vals_at_Et"] + Z_t_X = dfm_variables["Z_t_X"] + Z_t_E = dfm_variables["Z_t_E"] + + # Numerator + X1_onehot = F.one_hot(X_1_sampled, num_classes=self.num_classes_X).float() + E1_onehot = F.one_hot(E_1_sampled, num_classes=self.num_classes_E).float() + mask_X = X_1_sampled.unsqueeze(-1) != X_t_label + mask_E = E_1_sampled.unsqueeze(-1) != E_t_label + + Rtg_t_numer_X = X1_onehot * self.omega * mask_X + Rtg_t_numer_E = E1_onehot * self.omega * mask_E + + # Denominator + denom_X = Z_t_X * pt_vals_at_Xt # (bs, n) + denom_E = Z_t_E * pt_vals_at_Et # (bs, n, n) + + # Final R^TG + Rtg_t_X = Rtg_t_numer_X / denom_X[:, :, None] + Rtg_t_E = Rtg_t_numer_E / denom_E[:, :, :, None] + + return Rtg_t_X, Rtg_t_E + + def stabilize_rate_matrix(self, R_t_X, R_t_E, dfm_variables): + + # Unpack needed variables + pt_vals_X = dfm_variables["pt_vals_X"] + pt_vals_E = dfm_variables["pt_vals_E"] + pt_vals_at_Xt = dfm_variables["pt_vals_at_Xt"] + pt_vals_at_Et = dfm_variables["pt_vals_at_Et"] + + # protect to avoid NaN and too large values + R_t_X = torch.nan_to_num(R_t_X, nan=0.0, posinf=0.0, neginf=0.0) + R_t_E = torch.nan_to_num(R_t_E, nan=0.0, posinf=0.0, neginf=0.0) + R_t_X[R_t_X > 1e5] = 0.0 + R_t_E[R_t_E > 1e5] = 0.0 + + # Set p(x_t | x_1) = 0 or p(j | x_1) = 0 cases to zero, which need to be applied to Rdb too + dx = R_t_X.shape[-1] + de = R_t_E.shape[-1] + R_t_X[(pt_vals_at_Xt == 0.0)[:, :, None].repeat(1, 1, dx)] = 0.0 + R_t_E[(pt_vals_at_Et == 0.0)[:, :, :, None].repeat(1, 1, 1, de)] = 0.0 + + # zero-out certain columns of R, which is implied in the computation of Rdb + # if the probability of a place is 0, then we should not consider it in the R computation + R_t_X[pt_vals_X == 0.0] = 0.0 + R_t_E[pt_vals_E == 0.0] = 0.0 + + return R_t_X, R_t_E diff --git a/CupOFMoCA-PROTAC/src/defog/src/flow_matching/time_distorter.py b/CupOFMoCA-PROTAC/src/defog/src/flow_matching/time_distorter.py new file mode 100644 index 0000000000000000000000000000000000000000..048b46e45d760ad573e52faece436de28d734013 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/flow_matching/time_distorter.py @@ -0,0 +1,135 @@ +import torch +import numpy as np +from scipy.stats import norm +from scipy.special import beta as beta_func, betaln +from scipy.stats import beta as sp_beta +from scipy.interpolate import interp1d + + +def beta_pdf(x, alpha, beta): + """Beta distribution PDF.""" + # coeff = np.exp(betaln(alpha, beta)) + # return x ** (alpha - 1) * (1 - x) ** (beta - 1) / coeff + return x ** (alpha - 1) * (1 - x) ** (beta - 1) / beta_func(alpha, beta) + + +def objective_function(alpha, beta, y, t): + """Objective function to minimize (mean squared error).""" + y_pred = beta_pdf(t, alpha, beta) + regularization = (alpha + beta) + (1 / alpha + 1 / beta) + error = np.mean((y - y_pred) ** 2) + error = error + 0.0001 * regularization + return error + + +class TimeDistorter: + + def __init__( + self, + train_distortion, + sample_distortion, + mu=0, + sigma=1, + alpha=1, + beta=1, + ): + self.train_distortion = train_distortion # used for sample_ft + self.sample_distortion = sample_distortion # used for get_ft + self.alpha = alpha + self.beta = beta + print( + f"TimeDistorter: train_distortion={train_distortion}, sample_distortion={sample_distortion}" + ) + self.f_inv = None + + def train_ft(self, batch_size, device): + t_uniform = torch.rand((batch_size, 1), device=device) + t_distort = self.apply_distortion(t_uniform, self.train_distortion) + + return t_distort + + def sample_ft(self, t, sample_distortion): + t_distort = self.apply_distortion(t, sample_distortion) + return t_distort + + def fit(self, difficulty, t_array, learning_rate=0.01, iterations=1000): + """Fit a beta distribution to data using the method of moments.""" + alpha, beta = self.alpha, self.beta + t_array = t_array + 1e-6 # Avoid division by zero + + for _ in range(iterations): + y_pred = beta_pdf(t_array, alpha, beta) + + # Numerical approximation of the gradients + epsilon = 1e-5 + grad_alpha = ( + objective_function(alpha + epsilon, beta, difficulty, t_array) + - objective_function(alpha - epsilon, beta, difficulty, t_array) + ) / (2 * epsilon) + grad_beta = ( + objective_function(alpha, beta + epsilon, difficulty, t_array) + - objective_function(alpha, beta - epsilon, difficulty, t_array) + ) / (2 * epsilon) + + # # Add regularization gradient components + # grad_alpha += learning_rate * (1 - 1 / alpha**2) + # grad_beta += learning_rate * (1 + 1 / beta**2) + + # Update parameters + alpha -= learning_rate * grad_alpha + beta -= learning_rate * grad_beta + + alpha = min(max(0.3, alpha), 3) + beta = min(max(0.3, beta), 3) + + y_pred = beta_pdf(t_array, alpha, beta) + self.approximate_f_inverse(alpha, beta) + + return y_pred, alpha, beta + + def approximate_f_inverse(self, alpha, beta): + # Generate data points + t_values = np.linspace(0, 1, 100000) + f_values = sp_beta.cdf(t_values, alpha, beta) + + # Sort and remove duplicates + sorted_indices = np.argsort(f_values) + f_values_sorted = f_values[sorted_indices] + t_values_sorted = t_values[sorted_indices] + + # Remove duplicates + _, unique_indices = np.unique(f_values_sorted, return_index=True) + f_values_unique = f_values_sorted[unique_indices] + t_values_unique = t_values_sorted[unique_indices] + + # Create the interpolation function for the inverse + f_inv = interp1d( + f_values_unique, + t_values_unique, + bounds_error=False, + fill_value="extrapolate", + ) + + self.f_inv = f_inv + + def apply_distortion(self, t, distortion_type): + assert torch.all((t >= 0) & (t <= 1)), "t must be in the range (0, 1)" + + if distortion_type == "identity": + ft = t + elif distortion_type == "cos": + ft = (1 - torch.cos(t * torch.pi)) / 2 + elif distortion_type == "revcos": + ft = 2 * t - (1 - torch.cos(t * torch.pi)) / 2 + elif distortion_type == "polyinc": + ft = t**2 + elif distortion_type == "polydec": + ft = 2 * t - t**2 + elif distortion_type == "beta": + raise ValueError(f"Unsupported for now: {distortion_type}") + elif distortion_type == "logitnormal": + raise ValueError(f"Unsupported for now: {distortion_type}") + else: + raise ValueError(f"Unknown distortion type: {distortion_type}") + + return ft diff --git a/CupOFMoCA-PROTAC/src/defog/src/flow_matching/utils.py b/CupOFMoCA-PROTAC/src/defog/src/flow_matching/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..dfb36c5e9522a34031141ff6202eaa060d0b1c41 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/flow_matching/utils.py @@ -0,0 +1,44 @@ +import torch.nn.functional as F + + +def p_xt_g_x1(X1, E1, t, limit_dist): + # x1 (B, D) + # t float + # returns (B, D, S) for varying x_t value + device = X1.device + limit_dist.X = limit_dist.X.to(device) + limit_dist.E = limit_dist.E.to(device) + + t_time = t.squeeze(-1)[:, None, None] + X1_onehot = F.one_hot(X1, num_classes=len(limit_dist.X)).float() + E1_onehot = F.one_hot(E1, num_classes=len(limit_dist.E)).float() + + Xt = t_time * X1_onehot + (1 - t_time) * limit_dist.X[None, None, :] + Et = ( + t_time[:, None] * E1_onehot + + (1 - t_time[:, None]) * limit_dist.E[None, None, None, :] + ) + + assert ((Xt.sum(-1) - 1).abs() < 1e-4).all() and ( + (Et.sum(-1) - 1).abs() < 1e-4 + ).all() + + return Xt.clamp(min=0.0, max=1.0), Et.clamp(min=0.0, max=1.0) + + +def dt_p_xt_g_x1(X1, E1, limit_dist): + # x1 (B, D) + # returns (B, D, S) for varying x_t value + device = X1.device + limit_dist.X = limit_dist.X.to(device) + limit_dist.E = limit_dist.E.to(device) + + X1_onehot = F.one_hot(X1, num_classes=len(limit_dist.X)).float() + E1_onehot = F.one_hot(E1, num_classes=len(limit_dist.E)).float() + + dX = X1_onehot - limit_dist.X[None, None, :] + dE = E1_onehot - limit_dist.E[None, None, None, :] + + assert (dX.sum(-1).abs() < 1e-4).all() and (dE.sum(-1).abs() < 1e-4).all() + + return dX, dE diff --git a/CupOFMoCA-PROTAC/src/defog/src/graph_discrete_flow_model.py b/CupOFMoCA-PROTAC/src/defog/src/graph_discrete_flow_model.py new file mode 100644 index 0000000000000000000000000000000000000000..d58e00234c880906fe534be23aeaf260d52ebad1 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/graph_discrete_flow_model.py @@ -0,0 +1,967 @@ +import time +import wandb +import os + +import numpy as np +import pickle +import matplotlib.pyplot as plt +from tqdm import tqdm +import pandas as pd +import torch +import torch.nn as nn +import torch.nn.functional as F +import pytorch_lightning as pl +from torch.distributions.categorical import Categorical + +from models.transformer_model import GraphTransformer + +from metrics.train_metrics import TrainLossDiscrete +from src import utils +from flow_matching.noise_distribution import NoiseDistribution +from flow_matching.time_distorter import TimeDistorter +from flow_matching.rate_matrix import RateMatrixDesigner +from flow_matching.utils import p_xt_g_x1 +from flow_matching import flow_matching_utils + + +class GraphDiscreteFlowModel(pl.LightningModule): + def __init__( + self, + cfg, + dataset_infos, + train_metrics, + sampling_metrics, + visualization_tools, + extra_features, + domain_features, + test_labels=None, + ): + super().__init__() + + self.cfg = cfg + self.name = f"{cfg.dataset.name}_{cfg.general.name}" + self.model_dtype = torch.float32 + self.conditional = cfg.general.conditional + self.test_labels = test_labels + + # number of steps used for sampling + self.sample_T = cfg.sample.sample_steps + + self.input_dims = dataset_infos.input_dims + self.output_dims = dataset_infos.output_dims + self.dataset_info = dataset_infos + self.node_dist = dataset_infos.nodes_dist + print("max num nodes: ", len(self.node_dist.prob) - 1) + print("min num nodes: ", torch.where(self.node_dist.prob > 0)[0][0].item()) + + self.train_metrics = train_metrics + self.sampling_metrics = sampling_metrics + + self.visualization_tools = visualization_tools + self.extra_features = extra_features + self.domain_features = domain_features + + self.noise_dist = NoiseDistribution(cfg.model.transition, dataset_infos) + self.limit_dist = self.noise_dist.get_limit_dist() + + # add virtual class when absorbing state refers to a new class + self.noise_dist.update_input_output_dims(self.input_dims) + self.noise_dist.update_dataset_infos(self.dataset_info) + + self.train_loss = TrainLossDiscrete( + self.cfg.model.lambda_train, + ) + + self.model = GraphTransformer( + n_layers=cfg.model.n_layers, + input_dims=self.input_dims, + hidden_mlp_dims=cfg.model.hidden_mlp_dims, + hidden_dims=cfg.model.hidden_dims, + output_dims=self.output_dims, + act_fn_in=nn.ReLU(), + act_fn_out=nn.ReLU(), + ) + + self.save_hyperparameters( + ignore=[ + "train_metrics", + "sampling_metrics", + ], + ) + + # logging + self.start_epoch_time = None + self.train_iterations = None + self.val_iterations = None + self.log_every_steps = cfg.general.log_every_steps + self.number_chain_steps = cfg.general.number_chain_steps + self.val_counter = 0 + self.adapt_counter = 0 + + # time distortor for both training and sampling steps + self.time_distorter = TimeDistorter( + train_distortion=cfg.train.time_distortion, + sample_distortion=cfg.sample.time_distortion, + alpha=1, + beta=1, + ) + + # rate matrix designer + self.rate_matrix_designer = RateMatrixDesigner( + rdb=self.cfg.sample.rdb, + rdb_crit=self.cfg.sample.rdb_crit, + eta=self.cfg.sample.eta, + omega=self.cfg.sample.omega, + limit_dist=self.limit_dist, + ) + + def training_step(self, data, i): + if data.edge_index.numel() == 0: + self.print("Found a batch with no edges. Skipping.") + return + + if self.conditional: + if torch.rand(1) < 0.1: + data.y = torch.ones_like(data.y, device=self.device) * -1 + + dense_data, node_mask = utils.to_dense( + data.x, + data.edge_index, + data.edge_attr, + data.batch, + ) + + dense_data = dense_data.mask(node_mask) + X, E = dense_data.X, dense_data.E + noisy_data = self.apply_noise(X, E, data.y, node_mask) + extra_data = self.compute_extra_data(noisy_data) + pred = self.forward(noisy_data, extra_data, node_mask) + + loss = self.train_loss( + masked_pred_X=pred.X, + masked_pred_E=pred.E, + pred_y=pred.y, + true_X=X, + true_E=E, + true_y=data.y, + log=i % self.log_every_steps == 0, + ) + + self.train_metrics( + masked_pred_X=pred.X, + masked_pred_E=pred.E, + true_X=X, + true_E=E, + log=i % self.log_every_steps == 0, + ) + + return {"loss": loss} + + def on_load_checkpoint(self, checkpoint: dict) -> None: + state = checkpoint["state_dict"] + current = self.state_dict() + # Replace any missing or shape-mismatched keys with current model values + # so that strict load_state_dict succeeds when resuming across datasets + for k, v in current.items(): + if k not in state or state[k].shape != v.shape: + state[k] = v + + def configure_optimizers(self): + return torch.optim.AdamW( + self.parameters(), + lr=self.cfg.train.lr, + amsgrad=True, + weight_decay=self.cfg.train.weight_decay, + ) + + def on_fit_start(self) -> None: + self.train_iterations = len(self.trainer.datamodule.train_dataloader()) + self.print( + "Size of the input features", + self.input_dims["X"], + self.input_dims["E"], + self.input_dims["y"], + ) + if self.local_rank == 0: + utils.setup_wandb(self.cfg) + + def on_train_epoch_start(self) -> None: + self.print("Starting train epoch...") + self.start_epoch_time = time.time() + self.train_loss.reset() + self.train_metrics.reset() + + def on_train_epoch_end(self) -> None: + to_log = self.train_loss.log_epoch_metrics() + self.print( + f"Epoch {self.current_epoch}: X_CE: {to_log['train_epoch/x_CE'] :.3f}" + f" -- E_CE: {to_log['train_epoch/E_CE'] :.3f} --" + f" y_CE: {to_log['train_epoch/y_CE'] :.3f}" + f" -- {time.time() - self.start_epoch_time:.1f}s " + ) + epoch_at_metrics, epoch_bond_metrics = self.train_metrics.log_epoch_metrics() + self.print( + f"Epoch {self.current_epoch}: {epoch_at_metrics} -- {epoch_bond_metrics}" + ) + if wandb.run: + wandb.log({"epoch": self.current_epoch}, commit=False) + + def on_validation_epoch_start(self) -> None: + print("Starting validation...") + self.sampling_metrics.reset() + + def validation_step(self, data, i): + return + + def on_validation_epoch_end(self) -> None: + self.val_counter += 1 + if self.val_counter % self.cfg.general.sample_every_val == 0: + print("Starting to sample") + samples, labels = self.sample( + is_test=False, save_samples=False, save_visualization=True + ) + to_log = self.evaluate_samples( + samples=samples, labels=labels, is_test=False + ) + + # Store results + filename = os.path.join( + os.getcwd(), + f"val_epoch{self.current_epoch}_res_{self.cfg.sample.eta}_{self.cfg.sample.rdb}.txt", + ) + with open(filename, "w") as file: + for key, value in to_log.items(): + file.write(f"{key}: {value}\n") + + self.print("Finished validation.") + + def on_test_epoch_start(self) -> None: + self.print("Starting test...") + self.sampling_metrics.reset() + if self.local_rank == 0: + utils.setup_wandb(self.cfg) + + def test_step(self, data, i): + return + + def on_test_epoch_end(self) -> None: + + if self.cfg.sample.search: + print("Starting sampling optimization...") + self.search_hyperparameters() + else: + print("Starting to sample") + samples, labels = self.sample( + is_test=True, + save_samples=self.cfg.general.save_samples, + save_visualization=True, + ) + to_log = self.evaluate_samples(samples=samples, labels=labels, is_test=True) + + # Store results + filename = os.path.join( + os.getcwd(), + f"test_epoch{self.current_epoch}_res_{self.cfg.sample.eta}_{self.cfg.sample.rdb}.txt", + ) + with open(filename, "w") as file: + for key, value in to_log.items(): + file.write(f"{key}: {value}\n") + + self.print("Finished testing.") + + def sample(self, is_test, save_samples, save_visualization): + + # Load generated samples if they exist + if self.cfg.general.generated_path: + self.print("Loading generated samples...") + with open(self.cfg.general.generated_path, "rb") as f: + samples = pickle.load(f) + # Set labels to None + labels = [None] * len(samples) + return samples, labels + + # Otherwise, generate new samples + if is_test: + samples_to_generate = ( + self.cfg.general.final_model_samples_to_generate + * self.cfg.general.num_sample_fold + ) + samples_left_to_generate = ( + self.cfg.general.final_model_samples_to_generate + * self.cfg.general.num_sample_fold + ) + samples_left_to_save = self.cfg.general.final_model_samples_to_save + chains_left_to_save = self.cfg.general.final_model_chains_to_save + + else: + samples_to_generate = self.cfg.general.samples_to_generate + samples_left_to_generate = self.cfg.general.samples_to_generate + samples_left_to_save = self.cfg.general.samples_to_save + chains_left_to_save = self.cfg.general.chains_to_save + + samples = [] + labels = [] + graph_id = 0 + while samples_left_to_generate > 0: + self.print( + f"Samples left to generate: {samples_left_to_generate}/" + f"{samples_to_generate}", + end="", + flush=True, + ) + bs = 2 * self.cfg.train.batch_size + to_generate = min(samples_left_to_generate, bs) + to_save = min(samples_left_to_save, bs) + chains_save = min(chains_left_to_save, bs) + num_chain_steps = min(self.number_chain_steps, self.sample_T) + cur_samples, cur_labels = self.sample_batch( + graph_id, + to_generate, + num_nodes=None, + save_final=to_save, + keep_chain=chains_save, + number_chain_steps=num_chain_steps, + save_visualization=save_visualization, + ) + samples.extend(cur_samples) + labels.extend(cur_labels) + + graph_id += to_generate + samples_left_to_save -= to_save + samples_left_to_generate -= to_generate + chains_left_to_save -= chains_save + + if save_samples: + self.print("Saving the generated graphs") + + # saving in txt version + filename = "graphs.txt" + with open(filename, "w") as f: + for item in samples: + f.write(f"N={item[0].shape[0]}\n") + atoms = item[0].tolist() + f.write("X: \n") + for at in atoms: + f.write(f"{at} ") + f.write("\n") + f.write("E: \n") + for bond_list in item[1]: + for bond in bond_list: + f.write(f"{bond} ") + f.write("\n") + f.write("\n") + + # saving in pkl version + with open(f"generated_samples_rank{self.local_rank}.pkl", "wb") as f: + pickle.dump(samples, f) + + print("Generated graphs saved.") + + return samples, labels + + def evaluate_samples( + self, + samples, + labels, + is_test, + save_filename="", + ): + print("Computing sampling metrics...") + + to_log = {} + samples_to_evaluate = self.cfg.general.final_model_samples_to_generate + if is_test: + for i in range(self.cfg.general.num_sample_fold): + cur_samples = samples[ + i * samples_to_evaluate : (i + 1) * samples_to_evaluate + ] + cur_labels = labels[ + i * samples_to_evaluate : (i + 1) * samples_to_evaluate + ] + + cur_to_log = self.sampling_metrics.forward( + cur_samples, + ref_metrics=self.dataset_info.ref_metrics, + name=f"self.name_{i}", + current_epoch=self.current_epoch, + val_counter=-1, + test=is_test, + local_rank=self.local_rank, + labels=cur_labels if self.conditional else None, + ) + + if i == 0: + to_log = {i: [cur_to_log[i]] for i in cur_to_log} + else: + to_log = {i: to_log[i] + [cur_to_log[i]] for i in cur_to_log} + + filename = os.path.join( + os.getcwd(), + f"epoch{self.current_epoch}_res_fold{i}_{save_filename}.txt", + ) + with open(filename, "w") as file: + for key, value in cur_to_log.items(): + file.write(f"{key}: {value}\n") + + to_log = { + i: (np.array(to_log[i]).mean(), np.array(to_log[i]).std()) + for i in to_log + } + else: + to_log = self.sampling_metrics.forward( + samples, + ref_metrics=self.dataset_info.ref_metrics, + name=self.cfg.general.name, + current_epoch=self.current_epoch, + val_counter=-1, + test=is_test, + local_rank=self.local_rank, + labels=labels if self.conditional else None, + ) + + return to_log + + def apply_noise(self, X, E, y, node_mask, t=None): + """Sample noise and apply it to the data.""" + + # Sample a timestep t. + bs = X.size(0) + if t is None: + t_float = self.time_distorter.train_ft(bs, self.device) + else: + t_float = t + + # sample random step + X_1_label = torch.argmax(X, dim=-1) + E_1_label = torch.argmax(E, dim=-1) + prob_X_t, prob_E_t = p_xt_g_x1( + X1=X_1_label, E1=E_1_label, t=t_float, limit_dist=self.limit_dist + ) + + # step 4 - sample noised data + sampled_t = flow_matching_utils.sample_discrete_features( + probX=prob_X_t, probE=prob_E_t, node_mask=node_mask + ) + noise_dims = self.noise_dist.get_noise_dims() + X_t = F.one_hot(sampled_t.X, num_classes=noise_dims["X"]) + E_t = F.one_hot(sampled_t.E, num_classes=noise_dims["E"]) + + # step 5 - create the PlaceHolder + z_t = utils.PlaceHolder(X=X_t, E=E_t, y=y).type_as(X_t).mask(node_mask) + + noisy_data = { + "t": t_float, + "X_t": z_t.X, + "E_t": z_t.E, + "y_t": z_t.y, + "node_mask": node_mask, + } + + return noisy_data + + def forward(self, noisy_data, extra_data, node_mask): + X = torch.cat((noisy_data["X_t"], extra_data.X), dim=2).float() + E = torch.cat((noisy_data["E_t"], extra_data.E), dim=3).float() + y = torch.hstack((noisy_data["y_t"], extra_data.y)).float() + return self.model(X, E, y, node_mask) + + @torch.no_grad() + def sample_batch( + self, + batch_id: int, + batch_size: int, + keep_chain: int, + number_chain_steps: int, + save_final: int, + num_nodes=None, + save_visualization: bool = True, + ): + """ + :param batch_id: int + :param batch_size: int + :param num_nodes: int, tensor (batch_size) (optional) for specifying number of nodes + :param save_final: int: number of predictions to save to file + :param keep_chain: int: number of chains to save to file + :param keep_chain_steps: number of timesteps to save for each chain + :return: molecule_list. Each element of this list is a tuple (atom_types, charges, positions) + """ + if num_nodes is None: + n_nodes = self.node_dist.sample_n(batch_size, self.device) + elif type(num_nodes) == int: + n_nodes = num_nodes * torch.ones( + batch_size, device=self.device, dtype=torch.int + ) + else: + assert isinstance(num_nodes, torch.Tensor) + n_nodes = num_nodes + n_max = torch.max(n_nodes).item() + + # Build the masks + arange = ( + torch.arange(n_max, device=self.device).unsqueeze(0).expand(batch_size, -1) + ) + node_mask = arange < n_nodes.unsqueeze(1) + + # Sample noise -- z has size (n_samples, n_nodes, n_features) + z_T = flow_matching_utils.sample_discrete_feature_noise( + limit_dist=self.noise_dist.get_limit_dist(), node_mask=node_mask + ) + if self.conditional: + if "qm9" in self.cfg.dataset.name: + y = self.test_labels + perm = torch.randperm(y.size(0)) + idx = perm[:100] + condition = y[idx] + condition = condition.to(self.device) + z_T.y = condition.repeat([10, 1])[:batch_size, :] + elif "tls" in self.cfg.dataset.name: + z_T.y = torch.zeros(batch_size, 1).to(self.device) + z_T.y[: batch_size // 2] = 1 + else: + raise NotImplementedError + X, E, y = z_T.X, z_T.E, z_T.y + + # Init chain storing variables + assert (E == torch.transpose(E, 1, 2)).all() + chain_X_size = torch.Size((number_chain_steps + 1, keep_chain, X.size(1))) + chain_E_size = torch.Size( + (number_chain_steps + 1, keep_chain, E.size(1), E.size(2)) + ) + chain_X = torch.zeros(chain_X_size) + chain_E = torch.zeros(chain_E_size) + chain_times = torch.zeros((number_chain_steps + 1, keep_chain)) + chain_time_unit = 1 / number_chain_steps + + # Store initial graph + if keep_chain > 0: + sampled_initial = z_T.mask(node_mask, collapse=True) + chain_X[0] = sampled_initial.X[:keep_chain] + chain_E[0] = sampled_initial.E[:keep_chain] + chain_times[0] = torch.zeros((keep_chain)) + + for t_int in tqdm(range(0, self.cfg.sample.sample_steps)): + # this state + t_array = t_int * torch.ones((batch_size, 1)).type_as(y) + t_norm = t_array / (self.cfg.sample.sample_steps) + if ("absorb" in self.cfg.model.transition) and (t_int == 0): + # to avoid failure mode of absorbing transition, add epsilon + t_norm = t_norm + 1e-6 + # next state + s_array = t_array + 1 + s_norm = s_array / (self.cfg.sample.sample_steps) + + # using round for precision + write_index = int(np.ceil(np.round(s_norm[0].item() / chain_time_unit, 6))) + + # Distort time + t_norm = self.time_distorter.sample_ft( + t_norm, self.cfg.sample.time_distortion + ) + s_norm = self.time_distorter.sample_ft( + s_norm, self.cfg.sample.time_distortion + ) + + # Sample z_s + sampled_s, discrete_sampled_s = self.sample_p_zs_given_zt( + t_norm, + s_norm, + X, + E, + y, + node_mask, + ) + + X, E, y = sampled_s.X, sampled_s.E, sampled_s.y + + # Save the first keep_chain graphs + chain_X[write_index] = discrete_sampled_s.X[:keep_chain] + chain_E[write_index] = discrete_sampled_s.E[:keep_chain] + chain_times[write_index] = s_norm.flatten()[:keep_chain] + + # Sample + sampled_s = sampled_s.mask(node_mask, collapse=True) + X, E, y = sampled_s.X, sampled_s.E, sampled_s.y + + # Prepare the chain for saving + if keep_chain > 0: + + # Repeat last frame 10x to see final sample better + chain_X = torch.cat([chain_X, chain_X[-1:].repeat(10, 1, 1)], dim=0) + chain_E = torch.cat([chain_E, chain_E[-1:].repeat(10, 1, 1, 1)], dim=0) + chain_times = torch.cat( + [chain_times, chain_times[-1:].repeat(10, 1)], dim=0 + ) + assert chain_X.size(0) == (number_chain_steps + 1 + 10) + + X, E, y = self.noise_dist.ignore_virtual_classes(X, E, y) + chain_X, chain_E, _ = self.noise_dist.ignore_virtual_classes( + chain_X, chain_E, y + ) + + # Save generated graphs + molecule_list = [] + label_list = [] + for i in range(batch_size): + n = n_nodes[i] + atom_types = X[i, :n].cpu() + edge_types = E[i, :n, :n].cpu() + molecule_list.append([atom_types, edge_types]) + label_list.append(y[i].cpu()) + + if self.visualization_tools is not None and save_visualization: + # Visualize chains + self.print("Visualizing chains...") + current_path = os.getcwd() + num_molecules = chain_X.size(1) # number of molecules + for i in range(num_molecules): + result_path = os.path.join( + current_path, + f"chains/{self.cfg.general.name}/" + f"epoch{self.current_epoch}/" + f"chains/molecule_{batch_id + i}", + ) + if not os.path.exists(result_path): + os.makedirs(result_path) + _ = self.visualization_tools.visualize_chain( + result_path, + chain_X[:, i, :].numpy(), + chain_E[:, i, :].numpy(), + chain_times[:, i].numpy(), + ) + self.print( + "\r{}/{} complete".format(i + 1, num_molecules), end="", flush=True + ) + self.print("\nVisualizing graphs...") + + # Visualize the final molecules + current_path = os.getcwd() + result_path = os.path.join( + current_path, + f"graphs/{self.cfg.general.name}/epoch{self.current_epoch}_b{batch_id}/", + ) + self.visualization_tools.visualize(result_path, molecule_list, save_final) + self.print("Done.") + + return molecule_list, label_list + + def compute_step_probs(self, R_t_X, R_t_E, X_t, E_t, dt, limit_x, limit_e): + step_probs_X = R_t_X * dt # type: ignore # (B, D, S) + step_probs_E = R_t_E * dt # (B, D, S) + + # Calculate the on-diagnoal step probabilities + # 1) Zero out the diagonal entries + # assert (E_t.argmax(-1) < 4).all() + step_probs_X.scatter_(-1, X_t.argmax(-1)[:, :, None], 0.0) + step_probs_E.scatter_(-1, E_t.argmax(-1)[:, :, :, None], 0.0) + + # 2) Calculate the diagonal entries such that the probability row sums to 1 + step_probs_X.scatter_( + -1, + X_t.argmax(-1)[:, :, None], + (1.0 - step_probs_X.sum(dim=-1, keepdim=True)).clamp(min=0.0), + ) + step_probs_E.scatter_( + -1, + E_t.argmax(-1)[:, :, :, None], + (1.0 - step_probs_E.sum(dim=-1, keepdim=True)).clamp(min=0.0), + ) + + # step 2 - merge to the original formulation + prob_X = step_probs_X.clone() + prob_E = step_probs_E.clone() + + return prob_X, prob_E + + def sample_p_zs_given_zt( + self, + t, + s, + X_t, + E_t, + y_t, + node_mask, + # , condition + ): + """Samples from zs ~ p(zs | zt). Only used during sampling. + if last_step, return the graph prediction as well""" + bs, n, dx = X_t.shape + _, _, _, de = E_t.shape + dt = (s - t)[0] + + # Neural net predictions + noisy_data = { + "X_t": X_t, + "E_t": E_t, + "y_t": y_t, + "t": t, + "node_mask": node_mask, + } + + extra_data = self.compute_extra_data(noisy_data) + pred = self.forward(noisy_data, extra_data, node_mask) + # Normalize predictions + pred_X = F.softmax(pred.X, dim=-1) # bs, n, d0 + pred_E = F.softmax(pred.E, dim=-1) # bs, n, n, d0 + limit_x = self.limit_dist.X + limit_e = self.limit_dist.E + + G_1_pred = pred_X, pred_E + G_t = X_t, E_t + + R_t_X, R_t_E = self.rate_matrix_designer.compute_graph_rate_matrix( + t, + node_mask, + G_t, + G_1_pred, + ) + + if self.conditional: + uncond_y = torch.ones_like(y_t, device=self.device) * -1 + noisy_data["y_t"] = uncond_y + + extra_data = self.compute_extra_data(noisy_data) + pred = self.forward(noisy_data, extra_data, node_mask) + + pred_X = F.softmax(pred.X, dim=-1) # bs, n, d0 + pred_E = F.softmax(pred.E, dim=-1) # bs, n, n, d0 + + R_t_X_uncond, R_t_E_uncond = ( + self.rate_matrix_designer.compute_graph_rate_matrix( + t, + node_mask, + G_t, + G_1_pred, + ) + ) + + guidance_weight = self.cfg.general.guidance_weight + R_t_X = torch.exp( + torch.log(R_t_X_uncond + 1e-6) * (1 - guidance_weight) + + torch.log(R_t_X + 1e-6) * guidance_weight + ) + R_t_E = torch.exp( + torch.log(R_t_E_uncond + 1e-6) * (1 - guidance_weight) + + torch.log(R_t_E + 1e-6) * guidance_weight + ) + + prob_X, prob_E = self.compute_step_probs( + R_t_X, R_t_E, X_t, E_t, dt, limit_x, limit_e + ) + + if s[0] == 1.0: + prob_X, prob_E = pred_X, pred_E + + sampled_s = flow_matching_utils.sample_discrete_features( + prob_X, prob_E, node_mask=node_mask + ) + + X_s = F.one_hot(sampled_s.X, num_classes=len(limit_x)).float() + E_s = F.one_hot(sampled_s.E, num_classes=len(limit_e)).float() + + assert (E_s == torch.transpose(E_s, 1, 2)).all() + assert (X_t.shape == X_s.shape) and (E_t.shape == E_s.shape) + + if self.conditional: + y_to_save = y_t + else: + y_to_save = torch.zeros([y_t.shape[0], 0], device=self.device) + + out_one_hot = utils.PlaceHolder(X=X_s, E=E_s, y=y_to_save) + out_discrete = utils.PlaceHolder(X=X_s, E=E_s, y=y_to_save) + + out_one_hot = out_one_hot.mask(node_mask).type_as(y_t) + out_discrete = out_discrete.mask(node_mask, collapse=True).type_as(y_t) + + return out_one_hot, out_discrete + + def compute_extra_data(self, noisy_data): + """At every training step (after adding noise) and step in sampling, compute extra information and append to + the network input.""" + + extra_features = self.extra_features(noisy_data) + + # one additional category is added for the absorbing transition + X, E, y = self.noise_dist.ignore_virtual_classes( + noisy_data["X_t"], noisy_data["E_t"], noisy_data["y_t"] + ) + noisy_data_to_mol_feat = noisy_data.copy() + noisy_data_to_mol_feat["X_t"] = X + noisy_data_to_mol_feat["E_t"] = E + noisy_data_to_mol_feat["y_t"] = y + extra_molecular_features = self.domain_features(noisy_data_to_mol_feat) + + extra_X = torch.cat((extra_features.X, extra_molecular_features.X), dim=-1) + extra_E = torch.cat((extra_features.E, extra_molecular_features.E), dim=-1) + extra_y = torch.cat((extra_features.y, extra_molecular_features.y), dim=-1) + + t = noisy_data["t"] + extra_y = torch.cat((extra_y, t), dim=1) + + return utils.PlaceHolder(X=extra_X, E=extra_E, y=extra_y) + + def search_hyperparameters(self): + """ + Grid search for sampling hypeparameters. + The num_step_list is tunable based on requirements. + """ + + num_step_list = [5, 10, 50, 100, 1000] + if self.cfg.dataset.name == "qm9": + num_step_list = [1, 5, 10, 50, 100, 500] + if self.cfg.dataset.name in ["guacamol", 'moses', 'zinc']: # accelerate + num_step_list = [50] + + if self.cfg.sample.search == "all": + results_df = self.search_distortion(num_step_list) + results_df = self.search_stochasticity(num_step_list) + results_df = self.search_target_guidance(num_step_list) + elif self.cfg.sample.search == "distortion": + results_df = self.search_distortion(num_step_list) + elif self.cfg.sample.search == "stochasticity": + results_df = self.search_stochasticity(num_step_list) + elif self.cfg.sample.search == "target_guidance": + results_df = self.search_target_guidance(num_step_list) + else: + raise NotImplementedError( + f"Search type {self.cfg.sample.search} not implemented." + ) + + print("Finished searching. Results saved to search_hyperparameters.csv") + + def search_distortion(self, num_step_list): + """ + Grid search for sampling distortion. + """ + results_df = pd.DataFrame() + distortion_list = ["identity", "polydec", "cos", "revcos", "polyinc"] + # distortion_list = ["identity", "polydec"] + + for num_step in num_step_list: + for distortor in distortion_list: + self.cfg.sample.sample_steps = num_step + self.cfg.sample.time_distortion = distortor + print( + f"############# Testing num steps: {num_step}, distortor: {distortor} #############" + ) + samples, labels = self.sample( + is_test=True, + save_samples=self.cfg.general.save_samples, + save_visualization=False, + ) + res = self.evaluate_samples( + samples=samples, labels=labels, is_test=True + ) + mean_res = {f"{key}_mean": res[key][0] for key in res} + std_res = {f"{key}_std": res[key][1] for key in res} + mean_res.update(std_res) + res_df = pd.DataFrame([mean_res]) + res_df["num_step"] = num_step + res_df["distortor"] = distortor + results_df = pd.concat([results_df, res_df], ignore_index=True) + # save at each step as well + results_df.to_csv(f"search_distortion.csv") + + # set back to default value + self.cfg.sample.time_distortion = "identity" + + # save the final results + results_df.reset_index(inplace=True) + results_df.set_index(["num_step", "distortor"], inplace=True) + results_df.to_csv(f"search_distortion.csv") + + def search_stochasticity(self, num_step_list): + """ + Grid search for stochasticity level eta. + The num_step_list is tunable based on requirements. + """ + results_df = pd.DataFrame() + eta_list = [0.0, 5, 10, 25, 50, 100, 200, 300, 500] + # eta_list = [5, 10] + for num_step in num_step_list: + for eta in eta_list: + self.cfg.sample.sample_steps = num_step + self.cfg.sample.eta = eta + self.rate_matrix_designer.eta = eta + print( + f"############# Testing num steps: {num_step}, eta: {eta} #############" + ) + samples, labels = self.sample( + is_test=True, + save_samples=self.cfg.general.save_samples, + save_visualization=False, + ) + res = self.evaluate_samples( + samples=samples, labels=labels, is_test=True + ) + mean_res = {f"{key}_mean": res[key][0] for key in res} + std_res = {f"{key}_std": res[key][1] for key in res} + mean_res.update(std_res) + res_df = pd.DataFrame([mean_res]) + res_df["num_step"] = num_step + res_df["eta"] = eta + results_df = pd.concat([results_df, res_df], ignore_index=True) + # save at each step as well + results_df.to_csv(f"search_stochasticity.csv") + + # set back to default value + self.cfg.sample.eta = 0.0 + + # save the final results + results_df.reset_index(inplace=True) + results_df.set_index(["num_step", "eta"], inplace=True) + results_df.to_csv(f"search_stochasticity.csv") + + def search_target_guidance(self, num_step_list): + """ + Grid search for target guidance omega. + The num_step_list is tunable based on requirements. + """ + results_df = pd.DataFrame() + omega_list = [ + 0.0, + 0.01, + 0.02, + 0.05, + 0.1, + 0.2, + 0.3, + 0.4, + 0.5, + 1.0, + 2.0, + ] # tunable based on requirements + # omega_list = [0.5, 0.01] # tunable based on requirements + + for num_step in num_step_list: + for omega in omega_list: + self.cfg.sample.sample_steps = num_step + self.cfg.sample.omega = omega + self.rate_matrix_designer.omega = omega + print( + f"############# Testing num steps: {num_step}, omega: {omega} #############" + ) + samples, labels = self.sample( + is_test=True, + save_samples=self.cfg.general.save_samples, + save_visualization=False, + ) + res = self.evaluate_samples( + samples=samples, labels=labels, is_test=True + ) + mean_res = {f"{key}_mean": res[key][0] for key in res} + std_res = {f"{key}_std": res[key][1] for key in res} + mean_res.update(std_res) + res_df = pd.DataFrame([mean_res]) + res_df["num_step"] = num_step + res_df["omega"] = omega + results_df = pd.concat([results_df, res_df], ignore_index=True) + # save at each step as well + results_df.to_csv(f"search_target_guidance.csv") + + # set back to default value + self.cfg.sample.omega = 0.0 + + # save the final results + results_df.reset_index(inplace=True) + results_df.set_index(["num_step", "omega"], inplace=True) + results_df.to_csv(f"search_target_guidance.csv") diff --git a/CupOFMoCA-PROTAC/src/defog/src/main.py b/CupOFMoCA-PROTAC/src/defog/src/main.py new file mode 100644 index 0000000000000000000000000000000000000000..ebf520cc78a92a89218524b39f49e2f740cfa07b --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/main.py @@ -0,0 +1,293 @@ +import os +import pathlib +import warnings + +import torch + +torch.cuda.empty_cache() +torch.set_float32_matmul_precision('high') +import hydra +from omegaconf import DictConfig +import pytorch_lightning as pl +from pytorch_lightning import Trainer +from pytorch_lightning.callbacks import ModelCheckpoint +from pytorch_lightning.utilities.warnings import PossibleUserWarning + +from src import utils +from metrics.abstract_metrics import TrainAbstractMetricsDiscrete +from graph_discrete_flow_model import GraphDiscreteFlowModel +from models.extra_features import DummyExtraFeatures, ExtraFeatures + + +warnings.filterwarnings("ignore", category=PossibleUserWarning) + + +@hydra.main(version_base="1.3", config_path="../configs", config_name="config") +def main(cfg: DictConfig): + pl.seed_everything(cfg.train.seed) + dataset_config = cfg["dataset"] + + if dataset_config["name"] in [ + "sbm", + "comm20", + "planar", + "tree", + ]: + import graph_tool # noqa: F401 — only needed for spectre datasets + from analysis.visualization import NonMolecularVisualization + from datasets.spectre_dataset import ( + SpectreGraphDataModule, + SpectreDatasetInfos, + ) + from analysis.spectre_utils import ( + PlanarSamplingMetrics, + SBMSamplingMetrics, + Comm20SamplingMetrics, + TreeSamplingMetrics, + ) + + datamodule = SpectreGraphDataModule(cfg) + if dataset_config["name"] == "sbm": + sampling_metrics = SBMSamplingMetrics(datamodule) + elif dataset_config["name"] == "comm20": + sampling_metrics = Comm20SamplingMetrics(datamodule) + elif dataset_config["name"] == "planar": + sampling_metrics = PlanarSamplingMetrics(datamodule) + elif dataset_config["name"] == "tree": + sampling_metrics = TreeSamplingMetrics(datamodule) + else: + raise NotImplementedError( + f"Dataset {dataset_config['name']} not implemented" + ) + + dataset_infos = SpectreDatasetInfos(datamodule, dataset_config) + + train_metrics = TrainAbstractMetricsDiscrete() + visualization_tools = NonMolecularVisualization(dataset_name=cfg.dataset.name) + + extra_features = ExtraFeatures( + cfg.model.extra_features, + cfg.model.rrwp_steps, + dataset_info=dataset_infos, + ) + domain_features = DummyExtraFeatures() + + dataset_infos.compute_input_output_dims( + datamodule=datamodule, + extra_features=extra_features, + domain_features=domain_features, + ) + + + elif dataset_config["name"] in ["qm9", "guacamol", "moses", "zinc", "protac_linker"]: + from metrics.molecular_metrics import ( + TrainMolecularMetrics, + SamplingMolecularMetrics, + ) + from metrics.molecular_metrics_discrete import TrainMolecularMetricsDiscrete + from models.extra_features_molecular import ExtraMolecularFeatures + from analysis.visualization import MolecularVisualization + + if "qm9" in dataset_config["name"]: + from datasets import qm9_dataset + + datamodule = qm9_dataset.QM9DataModule(cfg) + dataset_infos = qm9_dataset.QM9infos(datamodule=datamodule, cfg=cfg) + dataset_smiles = qm9_dataset.get_smiles( + cfg=cfg, + datamodule=datamodule, + dataset_infos=dataset_infos, + evaluate_datasets=False, + ) + elif dataset_config["name"] == "guacamol": + from datasets import guacamol_dataset + + datamodule = guacamol_dataset.GuacamolDataModule(cfg) + dataset_infos = guacamol_dataset.Guacamolinfos(datamodule, cfg) + dataset_smiles = guacamol_dataset.get_smiles( + raw_dir=datamodule.train_dataset.raw_dir, + filter_dataset=cfg.dataset.filter, + ) + + elif dataset_config.name == "moses": + from datasets import moses_dataset + + datamodule = moses_dataset.MosesDataModule(cfg) + dataset_infos = moses_dataset.MOSESinfos(datamodule, cfg) + dataset_smiles = moses_dataset.get_smiles( + raw_dir=datamodule.train_dataset.raw_dir, + filter_dataset=cfg.dataset.filter, + ) + elif "zinc" in dataset_config["name"]: + from datasets import zinc_dataset + + datamodule = zinc_dataset.ZINCDataModule(cfg) + dataset_infos = zinc_dataset.ZINCinfos(datamodule=datamodule, cfg=cfg) + dataset_smiles = zinc_dataset.get_smiles( + cfg=cfg, + datamodule=datamodule, + dataset_infos=dataset_infos, + evaluate_datasets=False, + ) + elif dataset_config["name"] == "protac_linker": + from datasets import protac_dataset + + datamodule = protac_dataset.ProtacLinkerDataModule(cfg) + dataset_infos = protac_dataset.ProtacLinkerInfos( + datamodule, cfg, recompute_statistics=False + ) + dataset_smiles = protac_dataset.get_smiles( + raw_dir=datamodule.train_dataset.raw_dir + ) + else: + raise ValueError("Dataset not implemented") + + extra_features = ExtraFeatures( + cfg.model.extra_features, + cfg.model.rrwp_steps, + dataset_info=dataset_infos, + ) + domain_features = ExtraMolecularFeatures(dataset_infos=dataset_infos) + + dataset_infos.compute_input_output_dims( + datamodule=datamodule, + extra_features=extra_features, + domain_features=domain_features, + ) + + train_metrics = TrainMolecularMetricsDiscrete(dataset_infos) + + # We do not evaluate novelty during training + add_virtual_states = "absorbing" == cfg.model.transition + sampling_metrics = SamplingMolecularMetrics( + dataset_infos, dataset_smiles, cfg, add_virtual_states=add_virtual_states + ) + visualization_tools = MolecularVisualization( + cfg.dataset.remove_h, dataset_infos=dataset_infos + ) + + elif dataset_config["name"] == "tls": + from datasets import tls_dataset + from metrics.tls_metrics import TLSSamplingMetrics + from analysis.visualization import NonMolecularVisualization + + datamodule = tls_dataset.TLSDataModule(cfg) + dataset_infos = tls_dataset.TLSInfos(datamodule=datamodule) + + train_metrics = TrainAbstractMetricsDiscrete() + extra_features = ( + ExtraFeatures( + cfg.model.extra_features, + cfg.model.rrwp_steps, + dataset_info=dataset_infos, + ) + if cfg.model.extra_features is not None + else DummyExtraFeatures() + ) + domain_features = DummyExtraFeatures() + + sampling_metrics = TLSSamplingMetrics(datamodule) + + visualization_tools = NonMolecularVisualization(dataset_name=cfg.dataset.name) + + dataset_infos.compute_input_output_dims( + datamodule=datamodule, + extra_features=extra_features, + domain_features=domain_features, + ) + + else: + raise NotImplementedError("Unknown dataset {}".format(cfg["dataset"])) + + dataset_infos.compute_reference_metrics( + datamodule=datamodule, + sampling_metrics=sampling_metrics, + ) + + model_kwargs = { + "dataset_infos": dataset_infos, + "train_metrics": train_metrics, + "sampling_metrics": sampling_metrics, + "visualization_tools": visualization_tools, + "extra_features": extra_features, + "domain_features": domain_features, + "test_labels": ( + datamodule.test_labels + if ("qm9" in cfg.dataset.name and cfg.general.conditional) + else None + ), + } + + utils.create_folders(cfg) + model = GraphDiscreteFlowModel(cfg=cfg, **model_kwargs) + + callbacks = [] + if cfg.train.save_model: + _defog_root = pathlib.Path(os.path.realpath(__file__)).parent.parent + _ckpt_dir = getattr(cfg.general, 'checkpoint_dir', None) or str( + _defog_root / 'checkpoints' / cfg.general.name + ) + checkpoint_callback = ModelCheckpoint( + dirpath=_ckpt_dir, + filename="{epoch}", + save_top_k=-1, + every_n_epochs=cfg.general.sample_every_val + * cfg.general.check_val_every_n_epochs, + ) + callbacks.append(checkpoint_callback) + + if cfg.train.ema_decay > 0: + ema_callback = utils.EMA(decay=cfg.train.ema_decay) + callbacks.append(ema_callback) + + name = cfg.general.name + if name == "debug": + print("[WARNING]: Run is called 'debug' -- it will run with fast_dev_run. ") + + use_gpu = cfg.general.gpus > 0 and torch.cuda.is_available() + trainer = Trainer( + gradient_clip_val=cfg.train.clip_grad, + strategy="ddp_find_unused_parameters_true", # Needed to load old checkpoints + accelerator="gpu" if use_gpu else "cpu", + devices=cfg.general.gpus if use_gpu else 1, + max_epochs=cfg.train.n_epochs, + check_val_every_n_epoch=cfg.general.check_val_every_n_epochs, + fast_dev_run=name == "debug", + enable_progress_bar=False, + callbacks=callbacks, + log_every_n_steps=50 if name != "debug" else 1, + logger=[], + ) + + if not cfg.general.test_only and cfg.general.generated_path is None: + finetune_from = getattr(cfg.general, 'finetune_from', None) + if finetune_from: + # Load weights only — no epoch/optimizer state — for cross-dataset fine-tuning + ckpt = torch.load(finetune_from, map_location='cpu', weights_only=False) + current_state = model.state_dict() + ckpt_state = ckpt['state_dict'] + for k, v in current_state.items(): + if k not in ckpt_state or ckpt_state[k].shape != v.shape: + ckpt_state[k] = v + model.load_state_dict(ckpt_state) + trainer.fit(model, datamodule=datamodule) + else: + trainer.fit(model, datamodule=datamodule, ckpt_path=cfg.general.resume) + else: + # Start by evaluating test_only_path + trainer.test(model, datamodule=datamodule, ckpt_path=cfg.general.test_only) + if cfg.general.evaluate_all_checkpoints: + directory = pathlib.Path(cfg.general.test_only).parents[0] + print("Directory:", directory) + files_list = os.listdir(directory) + for file in files_list: + if ".ckpt" in file: + ckpt_path = os.path.join(directory, file) + if ckpt_path == cfg.general.test_only: + continue + print("Loading checkpoint", ckpt_path) + trainer.test(model, datamodule=datamodule, ckpt_path=ckpt_path) + + +if __name__ == "__main__": + main() diff --git a/CupOFMoCA-PROTAC/src/defog/src/metrics/__init__.py b/CupOFMoCA-PROTAC/src/defog/src/metrics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/CupOFMoCA-PROTAC/src/defog/src/metrics/abstract_metrics.py b/CupOFMoCA-PROTAC/src/defog/src/metrics/abstract_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..f174ae9b4b906f3d3ae2bb087733f9400aa2a783 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/metrics/abstract_metrics.py @@ -0,0 +1,196 @@ +import torch +from torch import Tensor +from torch.nn import functional as F +from torch.nn import KLDivLoss +from torchmetrics import Metric, MeanSquaredError + + +class TrainAbstractMetricsDiscrete(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, masked_pred_X, masked_pred_E, true_X, true_E, log: bool): + pass + + def reset(self): + pass + + def log_epoch_metrics(self): + return None, None + + +class SumExceptBatchMetric(Metric): + def __init__(self): + super().__init__() + self.add_state("total_value", default=torch.tensor(0.0), dist_reduce_fx="sum") + self.add_state("total_samples", default=torch.tensor(0.0), dist_reduce_fx="sum") + + def update(self, values) -> None: + self.total_value += torch.sum(values) + self.total_samples += values.shape[0] + + def compute(self): + return self.total_value / self.total_samples + + +class SumExceptBatchMSE(MeanSquaredError): + def update(self, preds: Tensor, target: Tensor) -> None: + """Update state with predictions and targets. + + Args: + preds: Predictions from model + target: Ground truth values + """ + assert preds.shape == target.shape + sum_squared_error, n_obs = self._mean_squared_error_update(preds, target) + + self.sum_squared_error += sum_squared_error + self.total += n_obs + + def _mean_squared_error_update(self, preds: Tensor, target: Tensor): + """Updates and returns variables required to compute Mean Squared Error. Checks for same shape of input + tensors. + preds: Predicted tensor + target: Ground truth tensor + """ + diff = preds - target + sum_squared_error = torch.sum(diff * diff) + n_obs = preds.shape[0] + return sum_squared_error, n_obs + + +class SumExceptBatchKL(Metric): + def __init__(self): + super().__init__() + self.add_state("total_value", default=torch.tensor(0.0), dist_reduce_fx="sum") + self.add_state("total_samples", default=torch.tensor(0.0), dist_reduce_fx="sum") + + def update(self, p, q) -> None: + self.total_value += F.kl_div(q, p, reduction="sum") + self.total_samples += p.size(0) + + def compute(self): + return self.total_value / self.total_samples + + +class CrossEntropyMetric(Metric): + def __init__(self): + super().__init__() + self.add_state("total_ce", default=torch.tensor(0.0), dist_reduce_fx="sum") + self.add_state("total_samples", default=torch.tensor(0.0), dist_reduce_fx="sum") + + def update(self, preds: Tensor, target: Tensor, weight: Tensor = None) -> None: + """Update state with predictions and targets. + preds: Predictions from model (bs * n, d) or (bs * n * n, d) + target: Ground truth values (bs * n, d) or (bs * n * n, d).""" + target = torch.argmax(target, dim=-1) + if weight is not None: + output = F.cross_entropy( + preds, + target, + reduction="none", + weight=None, + ) + output = (output * weight).sum() + else: + output = F.cross_entropy( + preds, + target, + reduction="sum", + weight=None, + ) + # output = F.cross_entropy(preds, target, reduction="sum") + self.total_ce += output + self.total_samples += preds.size(0) + + def compute(self): + return self.total_ce / self.total_samples + + +class KLDMetric(Metric): + def __init__(self): + super().__init__() + self.add_state("total_ce", default=torch.tensor(0.0), dist_reduce_fx="sum") + self.add_state("total_samples", default=torch.tensor(0.0), dist_reduce_fx="sum") + + def update(self, preds: Tensor, target: Tensor, weight: Tensor = None) -> None: + """Update state with predictions and targets. + preds: Predictions from model (bs * n, d) or (bs * n * n, d) + target: Ground truth values (bs * n, d) or (bs * n * n, d).""" + # target = torch.argmax(target, dim=-1) + if weight is not None: + output = KLDivLoss(reduction="none")( + preds, + target, + ) + output = (output * weight).sum() + else: + output = KLDivLoss(reduction="none")( + preds, + target, + ) + + output[output.isnan()] = 0 # zero-out masked places + + output = output.sum() + # output = F.cross_entropy(preds, target, reduction="sum") + self.total_ce += output + self.total_samples += preds.size(0) + + def compute(self): + return self.total_ce / self.total_samples + + +class ProbabilityMetric(Metric): + def __init__(self): + """This metric is used to track the marginal predicted probability of a class during training.""" + super().__init__() + self.add_state("prob", default=torch.tensor(0.0), dist_reduce_fx="sum") + self.add_state("total", default=torch.tensor(0.0), dist_reduce_fx="sum") + + def update(self, preds: Tensor) -> None: + self.prob += preds.sum() + self.total += preds.numel() + + def compute(self): + return self.prob / self.total + + +class NLL(Metric): + def __init__(self): + super().__init__() + self.add_state("total_nll", default=torch.tensor(0.0), dist_reduce_fx="sum") + self.add_state("total_samples", default=torch.tensor(0.0), dist_reduce_fx="sum") + + def update(self, batch_nll) -> None: + self.total_nll += torch.sum(batch_nll) + self.total_samples += batch_nll.numel() + + def compute(self): + return self.total_nll / self.total_samples + + +def compute_ratios(gen_metrics, ref_metrics, metrics_keys): + print("Computing ratios of metrics: ", metrics_keys) + if ref_metrics is not None and len(metrics_keys) > 0: + ratios = {} + for key in metrics_keys: + try: + ref_metric = round(ref_metrics[key], 4) + except: + print(key, "not found") + continue + if ref_metric != 0.0: + ratios[key + "_ratio"] = gen_metrics[key] / ref_metric + else: + print(f"WARNING: Reference {key} is 0. Skipping its ratio.") + if len(ratios) > 0: + ratios["average_ratio"] = sum(ratios.values()) / len(ratios) + else: + ratios["average_ratio"] = -1 + print(f"WARNING: no ratio being saved.") + else: + print("WARNING: No reference metrics for ratio computation.") + ratios = {} + + return ratios diff --git a/CupOFMoCA-PROTAC/src/defog/src/metrics/molecular_metrics.py b/CupOFMoCA-PROTAC/src/defog/src/metrics/molecular_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..7f4699495166015c13e9e3e2be2d76809da90cb1 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/metrics/molecular_metrics.py @@ -0,0 +1,584 @@ +import os + +from rdkit import Chem +from torchmetrics import MeanSquaredError, MeanAbsoluteError +import time +import warnings + +### packages for visualization +from analysis.rdkit_functions import compute_molecular_metrics +import torch +from torchmetrics import Metric, MetricCollection +from torch import Tensor +import wandb +import torch.nn as nn +import numpy as np +import pandas as pd + +# import fcd + +from metrics.abstract_metrics import compute_ratios + + +class TrainMolecularMetrics(nn.Module): + def __init__(self, remove_h): + super().__init__() + self.train_atom_metrics = AtomMetrics(remove_h) + self.train_bond_metrics = BondMetrics() + + def forward( + self, + masked_pred_epsX, + masked_pred_epsE, + pred_y, + true_epsX, + true_epsE, + true_y, + log: bool, + ): + self.train_atom_metrics(masked_pred_epsX, true_epsX) + self.train_bond_metrics(masked_pred_epsE, true_epsE) + if log: + to_log = {} + for key, val in self.train_atom_metrics.compute().items(): + to_log["train/" + key] = val.item() + for key, val in self.train_bond_metrics.compute().items(): + to_log["train/" + key] = val.item() + if wandb.run: + wandb.log(to_log, commit=False) + + def reset(self): + for metric in [self.train_atom_metrics, self.train_bond_metrics]: + metric.reset() + + def log_epoch_metrics(self): + epoch_atom_metrics = self.train_atom_metrics.compute() + epoch_bond_metrics = self.train_bond_metrics.compute() + + to_log = {} + for key, val in epoch_atom_metrics.items(): + to_log["train_epoch/epoch" + key] = val.item() + for key, val in epoch_bond_metrics.items(): + to_log["train_epoch/epoch" + key] = val.item() + + if wandb.run: + wandb.log(to_log, commit=False) + + for key, val in epoch_atom_metrics.items(): + epoch_atom_metrics[key] = f"{val.item() :.3f}" + for key, val in epoch_bond_metrics.items(): + epoch_bond_metrics[key] = f"{val.item() :.3f}" + + return epoch_atom_metrics, epoch_bond_metrics + + +class SamplingMolecularMetrics(nn.Module): + def __init__(self, dataset_infos, dataset_smiles, cfg, add_virtual_states=False): + super().__init__() + di = dataset_infos + self.generated_n_dist = GeneratedNDistribution(di.max_n_nodes) + num_node_types = ( + di.output_dims["X"] + 1 if add_virtual_states else di.output_dims["X"] + ) + self.generated_node_dist = GeneratedNodesDistribution(num_node_types) + num_edge_types = ( + di.output_dims["E"] + 1 if add_virtual_states else di.output_dims["E"] + ) + self.generated_edge_dist = GeneratedEdgesDistribution(num_edge_types) + self.generated_valency_dist = ValencyDistribution(di.max_n_nodes) + self.cfg = cfg + + n_target_dist = di.n_nodes.type_as(self.generated_n_dist.n_dist) + n_target_dist = n_target_dist / torch.sum(n_target_dist) + self.register_buffer("n_target_dist", n_target_dist) + + node_target_dist = di.node_types.type_as(self.generated_node_dist.node_dist) + node_target_dist = node_target_dist / torch.sum(node_target_dist) + self.register_buffer("node_target_dist", node_target_dist) + if add_virtual_states: + node_target_dist = torch.cat([node_target_dist, torch.tensor([0.0])]) + + edge_target_dist = di.edge_types.type_as(self.generated_edge_dist.edge_dist) + edge_target_dist = edge_target_dist / torch.sum(edge_target_dist) + self.register_buffer("edge_target_dist", edge_target_dist) + if add_virtual_states: + edge_target_dist = torch.cat([edge_target_dist, torch.tensor([0.0])]) + + valency_target_dist = di.valency_distribution.type_as( + self.generated_valency_dist.edgepernode_dist + ) + valency_target_dist = valency_target_dist / torch.sum(valency_target_dist) + self.register_buffer("valency_target_dist", valency_target_dist) + self.n_dist_mae = HistogramsMAE(n_target_dist) + self.node_dist_mae = HistogramsMAE(node_target_dist) + self.edge_dist_mae = HistogramsMAE(edge_target_dist) + self.valency_dist_mae = HistogramsMAE(valency_target_dist) + + self.train_smiles = dataset_smiles["train"] + self.val_smiles = dataset_smiles["val"] + self.test_smiles = dataset_smiles["test"] + self.dataset_info = di + + def forward( + self, + molecules: list, + ref_metrics, + name, + current_epoch, + val_counter, + local_rank, + test=False, + labels=None, + ): + stability, rdkit_metrics, all_smiles, to_log = compute_molecular_metrics( + molecules, self.train_smiles, self.dataset_info, labels, self.cfg, test + ) + + if test and local_rank == 0: + with open(r"final_smiles.txt", "w") as fp: + for smiles in all_smiles: + # write each item on a new line + fp.write("%s\n" % smiles) + print("All smiles saved") + + print(all_smiles) + df = pd.DataFrame(all_smiles, columns=["SMILES"]) + df.to_csv("final_smiles.csv", index=False) + print("All SMILES saved to CSV") + + # self.dataset_info.compute_fcd = True + self.dataset_info.compute_fcd = False + if self.dataset_info.compute_fcd: + to_log["fcd"] = compute_fcd( + val_smiles=self.test_smiles if test else self.val_smiles, + generated_smiles=all_smiles, + # val_smiles=self.test_smiles, + # generated_smiles=self.train_smiles, + ) + else: + print("FCD computation is disabled. Skipping.") + to_log["fcd"] = -1 + + print("fcd", to_log["fcd"]) + + print("Starting custom metrics") + self.generated_n_dist(molecules) + generated_n_dist = self.generated_n_dist.compute() + self.n_dist_mae(generated_n_dist) + + self.generated_node_dist(molecules) + generated_node_dist = self.generated_node_dist.compute() + self.node_dist_mae(generated_node_dist) + + self.generated_edge_dist(molecules) + generated_edge_dist = self.generated_edge_dist.compute() + self.edge_dist_mae(generated_edge_dist) + + self.generated_valency_dist(molecules) + generated_valency_dist = self.generated_valency_dist.compute() + self.valency_dist_mae(generated_valency_dist) + + for i, atom_type in enumerate(self.dataset_info.atom_encoder.keys()): + generated_probability = generated_node_dist[i] + target_probability = self.node_target_dist[i] + to_log[f"molecular_metrics/{atom_type}_dist"] = ( + generated_probability - target_probability + ).item() + + for j, bond_type in enumerate( + ["No bond", "Single", "Double", "Triple", "Aromatic"] + # ["No bond", "Single", "Double", "Triple"] + ): + if j < len(generated_edge_dist): + generated_probability = generated_edge_dist[j] + target_probability = self.edge_target_dist[j] + to_log[f"molecular_metrics/bond_{bond_type}_dist"] = ( + generated_probability - target_probability + ).item() + + for valency in range(6): + generated_probability = generated_valency_dist[valency] + target_probability = self.valency_target_dist[valency] + to_log[f"molecular_metrics/valency_{valency}_dist"] = ( + generated_probability - target_probability + ).item() + + n_mae = self.n_dist_mae.compute() + node_mae = self.node_dist_mae.compute() + edge_mae = self.edge_dist_mae.compute() + valency_mae = self.valency_dist_mae.compute() + + ratios = compute_ratios( + gen_metrics=to_log, + ref_metrics=ref_metrics["test"] if test else ref_metrics["val"], + metrics_keys=["fcd"] if self.dataset_info.compute_fcd else [], + ) + to_log.update(ratios) + + if wandb.run: + wandb.log(to_log, commit=False) + wandb.run.summary["Gen n distribution"] = generated_n_dist + wandb.run.summary["Gen node distribution"] = generated_node_dist + wandb.run.summary["Gen edge distribution"] = generated_edge_dist + wandb.run.summary["Gen valency distribution"] = generated_valency_dist + + wandb.log( + { + "basic_metrics/n_mae": n_mae, + "basic_metrics/node_mae": node_mae, + "basic_metrics/edge_mae": edge_mae, + "basic_metrics/valency_mae": valency_mae, + }, + commit=False, + ) + + if local_rank == 0: + print("Custom metrics computed.") + if local_rank == 0: + valid_unique_molecules = rdkit_metrics[1] + text_filename = f"graphs/{name}/valid_unique_molecules_e{current_epoch}_b{val_counter}.txt" + if not os.path.exists(os.path.dirname(text_filename)): + os.makedirs(os.path.dirname(text_filename)) + textfile = open( + text_filename, + "w", + ) + textfile.writelines(valid_unique_molecules) + textfile.close() + print("Stability metrics:", stability, "--", rdkit_metrics[0]) + + return to_log + + def reset(self): + for metric in [ + self.n_dist_mae, + self.node_dist_mae, + self.edge_dist_mae, + self.valency_dist_mae, + ]: + metric.reset() + + +def compute_fcd(val_smiles, generated_smiles): + """smiles have must be a list of str""" + + print("Starting FCD computation") + start = time.time() + + # not using fcd.canonical_smiles because both smiles are already in canonical form (result from the Chem.MolToSmiles) + # filter out None values (not sanitizable molecules) + generated_smiles = [smile for smile in generated_smiles if smile is not None] + + # supress warnings + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=RuntimeWarning) + try: + fcd_score = fcd.get_fcd(generated_smiles, val_smiles) + except Exception as e: + print(f"Error in FCD computation. Setting FCD to -1.") + fcd_score = -1 + + end = time.time() + print("FCD computation time:", end - start, "FCD score is", fcd_score) + + return fcd_score + +class GeneratedNDistribution(Metric): + full_state_update = False + + def __init__(self, max_n): + super().__init__() + self.add_state( + "n_dist", + default=torch.zeros(max_n + 1, dtype=torch.float), + dist_reduce_fx="sum", + ) + + def update(self, molecules): + for molecule in molecules: + atom_types, _ = molecule + n = atom_types.shape[0] + self.n_dist[n] += 1 + + def compute(self): + return self.n_dist / torch.sum(self.n_dist) + + +class GeneratedNodesDistribution(Metric): + full_state_update = False + + def __init__(self, num_atom_types): + super().__init__() + self.add_state( + "node_dist", + default=torch.zeros(num_atom_types, dtype=torch.float), + dist_reduce_fx="sum", + ) + + def update(self, molecules): + for molecule in molecules: + atom_types, _ = molecule + + for atom_type in atom_types: + assert ( + int(atom_type) != -1 + ), "Mask error, the molecules should already be masked at the right shape" + self.node_dist[int(atom_type)] += 1 + + def compute(self): + return self.node_dist / torch.sum(self.node_dist) + + +class GeneratedEdgesDistribution(Metric): + full_state_update = False + + def __init__(self, num_edge_types): + super().__init__() + self.add_state( + "edge_dist", + default=torch.zeros(num_edge_types, dtype=torch.float), + dist_reduce_fx="sum", + ) + + def update(self, molecules): + for molecule in molecules: + _, edge_types = molecule + mask = torch.ones_like(edge_types) + mask = torch.triu(mask, diagonal=1).bool() + edge_types_ = edge_types[mask] + unique_edge_types, counts = torch.unique(edge_types_, return_counts=True) + for type, count in zip(unique_edge_types, counts): + self.edge_dist[type] += count + + def compute(self): + return self.edge_dist / torch.sum(self.edge_dist) + + +class MeanNumberEdge(Metric): + full_state_update = False + + def __init__(self): + super().__init__() + self.add_state("total_edge", default=torch.tensor(0.0), dist_reduce_fx="sum") + self.add_state("total_samples", default=torch.tensor(0.0), dist_reduce_fx="sum") + + def update(self, molecules, weight=1.0) -> None: + for molecule in molecules: + _, edge_types = molecule + triu_edge_types = torch.triu(edge_types, diagonal=1) + bonds = torch.nonzero(triu_edge_types) + self.total_edge += len(bonds) + self.total_samples += len(molecules) + + def compute(self): + return self.total_edge / self.total_samples + + +class ValencyDistribution(Metric): + full_state_update = False + + def __init__(self, max_n): + super().__init__() + self.add_state( + "edgepernode_dist", + default=torch.zeros(3 * max_n - 2, dtype=torch.float), + dist_reduce_fx="sum", + ) + + def update(self, molecules) -> None: + for molecule in molecules: + _, edge_types = molecule + edge_types_ = edge_types.clone() + edge_types_[edge_types == 4] = 1.5 + edge_types_[edge_types == 5] = 0.0 # zero out virtual states + valencies = torch.sum(edge_types_, dim=0) + unique, counts = torch.unique(valencies, return_counts=True) + for valency, count in zip(unique, counts): + self.edgepernode_dist[valency] += count + + def compute(self): + return self.edgepernode_dist / torch.sum(self.edgepernode_dist) + + +class HistogramsMAE(MeanAbsoluteError): + def __init__(self, target_histogram, **kwargs): + """Compute the distance between histograms.""" + super().__init__(**kwargs) + assert (target_histogram.sum() - 1).abs() < 1e-3 + self.target_histogram = target_histogram + + def update(self, pred): + pred = pred / pred.sum() + self.target_histogram = self.target_histogram.type_as(pred) + super().update(pred, self.target_histogram) + + +class MSEPerClass(MeanSquaredError): + full_state_update = False + + def __init__(self, class_id): + super().__init__() + self.class_id = class_id + + def update(self, preds: Tensor, target: Tensor) -> None: + """Update state with predictions and targets. + + Args: + preds: Predictions from model + target: Ground truth values + """ + preds = preds[..., self.class_id] + target = target[..., self.class_id] + super().update(preds, target) + + +class HydroMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class CarbonMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class NitroMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class OxyMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class FluorMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class BoronMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class BrMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class ClMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class IodineMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class PhosphorusMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class SulfurMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class SeMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class SiMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +# Bonds MSE + + +class NoBondMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class SingleMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class DoubleMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class TripleMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class AromaticMSE(MSEPerClass): + def __init__(self, i): + super().__init__(i) + + +class AtomMetrics(MetricCollection): + def __init__(self, dataset_infos): + remove_h = dataset_infos.remove_h + self.atom_decoder = dataset_infos.atom_decoder + num_atom_types = len(self.atom_decoder) + + types = { + "H": 0, + "C": 1, + "N": 2, + "O": 3, + "F": 4, + "B": 5, + "Br": 6, + "Cl": 7, + "I": 8, + "P": 9, + "S": 10, + "Se": 11, + "Si": 12, + } + + class_dict = { + "H": HydroMSE, + "C": CarbonMSE, + "N": NitroMSE, + "O": OxyMSE, + "F": FluorMSE, + "B": BoronMSE, + "Br": BrMSE, + "Cl": ClMSE, + "I": IodineMSE, + "P": PhosphorusMSE, + "S": SulfurMSE, + "Se": SeMSE, + "Si": SiMSE, + } + + metrics_list = [] + for i, atom_type in enumerate(self.atom_decoder): + metrics_list.append(class_dict[atom_type](i)) + + super().__init__(metrics_list) + + +class BondMetrics(MetricCollection): + def __init__(self): + mse_no_bond = NoBondMSE(0) + mse_SI = SingleMSE(1) + mse_DO = DoubleMSE(2) + mse_TR = TripleMSE(3) + mse_AR = AromaticMSE(4) + super().__init__([mse_no_bond, mse_SI, mse_DO, mse_TR, mse_AR]) + + +if __name__ == "__main__": + from torchmetrics.utilities import check_forward_full_state_property diff --git a/CupOFMoCA-PROTAC/src/defog/src/metrics/molecular_metrics_discrete.py b/CupOFMoCA-PROTAC/src/defog/src/metrics/molecular_metrics_discrete.py new file mode 100644 index 0000000000000000000000000000000000000000..c79888b0cbc6f69c9f8f4f878ffb6a1bde123c55 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/metrics/molecular_metrics_discrete.py @@ -0,0 +1,208 @@ +import torch +from torchmetrics import Metric, MetricCollection +from torch import Tensor +import wandb +import torch.nn as nn + + +class CEPerClass(Metric): + full_state_update = False + + def __init__(self, class_id): + super().__init__() + self.class_id = class_id + self.add_state("total_ce", default=torch.tensor(0.0), dist_reduce_fx="sum") + self.add_state("total_samples", default=torch.tensor(0.0), dist_reduce_fx="sum") + self.softmax = torch.nn.Softmax(dim=-1) + self.binary_cross_entropy = torch.nn.BCELoss(reduction="sum") + + def update(self, preds: Tensor, target: Tensor) -> None: + """Update state with predictions and targets. + Args: + preds: Predictions from model (bs, n, d) or (bs, n, n, d) + target: Ground truth values (bs, n, d) or (bs, n, n, d) + """ + target = target.reshape(-1, target.shape[-1]) + mask = (target != 0.0).any(dim=-1) + + prob = self.softmax(preds)[..., self.class_id] + prob = prob.flatten()[mask] + + target = target[:, self.class_id] + target = target[mask] + + output = self.binary_cross_entropy(prob, target) + self.total_ce += output + self.total_samples += prob.numel() + + def compute(self): + return self.total_ce / self.total_samples + + +class HydrogenCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class CarbonCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class NitroCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class OxyCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class FluorCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class BoronCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class BrCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class ClCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class IodineCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class PhosphorusCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class SulfurCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class SeCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class SiCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class NoBondCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class SingleCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class DoubleCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class TripleCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class AromaticCE(CEPerClass): + def __init__(self, i): + super().__init__(i) + + +class AtomMetricsCE(MetricCollection): + def __init__(self, dataset_infos): + atom_decoder = dataset_infos.atom_decoder + + class_dict = { + "H": HydrogenCE, + "C": CarbonCE, + "N": NitroCE, + "O": OxyCE, + "F": FluorCE, + "B": BoronCE, + "Br": BrCE, + "Cl": ClCE, + "I": IodineCE, + "P": PhosphorusCE, + "S": SulfurCE, + "Se": SeCE, + "Si": SiCE, + } + + metrics_list = [] + for i, atom_type in enumerate(atom_decoder): + metrics_list.append(class_dict[atom_type](i)) + super().__init__(metrics_list) + + +class BondMetricsCE(MetricCollection): + def __init__(self): + ce_no_bond = NoBondCE(0) + ce_SI = SingleCE(1) + ce_DO = DoubleCE(2) + ce_TR = TripleCE(3) + # ce_AR = AromaticCE(4) + # super().__init__([ce_no_bond, ce_SI, ce_DO, ce_TR, ce_AR]) + super().__init__([ce_no_bond, ce_SI, ce_DO, ce_TR]) + + +class TrainMolecularMetricsDiscrete(nn.Module): + def __init__(self, dataset_infos): + super().__init__() + self.train_atom_metrics = AtomMetricsCE(dataset_infos=dataset_infos) + self.train_bond_metrics = BondMetricsCE() + + def forward(self, masked_pred_X, masked_pred_E, true_X, true_E, log: bool): + self.train_atom_metrics(masked_pred_X, true_X) + self.train_bond_metrics(masked_pred_E, true_E) + if log: + to_log = {} + for key, val in self.train_atom_metrics.compute().items(): + to_log["train/" + key] = val.item() + for key, val in self.train_bond_metrics.compute().items(): + to_log["train/" + key] = val.item() + if wandb.run: + wandb.log(to_log, commit=False) + + def reset(self): + for metric in [self.train_atom_metrics, self.train_bond_metrics]: + metric.reset() + + def log_epoch_metrics(self): + epoch_atom_metrics = self.train_atom_metrics.compute() + epoch_bond_metrics = self.train_bond_metrics.compute() + + to_log = {} + for key, val in epoch_atom_metrics.items(): + to_log["train_epoch/" + key] = val.item() + for key, val in epoch_bond_metrics.items(): + to_log["train_epoch/" + key] = val.item() + if wandb.run: + wandb.log(to_log, commit=False) + + for key, val in epoch_atom_metrics.items(): + epoch_atom_metrics[key] = val.item() + for key, val in epoch_bond_metrics.items(): + epoch_bond_metrics[key] = val.item() + + return epoch_atom_metrics, epoch_bond_metrics diff --git a/CupOFMoCA-PROTAC/src/defog/src/metrics/tls_metrics.py b/CupOFMoCA-PROTAC/src/defog/src/metrics/tls_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..a04f63ca65158cdcc87c89f0043c31aab5d13a36 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/metrics/tls_metrics.py @@ -0,0 +1,194 @@ +import copy +from collections import Counter +from typing import List + +import networkx as nx +import numpy as np +import scipy.sparse as sp +import wandb +import torch +from torch import Tensor +from torchmetrics import MeanMetric + +from datasets.tls_dataset import CellGraph +from analysis.spectre_utils import PlanarSamplingMetrics +from analysis.spectre_utils import is_planar_graph + + +class TLSSamplingMetrics(PlanarSamplingMetrics): + def __init__(self, datamodule): + super().__init__(datamodule) + self.train_cell_graphs = self.loader_to_cell_graphs( + datamodule.train_dataloader() + ) + self.val_cell_graphs = self.loader_to_cell_graphs(datamodule.val_dataloader()) + self.test_cell_graphs = self.loader_to_cell_graphs(datamodule.test_dataloader()) + + def loader_to_cell_graphs(self, loader): + cell_graphs = [] + for batch in loader: + for tg_graph in batch.to_data_list(): + cell_graph = CellGraph.from_torch_geometric(tg_graph) + cell_graphs.append(cell_graph) + + return cell_graphs + + def is_cell_graph_valid(self, cg: CellGraph): + # connected and planar + return is_planar_graph(cg) + + def forward( + self, + generated_graphs: list, + ref_metrics, + name, + current_epoch, + val_counter, + local_rank, + test=False, + labels=None, + ): + + # Unattributed graphs specific + to_log = super().forward( + generated_graphs, + ref_metrics, + name, + current_epoch, + val_counter, + local_rank, + test, + labels, + ) + + # Cell graph specific + reference_cgs = self.test_cell_graphs if test else self.val_cell_graphs + generated_cgs = [] + if local_rank == 0: + print("Building generated cell graphs...") + for graph in generated_graphs: + generated_cgs.append(CellGraph.from_dense_graph(graph)) + + # TODO: Implement these metrics with torchmetrics for parallelization + generated_labels = torch.tensor([cg.to_label() for cg in generated_cgs]) + ambiguous_gen_cgs = sum( + [(cg_label == -1).int() for cg_label in generated_labels] + ).item() + if labels is not None: + true_labels = torch.tensor(labels) + high_tls_idxs = true_labels == 1 + low_tls_idxs = true_labels == 0 + total_tls_acc = (generated_labels == true_labels).float().mean().item() + high_tls_acc = ( + (generated_labels[high_tls_idxs] == true_labels[high_tls_idxs]) + .float() + .mean() + .item() + ) + low_tls_acc = ( + (generated_labels[low_tls_idxs] == true_labels[low_tls_idxs]) + .float() + .mean() + .item() + ) + else: + total_tls_acc = -1 + high_tls_acc = -1 + low_tls_acc = -1 + + # Compute novelty and uniqueness + if local_rank == 0: + print("Computing uniqueness, novelty and validity for cell graphs...") + frac_novel = eval_fraction_novel_cell_graphs( + generated_cell_graphs=generated_cgs, + train_cell_graphs=self.train_cell_graphs, + ) + ( + frac_unique, + frac_unique_and_novel, + frac_unique_and_novel_valid, + ) = eval_fraction_unique_novel_valid_cell_graphs( + generated_cell_graphs=generated_cgs, + train_cell_graphs=self.train_cell_graphs, + valid_cg_fn=self.is_cell_graph_valid, + ) + + tls_to_log = { + "tls_metrics/total_tls_acc": total_tls_acc, + "tls_metrics/high_tls_acc": high_tls_acc, + "tls_metrics/low_tls_acc": low_tls_acc, + "tls_metrics/num_ambiguous_tls": ambiguous_gen_cgs, + "tls_metrics/frac_novel": frac_novel, + "tls_metrics/frac_unique": frac_unique, + "tls_metrics/frac_unique_and_novel": frac_unique_and_novel, + "tls_metrics/frac_unique_and_novel_valid": frac_unique_and_novel_valid, + } + + print(f"TLS sampling metrics: {tls_to_log}") + if wandb.run: + # only log TLS sampling metrics because others are already logged by planar sampling metrics + wandb.log(tls_to_log, commit=False) + + to_log.update(tls_to_log) + + return to_log + + +# specific for cell graphs (isomorphism function is of cell graphs) +def eval_fraction_novel_cell_graphs(generated_cell_graphs, train_cell_graphs): + count_non_novel = 0 + for gen_cg in generated_cell_graphs: + for train_cg in train_cell_graphs: + if nx.faster_could_be_isomorphic(train_cg, gen_cg): + if gen_cg.is_isomorphic(train_cg): + count_non_novel += 1 + break + return 1 - count_non_novel / len(generated_cell_graphs) + + +# specific for cell graphs (isomorphism function is of cell graphs) +def eval_fraction_unique_novel_valid_cell_graphs( + generated_cell_graphs, + train_cell_graphs, + valid_cg_fn, +): + count_non_unique = 0 + count_not_novel = 0 + count_not_valid = 0 + for cg_idx, gen_cg in enumerate(generated_cell_graphs): + is_unique = True + for gen_cg_seen in generated_cell_graphs[:cg_idx]: + # test =gen_cg.is_isomorphic(gen_cg_seen) + # print(test) + # breakpoint() + if nx.faster_could_be_isomorphic(gen_cg_seen, gen_cg): + # we also need to consider phenotypes of nodes + if gen_cg.is_isomorphic(gen_cg_seen): + count_non_unique += 1 + is_unique = False + break + if is_unique: + is_novel = True + for train_cg in train_cell_graphs: + if nx.faster_could_be_isomorphic(train_cg, gen_cg): + if gen_cg.is_isomorphic(train_cg): + count_not_novel += 1 + is_novel = False + break + if is_novel: + if not valid_cg_fn(gen_cg): + count_not_valid += 1 + + frac_unique = 1 - count_non_unique / len(generated_cell_graphs) + frac_unique_non_isomorphic = frac_unique - count_not_novel / len( + generated_cell_graphs + ) + frac_unique_non_isomorphic_valid = ( + frac_unique_non_isomorphic - count_not_valid / len(generated_cell_graphs) + ) + + return ( + frac_unique, + frac_unique_non_isomorphic, + frac_unique_non_isomorphic_valid, + ) diff --git a/CupOFMoCA-PROTAC/src/defog/src/metrics/train_metrics.py b/CupOFMoCA-PROTAC/src/defog/src/metrics/train_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..857fca25f6844022af169ea7045823609912c794 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/metrics/train_metrics.py @@ -0,0 +1,118 @@ +import time + +import wandb +import torch +from torch import Tensor +import torch.nn as nn +from torchmetrics import Metric, MeanSquaredError, MetricCollection + +from metrics.abstract_metrics import ( + CrossEntropyMetric, + KLDMetric, +) + + +class NodeMSE(MeanSquaredError): + def __init__(self, *args): + super().__init__(*args) + + +class EdgeMSE(MeanSquaredError): + def __init__(self, *args): + super().__init__(*args) + + +class TrainLossDiscrete(nn.Module): + """Train with Cross entropy""" + + def __init__(self, lambda_train, kld=False): + super().__init__() + self.lambda_train = lambda_train + if not kld: + self.node_loss = CrossEntropyMetric() + self.edge_loss = CrossEntropyMetric() + else: + self.node_loss = KLDMetric() + self.edge_loss = KLDMetric() + self.y_loss = CrossEntropyMetric() + + def forward( + self, + masked_pred_X, + masked_pred_E, + pred_y, + true_X, + true_E, + true_y, + log: bool, + ): + """Compute train metrics + masked_pred_X : tensor -- (bs, n, dx) + masked_pred_E : tensor -- (bs, n, n, de) + pred_y : tensor -- (bs, ) + true_X : tensor -- (bs, n, dx) + true_E : tensor -- (bs, n, n, de) + true_y : tensor -- (bs, ) + log : boolean.""" + true_X = torch.reshape(true_X, (-1, true_X.size(-1))) # (bs * n, dx) + true_E = torch.reshape(true_E, (-1, true_E.size(-1))) # (bs * n * n, de) + masked_pred_X = torch.reshape( + masked_pred_X, (-1, masked_pred_X.size(-1)) + ) # (bs * n, dx) + masked_pred_E = torch.reshape( + masked_pred_E, (-1, masked_pred_E.size(-1)) + ) # (bs * n * n, de) + + # Remove masked rows + mask_X = (true_X != 0.0).any(dim=-1) + mask_E = (true_E != 0.0).any(dim=-1) + + flat_true_X = true_X[mask_X, :] + flat_pred_X = masked_pred_X[mask_X, :] + + flat_true_E = true_E[mask_E, :] + flat_pred_E = masked_pred_E[mask_E, :] + + loss_X = self.node_loss(flat_pred_X, flat_true_X) if true_X.numel() > 0 else 0.0 + loss_E = self.edge_loss(flat_pred_E, flat_true_E) if true_E.numel() > 0 else 0.0 + loss_y = self.y_loss(pred_y, true_y) if pred_y.numel() > 0 else 0.0 + + if log: + to_log = { + "train_loss/batch_CE": (loss_X + loss_E + loss_y).detach(), + "train_loss/X_CE": ( + self.node_loss.compute() if true_X.numel() > 0 else -1 + ), + "train_loss/E_CE": ( + self.edge_loss.compute() if true_E.numel() > 0 else -1 + ), + "train_loss/y_CE": self.y_loss.compute() if true_y.numel() > 0 else -1, + } + if wandb.run: + wandb.log(to_log, commit=True) + return loss_X + self.lambda_train[0] * loss_E + self.lambda_train[1] * loss_y + + def reset(self): + for metric in [self.node_loss, self.edge_loss, self.y_loss]: + metric.reset() + + def log_epoch_metrics(self): + epoch_node_loss = ( + self.node_loss.compute() if self.node_loss.total_samples > 0 else -1 + ) + epoch_edge_loss = ( + self.edge_loss.compute() if self.edge_loss.total_samples > 0 else -1 + ) + epoch_y_loss = ( + self.y_loss.compute() if self.y_loss.total_samples > 0 else -1 + ) + + to_log = { + "train_epoch/x_CE": epoch_node_loss, + "train_epoch/E_CE": epoch_edge_loss, + "train_epoch/y_CE": epoch_y_loss, + } + if wandb.run: + wandb.log(to_log, commit=False) + + return to_log diff --git a/CupOFMoCA-PROTAC/src/defog/src/models/__init__.py b/CupOFMoCA-PROTAC/src/defog/src/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/CupOFMoCA-PROTAC/src/defog/src/models/extra_features.py b/CupOFMoCA-PROTAC/src/defog/src/models/extra_features.py new file mode 100644 index 0000000000000000000000000000000000000000..8453623276d9f5209518e7465e9ae897739e902b --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/models/extra_features.py @@ -0,0 +1,439 @@ +import torch +from src import utils + + +class DummyExtraFeatures: + def __init__(self): + """This class does not compute anything, just returns empty tensors.""" + + def __call__(self, noisy_data): + X = noisy_data["X_t"] + E = noisy_data["E_t"] + y = noisy_data["y_t"] + empty_x = X.new_zeros((*X.shape[:-1], 0)) + empty_e = E.new_zeros((*E.shape[:-1], 0)) + empty_y = y.new_zeros((y.shape[0], 0)) + return utils.PlaceHolder(X=empty_x, E=empty_e, y=empty_y) + + +class ExtraFeatures: + def __init__(self, extra_features_type, rrwp_steps, dataset_info): + self.max_n_nodes = dataset_info.max_n_nodes + self.ncycles = NodeCycleFeatures() + self.features_type = extra_features_type + self.rrwp_steps = rrwp_steps + self.RRWP = RRWPFeatures() + self.RWP = RRWPFeatures(normalize=False) + if extra_features_type in ["eigenvalues", "all"]: + self.eigenfeatures = EigenFeatures(mode=extra_features_type) + + def __call__(self, noisy_data): + n = noisy_data["node_mask"].sum(dim=1).unsqueeze(1) / self.max_n_nodes + x_cycles, y_cycles = self.ncycles(noisy_data) # (bs, n_cycles) + + if self.features_type == "cycles": + E = noisy_data["E_t"] + extra_edge_attr = torch.zeros((*E.shape[:-1], 0)).type_as(E) + return utils.PlaceHolder( + X=x_cycles, E=extra_edge_attr, y=torch.hstack((n, y_cycles)) + ) + + elif self.features_type == "eigenvalues": + eigenfeatures = self.eigenfeatures(noisy_data) + E = noisy_data["E_t"] + extra_edge_attr = torch.zeros((*E.shape[:-1], 0)).type_as(E) + n_components, batched_eigenvalues = eigenfeatures # (bs, 1), (bs, 10) + return utils.PlaceHolder( + X=x_cycles, + E=extra_edge_attr, + y=torch.hstack((n, y_cycles)), + ) + + elif self.features_type == "rrwp": + E = noisy_data["E_t"].float()[..., 1:].sum(-1) # bs, n, n + rrwp_edge_attr = self.RRWP(E, k=self.rrwp_steps) + diag_index = torch.arange(rrwp_edge_attr.shape[1]) + rrwp_node_attr = rrwp_edge_attr[:, diag_index, diag_index, :] + self.eigenfeatures = EigenFeatures(mode="all") + + return utils.PlaceHolder( + X=rrwp_node_attr, + E=rrwp_edge_attr, + y=torch.hstack((n, y_cycles)), + ) + + elif self.features_type == "rrwp_double": + E = noisy_data["E_t"].float()[..., 1:].sum(-1) # bs, n, n + rrwp_edge_attr = self.RRWP(E, k=self.rrwp_steps) + rrwp_edge_attr_wo_norm = self.RWP(E, k=self.rrwp_steps) + + # Normalize the rrwp_edge_attr_wo_norm + max_value = rrwp_edge_attr_wo_norm.max(dim=1, keepdim=True).values + max_value = max_value.max(dim=2, keepdim=True).values + rrwp_edge_attr_wo_norm = rrwp_edge_attr_wo_norm / max_value + + rrwp_edge_attr = torch.cat((rrwp_edge_attr, rrwp_edge_attr_wo_norm), dim=-1) + diag_index = torch.arange(rrwp_edge_attr.shape[1]) + rrwp_node_attr = rrwp_edge_attr[:, diag_index, diag_index, :] + # self.eigenfeatures = EigenFeatures(mode='all') + + return utils.PlaceHolder( + X=rrwp_node_attr, + E=rrwp_edge_attr, + y=torch.hstack((n, y_cycles)), + ) + + elif self.features_type == "rrwp_only": + E = noisy_data["E_t"].float()[..., 1:].sum(-1) # bs, n, n + rrwp_edge_attr = self.RRWP(E, k=self.rrwp_steps) + diag_index = torch.arange(rrwp_edge_attr.shape[1]) + rrwp_node_attr = rrwp_edge_attr[:, diag_index, diag_index, :] + + return utils.PlaceHolder( + X=rrwp_node_attr, + E=rrwp_edge_attr, + y=n, + ) + + elif self.features_type == "rrwp_comp": + E = noisy_data["E_t"].float()[..., 1:].sum(-1) # bs, n, n + rrwp_edge_attr = self.RRWP(E, k=int(self.rrwp_steps / 2)) + diag_index = torch.arange(rrwp_edge_attr.shape[1]) + rrwp_node_attr = rrwp_edge_attr[:, diag_index, diag_index, :] + + comp_E = 1 - noisy_data["E_t"].float()[..., 1:].sum(-1) # bs, n, n + comp_rrwp_edge_attr = self.RRWP(comp_E, k=int(self.rrwp_steps / 2)) + comp_rrwp_node_attr = comp_rrwp_edge_attr[:, diag_index, diag_index, :] + + return utils.PlaceHolder( + X=torch.cat((rrwp_node_attr, comp_rrwp_node_attr), dim=-1), + E=torch.cat((rrwp_edge_attr, comp_rrwp_edge_attr), dim=-1), + y=torch.hstack((n, y_cycles)), + ) + + elif self.features_type == "all": + eigenfeatures = self.eigenfeatures(noisy_data) + E = noisy_data["E_t"] + extra_edge_attr = torch.zeros((*E.shape[:-1], 0)).type_as(E) + n_components, batched_eigenvalues, nonlcc_indicator, k_lowest_eigvec = ( + eigenfeatures # (bs, 1), (bs, 10), + ) + + return utils.PlaceHolder( + X=torch.cat( + (x_cycles, nonlcc_indicator, k_lowest_eigvec), + dim=-1, + ), + E=extra_edge_attr, + y=torch.hstack((n, y_cycles, n_components, batched_eigenvalues)), + ) + + else: + raise ValueError(f"Features type {self.features_type} not implemented") + + +class RRWPFeatures: + def __init__(self, k=10, normalize=True): + self.k = k + self.normalize = normalize + + def __call__(self, E, k=None): + k = k or self.k + + ( + bs, + n, + _, + ) = E.shape + if self.normalize: + degree = torch.zeros(bs, n, n, device=E.device) + to_fill = 1 / (E.sum(dim=-1).float()) + to_fill[E.sum(dim=-1).float() == 0] = 0 + degree = torch.diagonal_scatter(degree, to_fill, dim1=1, dim2=2) + E = degree @ E + + id = torch.eye(n, device=E.device).unsqueeze(0).repeat(bs, 1, 1) + rrwp_list = [id] + + for i in range(k - 1): + cur_rrwp = rrwp_list[-1] @ E + rrwp_list.append(cur_rrwp) + + return torch.stack(rrwp_list, -1) + + +class NodeCycleFeatures: + def __init__(self): + self.kcycles = KNodeCycles() + + def __call__(self, noisy_data): + adj_matrix = noisy_data["E_t"][..., 1:].sum(dim=-1).float() + + x_cycles, y_cycles = self.kcycles.k_cycles( + adj_matrix=adj_matrix + ) # (bs, n_cycles) + x_cycles = x_cycles.type_as(adj_matrix) * noisy_data["node_mask"].unsqueeze(-1) + # Avoid large values when the graph is dense + x_cycles = x_cycles / 10 + y_cycles = y_cycles / 10 + x_cycles[x_cycles > 1] = 1 + y_cycles[y_cycles > 1] = 1 + return x_cycles, y_cycles + + +class EigenFeatures: + """ + Code taken from : https://github.com/Saro00/DGN/blob/master/models/pytorch/eigen_agg.py + """ + + def __init__(self, mode): + """mode: 'eigenvalues' or 'all'""" + self.mode = mode + + def __call__(self, noisy_data): + E_t = noisy_data["E_t"] + mask = noisy_data["node_mask"] + A = E_t[..., 1:].sum(dim=-1).float() * mask.unsqueeze(1) * mask.unsqueeze(2) + # L = compute_laplacian(A, normalize="sym") + L = compute_laplacian(A, normalize=False) + mask_diag = 2 * L.shape[-1] * torch.eye(A.shape[-1]).type_as(L).unsqueeze(0) + mask_diag = mask_diag * (~mask.unsqueeze(1)) * (~mask.unsqueeze(2)) + L = L * mask.unsqueeze(1) * mask.unsqueeze(2) + mask_diag + + if self.mode == "eigenvalues": + eigvals = torch.linalg.eigvalsh(L) # bs, n + eigvals = eigvals.type_as(A) / torch.sum(mask, dim=1, keepdim=True) + + n_connected_comp, batch_eigenvalues = get_eigenvalues_features( + eigenvalues=eigvals + ) + return n_connected_comp.type_as(A), batch_eigenvalues.type_as(A) + + elif self.mode == "all": + eigvals, eigvectors = torch.linalg.eigh(L) + # print(eigvals) + eigvals = eigvals.type_as(A) / torch.sum(mask, dim=1, keepdim=True) + eigvectors = eigvectors * mask.unsqueeze(2) * mask.unsqueeze(1) + # Retrieve eigenvalues features + n_connected_comp, batch_eigenvalues = get_eigenvalues_features( + eigenvalues=eigvals + ) + + # Retrieve eigenvectors features + nonlcc_indicator, k_lowest_eigenvector = get_eigenvectors_features( + vectors=eigvectors, + node_mask=noisy_data["node_mask"], + n_connected=n_connected_comp, + ) + return ( + n_connected_comp, + batch_eigenvalues, + nonlcc_indicator, + k_lowest_eigenvector, + ) + else: + raise NotImplementedError(f"Mode {self.mode} is not implemented") + + +def compute_laplacian(adjacency, normalize: bool): + """ + adjacency : batched adjacency matrix (bs, n, n) + normalize: can be None, 'sym' or 'rw' for the combinatorial, symmetric normalized or random walk Laplacians + Return: + L (n x n ndarray): combinatorial or symmetric normalized Laplacian. + """ + diag = torch.sum(adjacency, dim=-1) # (bs, n) + n = diag.shape[-1] + D = torch.diag_embed(diag) # Degree matrix # (bs, n, n) + combinatorial = D - adjacency # (bs, n, n) + + if not normalize: + return (combinatorial + combinatorial.transpose(1, 2)) / 2 + + diag0 = diag.clone() + diag[diag == 0] = 1e-12 + + diag_norm = 1 / torch.sqrt(diag) # (bs, n) + D_norm = torch.diag_embed(diag_norm) # (bs, n, n) + L = torch.eye(n, device=adjacency.device).unsqueeze(0) - D_norm @ adjacency @ D_norm + L[diag0 == 0] = 0 + return (L + L.transpose(1, 2)) / 2 + + +def get_eigenvalues_features(eigenvalues, k=5): + """ + values : eigenvalues -- (bs, n) + node_mask: (bs, n) + k: num of non zero eigenvalues to keep + """ + ev = eigenvalues + bs, n = ev.shape + n_connected_components = (ev < 1e-5).sum(dim=-1) + try: + assert (n_connected_components > 0).all(), (n_connected_components, ev) + except: + import pdb + + pdb.set_trace() + + to_extend = max(n_connected_components) + k - n + if to_extend > 0: + eigenvalues = torch.hstack( + (eigenvalues, 2 * torch.ones(bs, to_extend).type_as(eigenvalues)) + ) + indices = torch.arange(k).type_as(eigenvalues).long().unsqueeze( + 0 + ) + n_connected_components.unsqueeze(1) + first_k_ev = torch.gather(eigenvalues, dim=1, index=indices) + return n_connected_components.unsqueeze(-1), first_k_ev + + +def get_eigenvectors_features(vectors, node_mask, n_connected, k=2): + """ + vectors (bs, n, n) : eigenvectors of Laplacian IN COLUMNS + returns: + not_lcc_indicator : indicator vectors of largest connected component (lcc) for each graph -- (bs, n, 1) + k_lowest_eigvec : k first eigenvectors for the largest connected component -- (bs, n, k) + """ + bs, n = vectors.size(0), vectors.size(1) + + # Create an indicator for the nodes outside the largest connected components + first_ev = torch.round(vectors[:, :, 0], decimals=3) * node_mask # bs, n + # Add random value to the mask to prevent 0 from becoming the mode + random = torch.randn(bs, n, device=node_mask.device) * (~node_mask) # bs, n + first_ev = first_ev + random + most_common = torch.mode(first_ev, dim=1).values # values: bs -- indices: bs + mask = ~(first_ev == most_common.unsqueeze(1)) + not_lcc_indicator = (mask * node_mask).unsqueeze(-1).float() + + # Get the eigenvectors corresponding to the first nonzero eigenvalues + to_extend = max(n_connected) + k - n + if to_extend > 0: + vectors = torch.cat( + (vectors, torch.zeros(bs, n, to_extend).type_as(vectors)), dim=2 + ) # bs, n , n + to_extend + indices = torch.arange(k).type_as(vectors).long().unsqueeze(0).unsqueeze( + 0 + ) + n_connected.unsqueeze( + 2 + ) # bs, 1, k + indices = indices.expand(-1, n, -1) # bs, n, k + first_k_ev = torch.gather(vectors, dim=2, index=indices) # bs, n, k + first_k_ev = first_k_ev * node_mask.unsqueeze(2) + + return not_lcc_indicator, first_k_ev + + +def batch_trace(X): + """ + Expect a matrix of shape B N N, returns the trace in shape B + :param X: + :return: + """ + diag = torch.diagonal(X, dim1=-2, dim2=-1) + trace = diag.sum(dim=-1) + return trace + + +def batch_diagonal(X): + """ + Extracts the diagonal from the last two dims of a tensor + :param X: + :return: + """ + return torch.diagonal(X, dim1=-2, dim2=-1) + + +class KNodeCycles: + """Builds cycle counts for each node in a graph.""" + + def __init__(self): + super().__init__() + + def calculate_kpowers(self): + self.k1_matrix = self.adj_matrix.float() + self.d = self.adj_matrix.sum(dim=-1) + self.k2_matrix = self.k1_matrix @ self.adj_matrix.float() + self.k3_matrix = self.k2_matrix @ self.adj_matrix.float() + self.k4_matrix = self.k3_matrix @ self.adj_matrix.float() + self.k5_matrix = self.k4_matrix @ self.adj_matrix.float() + self.k6_matrix = self.k5_matrix @ self.adj_matrix.float() + + def k3_cycle(self): + """tr(A ** 3).""" + c3 = batch_diagonal(self.k3_matrix) + return (c3 / 2).unsqueeze(-1).float(), (torch.sum(c3, dim=-1) / 6).unsqueeze( + -1 + ).float() + + def k4_cycle(self): + diag_a4 = batch_diagonal(self.k4_matrix) + c4 = ( + diag_a4 + - self.d * (self.d - 1) + - (self.adj_matrix @ self.d.unsqueeze(-1)).sum(dim=-1) + ) + return (c4 / 2).unsqueeze(-1).float(), (torch.sum(c4, dim=-1) / 8).unsqueeze( + -1 + ).float() + + def k5_cycle(self): + diag_a5 = batch_diagonal(self.k5_matrix) + triangles = batch_diagonal(self.k3_matrix) + c5 = ( + diag_a5 + - 2 * triangles * self.d + - (self.adj_matrix @ triangles.unsqueeze(-1)).sum(dim=-1) + + triangles + ) + return (c5 / 2).unsqueeze(-1).float(), (c5.sum(dim=-1) / 10).unsqueeze( + -1 + ).float() + + def k6_cycle(self): + term_1_t = batch_trace(self.k6_matrix) + term_2_t = batch_trace(self.k3_matrix**2) + term3_t = torch.sum(self.adj_matrix * self.k2_matrix.pow(2), dim=[-2, -1]) + d_t4 = batch_diagonal(self.k2_matrix) + a_4_t = batch_diagonal(self.k4_matrix) + term_4_t = (d_t4 * a_4_t).sum(dim=-1) + term_5_t = batch_trace(self.k4_matrix) + term_6_t = batch_trace(self.k3_matrix) + term_7_t = batch_diagonal(self.k2_matrix).pow(3).sum(-1) + term8_t = torch.sum(self.k3_matrix, dim=[-2, -1]) + term9_t = batch_diagonal(self.k2_matrix).pow(2).sum(-1) + term10_t = batch_trace(self.k2_matrix) + + c6_t = ( + term_1_t + - 3 * term_2_t + + 9 * term3_t + - 6 * term_4_t + + 6 * term_5_t + - 4 * term_6_t + + 4 * term_7_t + + 3 * term8_t + - 12 * term9_t + + 4 * term10_t + ) + return None, (c6_t / 12).unsqueeze(-1).float() + + def k_cycles(self, adj_matrix, verbose=False): + self.adj_matrix = adj_matrix + self.calculate_kpowers() + + k3x, k3y = self.k3_cycle() + assert (k3x >= -0.1).all() + + k4x, k4y = self.k4_cycle() + assert (k4x >= -0.1).all() + + k5x, k5y = self.k5_cycle() + assert (k5x >= -0.1).all(), k5x + + _, k6y = self.k6_cycle() + assert (k6y >= -0.1).all() + + kcyclesx = torch.cat([k3x, k4x, k5x], dim=-1) + kcyclesy = torch.cat([k3y, k4y, k5y, k6y], dim=-1) + return kcyclesx, kcyclesy diff --git a/CupOFMoCA-PROTAC/src/defog/src/models/extra_features_molecular.py b/CupOFMoCA-PROTAC/src/defog/src/models/extra_features_molecular.py new file mode 100644 index 0000000000000000000000000000000000000000..30c01aa0d46e2dd1fc309d6bc63eb325ab2ca905 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/models/extra_features_molecular.py @@ -0,0 +1,93 @@ +import torch +from src import utils + + +class ExtraMolecularFeatures: + def __init__(self, dataset_infos): + self.charge = ChargeFeature( + remove_h=dataset_infos.remove_h, valencies=dataset_infos.valencies + ) + self.valency = ValencyFeature() + self.weight = WeightFeature( + max_weight=dataset_infos.max_weight, atom_weights=dataset_infos.atom_weights + ) + + def __call__(self, noisy_data): + charge = self.charge(noisy_data).unsqueeze(-1) # (bs, n, 1) + valency = self.valency(noisy_data).unsqueeze(-1) # (bs, n, 1) + weight = self.weight(noisy_data) # (bs, 1) + + extra_edge_attr = torch.zeros((*noisy_data["E_t"].shape[:-1], 0)).type_as( + noisy_data["E_t"] + ) + + return utils.PlaceHolder( + X=torch.cat((charge, valency), dim=-1), E=extra_edge_attr, y=weight + ) + + +class ChargeFeature: + def __init__(self, remove_h, valencies): + self.remove_h = remove_h + self.valencies = valencies + + def __call__(self, noisy_data): + # bond_orders = torch.tensor( + # [0, 1, 2, 3, 1.5], device=noisy_data["E_t"].device + # ).reshape(1, 1, 1, -1) + de = noisy_data["E_t"].shape[-1] + if de == 5: + bond_orders = torch.tensor( + [0, 1, 2, 3, 1.5], device=noisy_data["E_t"].device + ).reshape(1, 1, 1, -1) + else: + bond_orders = torch.tensor( + [0, 1, 2, 3], device=noisy_data["E_t"].device + ).reshape(1, 1, 1, -1) + weighted_E = noisy_data["E_t"] * bond_orders # (bs, n, n, de) + current_valencies = weighted_E.argmax(dim=-1).sum(dim=-1) # (bs, n) + + valencies = torch.tensor( + self.valencies, device=noisy_data["X_t"].device + ).reshape(1, 1, -1) + X = noisy_data["X_t"] * valencies # (bs, n, dx) + normal_valencies = torch.argmax(X, dim=-1) # (bs, n) + + return (normal_valencies - current_valencies).type_as(noisy_data["X_t"]) + + +class ValencyFeature: + def __init__(self): + pass + + def __call__(self, noisy_data): + # bond_orders = torch.tensor( + # [0, 1, 2, 3, 1.5], device=noisy_data["E_t"].device + # ).reshape(1, 1, 1, -1) + de = noisy_data["E_t"].shape[-1] + if de == 5: + bond_orders = torch.tensor( + [0, 1, 2, 3, 1.5], device=noisy_data["E_t"].device + ).reshape(1, 1, 1, -1) + else: + bond_orders = torch.tensor( + [0, 1, 2, 3], device=noisy_data["E_t"].device + ).reshape(1, 1, 1, -1) + # bond_orders = torch.tensor([0, 1, 2, 3], device=noisy_data['E_t'].device).reshape(1, 1, 1, -1) # debug + E = noisy_data["E_t"] * bond_orders # (bs, n, n, de) + valencies = E.argmax(dim=-1).sum(dim=-1) # (bs, n) + return valencies.type_as(noisy_data["X_t"]) + + +class WeightFeature: + def __init__(self, max_weight, atom_weights): + self.max_weight = max_weight + self.atom_weight_list = torch.tensor(list(atom_weights.values())) + + def __call__(self, noisy_data): + X = torch.argmax(noisy_data["X_t"], dim=-1) # (bs, n) + X_weights = self.atom_weight_list.to(X.device)[X] # (bs, n) + return ( + X_weights.sum(dim=-1).unsqueeze(-1).type_as(noisy_data["X_t"]) + / self.max_weight + ) # (bs, 1) diff --git a/CupOFMoCA-PROTAC/src/defog/src/models/layers.py b/CupOFMoCA-PROTAC/src/defog/src/models/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..7d50b02eee86144bd66b84e12b335af250849a9f --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/models/layers.py @@ -0,0 +1,46 @@ +import torch +import torch.nn as nn + + +class Xtoy(nn.Module): + def __init__(self, dx, dy): + """Map node features to global features""" + super().__init__() + self.lin = nn.Linear(4 * dx, dy) + + def forward(self, X): + """X: bs, n, dx.""" + m = X.mean(dim=1) + mi = X.min(dim=1)[0] + ma = X.max(dim=1)[0] + std = X.std(dim=1) + z = torch.hstack((m, mi, ma, std)) + out = self.lin(z) + return out + + +class Etoy(nn.Module): + def __init__(self, d, dy): + """Map edge features to global features.""" + super().__init__() + self.lin = nn.Linear(4 * d, dy) + + def forward(self, E): + """E: bs, n, n, de + Features relative to the diagonal of E could potentially be added. + """ + m = E.mean(dim=(1, 2)) + mi = E.min(dim=2)[0].min(dim=1)[0] + ma = E.max(dim=2)[0].max(dim=1)[0] + std = torch.std(E, dim=(1, 2)) + z = torch.hstack((m, mi, ma, std)) + out = self.lin(z) + return out + + +def masked_softmax(x, mask, **kwargs): + if mask.sum() == 0: + return x + x_masked = x.clone() + x_masked[mask == 0] = -float("inf") + return torch.softmax(x_masked, **kwargs) diff --git a/CupOFMoCA-PROTAC/src/defog/src/models/transformer_model.py b/CupOFMoCA-PROTAC/src/defog/src/models/transformer_model.py new file mode 100644 index 0000000000000000000000000000000000000000..6aa59b6ace52909c6efcd3e140c80987d20e5029 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/models/transformer_model.py @@ -0,0 +1,380 @@ +import math + +import torch +import torch.nn as nn +from torch.nn.modules.dropout import Dropout +from torch.nn.modules.linear import Linear +from torch.nn.modules.normalization import LayerNorm +from torch.nn import functional as F +from torch import Tensor + +from src import utils +from flow_matching import flow_matching_utils +from models.layers import Xtoy, Etoy, masked_softmax + + +class XEyTransformerLayer(nn.Module): + """Transformer that updates node, edge and global features + d_x: node features + d_e: edge features + dz : global features + n_head: the number of heads in the multi_head_attention + dim_feedforward: the dimension of the feedforward network model after self-attention + dropout: dropout probablility. 0 to disable + layer_norm_eps: eps value in layer normalizations. + """ + + def __init__( + self, + dx: int, + de: int, + dy: int, + n_head: int, + dim_ffX: int = 2048, + dim_ffE: int = 128, + dim_ffy: int = 2048, + dropout: float = 0.1, + layer_norm_eps: float = 1e-5, + device=None, + dtype=None, + ) -> None: + kw = {"device": device, "dtype": dtype} + super().__init__() + + self.self_attn = NodeEdgeBlock(dx, de, dy, n_head, **kw) + + self.linX1 = Linear(dx, dim_ffX, **kw) + self.linX2 = Linear(dim_ffX, dx, **kw) + self.normX1 = LayerNorm(dx, eps=layer_norm_eps, **kw) + self.normX2 = LayerNorm(dx, eps=layer_norm_eps, **kw) + self.dropoutX1 = Dropout(dropout) + self.dropoutX2 = Dropout(dropout) + self.dropoutX3 = Dropout(dropout) + + self.linE1 = Linear(de, dim_ffE, **kw) + self.linE2 = Linear(dim_ffE, de, **kw) + self.normE1 = LayerNorm(de, eps=layer_norm_eps, **kw) + self.normE2 = LayerNorm(de, eps=layer_norm_eps, **kw) + self.dropoutE1 = Dropout(dropout) + self.dropoutE2 = Dropout(dropout) + self.dropoutE3 = Dropout(dropout) + + self.lin_y1 = Linear(dy, dim_ffy, **kw) + self.lin_y2 = Linear(dim_ffy, dy, **kw) + self.norm_y1 = LayerNorm(dy, eps=layer_norm_eps, **kw) + self.norm_y2 = LayerNorm(dy, eps=layer_norm_eps, **kw) + self.dropout_y1 = Dropout(dropout) + self.dropout_y2 = Dropout(dropout) + self.dropout_y3 = Dropout(dropout) + + self.activation = F.relu + + def forward(self, X: Tensor, E: Tensor, y, node_mask: Tensor): + """Pass the input through the encoder layer. + X: (bs, n, d) + E: (bs, n, n, d) + y: (bs, dy) + node_mask: (bs, n) Mask for the src keys per batch (optional) + Output: newX, newE, new_y with the same shape. + """ + + newX, newE, new_y = self.self_attn(X, E, y, node_mask=node_mask) + + newX_d = self.dropoutX1(newX) + X = self.normX1(X + newX_d) + # X = self.normX1(newX_d + nn.functional.sigmoid(X)) + + newE_d = self.dropoutE1(newE) + E = self.normE1(E + newE_d) + # newE_d = self.normE1(newE_d + nn.functional.sigmoid(E)) + + new_y_d = self.dropout_y1(new_y) + y = self.norm_y1(y + new_y_d) + # new_y_d = self.norm_y1(new_y_d + nn.functional.sigmoid(y)) + + ff_outputX = self.linX2(self.dropoutX2(self.activation(self.linX1(X)))) + ff_outputX = self.dropoutX3(ff_outputX) + X = self.normX2(X + ff_outputX) + + ff_outputE = self.linE2(self.dropoutE2(self.activation(self.linE1(E)))) + ff_outputE = self.dropoutE3(ff_outputE) + E = self.normE2(E + ff_outputE) + + ff_output_y = self.lin_y2(self.dropout_y2(self.activation(self.lin_y1(y)))) + ff_output_y = self.dropout_y3(ff_output_y) + y = self.norm_y2(y + ff_output_y) + + return X, E, y + + +class NodeEdgeBlock(nn.Module): + """Self attention layer that also updates the representations on the edges.""" + + def __init__(self, dx, de, dy, n_head, **kwargs): + super().__init__() + assert dx % n_head == 0, f"dx: {dx} -- nhead: {n_head}" + self.dropout_attn = Dropout(0.1) + self.dropout_X = Dropout(0.1) + self.dropout_E = Dropout(0.1) + + self.dx = dx + self.de = de + self.dy = dy + self.df = int(dx / n_head) + self.n_head = n_head + + # Attention + self.q = Linear(dx, dx) + self.k = Linear(dx, dx) + self.v = Linear(dx, dx) + + # FiLM E to X + self.e_add = Linear(de, dx) + self.e_mul = Linear(de, dx) + + # FiLM y to E + self.y_e_mul = Linear(dy, dx) # Warning: here it's dx and not de + self.y_e_add = Linear(dy, dx) + + # FiLM y to X + self.y_x_mul = Linear(dy, dx) + self.y_x_add = Linear(dy, dx) + + # Process y + self.y_y = Linear(dy, dy) + self.x_y = Xtoy(dx, dy) + self.e_y = Etoy(de, dy) + + # Output layers + self.x_out = Linear(dx, dx) + self.e_out = Linear(dx, de) + self.y_out = nn.Sequential(nn.Linear(dy, dy), nn.ReLU(), nn.Linear(dy, dy)) + + def forward(self, X, E, y, node_mask): + """ + :param X: bs, n, d node features + :param E: bs, n, n, d edge features + :param y: bs, dz global features + :param node_mask: bs, n + :return: newX, newE, new_y with the same shape. + """ + bs, n, _ = X.shape + x_mask = node_mask.unsqueeze(-1) # bs, n, 1 + e_mask1 = x_mask.unsqueeze(2) # bs, n, 1, 1 + e_mask2 = x_mask.unsqueeze(1) # bs, 1, n, 1 + + # 1. Map X to keys and queries + Q = self.q(X) * x_mask # (bs, n, dx) + K = self.k(X) * x_mask # (bs, n, dx) + flow_matching_utils.assert_correctly_masked(Q, x_mask) + # 2. Reshape to (bs, n, n_head, df) with dx = n_head * df + + Q = Q.reshape((Q.size(0), Q.size(1), self.n_head, self.df)) + K = K.reshape((K.size(0), K.size(1), self.n_head, self.df)) + + Q = Q.unsqueeze(2) # (bs, 1, n, n_head, df) + K = K.unsqueeze(1) # (bs, n, 1, n head, df) + + # Compute unnormalized attentions. Y is (bs, n, n, n_head, df) + Y = Q * K + Y = Y / math.sqrt(Y.size(-1)) + + # # Compute the distance based on the Gaussian kernel + # Y_exp = torch.exp( + # -(Q - K) * (Q - K) / math.sqrt(Y.size(-1)) + # ) # bs, n, n, n_head, df + # Y = Y + Y_exp + flow_matching_utils.assert_correctly_masked( + Y, (e_mask1 * e_mask2).unsqueeze(-1) + ) + + # Incorporate edge features to the self attention scores. + E1 = self.e_mul(E) * e_mask1 * e_mask2 # bs, n, n, dx + E1 = E1.reshape((E.size(0), E.size(1), E.size(2), self.n_head, self.df)) + E2 = self.e_add(E) * e_mask1 * e_mask2 # bs, n, n, dx + E2 = E2.reshape((E.size(0), E.size(1), E.size(2), self.n_head, self.df)) + Y = Y * (E1 + 1) + E2 # (bs, n, n, n_head, df) + + # Incorporate y to E + newE = Y.flatten(start_dim=3) # bs, n, n, dx + ye1 = self.y_e_add(y).unsqueeze(1).unsqueeze(1) # bs, 1, 1, de + ye2 = self.y_e_mul(y).unsqueeze(1).unsqueeze(1) + newE = ye1 + (ye2 + 1) * newE + + # Output E + newE = self.e_out(newE) * e_mask1 * e_mask2 # bs, n, n, de + flow_matching_utils.assert_correctly_masked(newE, e_mask1 * e_mask2) + + # Compute attentions. attn is still (bs, n, n, n_head, df) + softmax_mask = e_mask2.expand(-1, n, -1, self.n_head) # bs, 1, n, 1 + attn = masked_softmax(Y, softmax_mask, dim=2) # bs, n, n, n_head + + V = self.v(X) * x_mask # bs, n, dx + V = V.reshape((V.size(0), V.size(1), self.n_head, self.df)) + V = V.unsqueeze(1) # (bs, 1, n, n_head, df) + + # Compute weighted values + weighted_V = attn * V + weighted_V = weighted_V.sum(dim=2) + + # Send output to input dim + weighted_V = weighted_V.flatten(start_dim=2) # bs, n, dx + + # Incorporate y to X + yx1 = self.y_x_add(y).unsqueeze(1) + yx2 = self.y_x_mul(y).unsqueeze(1) + newX = yx1 + (yx2 + 1) * weighted_V + + # Output X + newX = self.x_out(newX) * x_mask + flow_matching_utils.assert_correctly_masked(newX, x_mask) + + # Process y based on X axnd E + y = self.y_y(y) + e_y = self.e_y(E) + x_y = self.x_y(X) + new_y = y + x_y + e_y + new_y = self.y_out(new_y) # bs, dy + + # newX = self.dropout_X(newX) + # newE = self.dropout_E(newE) + + return newX, newE, new_y + + +class GraphTransformer(nn.Module): + """ + n_layers : int -- number of layers + dims : dict -- contains dimensions for each feature type + """ + + def __init__( + self, + n_layers: int, + input_dims: dict, + hidden_mlp_dims: dict, + hidden_dims: dict, + output_dims: dict, + act_fn_in: nn.ReLU(), + act_fn_out: nn.ReLU(), + ): + super().__init__() + self.n_layers = n_layers + self.out_dim_X = output_dims["X"] + self.out_dim_E = output_dims["E"] + self.out_dim_y = output_dims["y"] + + self.mlp_in_X = nn.Sequential( + nn.Linear(input_dims["X"], hidden_mlp_dims["X"]), + act_fn_in, + nn.Linear(hidden_mlp_dims["X"], hidden_dims["dx"]), + act_fn_in, + ) + + self.mlp_in_E = nn.Sequential( + nn.Linear(input_dims["E"], hidden_mlp_dims["E"]), + act_fn_in, + nn.Linear(hidden_mlp_dims["E"], hidden_dims["de"]), + act_fn_in, + ) + + self.mlp_in_y = nn.Sequential( + nn.Linear(input_dims["y"] + 64, hidden_mlp_dims["y"]), + act_fn_in, + nn.Linear(hidden_mlp_dims["y"], hidden_dims["dy"]), + act_fn_in, + ) + + self.tf_layers = nn.ModuleList( + [ + XEyTransformerLayer( + dx=hidden_dims["dx"], + de=hidden_dims["de"], + dy=hidden_dims["dy"], + n_head=hidden_dims["n_head"], + dim_ffX=hidden_dims["dim_ffX"], + dim_ffE=hidden_dims["dim_ffE"], + ) + for i in range(n_layers) + ] + ) + + self.mlp_out_X = nn.Sequential( + nn.Linear(hidden_dims["dx"], hidden_mlp_dims["X"]), + act_fn_out, + nn.Linear(hidden_mlp_dims["X"], output_dims["X"]), + ) + + self.mlp_out_E = nn.Sequential( + nn.Linear(hidden_dims["de"], hidden_mlp_dims["E"]), + act_fn_out, + nn.Linear(hidden_mlp_dims["E"], output_dims["E"]), + ) + + self.mlp_out_y = nn.Sequential( + nn.Linear(hidden_dims["dy"], hidden_mlp_dims["y"]), + act_fn_out, + nn.Linear(hidden_mlp_dims["y"], output_dims["y"]), + ) + + def forward(self, X, E, y, node_mask): + bs, n = X.shape[0], X.shape[1] + + diag_mask = torch.eye(n) + diag_mask = ~diag_mask.type_as(E).bool() + diag_mask = diag_mask.unsqueeze(0).unsqueeze(-1).expand(bs, -1, -1, -1) + + X_to_out = X[..., : self.out_dim_X] + E_to_out = E[..., : self.out_dim_E] + y_to_out = y[..., : self.out_dim_y] + + new_E = self.mlp_in_E(E) + new_E = (new_E + new_E.transpose(1, 2)) / 2 + + # encode time steps additionally + time_emb = timestep_embedding(y[:, -1].unsqueeze(-1), 64) + y = torch.hstack([y, time_emb]) + + after_in = utils.PlaceHolder( + X=self.mlp_in_X(X), E=new_E, y=self.mlp_in_y(y) + ).mask(node_mask) + X, E, y = after_in.X, after_in.E, after_in.y + + for layer in self.tf_layers: + X, E, y = layer(X, E, y, node_mask) + + X = self.mlp_out_X(X) + E = self.mlp_out_E(E) + y = self.mlp_out_y(y) + + X = X + X_to_out + E = (E + E_to_out) * diag_mask + y = y + y_to_out + + E = 1 / 2 * (E + torch.transpose(E, 1, 2)) + + return utils.PlaceHolder(X=X, E=E, y=y).mask(node_mask) + + +def timestep_embedding(timesteps, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=timesteps.device) + args = timesteps.float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + + # import pdb; pdb.set_trace() + return embedding diff --git a/CupOFMoCA-PROTAC/src/defog/src/utils.py b/CupOFMoCA-PROTAC/src/defog/src/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b3bd69182deeba6b4117ea3bc29e5e876ff4ec90 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/defog/src/utils.py @@ -0,0 +1,210 @@ +import os +import torch_geometric.utils +from omegaconf import OmegaConf, open_dict +from torch_geometric.utils import to_dense_adj, to_dense_batch +import torch +import omegaconf +import wandb + + +def create_folders(args): + try: + # os.makedirs('checkpoints') + os.makedirs("graphs") + os.makedirs("chains") + except OSError: + pass + + try: + # os.makedirs('checkpoints/' + args.general.name) + os.makedirs("graphs/" + args.general.name) + os.makedirs("chains/" + args.general.name) + except OSError: + pass + + +def normalize(X, E, y, norm_values, norm_biases, node_mask): + X = (X - norm_biases[0]) / norm_values[0] + E = (E - norm_biases[1]) / norm_values[1] + y = (y - norm_biases[2]) / norm_values[2] + + diag = ( + torch.eye(E.shape[1], dtype=torch.bool).unsqueeze(0).expand(E.shape[0], -1, -1) + ) + E[diag] = 0 + + return PlaceHolder(X=X, E=E, y=y).mask(node_mask) + + +def unnormalize(X, E, y, norm_values, norm_biases, node_mask, collapse=False): + """ + X : node features + E : edge features + y : global features` + norm_values : [norm value X, norm value E, norm value y] + norm_biases : same order + node_mask + """ + X = X * norm_values[0] + norm_biases[0] + E = E * norm_values[1] + norm_biases[1] + y = y * norm_values[2] + norm_biases[2] + + return PlaceHolder(X=X, E=E, y=y).mask(node_mask, collapse) + + +def symmetrize_and_mask_diag(E): + # symmetrize the edge matrix + upper_triangular_mask = torch.zeros_like(E) + indices = torch.triu_indices(row=E.size(1), col=E.size(2), offset=1) + if len(E.shape) == 4: + upper_triangular_mask[:, indices[0], indices[1], :] = 1 + else: + upper_triangular_mask[:, indices[0], indices[1]] = 1 + E = E * upper_triangular_mask + E = E + torch.transpose(E, 1, 2) + # mask the diagonal + diag = ( + torch.eye(E.shape[1], dtype=torch.bool).unsqueeze(0).expand(E.shape[0], -1, -1) + ) + E[diag] = 0 + + return E + + +def to_dense(x, edge_index, edge_attr, batch): + X, node_mask = to_dense_batch(x=x, batch=batch) + # node_mask = node_mask.float() + edge_index, edge_attr = torch_geometric.utils.remove_self_loops( + edge_index, edge_attr + ) + max_num_nodes = X.size(1) + E = to_dense_adj( + edge_index=edge_index, + batch=batch, + edge_attr=edge_attr, + max_num_nodes=max_num_nodes, + ) + E = encode_no_edge(E) + + return PlaceHolder(X=X, E=E, y=None), node_mask + + +def encode_no_edge(E): + assert len(E.shape) == 4 + if E.shape[-1] == 0: + return E + no_edge = torch.sum(E, dim=3) == 0 + first_elt = E[:, :, :, 0] + first_elt[no_edge] = 1 + E[:, :, :, 0] = first_elt + diag = ( + torch.eye(E.shape[1], dtype=torch.bool).unsqueeze(0).expand(E.shape[0], -1, -1) + ) + E[diag] = 0 + return E + + +def update_config_with_new_keys(cfg, saved_cfg): + saved_general = saved_cfg.general + saved_train = saved_cfg.train + saved_model = saved_cfg.model + + for key, val in saved_general.items(): + OmegaConf.set_struct(cfg.general, True) + with open_dict(cfg.general): + if key not in cfg.general.keys(): + setattr(cfg.general, key, val) + + OmegaConf.set_struct(cfg.train, True) + with open_dict(cfg.train): + for key, val in saved_train.items(): + if key not in cfg.train.keys(): + setattr(cfg.train, key, val) + + OmegaConf.set_struct(cfg.model, True) + with open_dict(cfg.model): + for key, val in saved_model.items(): + if key not in cfg.model.keys(): + setattr(cfg.model, key, val) + return cfg + + +class PlaceHolder: + def __init__(self, X, E, y): + self.X = X + self.E = E + self.y = y + + def type_as(self, x: torch.Tensor): + """Changes the device and dtype of X, E, y.""" + self.X = self.X.type_as(x) + self.E = self.E.type_as(x) + self.y = self.y.type_as(x) + return self + + def to_device(self, device): + """Changes the device and dtype of X, E, y.""" + self.X = self.X.to(device) + self.E = self.E.to(device) + self.y = self.y.to(device) if self.y is not None else None + return self + + def mask(self, node_mask, collapse=False): + x_mask = node_mask.unsqueeze(-1) # bs, n, 1 + e_mask1 = x_mask.unsqueeze(2) # bs, n, 1, 1 + e_mask2 = x_mask.unsqueeze(1) # bs, 1, n, 1 + + if collapse: + self.X = torch.argmax(self.X, dim=-1) + self.E = torch.argmax(self.E, dim=-1) + + self.X[node_mask == 0] = -1 + self.E[(e_mask1 * e_mask2).squeeze(-1) == 0] = -1 + else: + self.X = self.X * x_mask + self.E = self.E * e_mask1 * e_mask2 + assert torch.allclose(self.E, torch.transpose(self.E, 1, 2)) + return self + + def __repr__(self): + return ( + f"X: {self.X.shape if type(self.X) == torch.Tensor else self.X} -- " + + f"E: {self.E.shape if type(self.E) == torch.Tensor else self.E} -- " + + f"y: {self.y.shape if type(self.y) == torch.Tensor else self.y}" + ) + + def split(self, node_mask): + """Split a PlaceHolder representing a batch into a list of placeholders representing individual graphs.""" + graph_list = [] + batch_size = self.X.shape[0] + for i in range(batch_size): + n = torch.sum(node_mask[i], dim=0) + x = self.X[i, :n] + e = self.E[i, :n, :n] + y = self.y[i] if self.y is not None else None + graph_list.append(PlaceHolder(X=x, E=e, y=y)) + return graph_list + + +def setup_wandb(cfg): + config_dict = omegaconf.OmegaConf.to_container( + cfg, resolve=True, throw_on_missing=True + ) + if cfg.general.test_only is None: + name = f"{cfg.general.name}" + else: + if cfg.sample.search: + name = f"{cfg.general.name}_search_{cfg.sample.search}" + else: + name = f"{cfg.general.name}_eta{cfg.sample.eta}_{cfg.sample.rdb}_{cfg.sample.time_distortion}" + kwargs = { + "name": name, + "project": getattr(cfg.general, 'wandb_project', f"graph_dfm_{cfg.dataset.name}"), + "config": config_dict, + "settings": wandb.Settings(_disable_stats=True), + "reinit": True, + "mode": cfg.general.wandb, + } + config_dict["general"]["local_dir"] = os.getcwd() + wandb.init(**kwargs) + wandb.save("*.txt") diff --git a/CupOFMoCA-PROTAC/src/degrademaster/data/PROTAC/name.json b/CupOFMoCA-PROTAC/src/degrademaster/data/PROTAC/name.json new file mode 100644 index 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b/CupOFMoCA-PROTAC/src/degrademaster/data/case_study/vhl_e3_feature_egfr.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aca7d4ba3f95a2d903e542f761c3d448323d7ff8a9e1d58e58d9b8bd55d0a74a +size 368 diff --git a/CupOFMoCA-PROTAC/src/protac/ARCRBN.py b/CupOFMoCA-PROTAC/src/protac/ARCRBN.py new file mode 100644 index 0000000000000000000000000000000000000000..3f2b9469595edd5404f49de8a899358c55864ef7 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/protac/ARCRBN.py @@ -0,0 +1,333 @@ +""" +AR-CRBN PROTAC Builder (mixed chemistry: amide on warhead side, SNAr on E3 side) +Target: Androgen Receptor (AR T878A) +E3 Ligase: CRBN (pomalidomide/thalidomide-based binder) +Source: PROTAC-DB ID 2525 + +Reference PROTAC (PROTAC-DB #2525, MW = 777 Da): + CN(C1=CC=C(C#N)C(Cl)=C1)[C@H]1CC[C@H](NC(=O)C2=CC=C(N3CCN(CC4CN(C5=CC=C6 + C(=O)N(C7CCC(=O)NC7=O)C(=O)C6=C5)C4)CC3)C=C2)CC1 + Linker: C1CN(CC2CNC2)CCN1 (piperazine-azetidine diamine, MW 155 Da) + +Linker chemistry: DIAMINE termini (fix_termini='amine') + The generated linker has two free secondary N atoms. + Warhead provides a COOH electrophile; E3 provides an electrophilic aromatic C-F. + + Warhead-COOH + linker-N → secondary amide (warhead side) + E3-Ar-F + linker-N → C-N aromatic bond via SNAr (E3 side) + +Warhead (COOH-armed, MW ≈ 388 Da): + 4-[((1R,4R)-4-(N-methyl-N-(4-cyano-3-chlorophenyl)amino)cyclohexyl)amino]benzoic + acid. The para-COOH reacts with the linker's secondary amine → amide. + +E3 Ligand (5-fluorothalidomide, MW ≈ 276 Da): + 2-(2,6-dioxopiperidin-3-yl)-5-fluoroisoindoline-1,3-dione. + C5-F undergoes SNAr with the linker's secondary amine → aromatic C-N bond. +""" + +from rdkit import Chem +from rdkit.Chem import AllChem, Descriptors +from rdkit import RDLogger +from typing import Optional + +try: + from .protac_builder import PROTAC_Builder +except ImportError: + from protac_builder import PROTAC_Builder + +RDLogger.DisableLog("rdApp.error") + + +# --------------------------------------------------------------------------- +# Component SMILES +# --------------------------------------------------------------------------- + +# AR antagonist scaffold with COOH arm — reacts with linker piperazine-N → amide +AR_WARHEAD_SMILES = "OC(=O)c1ccc(N[C@H]2CC[C@H](N(C)c3ccc(C#N)c(Cl)c3)CC2)cc1" + +# 5-fluorothalidomide (CRBN binder): C5-F undergoes SNAr with linker azetidine-N +CRBN_E3_SMILES = "Fc1ccc2c(c1)C(=O)N([C@@H]1CCC(=O)NC1=O)C2=O" + +AR_WARHEAD_MOL = Chem.MolFromSmiles(AR_WARHEAD_SMILES) +CRBN_E3_MOL = Chem.MolFromSmiles(CRBN_E3_SMILES) + +# Standard interface aliases +WARHEAD_MOL = AR_WARHEAD_MOL +E3_MOL = CRBN_E3_MOL +WARHEAD_SMILES = AR_WARHEAD_SMILES +E3_SMILES = CRBN_E3_SMILES + +# Reactive SMARTS (used by batch_report.py to locate reactive atoms in the free fragments) +WH_REACTIVE_SMARTS = "[CX3](=[OX1])[OX2H1]" # warhead COOH carbonyl C +E3_REACTIVE_SMARTS = "[c][F]" # E3 isoindoline C5 bearing F + +# Linker attachment SMARTS — linker must expose two secondary amines +_NH_SMARTS = "[NX3;!H0;!$(NC=O);!$(NS=O);!$(N=O)]" + +# Fallback substructure patterns for batch_report.py assembly check +WARHEAD_PAT = Chem.MolFromSmarts("[NX3](C)c1ccc(C#N)c(Cl)c1") # N-methyl-4-cyano-3-Cl-aniline +E3_PAT = Chem.MolFromSmarts("O=C1NC(=O)CCC1") # glutarimide (CRBN) + + +class ARCRBN_PROTAC_Builder(PROTAC_Builder): + """ + PROTAC builder for AR-CRBN (PROTAC-DB #2525). + + Warhead side: COOH (electrophile) + linker secondary amine → amide bond. + E3 side: isoindoline C5-F (SNAr electrophile) + linker secondary amine → aromatic C-N. + + Use with --fix_termini amine in defog_guided.py. + """ + + _COOH_SMARTS = "[CX3](=[OX1])[OX2H1]" + _AR_F_SMARTS = "[c][F]" + _NH_SMARTS = "[NX3;!H0;!$(NC=O);!$(NS=O);!$(N=O)]" + _AMIDE_SMARTS = "[NX3H0,NX3H1][CX3](=[OX1])[#6]" + # Aromatic C-N in assembled PROTAC (isoindoline C5 bonded to non-amide N) + _AR_CN_SMARTS = "c1ccc2c(c1)C(=O)N(*)C2=O" + + def __init__(self, warhead: Optional[Chem.Mol] = None, + e3: Optional[Chem.Mol] = None): + # Initialize patterns before super().__init__() which calls _verify_* + self._cooh_pat = Chem.MolFromSmarts(self._COOH_SMARTS) + self._ar_f_pat = Chem.MolFromSmarts(self._AR_F_SMARTS) + self._nh_pat = Chem.MolFromSmarts(self._NH_SMARTS) + self._amide_pat = Chem.MolFromSmarts(self._AMIDE_SMARTS) + self._ar_cn_pat = Chem.MolFromSmarts(self._AR_CN_SMARTS) + self._ar_core = Chem.MolFromSmarts("[NX3](C)c1ccc(C#N)c(Cl)c1") + self._glutarimide = Chem.MolFromSmarts("O=C1NC(=O)CCC1") + super().__init__( + warhead=warhead if warhead is not None else AR_WARHEAD_MOL, + e3=e3 if e3 is not None else CRBN_E3_MOL, + warhead_name="AR antagonist (COOH-armed)", + e3_name="5-fluorothalidomide (CRBN binder, SNAr)", + ) + + # Warhead side: linker secondary amine + warhead COOH → secondary amide + self._amide_rxn = AllChem.ReactionFromSmarts( + "[NX3;!H0;!$(NC=O);!$(NS=O):1].[CX3:2](=[OX1:3])[OX2H1]" + ">>[N:1][CX3:2](=[OX1:3])" + ) + # E3 side: linker secondary amine + E3 aromatic C-F → aromatic C-N (SNAr) + self._snAr_rxn = AllChem.ReactionFromSmarts( + "[NX3;!H0;!$(NC=O);!$(NS=O):1].[c:2][F,Cl]>>[c:2][N:1]" + ) + + def _verify_warhead_reactive_group(self): + if self.warhead_mol is not None and self.warhead_mol.HasSubstructMatch(self._cooh_pat): + print("✓ AR warhead: COOH reactive group confirmed") + else: + print("⚠ AR warhead: no COOH detected — check warhead SMILES") + + def _verify_e3_reactive_group(self): + if self.e3_mol is not None and self.e3_mol.HasSubstructMatch(self._ar_f_pat): + print("✓ CRBN E3: aromatic C-F (SNAr) reactive group confirmed") + else: + print("⚠ CRBN E3: no Ar-F detected — check E3 SMILES") + + # ------------------------------------------------------------------ + # Linker attachment site detection + # ------------------------------------------------------------------ + + def _reactive_amine_atoms(self, linker_mol: Chem.Mol) -> list: + """Find secondary/primary amine N atoms on the linker (attachment sites).""" + results, seen = [], set() + for m in linker_mol.GetSubstructMatches(self._nh_pat): + n_idx = m[0] + if n_idx not in seen: + seen.add(n_idx) + results.append({"idx": n_idx}) + return results + + def find_linker_attachment_sites(self, linker_mol: Chem.Mol, + amide_only: bool = False) -> list: + """Return all ordered (warhead_site, e3_site) amine pairs for assembly.""" + amines = self._reactive_amine_atoms(linker_mol) + sites, seen = [], set() + for w in amines: + for e in amines: + key = (w["idx"], e["idx"]) + if key not in seen and w["idx"] != e["idx"]: + seen.add(key) + sites.append({ + "warhead_site": w["idx"], + "warhead_type": "amine", + "warhead_restored": False, + "e3_site": e["idx"], + "e3_type": "amine", + "e3_restored": False, + }) + return sites + + # ------------------------------------------------------------------ + # Assembly: amide (warhead) + SNAr (E3) + # ------------------------------------------------------------------ + + def _assemble_protac(self, linker_mol: Chem.Mol, + warhead_site_idx: int, e3_site_idx: int, + warhead_type: str = "amine", e3_type: str = "amine", + warhead_restored: bool = False, + e3_restored: bool = False) -> Optional[str]: + """ + Step 1: tagged linker-N (e3 side) + E3 aromatic C-F → SNAr intermediate. + Step 2: remaining linker-N (warhead side) + Warhead-COOH → PROTAC (amide). + """ + # Tag the e3-side amine on the linker + rw = Chem.RWMol(linker_mol) + rw.GetAtomWithIdx(e3_site_idx).SetAtomMapNum(99) + try: + Chem.SanitizeMol(rw) + except Exception: + return None + tagged_linker = rw.GetMol() + + # Step 1: tagged linker-N + E3 Ar-F → SNAr (aromatic C-N bond) + step1_rxn = AllChem.ReactionFromSmarts( + "[NX3;!H0;!$(NC=O):99].[c:2][F,Cl]>>[c:2][N:99]" + ) + products1 = step1_rxn.RunReactants((tagged_linker, self.e3_mol)) + if not products1: + return None + + intermediate = None + for prod_tuple in products1: + try: + mol = prod_tuple[0] + Chem.SanitizeMol(mol) + intermediate = mol + break + except Exception: + continue + if intermediate is None: + return None + + # Clear atom map tag + rw2 = Chem.RWMol(intermediate) + for atom in rw2.GetAtoms(): + if atom.GetAtomMapNum() == 99: + atom.SetAtomMapNum(0) + intermediate = rw2.GetMol() + + # Step 2: remaining linker-N + Warhead-COOH → amide + products2 = self._amide_rxn.RunReactants((intermediate, self.warhead_mol)) + if not products2: + return None + + for prod_tuple in products2: + try: + protac_mol = prod_tuple[0] + Chem.SanitizeMol(protac_mol) + return Chem.MolToSmiles(protac_mol) + except Exception: + continue + return None + + # ------------------------------------------------------------------ + # Validation + # ------------------------------------------------------------------ + + def check_attachment_site_compatibility(self, protac_mol: Chem.Mol) -> bool: + has_amide = len(protac_mol.GetSubstructMatches(self._amide_pat)) >= 1 + no_cooh = len(protac_mol.GetSubstructMatches(self._cooh_pat)) == 0 + no_ar_f = not protac_mol.HasSubstructMatch(self._ar_f_pat) + has_cores = self.has_warhead_core(protac_mol) and self.has_e3_core(protac_mol) + return (has_amide and no_cooh and no_ar_f) or has_cores + + def has_warhead_core(self, protac_mol: Chem.Mol) -> bool: + return protac_mol.HasSubstructMatch(self._ar_core) + + def has_e3_core(self, protac_mol: Chem.Mol) -> bool: + return protac_mol.HasSubstructMatch(self._glutarimide) + + def _basic_validation(self, protac_mol: Chem.Mol) -> dict: + checks = { + "has_warhead_core": self.has_warhead_core(protac_mol), + "has_e3_core": self.has_e3_core(protac_mol), + "has_amide_bond": len(protac_mol.GetSubstructMatches(self._amide_pat)) >= 1, + "no_unreacted_cooh": len(protac_mol.GetSubstructMatches(self._cooh_pat)) == 0, + "no_unreacted_ar_f": not protac_mol.HasSubstructMatch(self._ar_f_pat), + } + checks["attachment_site_valid"] = self.check_attachment_site_compatibility(protac_mol) + checks["_passed"] = all([ + checks["has_warhead_core"], + checks["has_e3_core"], + checks["has_amide_bond"], + checks["no_unreacted_cooh"], + checks["no_unreacted_ar_f"], + ]) + return checks + + +Builder = ARCRBN_PROTAC_Builder + + +# --------------------------------------------------------------------------- +# Self-test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + RDLogger.DisableLog("rdApp.error") + print("=" * 70) + print("AR-CRBN PROTAC Builder — Self-Test (amide + SNAr chemistry)") + print(f"Warhead : AR antagonist with COOH arm (amide coupling)") + print(f"E3 : 5-fluorothalidomide (CRBN binder, SNAr at C5-F)") + print(f"Linker : diamine (two secondary N termini)") + print(f"Chemistry: linker-N + COOH → amide (WH) | linker-N + Ar-F → C-N (E3)") + print("=" * 70) + + builder = ARCRBN_PROTAC_Builder() + builder._verify_warhead_reactive_group() + builder._verify_e3_reactive_group() + + # Reference linker from PROTAC-DB #2525: piperazine-azetidine diamine + # Two secondary amines: piperazine-NH (warhead side) and azetidine-NH (E3 side) + test_linkers = [ + ("C1CN(CC2CNC2)CCN1", "Piperazine-azetidine (PROTAC-DB #2525 reference)"), + ("NCCOCCOCCN", "PEG-diamine (two primary amines)"), + ("N1CCNCC1", "Piperazine (two secondary amines)"), + ("OC(=O)CCCCC(=O)O", "Diacid — no amines, should fail"), + ("NCCCN", "Propane-1,3-diamine"), + ("C1CCN(CC1)CCN", "Piperazine-ethylamine"), + ] + + print() + for smiles, desc in test_linkers: + linker_mol = Chem.MolFromSmiles(smiles) + print(f"Linker : {desc}") + if linker_mol is None: + print(" [SKIP] unparseable SMILES") + print("-" * 70) + continue + try: + result = builder.assemble(linker_mol, return_all=False) + except ValueError: + result = None + if result: + m = Chem.MolFromSmiles(result) + mw = Descriptors.MolWt(m) if m else "?" + val = builder._basic_validation(m) if m else {} + passed = val.get("_passed", False) + print(f" {'✓' if passed else '~'} Assembled (MW={mw:.0f} Da): {result[:80]}...") + if not passed: + fails = [k for k, v in val.items() if not v and not k.startswith("_")] + print(f" Validation fails: {fails}") + else: + print(f" ✗ Assembly failed") + print("-" * 70) + + # Verify reference PROTAC (PROTAC-DB #2525) passes validation + print("\nReference PROTAC (PROTAC-DB #2525) validation:") + ref_smi = ("CN(C1=CC=C(C#N)C(Cl)=C1)[C@H]1CC[C@H](NC(=O)C2=CC=C" + "(N3CCN(CC4CN(C5=CC=C6C(=O)N(C7CCC(=O)NC7=O)C(=O)C6=C5)" + "C4)CC3)C=C2)CC1") + ref_mol = Chem.MolFromSmiles(ref_smi) + if ref_mol: + mw = Descriptors.MolWt(ref_mol) + val = builder._basic_validation(ref_mol) + print(f" MW = {mw:.0f} Da (expected ~777 Da)") + for k, v in val.items(): + if not k.startswith("_"): + print(f" {'✓' if v else '✗'} {k}") + print(f" {'✓ PASSED' if val['_passed'] else '✗ FAILED'}") + else: + print(" [ERROR] could not parse reference SMILES") diff --git a/CupOFMoCA-PROTAC/src/protac/ARCRBNv2.py b/CupOFMoCA-PROTAC/src/protac/ARCRBNv2.py new file mode 100644 index 0000000000000000000000000000000000000000..8811d0585b5bc346a8015e7c08541df9407780db --- /dev/null +++ b/CupOFMoCA-PROTAC/src/protac/ARCRBNv2.py @@ -0,0 +1,343 @@ +""" +AR-CRBN PROTAC Builder (v2, alkylation chemistry — in-distribution vs PROTAC-DB) + +Target: Androgen Receptor (AR) — GSK2849466-derived warhead +E3 Ligase: CRBN — pomalidomide / 4-amino-thalidomide + +Why "v2": the original ARCRBN.py used a COOH-armed enzalutamide-aniline warhead ++ 5-fluoro-thalidomide E3 (amine-termini linker, amide+SNAr chemistry). That +exact warhead+E3 pair has 0/276 substructure overlap with PROTAC-DB's AR-V7+CRBN +training entries, so DegradMaster collapses to ~0 on generated PROTACs (OOD). + +This v2 builder uses the dominant PROTAC-DB AR-CRBN chemistry: + - Warhead: 2,2,3,3-tetramethyl-cyclobutyl-pivamide-(4-aminobenzamide) + + cyanochlorophenyl ether (GSK2849466 / SARM-binder family) + Reactive site: aniline-NH2 on the benzamide para position. + - E3: pomalidomide (4-amino-thalidomide). + Reactive site: aryl-NH2 on the isoindolinone 4-position. + - Linker: alkyl chain with terminal CH3 at both ends. + +Assembly (mirrors the alkylation path in ErlotinibVHL.py): + Warhead-Ar-NH2 + linker-CH3 → Warhead-Ar-N(H)-CH2-… (C-N alkylation) + E3-Ar-NH2 + linker-CH3 → E3-Ar-N(H)-CH2-… (C-N alkylation) + +Use with: --fix_termini methyl (linker CH3 termini are pinned) + --protac_module ARCRBNv2 +""" + +from rdkit import Chem +from rdkit.Chem import AllChem +from rdkit import RDLogger +from typing import Optional + +try: + from .protac_builder import PROTAC_Builder +except ImportError: + from protac_builder import PROTAC_Builder + +RDLogger.DisableLog("rdApp.error") + + +# --------------------------------------------------------------------------- +# Component SMILES — chosen to match dominant PROTAC-DB AR-CRBN chemistry +# --------------------------------------------------------------------------- + +# GSK2849466-style AR antagonist with terminal aniline-NH2 (linker attachment). +# 2,2,3,3-tetramethyl-1-((4-aminobenzamido))-4-(4-cyano-3-chlorophenoxy)-cyclobutane. +# 39/276 PROTAC-DB AR-CRBN entries contain the cyclobutyl-pivamide core; 226/276 +# contain the 4-aminobenzamide motif; together they cover the dominant active +# AR-CRBN PROTAC chemotype. +AR_WARHEAD_SMILES = "CC1(C)[C@H](NC(=O)c2ccc(N)cc2)C(C)(C)[C@H]1Oc1ccc(C#N)c(Cl)c1" + +# Pomalidomide / 4-amino-thalidomide — aryl-NH2 reactive for linker alkylation. +# 52/276 PROTAC-DB AR-CRBN entries match this exact pomalidomide core; thalidomide- +# like bicyclic appears in 267/276. +CRBN_E3_SMILES = "Nc1cccc2c1C(=O)N(C3CCC(=O)NC3=O)C2=O" + +AR_WARHEAD_MOL = Chem.MolFromSmiles(AR_WARHEAD_SMILES) +CRBN_E3_MOL = Chem.MolFromSmiles(CRBN_E3_SMILES) + +# SMARTS patterns +_NH2_ARYL_SMARTS = "[NX3;H2;$(Nc)]" # primary aniline NH2 +_AMIDE_SMARTS = "[NX3H0,NX3H1][CX3](=[OX1])[#6]" +_TERMINAL_CH3_SMARTS = "[CX4H3]" +_TERMINAL_CHO_SMARTS = "[CX3H1](=[OX1])" +# Warhead substructure for assembly verification (cyclobutyl-pivamide + cyanochlorophenoxy) +_AR_CORE_SMARTS = "CC(C)(C)[C](C(C)(C))[C]Oc1ccc(C#N)c(Cl)c1" +# E3 substructure: glutarimide + isoindolinone (thalidomide bicyclic) +_THALIDOMIDE_SMARTS = "O=C1c2ccccc2C(=O)N1[C@@H]1CCC(=O)NC1=O" + + +class ARCRBNv2_PROTAC_Builder(PROTAC_Builder): + """ + PROTAC builder: GSK2849466-aniline AR warhead + pomalidomide CRBN E3. + + Linker requirement: terminal CH3 groups (alkyl), one per end. + Warhead aryl-NH2 + linker terminal CH3 → secondary aniline + CH2 (C-N alkylation). + E3 aryl-NH2 + linker terminal CH3 → secondary aniline + CH2 (C-N alkylation). + """ + + def __init__(self, warhead: Optional[Chem.Mol] = None, + e3: Optional[Chem.Mol] = None): + # Initialize SMARTS patterns BEFORE super().__init__() — the parent + # constructor calls _verify_*_reactive_group which accesses these. + self._nh2_aryl_pat = Chem.MolFromSmarts(_NH2_ARYL_SMARTS) + self._amide_pat = Chem.MolFromSmarts(_AMIDE_SMARTS) + self._terminal_ch3 = Chem.MolFromSmarts(_TERMINAL_CH3_SMARTS) + self._terminal_cho = Chem.MolFromSmarts(_TERMINAL_CHO_SMARTS) + self._ar_core = Chem.MolFromSmarts(_AR_CORE_SMARTS) + self._thalidomide = Chem.MolFromSmarts(_THALIDOMIDE_SMARTS) + super().__init__( + warhead=warhead if warhead is not None else AR_WARHEAD_MOL, + e3=e3 if e3 is not None else CRBN_E3_MOL, + warhead_name="GSK2849466-aniline (AR)", + e3_name="pomalidomide (CRBN)", + ) + + # Alkylation reactions: aryl-NH2 + terminal CH3 → aryl-NH-CH2- + # E3 reaction: pomalidomide aryl-NH2 + linker CH3 + self._alkyl_rxn_e3 = AllChem.ReactionFromSmarts( + "[NX3H2;$(Nc):1].[CX4H3:2]>>[NX3H1:1][CX4H2:2]" + ) + # Warhead reaction: AR aryl-NH2 + linker CH3 + self._alkyl_rxn_wh = AllChem.ReactionFromSmarts( + "[NX3H2;$(Nc):1].[CX4H3:2]>>[NX3H1:1][CX4H2:2]" + ) + + # ------------------------------------------------------------------ + # Verification + # ------------------------------------------------------------------ + + def _verify_warhead_reactive_group(self): + if self.warhead_mol.HasSubstructMatch(self._nh2_aryl_pat): + print(f"✓ {self.warhead_name}: aryl-NH2 reactive group confirmed (4-aminobenzamide)") + else: + print(f"⚠ {self.warhead_name}: no reactive aryl-NH2 detected — check warhead") + + def _verify_e3_reactive_group(self): + if self.e3_mol.HasSubstructMatch(self._nh2_aryl_pat): + print(f"✓ {self.e3_name}: aryl-NH2 reactive group confirmed (4-amino-thalidomide)") + else: + print(f"⚠ {self.e3_name}: no reactive aryl-NH2 detected — check E3") + + # ------------------------------------------------------------------ + # Linker attachment site detection — looks for terminal CH3 on linker + # ------------------------------------------------------------------ + + def _reactive_terminal_c_atoms(self, linker_mol: Chem.Mol) -> list: + """ + Find terminal CH3 carbons that can act as alkylating sites. + + Two-layer check: + 1. SMARTS match on terminal CH3 + 2. Reaction trial vs E3 aryl-NH2 to confirm the alkylation fires. + """ + results = [] + seen = set() + + for m in linker_mol.GetSubstructMatches(self._terminal_ch3): + c_idx = m[0] + if c_idx in seen: + continue + + rw = Chem.RWMol(linker_mol) + rw.GetAtomWithIdx(c_idx).SetAtomMapNum(98) + try: + Chem.SanitizeMol(rw) + except Exception: + continue + + trial_rxn = AllChem.ReactionFromSmarts( + "[NX3H2;$(Nc):1].[CX4H3:98]>>[NX3H1:1][CX4H2:98]" + ) + if trial_rxn.RunReactants((self.e3_mol, rw.GetMol())): + seen.add(c_idx) + results.append({"idx": c_idx, "type": "alkyl"}) + + return results + + def find_linker_attachment_sites(self, linker_mol: Chem.Mol, + amide_only: bool = False) -> list: + """ + Find pairs of terminal CH3 atoms on the linker for alkylation assembly. + amide_only is accepted for interface compatibility but ignored here + (this chemistry has no amide path). + """ + cands = self._reactive_terminal_c_atoms(linker_mol) + sites = [] + seen = set() + for w in cands: + for e in cands: + key = (w["idx"], e["idx"]) + if key not in seen and w["idx"] != e["idx"]: + seen.add(key) + sites.append({ + "warhead_site": w["idx"], + "warhead_type": "alkyl", + "warhead_restored": False, + "e3_site": e["idx"], + "e3_type": "alkyl", + "e3_restored": False, + }) + return sites + + # ------------------------------------------------------------------ + # Assembly: two sequential alkylation steps + # ------------------------------------------------------------------ + + def _assemble_protac(self, linker_mol: Chem.Mol, warhead_site_idx: int, + e3_site_idx: int, + warhead_type: str = "alkyl", e3_type: str = "alkyl", + warhead_restored: bool = False, + e3_restored: bool = False) -> Optional[str]: + rw = Chem.RWMol(linker_mol) + rw.GetAtomWithIdx(e3_site_idx).SetAtomMapNum(99) + tagged_linker = rw.GetMol() + try: + Chem.SanitizeMol(rw) + except Exception: + return None + + # Step 1: E3 aryl-NH2 + linker CH3 → secondary aniline + step1_rxn = AllChem.ReactionFromSmarts( + "[NX3H2;$(Nc):1].[CX4H3:99]>>[NX3H1:1][CX4H2:99]" + ) + products1 = step1_rxn.RunReactants((self.e3_mol, tagged_linker)) + if not products1: + return None + intermediate = None + for prod_tuple in products1: + try: + m = prod_tuple[0] + Chem.SanitizeMol(m) + intermediate = m + break + except Exception: + continue + if intermediate is None: + return None + + # Clear the e3-side tag + rw2 = Chem.RWMol(intermediate) + for atom in rw2.GetAtoms(): + if atom.GetAtomMapNum() == 99: + atom.SetAtomMapNum(0) + intermediate = rw2.GetMol() + + # Step 2: warhead aryl-NH2 + remaining terminal CH3 → secondary aniline + products2 = self._alkyl_rxn_wh.RunReactants((self.warhead_mol, intermediate)) + if not products2: + return None + for prod_tuple in products2: + try: + protac_mol = prod_tuple[0] + Chem.SanitizeMol(protac_mol) + return Chem.MolToSmiles(protac_mol) + except Exception: + continue + return None + + # ------------------------------------------------------------------ + # Validation + # ------------------------------------------------------------------ + + def check_attachment_site_compatibility(self, protac_mol: Chem.Mol) -> bool: + return self.has_warhead_core(protac_mol) and self.has_e3_core(protac_mol) + + def has_warhead_core(self, protac_mol: Chem.Mol) -> bool: + # Match the cyanochloro-aryloxy + tetramethyl-cyclobutyl signature + pat = Chem.MolFromSmarts("Oc1ccc(C#N)c(Cl)c1") + return protac_mol.HasSubstructMatch(pat) + + def has_e3_core(self, protac_mol: Chem.Mol) -> bool: + # Match the glutarimide of thalidomide-class + pat = Chem.MolFromSmarts("O=C1NC(=O)CCC1") + return protac_mol.HasSubstructMatch(pat) + + def has_both_amide_bonds(self, protac_mol: Chem.Mol) -> bool: + # In alkylation chemistry, no NEW amides form — the warhead's pivamide and + # the E3's two carbonyls already exist. Just need ≥2 amide-like patterns total. + return len(protac_mol.GetSubstructMatches(self._amide_pat)) >= 2 + + def no_unreacted_cooh(self, protac_mol: Chem.Mol) -> bool: + # Alkylation chemistry doesn't use COOH; if any free COOH appeared it's + # a builder error. Accept absence as true. + cooh = Chem.MolFromSmarts("[CX3](=[OX1])[OX2H1]") + return len(protac_mol.GetSubstructMatches(cooh)) == 0 + + def _basic_validation(self, protac_mol: Chem.Mol) -> dict: + checks = { + "has_warhead_core": self.has_warhead_core(protac_mol), + "has_e3_core": self.has_e3_core(protac_mol), + "has_both_amide_bonds": self.has_both_amide_bonds(protac_mol), + "no_unreacted_cooh": self.no_unreacted_cooh(protac_mol), + "attachment_site_valid": self.check_attachment_site_compatibility(protac_mol), + } + checks["_passed"] = all([ + checks["has_warhead_core"], + checks["has_e3_core"], + checks["has_both_amide_bonds"], + checks["no_unreacted_cooh"], + ]) + return checks + + +# --------------------------------------------------------------------------- +# Standard interface aliases +# --------------------------------------------------------------------------- + +WARHEAD_MOL = AR_WARHEAD_MOL +E3_MOL = CRBN_E3_MOL +WARHEAD_SMILES = AR_WARHEAD_SMILES +E3_SMILES = CRBN_E3_SMILES +Builder = ARCRBNv2_PROTAC_Builder +WH_REACTIVE_SMARTS = _NH2_ARYL_SMARTS +E3_REACTIVE_SMARTS = _NH2_ARYL_SMARTS +WARHEAD_PAT = Chem.MolFromSmarts("Oc1ccc(C#N)c(Cl)c1") +E3_PAT = Chem.MolFromSmarts("O=C1NC(=O)CCC1") + + +# --------------------------------------------------------------------------- +# Self-test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + from rdkit import RDLogger + RDLogger.DisableLog("rdApp.error") + + print("=" * 70) + print("AR-CRBN v2 PROTAC Builder — Self-Test") + print(f"Warhead : AR (GSK2849466-aniline, aryl-NH2 reactive)") + print(f"E3 : CRBN (pomalidomide, aryl-NH2 reactive)") + print(f"Chemistry: alkyl linker + 2× C-N alkylation (CH3 termini)") + print("=" * 70) + + builder = ARCRBNv2_PROTAC_Builder() + + test_linkers = [ + ("CCCCCCCC", "octane (terminal CH3 at both ends)"), + ("CCOCCOCCOCC", "PEG3-dimethyl"), + ("CC1CCC(C)CC1", "1,4-dimethylcyclohexane"), + ("OC(=O)CCCCC(=O)O", "diacid — should fail (no terminal CH3)"), + ("NCCCCCN", "diamine — should fail (no terminal CH3)"), + ] + + print() + for smiles, desc in test_linkers: + linker_mol = Chem.MolFromSmiles(smiles) + print(f"Linker : {desc}") + ch3_count = len(linker_mol.GetSubstructMatches(Chem.MolFromSmarts(_TERMINAL_CH3_SMARTS))) + print(f" Terminal CH3 sites: {ch3_count}") + try: + protac_smi = builder.assemble(linker_mol, return_all=False) + protac_mol = Chem.MolFromSmiles(protac_smi) + passed, checks = builder.validate_protac(protac_mol) + tag = "VALID ✓" if passed else "ASSEMBLED but INVALID ✗" + print(f" Assembly: {tag}") + for k, v in checks.items(): + print(f" {'✓' if v else '✗'} {k}") + props = builder.get_protac_properties(protac_mol) + print(f" MW: {props['mw']} Da LogP: {props['logp']} HeavyAtoms: {props['heavy_atoms']}") + except Exception as e: + print(f" Assembly: FAILED — {e}") + print("-" * 70) diff --git a/CupOFMoCA-PROTAC/src/protac/BRD4CRBN.py b/CupOFMoCA-PROTAC/src/protac/BRD4CRBN.py new file mode 100644 index 0000000000000000000000000000000000000000..5dc340e4e1680fa7e631b8974debfe45e0a3d75d --- /dev/null +++ b/CupOFMoCA-PROTAC/src/protac/BRD4CRBN.py @@ -0,0 +1,334 @@ +""" +BRD4-CRBN PROTAC Builder +Target: BRD4 (JQ1-derived bromodomain inhibitor warhead, COOH-armed) +E3 Ligase: CRBN (4-fluoro-thalidomide; SNAr with linker-NH2 → 4-amino-thalidomide) +Source: Dominant chemistry in PROTAC-DB BRD4+CRBN (311 entries; JQ1 aromatic + core matches 50% of training PROTACs) + +Linker chemistry (mixed: amide on warhead side, SNAr on E3 side): + Warhead-COOH + linker-NH2 → secondary amide (warhead end) + E3-Ar-F + linker-NH2 → C-N aromatic bond via SNAr (E3 end → 4-NH-thalidomide) + +This mirrors the ARCRBN.py chemistry but swaps the warhead from enzalutamide-aniline +to JQ1-acetic-acid (the dominant BRD4 chemotype in PROTAC-DB BRD4+CRBN entries). + +Use with: --fix_termini amine + --protac_module BRD4CRBN + +Example training PROTAC (PROTAC-DB BRD4+CRBN, label=1): + CC1=C(C)C2=C(S1)N1C(C)=NN=C1[C@H](CC(=O)NCCCCCNC1=CC=CC3=C1CN(C1CCC(=O)NC1=O)C3=O)N=C2C1=CC=C(Cl)C=C1 +""" + +from rdkit import Chem +from rdkit.Chem import AllChem +from rdkit import RDLogger +from typing import Optional + +try: + from .protac_builder import PROTAC_Builder +except ImportError: + from protac_builder import PROTAC_Builder + +RDLogger.DisableLog("rdApp.error") + + +# --------------------------------------------------------------------------- +# Component SMILES — chosen to match dominant PROTAC-DB BRD4-CRBN chemistry +# --------------------------------------------------------------------------- + +# JQ1 with terminal CH2-COOH (the canonical BRD4 binder, 50% substructure match +# in PROTAC-DB BRD4+CRBN). Reactive site: COOH carbonyl → amide with linker NH. +BRD4_WARHEAD_SMILES = "CC1=C(C)C2=C(S1)N1C(C)=NN=C1[C@H](CC(=O)O)N=C2C1=CC=C(Cl)C=C1" + +# 4-Fluoro-thalidomide / 4-fluoro-pomalidomide (CRBN binder). +# C4-F undergoes SNAr with linker secondary amine → aryl C-N (post-assembly is +# 4-amino-thalidomide, which matches 25% of PROTAC-DB BRD4+CRBN entries). +CRBN_E3_SMILES = "Fc1cccc2c1C(=O)N([C@@H]1CCC(=O)NC1=O)C2=O" + +BRD4_WARHEAD_MOL = Chem.MolFromSmiles(BRD4_WARHEAD_SMILES) +CRBN_E3_MOL = Chem.MolFromSmiles(CRBN_E3_SMILES) + +# Standard interface aliases +WARHEAD_MOL = BRD4_WARHEAD_MOL +E3_MOL = CRBN_E3_MOL +WARHEAD_SMILES = BRD4_WARHEAD_SMILES +E3_SMILES = CRBN_E3_SMILES + +# Reactive SMARTS (used by batch_report.py to locate reactive atoms) +WH_REACTIVE_SMARTS = "[CX3](=[OX1])[OX2H1]" # warhead COOH carbonyl C +E3_REACTIVE_SMARTS = "[c][F]" # E3 isoindoline C4 bearing F + +# Linker attachment SMARTS — linker exposes secondary/primary amines +_NH_SMARTS = "[NX3;!H0;!$(NC=O);!$(NS=O);!$(N=O)]" + +# Substructure patterns for assembly check +WARHEAD_PAT = Chem.MolFromSmiles("Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=NCc1nnc(C)n1-2") # JQ1 core +E3_PAT = Chem.MolFromSmarts("O=C1NC(=O)CCC1") # glutarimide (CRBN) + + +class BRD4CRBN_PROTAC_Builder(PROTAC_Builder): + """ + PROTAC builder: JQ1-COOH BRD4 warhead + 4-F-thalidomide CRBN E3. + + Warhead side: COOH (electrophile) + linker secondary amine → amide bond. + E3 side: isoindoline C4-F (SNAr electrophile) + linker secondary amine → aryl C-N. + + Use with --fix_termini amine in defog_guided.py. + """ + + _COOH_SMARTS = "[CX3](=[OX1])[OX2H1]" + _AR_F_SMARTS = "[c][F]" + _NH_SMARTS = "[NX3;!H0;!$(NC=O);!$(NS=O);!$(N=O)]" + _AMIDE_SMARTS = "[NX3H0,NX3H1][CX3](=[OX1])[#6]" + # Aromatic C-N in assembled PROTAC (isoindoline aryl-N) + _AR_CN_SMARTS = "c1ccc2c(c1)C(=O)N(*)C2=O" + + def __init__(self, warhead: Optional[Chem.Mol] = None, + e3: Optional[Chem.Mol] = None): + # Initialize patterns BEFORE super().__init__() — parent calls _verify_* + self._cooh_pat = Chem.MolFromSmarts(self._COOH_SMARTS) + self._ar_f_pat = Chem.MolFromSmarts(self._AR_F_SMARTS) + self._nh_pat = Chem.MolFromSmarts(self._NH_SMARTS) + self._amide_pat = Chem.MolFromSmarts(self._AMIDE_SMARTS) + self._ar_cn_pat = Chem.MolFromSmarts(self._AR_CN_SMARTS) + # JQ1 thienodiazepine signature: dimethylthiophene + triazole + chlorophenyl + # (using SMILES-based pattern via MolFromSmiles for valid ring handling) + self._brd4_core = Chem.MolFromSmiles( + "Cc1sc2c(c1C)C(c1ccc(Cl)cc1)=NCc1nnc(C)n1-2" + ) + self._glutarimide = Chem.MolFromSmarts("O=C1NC(=O)CCC1") + + super().__init__( + warhead=warhead if warhead is not None else BRD4_WARHEAD_MOL, + e3=e3 if e3 is not None else CRBN_E3_MOL, + warhead_name="JQ1-acetic-acid (BRD4)", + e3_name="4-fluoro-thalidomide (CRBN, SNAr)", + ) + + # Warhead side: linker secondary amine + warhead COOH → secondary amide + self._amide_rxn = AllChem.ReactionFromSmarts( + "[NX3;!H0;!$(NC=O);!$(NS=O):1].[CX3:2](=[OX1:3])[OX2H1]" + ">>[N:1][CX3:2](=[OX1:3])" + ) + # E3 side: linker secondary amine + E3 aromatic C-F → aromatic C-N (SNAr) + self._snAr_rxn = AllChem.ReactionFromSmarts( + "[NX3;!H0;!$(NC=O);!$(NS=O):1].[c:2][F,Cl]>>[c:2][N:1]" + ) + + def _verify_warhead_reactive_group(self): + if self.warhead_mol is not None and self.warhead_mol.HasSubstructMatch(self._cooh_pat): + print(f"✓ {self.warhead_name}: COOH reactive group confirmed") + else: + print(f"⚠ {self.warhead_name}: no COOH detected — check warhead SMILES") + + def _verify_e3_reactive_group(self): + if self.e3_mol is not None and self.e3_mol.HasSubstructMatch(self._ar_f_pat): + print(f"✓ {self.e3_name}: aryl-F reactive group confirmed (SNAr)") + else: + print(f"⚠ {self.e3_name}: no aryl-F detected — check E3 SMILES") + + # ------------------------------------------------------------------ + # Linker attachment site detection + # ------------------------------------------------------------------ + + def _reactive_amine_atoms(self, linker_mol: Chem.Mol) -> list: + """ + Find linker N atoms that can act as a nucleophile for amide+SNAr. + + Two-layer check: + 1. SMARTS match on free secondary/primary amine + 2. Reaction trial (amide_rxn against warhead) to confirm it fires + """ + results = [] + seen = set() + for m in linker_mol.GetSubstructMatches(self._nh_pat): + n_idx = m[0] + if n_idx in seen: + continue + rw = Chem.RWMol(linker_mol) + rw.GetAtomWithIdx(n_idx).SetAtomMapNum(99) + try: + Chem.SanitizeMol(rw) + except Exception: + continue + trial = AllChem.ReactionFromSmarts( + "[NX3;!H0;!$(NC=O);!$(NS=O):99].[CX3:2](=[OX1:3])[OX2H1]" + ">>[N:99][CX3:2](=[OX1:3])" + ) + if trial.RunReactants((rw.GetMol(), self.warhead_mol)): + seen.add(n_idx) + results.append({"idx": n_idx, "type": "amine"}) + return results + + def find_linker_attachment_sites(self, linker_mol: Chem.Mol, + amide_only: bool = False) -> list: + """ + Find pairs of reactive linker amines for assembly. Both ends use the same + secondary amine; one will form an amide with warhead-COOH, the other will + form an aryl-N bond with E3-F. + + amide_only is accepted but doesn't change behavior here. + """ + cands = self._reactive_amine_atoms(linker_mol) + sites = [] + seen = set() + for w in cands: + for e in cands: + key = (w["idx"], e["idx"]) + if key not in seen and w["idx"] != e["idx"]: + seen.add(key) + sites.append({ + "warhead_site": w["idx"], + "warhead_type": "amine", + "warhead_restored": False, + "e3_site": e["idx"], + "e3_type": "amine", + "e3_restored": False, + }) + return sites + + # ------------------------------------------------------------------ + # Assembly: warhead COOH + linker-NH (amide), then E3-aryl-F + linker-NH (SNAr) + # ------------------------------------------------------------------ + + def _assemble_protac(self, linker_mol: Chem.Mol, warhead_site_idx: int, + e3_site_idx: int, + warhead_type: str = "amine", e3_type: str = "amine", + warhead_restored: bool = False, + e3_restored: bool = False) -> Optional[str]: + rw = Chem.RWMol(linker_mol) + rw.GetAtomWithIdx(e3_site_idx).SetAtomMapNum(99) + tagged_linker = rw.GetMol() + try: + Chem.SanitizeMol(rw) + except Exception: + return None + + # Step 1: E3 aryl-F + linker-NH (e3-side, tagged) → aryl-N(linker) + step1_rxn = AllChem.ReactionFromSmarts( + "[NX3;!H0;!$(NC=O);!$(NS=O):99].[c:2][F]" + ">>[c:2][N:99]" + ) + products1 = step1_rxn.RunReactants((tagged_linker, self.e3_mol)) + if not products1: + return None + intermediate = None + for prod_tuple in products1: + try: + m = prod_tuple[0] + Chem.SanitizeMol(m) + intermediate = m + break + except Exception: + continue + if intermediate is None: + return None + + # Clear e3-side tag + rw2 = Chem.RWMol(intermediate) + for atom in rw2.GetAtoms(): + if atom.GetAtomMapNum() == 99: + atom.SetAtomMapNum(0) + intermediate = rw2.GetMol() + + # Step 2: warhead COOH + remaining free amine → amide + products2 = self._amide_rxn.RunReactants((intermediate, self.warhead_mol)) + if not products2: + return None + for prod_tuple in products2: + try: + protac_mol = prod_tuple[0] + Chem.SanitizeMol(protac_mol) + return Chem.MolToSmiles(protac_mol) + except Exception: + continue + return None + + # ------------------------------------------------------------------ + # Validation + # ------------------------------------------------------------------ + + def has_warhead_core(self, protac_mol: Chem.Mol) -> bool: + return protac_mol.HasSubstructMatch(self._brd4_core) + + def has_e3_core(self, protac_mol: Chem.Mol) -> bool: + return protac_mol.HasSubstructMatch(self._glutarimide) + + def has_both_amide_bonds(self, protac_mol: Chem.Mol) -> bool: + # JQ1's CH2-CO-NH-linker amide + lenalidomide-side aryl-NH + # ≥2 amide patterns in the assembled PROTAC. + return len(protac_mol.GetSubstructMatches(self._amide_pat)) >= 2 + + def no_unreacted_cooh(self, protac_mol: Chem.Mol) -> bool: + return len(protac_mol.GetSubstructMatches(self._cooh_pat)) == 0 + + def check_attachment_site_compatibility(self, protac_mol: Chem.Mol) -> bool: + return self.has_warhead_core(protac_mol) and self.has_e3_core(protac_mol) + + def _basic_validation(self, protac_mol: Chem.Mol) -> dict: + checks = { + "has_warhead_core": self.has_warhead_core(protac_mol), + "has_e3_core": self.has_e3_core(protac_mol), + "has_both_amide_bonds": self.has_both_amide_bonds(protac_mol), + "no_unreacted_cooh": self.no_unreacted_cooh(protac_mol), + "attachment_site_valid": self.check_attachment_site_compatibility(protac_mol), + } + checks["_passed"] = all([ + checks["has_warhead_core"], + checks["has_e3_core"], + checks["has_both_amide_bonds"], + checks["no_unreacted_cooh"], + ]) + return checks + + +# --------------------------------------------------------------------------- +# Standard interface aliases +# --------------------------------------------------------------------------- + +Builder = BRD4CRBN_PROTAC_Builder + + +# --------------------------------------------------------------------------- +# Self-test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + from rdkit import RDLogger + RDLogger.DisableLog("rdApp.error") + + print("=" * 70) + print("BRD4-CRBN PROTAC Builder — Self-Test") + print(f"Warhead : BRD4 (JQ1-COOH, COOH reactive)") + print(f"E3 : CRBN (4-F-thalidomide, aryl-F SNAr reactive)") + print(f"Chemistry: diamine linker + amide (warhead) + SNAr (E3)") + print("=" * 70) + + builder = BRD4CRBN_PROTAC_Builder() + + test_linkers = [ + ("NCCCCCN", "pentanediamine (matches PROTAC-DB)"), + ("NCCOCCOCCN", "PEG-diamine"), + ("NC1CCC(N)CC1", "1,4-diaminocyclohexane"), + ("OC(=O)CCC(=O)O", "diacid — should fail (no NH)"), + ("CCCCCC", "alkane — should fail (no NH)"), + ] + print() + for smiles, desc in test_linkers: + linker_mol = Chem.MolFromSmiles(smiles) + nh_count = len(linker_mol.GetSubstructMatches(Chem.MolFromSmarts(_NH_SMARTS))) + print(f"Linker : {desc}") + print(f" Free NH sites: {nh_count}") + try: + protac_smi = builder.assemble(linker_mol, return_all=False) + protac_mol = Chem.MolFromSmiles(protac_smi) + passed, checks = builder.validate_protac(protac_mol) + tag = "VALID ✓" if passed else "ASSEMBLED but INVALID ✗" + print(f" Assembly: {tag}") + for k, v in checks.items(): + print(f" {'✓' if v else '✗'} {k}") + props = builder.get_protac_properties(protac_mol) + print(f" MW: {props['mw']} Da LogP: {props['logp']} HeavyAtoms: {props['heavy_atoms']}") + except Exception as e: + print(f" Assembly: FAILED — {e}") + print("-" * 70) diff --git a/CupOFMoCA-PROTAC/src/protac/BRD4VHL.py b/CupOFMoCA-PROTAC/src/protac/BRD4VHL.py new file mode 100644 index 0000000000000000000000000000000000000000..461631c25a5f8d5d99b3b387a9d4702da7592bbf --- /dev/null +++ b/CupOFMoCA-PROTAC/src/protac/BRD4VHL.py @@ -0,0 +1,389 @@ +""" +BRD4-VHL PROTAC Builder (double amide: warhead-COOH + linker-NH2 + linker-COOH + E3-NH2) +Target: BRD4 (Bromodomain-containing protein 4) +E3 Ligase: VHL +Source: PROTAC-DB ID 1784 (PDB: 7KHH, DC50 = 0.095 nM, Dmax = 96%) + +Reference PROTAC (PROTAC-DB #1784, MW = 1096.3 Da): + CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=O)[C@@H](NC(=O)CCCCCCCCCC + NC(=O)C3=CC4=C(C=C3CS(C)(=O)=O)C3=CN(C)C(=O)C5=C3C(=C[NH]5)CN4C3=NC=C(F) + C=C3F)C(C)(C)C)C=C2)SC=N1 + Linker: NCCCCCCCCCCC(=O)O (11-aminoundecanoic acid, MW 201.3 Da) + +Linker chemistry: AMINO-ACID termini (NH2 on warhead end, COOH on VHL end) + The generated linker has one free primary NH2 and one free COOH. + Warhead provides a COOH electrophile; linker provides NH2 nucleophile (warhead side). + Linker provides a COOH electrophile; E3 provides NH2 nucleophile (VHL side). + + Warhead-COOH + linker-NH2 → secondary amide (warhead side) + Linker-COOH + E3-NH2 → secondary amide (E3 / VHL side) + + Assembly order: Step 1 = VHL-NH2 + tagged linker-COOH → amide intermediate; + Step 2 = intermediate-NH2 + warhead-COOH → PROTAC. + +Warhead (BRD4 bromodomain inhibitor, COOH-armed, MW ≈ 500.5 Da): + Dihydropyrrolo-isoquinoline-difluoropyridyl tetracyclic BRD4 binder. + COOH at the benzoic acid arm reacts with the linker primary NH2 → amide. + +E3 Ligand (VHL binder, NH2-armed, MW ≈ 430.6 Da): + (2S,4R)-4-hydroxy-proline + tert-leucine + 4-(4-methylthiazol-5-yl)benzyl cap. + Free NH2 on tert-leucine alpha-amine is the reactive group. + +Usage with defog_guided.py: + --protac_module BRD4VHL --fix_termini amine_acid + (linker must expose one NH2 terminus and one COOH terminus) +""" + +from rdkit import Chem +from rdkit.Chem import AllChem, Descriptors +from rdkit import RDLogger +from typing import Optional + +try: + from .protac_builder import PROTAC_Builder +except ImportError: + from protac_builder import PROTAC_Builder + +RDLogger.DisableLog("rdApp.error") + + +# --------------------------------------------------------------------------- +# Component SMILES — extracted from PROTAC-DB #1784 +# --------------------------------------------------------------------------- + +# BRD4 bromodomain inhibitor: dihydropyrrolo-isoquinoline tetracyclic scaffold +# with 3,5-difluoropyridin-2-yl at N8 and methylsulfonylmethyl at C4. +# COOH at the benzoic acid arm is the reactive group (reacts with linker NH2 → amide). +# MW ≈ 500.5 Da; C23H18F2N4O5S +BRD4_WARHEAD_SMILES = "OC(=O)C1=CC2=C(C=C1CS(C)(=O)=O)C3=CN(C)C(=O)C4=C3C(=C[NH]4)CN2C1=NC=C(F)C=C1F" + +# VHL binder: (2S,4R)-4-hydroxyproline + (S)-tert-leucine + 4-(4-methylthiazol-5-yl)benzyl cap. +# Free NH2 on tert-leucine alpha-amine reacts with linker COOH → secondary amide. +# MW ≈ 430.6 Da; C22H30N4O3S +VHL_E3_SMILES = "N[C@@H](C(=O)N1C[C@@H](O)C[C@@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C" + +# Pre-built Mol objects +BRD4_WARHEAD_MOL = Chem.MolFromSmiles(BRD4_WARHEAD_SMILES) +VHL_E3_MOL = Chem.MolFromSmiles(VHL_E3_SMILES) + +# Standard interface aliases (used by defog_guided.py / batch_report.py) +WARHEAD_MOL = BRD4_WARHEAD_MOL +E3_MOL = VHL_E3_MOL +WARHEAD_SMILES = BRD4_WARHEAD_SMILES +E3_SMILES = VHL_E3_SMILES + +# Reactive SMARTS (used by batch_report.py to locate reactive atoms in free fragments) +WH_REACTIVE_SMARTS = "[CX3](=[OX1])[OX2H1]" # warhead benzoic acid COOH +E3_REACTIVE_SMARTS = "[NX3H2]" # tert-leucine primary NH2 + +# Substructure patterns for core detection and assembly check +WARHEAD_PAT = Chem.MolFromSmarts("CS(C)(=O)=O") # methylsulfonylmethyl (BRD4) +E3_PAT = Chem.MolFromSmarts("N1CC(O)CC1") # hydroxyproline ring (VHL) + +# SMARTS for reactive groups on linker +_COOH_SMARTS = "[CX3](=[OX1])[OX2H1]" +_NH2_SMARTS = "[NX3H2]" +_AMIDE_SMARTS = "[NX3H0,NX3H1][CX3](=[OX1])[#6]" + + +class BRD4VHL_PROTAC_Builder(PROTAC_Builder): + """ + PROTAC builder for BRD4-VHL (PROTAC-DB #1784). + + Warhead side: linker primary NH2 + warhead COOH → secondary amide. + E3 side: linker COOH + VHL tert-leucine NH2 → secondary amide. + + Linker must expose one NH2 terminus (warhead end) and one COOH terminus (VHL end). + """ + + _COOH_SMARTS = "[CX3](=[OX1])[OX2H1]" + _NH2_SMARTS = "[NX3H2]" + _AMIDE_SMARTS = "[NX3H0,NX3H1][CX3](=[OX1])[#6]" + # BRD4 core: methylsulfonylmethyl + pyrrole-NH in 5-membered ring + _BRD4_MS_SMARTS = "CS(C)(=O)=O" + _BRD4_NH_SMARTS = "[nH;r5]" + # VHL core: hydroxyproline + _VHL_HYP_SMARTS = "N1CC(O)CC1" + + def __init__(self, warhead: Optional[Chem.Mol] = None, + e3: Optional[Chem.Mol] = None): + self._cooh_pat = Chem.MolFromSmarts(self._COOH_SMARTS) + self._nh2_pat = Chem.MolFromSmarts(self._NH2_SMARTS) + self._amide_pat = Chem.MolFromSmarts(self._AMIDE_SMARTS) + self._brd4_ms = Chem.MolFromSmarts(self._BRD4_MS_SMARTS) + self._brd4_nh = Chem.MolFromSmarts(self._BRD4_NH_SMARTS) + self._vhl_hyp = Chem.MolFromSmarts(self._VHL_HYP_SMARTS) + + super().__init__( + warhead=warhead if warhead is not None else BRD4_WARHEAD_MOL, + e3=e3 if e3 is not None else VHL_E3_MOL, + warhead_name="BRD4 inhibitor (COOH-armed, tetracyclic)", + e3_name="VHL binder NH2 (VH032-type)", + ) + + # Warhead side: linker-NH2 + warhead-COOH → secondary amide + self._amide_rxn_wh = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX3:2](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:2](=[OX1:3])" + ) + # E3 side (Step 1): VHL-NH2 + tagged linker-COOH → secondary amide + self._amide_rxn_e3 = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX3:99](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:99](=[OX1:3])" + ) + + # ------------------------------------------------------------------ + # Verification + # ------------------------------------------------------------------ + + def _verify_warhead_reactive_group(self): + if self.warhead_mol.HasSubstructMatch(self._cooh_pat): + print(f"✓ {self.warhead_name}: COOH reactive group confirmed") + else: + print(f"⚠ {self.warhead_name}: no COOH detected — check warhead SMILES") + + def _verify_e3_reactive_group(self): + if self.e3_mol.HasSubstructMatch(self._nh2_pat): + print(f"✓ {self.e3_name}: NH2 reactive group confirmed (tert-leucine)") + else: + print(f"⚠ {self.e3_name}: no NH2 detected — check E3 SMILES") + + # ------------------------------------------------------------------ + # Linker attachment site detection + # ------------------------------------------------------------------ + + def _reactive_nh2_atoms(self, linker_mol: Chem.Mol) -> list: + """Find primary NH2 atoms on the linker (warhead-side attachment).""" + results, seen = [], set() + for m in linker_mol.GetSubstructMatches(self._nh2_pat): + n_idx = m[0] + if n_idx not in seen: + seen.add(n_idx) + results.append({"idx": n_idx}) + return results + + def _reactive_cooh_atoms(self, linker_mol: Chem.Mol) -> list: + """Find COOH carbonyl-C atoms on the linker (VHL-side attachment).""" + results, seen = [], set() + for m in linker_mol.GetSubstructMatches(self._cooh_pat): + c_idx = m[0] + if c_idx not in seen: + seen.add(c_idx) + results.append({"idx": c_idx}) + return results + + def find_linker_attachment_sites(self, linker_mol: Chem.Mol, + amide_only: bool = False) -> list: + """ + Return all (NH2 warhead-site, COOH VHL-site) atom index pairs. + + Warhead end: primary amine (NH2) that will react with warhead COOH. + VHL end: carboxylic acid (COOH) that will react with VHL NH2. + """ + nh2_cands = self._reactive_nh2_atoms(linker_mol) + cooh_cands = self._reactive_cooh_atoms(linker_mol) + + sites, seen = [], set() + for w in nh2_cands: + for e in cooh_cands: + key = (w["idx"], e["idx"]) + if key not in seen: + seen.add(key) + sites.append({ + "warhead_site": w["idx"], + "warhead_type": "nh2", + "warhead_restored": False, + "e3_site": e["idx"], + "e3_type": "cooh", + "e3_restored": False, + }) + return sites + + # ------------------------------------------------------------------ + # Assembly: two sequential amide bond formations + # ------------------------------------------------------------------ + + def _assemble_protac(self, linker_mol: Chem.Mol, + warhead_site_idx: int, e3_site_idx: int, + warhead_type: str = "nh2", e3_type: str = "cooh", + warhead_restored: bool = False, + e3_restored: bool = False) -> Optional[str]: + """ + Step 1: VHL-NH2 + tagged linker-COOH (e3 site) → secondary amide intermediate. + Step 2: intermediate-NH2 (warhead site) + warhead-COOH → PROTAC (secondary amide). + """ + # Tag the VHL-side COOH carbon on the linker for selective step-1 reaction + rw = Chem.RWMol(linker_mol) + rw.GetAtomWithIdx(e3_site_idx).SetAtomMapNum(99) + try: + Chem.SanitizeMol(rw) + except Exception: + return None + tagged_linker = rw.GetMol() + + # Step 1: E3 NH2 + tagged linker COOH → secondary amide + products1 = self._amide_rxn_e3.RunReactants((self.e3_mol, tagged_linker)) + if not products1: + return None + + intermediate = None + for prod_tuple in products1: + try: + mol = prod_tuple[0] + Chem.SanitizeMol(mol) + intermediate = mol + break + except Exception: + continue + if intermediate is None: + return None + + # Clear atom map tag + rw2 = Chem.RWMol(intermediate) + for atom in rw2.GetAtoms(): + if atom.GetAtomMapNum() == 99: + atom.SetAtomMapNum(0) + intermediate = rw2.GetMol() + + # Step 2: intermediate-NH2 + warhead-COOH → secondary amide (PROTAC) + products2 = self._amide_rxn_wh.RunReactants((intermediate, self.warhead_mol)) + if not products2: + return None + + for prod_tuple in products2: + try: + protac_mol = prod_tuple[0] + Chem.SanitizeMol(protac_mol) + return Chem.MolToSmiles(protac_mol) + except Exception: + continue + return None + + # ------------------------------------------------------------------ + # Validation + # ------------------------------------------------------------------ + + def has_warhead_core(self, protac_mol: Chem.Mol) -> bool: + """BRD4 warhead: methylsulfonylmethyl group AND pyrrole-NH in 5-membered ring.""" + return (protac_mol.HasSubstructMatch(self._brd4_ms) and + protac_mol.HasSubstructMatch(self._brd4_nh)) + + def has_e3_core(self, protac_mol: Chem.Mol) -> bool: + """VHL binder: hydroxyproline ring.""" + return protac_mol.HasSubstructMatch(self._vhl_hyp) + + def has_both_amide_bonds(self, protac_mol: Chem.Mol) -> bool: + """≥2 amide bonds formed (covers both linker junctions).""" + return len(protac_mol.GetSubstructMatches(self._amide_pat)) >= 2 + + def no_unreacted_cooh(self, protac_mol: Chem.Mol) -> bool: + """No free COOH remaining — warhead COOH and linker COOH both consumed.""" + return len(protac_mol.GetSubstructMatches(self._cooh_pat)) == 0 + + def check_attachment_site_compatibility(self, protac_mol: Chem.Mol) -> bool: + has_amide = self.has_both_amide_bonds(protac_mol) + no_cooh = self.no_unreacted_cooh(protac_mol) + has_cores = self.has_warhead_core(protac_mol) and self.has_e3_core(protac_mol) + return (has_amide and no_cooh) or has_cores + + def _basic_validation(self, protac_mol: Chem.Mol) -> dict: + checks = { + "has_warhead_core": self.has_warhead_core(protac_mol), + "has_e3_core": self.has_e3_core(protac_mol), + "has_both_amide_bonds": self.has_both_amide_bonds(protac_mol), + "no_unreacted_cooh": self.no_unreacted_cooh(protac_mol), + "attachment_site_valid": self.check_attachment_site_compatibility(protac_mol), + } + checks["_passed"] = all([ + checks["has_warhead_core"], + checks["has_e3_core"], + checks["has_both_amide_bonds"], + checks["no_unreacted_cooh"], + ]) + return checks + + +# --------------------------------------------------------------------------- +# Standard interface aliases +# --------------------------------------------------------------------------- + +Builder = BRD4VHL_PROTAC_Builder +WH_REACTIVE_SMARTS = _COOH_SMARTS +E3_REACTIVE_SMARTS = _NH2_SMARTS +WARHEAD_PAT = Chem.MolFromSmarts("CS(C)(=O)=O") # methylsulfonylmethyl (BRD4) +E3_PAT = Chem.MolFromSmarts("N1CC(O)CC1") # hydroxyproline (VHL) + + +# --------------------------------------------------------------------------- +# Self-test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + from rdkit import RDLogger + from rdkit.Chem import Descriptors + RDLogger.DisableLog("rdApp.error") + + print("=" * 70) + print("BRD4-VHL PROTAC Builder — Self-Test (double amide chemistry)") + print(f"Warhead : BRD4 tetracyclic inhibitor with COOH arm") + print(f"E3 : VHL binder (VH032-type, NH2 reactive)") + print(f"Linker : amino-acid type — NH2 (warhead end) + COOH (VHL end)") + print(f"Chemistry: warhead-COOH + linker-NH2 → amide | linker-COOH + VHL-NH2 → amide") + print("=" * 70) + + builder = BRD4VHL_PROTAC_Builder() + + test_linkers = [ + ("NCCCCCCCCCCC(=O)O", "11-aminoundecanoic acid (PROTAC-DB #1784 reference, MW 201 Da)"), + ("NCCCCCCCC(=O)O", "8-aminooctanoic acid (shorter, valid)"), + ("NCCOCCC(=O)O", "PEG-type amino acid (valid)"), + ("NCCCCCCCCNC(=O)O", "Secondary amine + carbamate — should fail or be unusual"), + ("OC(=O)CCCCC(=O)O", "Diacid — no NH2, should fail"), + ("NCCCCN", "Diamine — no COOH, should fail"), + ("NCCCCCCC(=O)O", "7-aminoheptanoic acid (short valid)"), + ] + + print() + for smiles, desc in test_linkers: + linker_mol = Chem.MolFromSmiles(smiles) + print(f"Linker : {desc}") + if linker_mol is None: + print(" [SKIP] unparseable SMILES") + print("-" * 70) + continue + + nh2_count = len(linker_mol.GetSubstructMatches(Chem.MolFromSmarts("[NX3H2]"))) + cooh_count = len(linker_mol.GetSubstructMatches(Chem.MolFromSmarts("[CX3](=[OX1])[OX2H1]"))) + print(f" NH2 sites: {nh2_count}, COOH sites: {cooh_count}") + + try: + protac_smi = builder.assemble(linker_mol, return_all=False) + if protac_smi: + protac_mol = Chem.MolFromSmiles(protac_smi) + passed, checks = builder.validate_protac(protac_mol) + tag = "VALID ✓" if passed else "ASSEMBLED but INVALID ✗" + print(f" Assembly: {tag}") + for k, v in checks.items(): + print(f" {'✓' if v else '✗'} {k}") + props = builder.get_protac_properties(protac_mol) + print(f" MW: {props['mw']} Da LogP: {props['logp']} " + f"HeavyAtoms: {props['heavy_atoms']}") + except Exception as e: + print(f" Assembly: FAILED — {e}") + + print("-" * 70) + + # Validate the reference PROTAC (PROTAC-DB #1784) + print("\nReference PROTAC (PROTAC-DB #1784) validation:") + ref_smi = ("CC1=C(C2=CC=C(CNC(=O)[C@@H]3C[C@@H](O)CN3C(=O)[C@@H](NC(=O)" + "CCCCCCCCCCNC(=O)C3=CC4=C(C=C3CS(C)(=O)=O)C3=CN(C)C(=O)C5=C3" + "C(=C[NH]5)CN4C3=NC=C(F)C=C3F)C(C)(C)C)C=C2)SC=N1") + ref_mol = Chem.MolFromSmiles(ref_smi) + if ref_mol: + mw = Descriptors.MolWt(ref_mol) + val = builder._basic_validation(ref_mol) + print(f" MW = {mw:.1f} Da (expected 1096.3 Da)") + for k, v in val.items(): + if not k.startswith("_"): + print(f" {'✓' if v else '✗'} {k}") + print(f" {'✓ PASSED' if val['_passed'] else '✗ FAILED'}") + else: + print(" [ERROR] could not parse reference SMILES") diff --git a/CupOFMoCA-PROTAC/src/protac/BRD9VHL.py b/CupOFMoCA-PROTAC/src/protac/BRD9VHL.py new file mode 100644 index 0000000000000000000000000000000000000000..01de1aea88e551b4db375eb64ec42b079d4e2746 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/protac/BRD9VHL.py @@ -0,0 +1,420 @@ +""" +BRD9-VHL PROTAC Builder +Target: BRD9 (1,6-naphthyridin-2(1H)-one warhead, BI-7273 / VZ185 family) +E3 Ligase: VHL (VH032 hydroxyproline/thiazolyl-benzyl binder) +Reference: VZ185 (Zoppi et al. 2019); DegradMaster case_study_1 + +Warhead (BI-7273-piperazine, MW ≈ 437 Da): + 1,6-Naphthyridin-2(1H)-one core fused to a 3,5-dimethoxybenzyl-piperazine + side chain. Free NH on the piperazine is the reactive group (matches + the gefitinib-piperazine pattern in EGFR-VHL). + +E3 Ligand (VH032, MW ≈ 431 Da): + Hydroxyproline + tert-leucine + 4-(4-methylthiazol-5-yl)benzyl cap. + Free NH2 on tert-leucine alpha-amine is the reactive group. + +Chemistry: identical to EGFR-VHL — diacid linker, 2× amide bond formation: + Warhead──N──C(=O)──[linker]──C(=O)──N──E3 + Warhead NH (piperazine) + linker COOH → tertiary amide + E3 NH2 (tert-leucine) + linker COOH → secondary amide + +Alkylation path also supported (terminal CH3 → C-N bond) for alkyl/PEG linkers. +""" + +from rdkit import Chem +from rdkit.Chem import AllChem +from rdkit import RDLogger +from typing import Optional + +try: + from .protac_builder import PROTAC_Builder +except ImportError: + from protac_builder import PROTAC_Builder + +RDLogger.DisableLog("rdApp.error") + + +# --------------------------------------------------------------------------- +# Component SMILES — derived from VZ185 (DegradMaster case_study_1, c0 reference) +# --------------------------------------------------------------------------- + +# BI-7273-like BRD9 warhead with piperazine-NH (linker stripped) +# 1,6-Naphthyridin-2(1H)-one + 3,5-dimethoxy-2-(piperazin-1-ylmethyl)phenyl +BRD9_WARHEAD_SMILES = "COc1cc(-c2cn(C)c(=O)c3cnccc23)cc(OC)c1CN1CCNCC1" + +# VH032: same VHL ligand used by EGFR-VHL module +VHL_E3_SMILES = "N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C" + +# Pre-built Mol objects +BRD9_WARHEAD_MOL = Chem.MolFromSmiles(BRD9_WARHEAD_SMILES) +VHL_E3_MOL = Chem.MolFromSmiles(VHL_E3_SMILES) + +# SMARTS patterns +_COOH_SMARTS = "[CX3](=[OX1])[OX2H1]" +_NH2_SMARTS = "[NX3;H2]" +_NH_SMARTS = "[NX3;H1;!$(NC=O);!$(NS=O);!$(NC#N)]" +_AMIDE_SMARTS = "[NX3H0,NX3H1][CX3](=[OX1])[#6]" +# 1,6-Naphthyridin-2(1H)-one core (BI-7273 family) +_NAPHTHYRIDINONE_SMARTS = "c1cnc(=O)c2cnccc12" +_HYDROXYPROLINE_SMARTS = "[C@@H]1C[C@H](O)CN1" +# Terminal aldehyde: post-assembly C=O that lost its OH during amide bond formation +_TERMINAL_CHO_SMARTS = "[CX3H1](=[OX1])" +# Terminal methyl: alkyl/PEG linker end — bonds via C-N alkylation +_TERMINAL_CH3_SMARTS = "[CX4H3]" + + +class BRD9VHL_PROTAC_Builder(PROTAC_Builder): + """ + PROTAC builder: BI-7273-piperazine BRD9 warhead + VH032 VHL E3. + + Linker requirement: two COOH groups (diacid), one per end. + Warhead NH (piperazine) + linker COOH → tertiary amide. + E3 NH2 (tert-leucine) + linker COOH → secondary amide. + """ + + def __init__(self, warhead: Optional[Chem.Mol] = None, + e3: Optional[Chem.Mol] = None): + super().__init__( + warhead=warhead if warhead is not None else BRD9_WARHEAD_MOL, + e3=e3 if e3 is not None else VHL_E3_MOL, + warhead_name="BI-7273-piperazine (BRD9)", + e3_name="VHL binder NH2 (VH032)", + ) + self._cooh_pat = Chem.MolFromSmarts(_COOH_SMARTS) + self._nh2_pat = Chem.MolFromSmarts(_NH2_SMARTS) + self._nh_pat = Chem.MolFromSmarts(_NH_SMARTS) + self._amide_pat = Chem.MolFromSmarts(_AMIDE_SMARTS) + self._naphthyridinone = Chem.MolFromSmarts(_NAPHTHYRIDINONE_SMARTS) + self._hyp = Chem.MolFromSmarts(_HYDROXYPROLINE_SMARTS) + self._terminal_cho = Chem.MolFromSmarts(_TERMINAL_CHO_SMARTS) + self._terminal_ch3 = Chem.MolFromSmarts(_TERMINAL_CH3_SMARTS) + + # --- Amide bond reactions (diacid linker path) --- + self._amide_rxn_nh2 = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX3:2](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:2](=[OX1:3])" + ) + self._amide_rxn_nh = AllChem.ReactionFromSmarts( + "[NX3H1;!$(NC=O);!$(NS=O):1].[CX3:2](=[OX1:3])[OX2H1]>>[NX3H0:1][CX3:2](=[OX1:3])" + ) + + # --- C-N alkylation reactions (alkyl/PEG linker path) --- + self._cn_rxn_nh2 = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX4H3:2]>>[NX3H1:1][CX4H2:2]" + ) + self._cn_rxn_nh = AllChem.ReactionFromSmarts( + "[NX3H1;!$(NC=O);!$(NS=O):1].[CX4H3:2]>>[NX3H0:1][CX4H2:2]" + ) + + # ------------------------------------------------------------------ + # Verification + # ------------------------------------------------------------------ + + def _verify_warhead_reactive_group(self): + pat = Chem.MolFromSmarts(_NH_SMARTS) + if self.warhead_mol.HasSubstructMatch(pat): + print(f"✓ {self.warhead_name}: NH reactive group confirmed (piperazine)") + else: + print(f"⚠ {self.warhead_name}: no reactive NH detected — check warhead") + + def _verify_e3_reactive_group(self): + pat = Chem.MolFromSmarts(_NH2_SMARTS) + if self.e3_mol.HasSubstructMatch(pat): + print(f"✓ {self.e3_name}: NH2 reactive group confirmed (tert-leucine)") + else: + print(f"⚠ {self.e3_name}: no reactive NH2 detected — check E3") + + # ------------------------------------------------------------------ + # Linker attachment site detection + # ------------------------------------------------------------------ + + def _reactive_cooh_atoms(self, linker_mol: Chem.Mol) -> list: + results = [] + seen = set() + + for m in linker_mol.GetSubstructMatches(self._cooh_pat): + c_idx = m[0] + if c_idx not in seen: + seen.add(c_idx) + results.append({"idx": c_idx, "restored": False}) + + for m in linker_mol.GetSubstructMatches(self._terminal_cho): + c_idx = m[0] + if c_idx in seen: + continue + + rw = Chem.RWMol(linker_mol) + c_atom = rw.GetAtomWithIdx(c_idx) + c_atom.SetNumExplicitHs(0) + c_atom.SetNoImplicit(True) + o_idx = rw.AddAtom(Chem.Atom("O")) + rw.GetAtomWithIdx(o_idx).SetNumExplicitHs(1) + rw.AddBond(c_idx, o_idx, Chem.BondType.SINGLE) + rw.GetAtomWithIdx(c_idx).SetAtomMapNum(99) + try: + Chem.SanitizeMol(rw) + except Exception: + continue + + step1_rxn = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX3:99](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:99](=[OX1:3])" + ) + if step1_rxn.RunReactants((self.e3_mol, rw.GetMol())): + seen.add(c_idx) + results.append({"idx": c_idx, "restored": True}) + + return results + + def _reactive_terminal_c_atoms(self, linker_mol: Chem.Mol) -> list: + results = [] + seen = set() + + for m in linker_mol.GetSubstructMatches(self._terminal_ch3): + c_idx = m[0] + if c_idx in seen: + continue + + rw = Chem.RWMol(linker_mol) + rw.GetAtomWithIdx(c_idx).SetAtomMapNum(98) + try: + Chem.SanitizeMol(rw) + except Exception: + continue + + trial_rxn = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX4H3:98]>>[NX3H1:1][CX4H2:98]" + ) + if trial_rxn.RunReactants((self.e3_mol, rw.GetMol())): + seen.add(c_idx) + results.append({"idx": c_idx, "type": "alkyl"}) + + return results + + def find_linker_attachment_sites(self, linker_mol: Chem.Mol, + amide_only: bool = False) -> list: + amide_cands = self._reactive_cooh_atoms(linker_mol) + alkyl_cands = [] if amide_only else self._reactive_terminal_c_atoms(linker_mol) + + sites = [] + seen = set() + + def _add(w, e, wtype, etype): + key = (w["idx"], e["idx"]) + if key not in seen and w["idx"] != e["idx"]: + seen.add(key) + sites.append({ + "warhead_site": w["idx"], + "warhead_type": wtype, + "warhead_restored": w.get("restored", False), + "e3_site": e["idx"], + "e3_type": etype, + "e3_restored": e.get("restored", False), + }) + + direct = [c for c in amide_cands if not c["restored"]] + restored = [c for c in amide_cands if c["restored"]] + for w in direct: + for e in direct: _add(w, e, "cooh", "cooh") + for w in direct: + for e in restored: _add(w, e, "cooh", "cooh") + for e in direct: _add(e, w, "cooh", "cooh") + for w in restored: + for e in restored: _add(w, e, "cooh", "cooh") + + if not amide_only: + for w in alkyl_cands: + for e in alkyl_cands: + _add(w, e, "alkyl", "alkyl") + + for w in amide_cands: + for e in alkyl_cands: + _add(w, e, "cooh", "alkyl") + _add(e, w, "alkyl", "cooh") + + return sites + + # ------------------------------------------------------------------ + # Assembly: two sequential bond formations + # ------------------------------------------------------------------ + + def _restore_cho_to_cooh(self, rw: Chem.RWMol, c_idx: int): + c_atom = rw.GetAtomWithIdx(c_idx) + c_atom.SetNumExplicitHs(0) + c_atom.SetNoImplicit(True) + o_idx = rw.AddAtom(Chem.Atom("O")) + rw.GetAtomWithIdx(o_idx).SetNumExplicitHs(1) + rw.AddBond(c_idx, o_idx, Chem.BondType.SINGLE) + + def _assemble_protac(self, linker_mol: Chem.Mol, warhead_site_idx: int, + e3_site_idx: int, + warhead_type: str = "cooh", e3_type: str = "cooh", + warhead_restored: bool = False, + e3_restored: bool = False) -> Optional[str]: + rw = Chem.RWMol(linker_mol) + + if e3_type == "cooh" and e3_restored: + self._restore_cho_to_cooh(rw, e3_site_idx) + if warhead_type == "cooh" and warhead_restored: + self._restore_cho_to_cooh(rw, warhead_site_idx) + try: + Chem.SanitizeMol(rw) + except Exception: + return None + + rw.GetAtomWithIdx(e3_site_idx).SetAtomMapNum(99) + tagged_linker = rw.GetMol() + + if e3_type == "cooh": + step1_rxn = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX3:99](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:99](=[OX1:3])" + ) + else: + step1_rxn = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX4H3:99]>>[NX3H1:1][CX4H2:99]" + ) + + products1 = step1_rxn.RunReactants((self.e3_mol, tagged_linker)) + if not products1: + return None + + intermediate = None + for prod_tuple in products1: + try: + mol = prod_tuple[0] + Chem.SanitizeMol(mol) + intermediate = mol + break + except Exception: + continue + if intermediate is None: + return None + + rw2 = Chem.RWMol(intermediate) + for atom in rw2.GetAtoms(): + if atom.GetAtomMapNum() == 99: + atom.SetAtomMapNum(0) + intermediate = rw2.GetMol() + + if warhead_type == "cooh": + step2_rxn = self._amide_rxn_nh + else: + step2_rxn = self._cn_rxn_nh + + products2 = step2_rxn.RunReactants((self.warhead_mol, intermediate)) + if not products2: + return None + + for prod_tuple in products2: + try: + protac_mol = prod_tuple[0] + Chem.SanitizeMol(protac_mol) + return Chem.MolToSmiles(protac_mol) + except Exception: + continue + + return None + + # ------------------------------------------------------------------ + # Validation + # ------------------------------------------------------------------ + + def check_attachment_site_compatibility(self, protac_mol: Chem.Mol) -> bool: + has_amide = len(protac_mol.GetSubstructMatches(self._amide_pat)) >= 2 + no_cooh = len(protac_mol.GetSubstructMatches(self._cooh_pat)) == 0 + has_cores = self.has_warhead_core(protac_mol) and self.has_e3_core(protac_mol) + return (has_amide and no_cooh) or has_cores + + def has_warhead_core(self, protac_mol: Chem.Mol) -> bool: + return protac_mol.HasSubstructMatch(self._naphthyridinone) + + def has_e3_core(self, protac_mol: Chem.Mol) -> bool: + return protac_mol.HasSubstructMatch(self._hyp) + + def has_both_amide_bonds(self, protac_mol: Chem.Mol) -> bool: + return len(protac_mol.GetSubstructMatches(self._amide_pat)) >= 2 + + def no_unreacted_cooh(self, protac_mol: Chem.Mol) -> bool: + return len(protac_mol.GetSubstructMatches(self._cooh_pat)) == 0 + + def _basic_validation(self, protac_mol: Chem.Mol) -> dict: + checks = { + "has_warhead_core": self.has_warhead_core(protac_mol), + "has_e3_core": self.has_e3_core(protac_mol), + "has_both_amide_bonds": self.has_both_amide_bonds(protac_mol), + "no_unreacted_cooh": self.no_unreacted_cooh(protac_mol), + "attachment_site_valid": self.check_attachment_site_compatibility(protac_mol), + } + checks["_passed"] = all([ + checks["has_warhead_core"], + checks["has_e3_core"], + checks["has_both_amide_bonds"], + checks["no_unreacted_cooh"], + ]) + return checks + + +# --------------------------------------------------------------------------- +# Standard interface aliases (used by defog_guided.py / batch_report.py) +# --------------------------------------------------------------------------- + +WARHEAD_MOL = BRD9_WARHEAD_MOL +E3_MOL = VHL_E3_MOL +WARHEAD_SMILES = BRD9_WARHEAD_SMILES +E3_SMILES = VHL_E3_SMILES +Builder = BRD9VHL_PROTAC_Builder +WH_REACTIVE_SMARTS = _NH_SMARTS +E3_REACTIVE_SMARTS = _NH2_SMARTS +WARHEAD_PAT = Chem.MolFromSmarts(_NAPHTHYRIDINONE_SMARTS) # 1,6-naphthyridinone (BRD9) +E3_PAT = Chem.MolFromSmarts("N1CC(O)CC1") # hydroxyproline (VHL) + + +# --------------------------------------------------------------------------- +# Self-test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + from rdkit import RDLogger + RDLogger.DisableLog("rdApp.error") + + print("=" * 70) + print("BRD9-VHL PROTAC Builder — Self-Test") + print(f"Warhead : BRD9 (BI-7273-piperazine, NH reactive)") + print(f"E3 : VHL (VH032, NH2 reactive)") + print(f"Chemistry: diacid linker + 2× amide bond formation") + print("=" * 70) + + builder = BRD9VHL_PROTAC_Builder() + + test_linkers = [ + ("OC(=O)CCCCCCCCC(=O)O", "Sebacic acid (C10 diacid)"), + ("OC(=O)CCOCCOCCOC(=O)O", "PEG-diacid (valid)"), + ("NCCOCCOCCN", "Diamine — no COOH, should fail"), + ("OC(=O)CCCC", "Single COOH — should fail"), + ] + + print() + for smiles, desc in test_linkers: + linker_mol = Chem.MolFromSmiles(smiles) + print(f"Linker : {desc}") + if linker_mol is None: + print(" [SKIP] unparseable SMILES") + print("-" * 70) + continue + + cooh_count = len(linker_mol.GetSubstructMatches(Chem.MolFromSmarts(_COOH_SMARTS))) + print(f"COOH sites: {cooh_count}") + + try: + protac_smi = builder.assemble(linker_mol, return_all=False) + protac_mol = Chem.MolFromSmiles(protac_smi) + passed, checks = builder.validate_protac(protac_mol) + tag = "VALID ✓" if passed else "ASSEMBLED but INVALID ✗" + print(f"Assembly: {tag}") + for k, v in checks.items(): + print(f" {'✓' if v else '✗'} {k}") + props = builder.get_protac_properties(protac_mol) + print(f" MW: {props['mw']} Da LogP: {props['logp']} " + f"HeavyAtoms: {props['heavy_atoms']}") + except Exception as e: + print(f"Assembly: FAILED — {e}") + + print("-" * 70) diff --git a/CupOFMoCA-PROTAC/src/protac/CDK6CRBN.py b/CupOFMoCA-PROTAC/src/protac/CDK6CRBN.py new file mode 100644 index 0000000000000000000000000000000000000000..ccf7b645ba757764d9681a20fdadb3c4f666bc1b --- /dev/null +++ b/CupOFMoCA-PROTAC/src/protac/CDK6CRBN.py @@ -0,0 +1,360 @@ +""" +CDK6-CRBN PROTAC Builder +Target: CDK6 (palbociclib-derived warhead, piperazine-NH reactive) +E3 Ligase: CRBN (pomalidomide / 4-amino-thalidomide, aryl-NH2 reactive) +Source: Dominant chemistry in PROTAC-DB CDK6+CRBN (palbociclib core matches + 58% of training PROTACs; piperazine-NH on warhead → diacid linker). + +Linker chemistry: same as EGFR-VHL / BRD9-VHL — diacid (cooh termini). + Warhead-piperazine-NH + linker-COOH → tertiary amide (warhead end) + E3-aryl-NH2 + linker-COOH → secondary amide (E3 end) + +Use with: --fix_termini cooh + --protac_module CDK6CRBN + +Picking COOH chemistry over the alternative amine-termini path because: + 1. Both warhead piperazine-NH and pomalidomide aryl-NH2 are nucleophilic + primary/secondary amines → diacid linker is the natural fit + 2. Diacid-termini generation produces far fewer liability-flagged linkers + than the amine-termini path (avoids vinyl-amines, hemiaminals, hydroxyamines) + +Example training PROTAC (PROTAC-DB CDK6+CRBN, label=1): + CC(=O)C1=C(C)C2=CN=C(NC3=CC=C(N4CCN(CC(=O)NCCOCCOCCOCCNC5=CC=CC6=C5C(=O)N(C5CCC(=O)NC5=O)C6=O)CC4)C=N3)N=C2N(C5CCCC5)C1=O +""" + +from rdkit import Chem +from rdkit.Chem import AllChem +from rdkit import RDLogger +from typing import Optional + +try: + from .protac_builder import PROTAC_Builder +except ImportError: + from protac_builder import PROTAC_Builder + +RDLogger.DisableLog("rdApp.error") + + +# --------------------------------------------------------------------------- +# Component SMILES — chosen to match dominant PROTAC-DB CDK6-CRBN chemistry +# --------------------------------------------------------------------------- + +# Palbociclib with terminal piperazine-NH (linker attachment site). +# 6-acetyl-pyrido-pyrimidine + cyclopentyl-N + amino-pyridyl-piperazine. +# 58% substructure match against PROTAC-DB CDK6+CRBN entries. +CDK6_WARHEAD_SMILES = "CC(=O)c1c(C)c2cnc(Nc3ccc(N4CCNCC4)nc3)nc2n(C2CCCC2)c1=O" + +# Pomalidomide / 4-amino-thalidomide (CRBN binder; aryl-NH2 reactive). +CRBN_E3_SMILES = "Nc1cccc2c1C(=O)N(C3CCC(=O)NC3=O)C2=O" + +CDK6_WARHEAD_MOL = Chem.MolFromSmiles(CDK6_WARHEAD_SMILES) +CRBN_E3_MOL = Chem.MolFromSmiles(CRBN_E3_SMILES) + +# SMARTS patterns +_COOH_SMARTS = "[CX3](=[OX1])[OX2H1]" +_NH2_SMARTS = "[NX3;H2]" +_NH_SMARTS = "[NX3;H1;!$(NC=O);!$(NS=O);!$(NC#N)]" +_AMIDE_SMARTS = "[NX3H0,NX3H1][CX3](=[OX1])[#6]" +# Palbociclib core (loose — allows any N-substituent on the cyclopentyl-N) +_CDK6_CORE_SMARTS = "[#6]C(=O)c1c([#6])c2cnc(*)nc2n([#6])c1=O" +_THALIDOMIDE_SMARTS = "O=C1c2ccccc2C(=O)N1[C@@H]1CCC(=O)NC1=O" +_TERMINAL_CHO_SMARTS = "[CX3H1](=[OX1])" + + +class CDK6CRBN_PROTAC_Builder(PROTAC_Builder): + """ + PROTAC builder: palbociclib-piperazine CDK6 warhead + pomalidomide CRBN E3. + + Linker requirement: two COOH groups (diacid), one per end. + Warhead piperazine-NH + linker COOH → tertiary amide. + E3 aryl-NH2 + linker COOH → secondary amide. + """ + + def __init__(self, warhead: Optional[Chem.Mol] = None, + e3: Optional[Chem.Mol] = None): + # Initialize patterns BEFORE super().__init__() — parent calls _verify_* + self._cooh_pat = Chem.MolFromSmarts(_COOH_SMARTS) + self._nh2_pat = Chem.MolFromSmarts(_NH2_SMARTS) + self._nh_pat = Chem.MolFromSmarts(_NH_SMARTS) + self._amide_pat = Chem.MolFromSmarts(_AMIDE_SMARTS) + self._cdk6_core = Chem.MolFromSmarts(_CDK6_CORE_SMARTS) + self._thalidomide = Chem.MolFromSmarts(_THALIDOMIDE_SMARTS) + self._terminal_cho = Chem.MolFromSmarts(_TERMINAL_CHO_SMARTS) + super().__init__( + warhead=warhead if warhead is not None else CDK6_WARHEAD_MOL, + e3=e3 if e3 is not None else CRBN_E3_MOL, + warhead_name="palbociclib-piperazine (CDK6)", + e3_name="pomalidomide (CRBN)", + ) + + # Amide reactions + # E3 aryl-NH2 + linker COOH → secondary aniline-amide + self._amide_rxn_nh2 = AllChem.ReactionFromSmarts( + "[NX3H2;$(Nc):1].[CX3:2](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:2](=[OX1:3])" + ) + # Warhead piperazine-NH + linker COOH → tertiary amide + self._amide_rxn_nh = AllChem.ReactionFromSmarts( + "[NX3H1;!$(NC=O);!$(NS=O):1].[CX3:2](=[OX1:3])[OX2H1]>>[NX3H0:1][CX3:2](=[OX1:3])" + ) + + def _verify_warhead_reactive_group(self): + if self.warhead_mol.HasSubstructMatch(self._nh_pat): + print(f"✓ {self.warhead_name}: NH reactive group confirmed (piperazine)") + else: + print(f"⚠ {self.warhead_name}: no reactive NH detected — check warhead") + + def _verify_e3_reactive_group(self): + nh2_aryl = Chem.MolFromSmarts("[NX3;H2;$(Nc)]") + if self.e3_mol.HasSubstructMatch(nh2_aryl): + print(f"✓ {self.e3_name}: aryl-NH2 reactive group confirmed (4-amino-thalidomide)") + else: + print(f"⚠ {self.e3_name}: no reactive aryl-NH2 detected — check E3") + + # ------------------------------------------------------------------ + # Linker attachment site detection — diacid (COOH at both ends) + # ------------------------------------------------------------------ + + def _reactive_cooh_atoms(self, linker_mol: Chem.Mol) -> list: + results = [] + seen = set() + + # Direct COOH + for m in linker_mol.GetSubstructMatches(self._cooh_pat): + c_idx = m[0] + if c_idx not in seen: + seen.add(c_idx) + results.append({"idx": c_idx, "restored": False}) + + # Terminal CHO → restore to COOH (post-assembly form) + for m in linker_mol.GetSubstructMatches(self._terminal_cho): + c_idx = m[0] + if c_idx in seen: + continue + rw = Chem.RWMol(linker_mol) + c_atom = rw.GetAtomWithIdx(c_idx) + c_atom.SetNumExplicitHs(0) + c_atom.SetNoImplicit(True) + o_idx = rw.AddAtom(Chem.Atom("O")) + rw.GetAtomWithIdx(o_idx).SetNumExplicitHs(1) + rw.AddBond(c_idx, o_idx, Chem.BondType.SINGLE) + rw.GetAtomWithIdx(c_idx).SetAtomMapNum(99) + try: + Chem.SanitizeMol(rw) + except Exception: + continue + trial_rxn = AllChem.ReactionFromSmarts( + "[NX3H2;$(Nc):1].[CX3:99](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:99](=[OX1:3])" + ) + if trial_rxn.RunReactants((self.e3_mol, rw.GetMol())): + seen.add(c_idx) + results.append({"idx": c_idx, "restored": True}) + return results + + def find_linker_attachment_sites(self, linker_mol: Chem.Mol, + amide_only: bool = False) -> list: + cands = self._reactive_cooh_atoms(linker_mol) + sites, seen = [], set() + direct = [c for c in cands if not c["restored"]] + restored = [c for c in cands if c["restored"]] + for w in direct: + for e in direct: + key = (w["idx"], e["idx"]) + if key not in seen and w["idx"] != e["idx"]: + seen.add(key) + sites.append({ + "warhead_site": w["idx"], "warhead_type": "cooh", + "warhead_restored": False, + "e3_site": e["idx"], "e3_type": "cooh", "e3_restored": False, + }) + for w in direct: + for e in restored: + key = (w["idx"], e["idx"]) + if key not in seen and w["idx"] != e["idx"]: + seen.add(key) + sites.append({ + "warhead_site": w["idx"], "warhead_type": "cooh", + "warhead_restored": False, + "e3_site": e["idx"], "e3_type": "cooh", "e3_restored": True, + }) + for e in direct: + key = (e["idx"], w["idx"]) + if key not in seen and w["idx"] != e["idx"]: + seen.add(key) + sites.append({ + "warhead_site": e["idx"], "warhead_type": "cooh", + "warhead_restored": False, + "e3_site": w["idx"], "e3_type": "cooh", "e3_restored": False, + }) + for w in restored: + for e in restored: + key = (w["idx"], e["idx"]) + if key not in seen and w["idx"] != e["idx"]: + seen.add(key) + sites.append({ + "warhead_site": w["idx"], "warhead_type": "cooh", + "warhead_restored": True, + "e3_site": e["idx"], "e3_type": "cooh", "e3_restored": True, + }) + return sites + + # ------------------------------------------------------------------ + # Assembly + # ------------------------------------------------------------------ + + def _restore_cho_to_cooh(self, rw: Chem.RWMol, c_idx: int): + c_atom = rw.GetAtomWithIdx(c_idx) + c_atom.SetNumExplicitHs(0) + c_atom.SetNoImplicit(True) + o_idx = rw.AddAtom(Chem.Atom("O")) + rw.GetAtomWithIdx(o_idx).SetNumExplicitHs(1) + rw.AddBond(c_idx, o_idx, Chem.BondType.SINGLE) + + def _assemble_protac(self, linker_mol: Chem.Mol, warhead_site_idx: int, + e3_site_idx: int, + warhead_type: str = "cooh", e3_type: str = "cooh", + warhead_restored: bool = False, + e3_restored: bool = False) -> Optional[str]: + rw = Chem.RWMol(linker_mol) + if e3_restored: + self._restore_cho_to_cooh(rw, e3_site_idx) + if warhead_restored: + self._restore_cho_to_cooh(rw, warhead_site_idx) + try: + Chem.SanitizeMol(rw) + except Exception: + return None + + rw.GetAtomWithIdx(e3_site_idx).SetAtomMapNum(99) + tagged = rw.GetMol() + + # Step 1: E3 aryl-NH2 + linker COOH(tagged) → secondary aniline-amide + step1_rxn = AllChem.ReactionFromSmarts( + "[NX3H2;$(Nc):1].[CX3:99](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:99](=[OX1:3])" + ) + products1 = step1_rxn.RunReactants((self.e3_mol, tagged)) + if not products1: + return None + intermediate = None + for prod_tuple in products1: + try: + m = prod_tuple[0] + Chem.SanitizeMol(m) + intermediate = m + break + except Exception: + continue + if intermediate is None: + return None + + rw2 = Chem.RWMol(intermediate) + for atom in rw2.GetAtoms(): + if atom.GetAtomMapNum() == 99: + atom.SetAtomMapNum(0) + intermediate = rw2.GetMol() + + # Step 2: warhead piperazine-NH + remaining COOH → tertiary amide + products2 = self._amide_rxn_nh.RunReactants((self.warhead_mol, intermediate)) + if not products2: + return None + for prod_tuple in products2: + try: + protac_mol = prod_tuple[0] + Chem.SanitizeMol(protac_mol) + return Chem.MolToSmiles(protac_mol) + except Exception: + continue + return None + + # ------------------------------------------------------------------ + # Validation + # ------------------------------------------------------------------ + + def has_warhead_core(self, protac_mol: Chem.Mol) -> bool: + return protac_mol.HasSubstructMatch(self._cdk6_core) + + def has_e3_core(self, protac_mol: Chem.Mol) -> bool: + glut = Chem.MolFromSmarts("O=C1NC(=O)CCC1") + return protac_mol.HasSubstructMatch(glut) + + def has_both_amide_bonds(self, protac_mol: Chem.Mol) -> bool: + return len(protac_mol.GetSubstructMatches(self._amide_pat)) >= 2 + + def no_unreacted_cooh(self, protac_mol: Chem.Mol) -> bool: + return len(protac_mol.GetSubstructMatches(self._cooh_pat)) == 0 + + def check_attachment_site_compatibility(self, protac_mol: Chem.Mol) -> bool: + return self.has_warhead_core(protac_mol) and self.has_e3_core(protac_mol) + + def _basic_validation(self, protac_mol: Chem.Mol) -> dict: + checks = { + "has_warhead_core": self.has_warhead_core(protac_mol), + "has_e3_core": self.has_e3_core(protac_mol), + "has_both_amide_bonds": self.has_both_amide_bonds(protac_mol), + "no_unreacted_cooh": self.no_unreacted_cooh(protac_mol), + "attachment_site_valid": self.check_attachment_site_compatibility(protac_mol), + } + checks["_passed"] = all([ + checks["has_warhead_core"], + checks["has_e3_core"], + checks["has_both_amide_bonds"], + checks["no_unreacted_cooh"], + ]) + return checks + + +# --------------------------------------------------------------------------- +# Standard interface aliases +# --------------------------------------------------------------------------- + +WARHEAD_MOL = CDK6_WARHEAD_MOL +E3_MOL = CRBN_E3_MOL +WARHEAD_SMILES = CDK6_WARHEAD_SMILES +E3_SMILES = CRBN_E3_SMILES +Builder = CDK6CRBN_PROTAC_Builder +WH_REACTIVE_SMARTS = _NH_SMARTS +E3_REACTIVE_SMARTS = "[NX3;H2;$(Nc)]" +WARHEAD_PAT = Chem.MolFromSmarts(_CDK6_CORE_SMARTS) +E3_PAT = Chem.MolFromSmarts("O=C1NC(=O)CCC1") + + +# --------------------------------------------------------------------------- +# Self-test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + from rdkit import RDLogger + RDLogger.DisableLog("rdApp.error") + + print("=" * 70) + print("CDK6-CRBN PROTAC Builder — Self-Test") + print(f"Warhead : CDK6 (palbociclib-piperazine-NH, NH reactive)") + print(f"E3 : CRBN (pomalidomide aryl-NH2, NH2 reactive)") + print(f"Chemistry: diacid linker + 2× amide bond formation") + print("=" * 70) + + builder = CDK6CRBN_PROTAC_Builder() + + test_linkers = [ + ("OC(=O)CCCCCCCCC(=O)O", "sebacic acid (C10 diacid)"), + ("OC(=O)CCOCCOCCOC(=O)O", "PEG-diacid"), + ("NCCN", "diamine — should fail"), + ] + print() + for smiles, desc in test_linkers: + linker_mol = Chem.MolFromSmiles(smiles) + cooh_count = len(linker_mol.GetSubstructMatches(Chem.MolFromSmarts(_COOH_SMARTS))) + print(f"Linker : {desc}") + print(f" COOH sites: {cooh_count}") + try: + protac_smi = builder.assemble(linker_mol, return_all=False) + protac_mol = Chem.MolFromSmiles(protac_smi) + passed, checks = builder.validate_protac(protac_mol) + tag = "VALID ✓" if passed else "ASSEMBLED but INVALID ✗" + print(f" Assembly: {tag}") + for k, v in checks.items(): + print(f" {'✓' if v else '✗'} {k}") + props = builder.get_protac_properties(protac_mol) + print(f" MW: {props['mw']} Da LogP: {props['logp']} HeavyAtoms: {props['heavy_atoms']}") + except Exception as e: + print(f" Assembly: FAILED — {e}") + print("-" * 70) diff --git a/CupOFMoCA-PROTAC/src/protac/ErlotinibVHL.py b/CupOFMoCA-PROTAC/src/protac/ErlotinibVHL.py new file mode 100644 index 0000000000000000000000000000000000000000..5da19235962cd691fbcbd80f565392c4431cae8b --- /dev/null +++ b/CupOFMoCA-PROTAC/src/protac/ErlotinibVHL.py @@ -0,0 +1,514 @@ +""" +MS39 PROTAC Builder +Target: EGFR (gefitinib-derived piperazine warhead) +E3 Ligase: VHL (hydroxyproline/thiazolyl-benzyl binder) +Source: Burslem et al. 2018 (Cell Chem Biol); PubChem CID 145996533 + +Warhead (gefitinib-piperazine, MW ≈ 460 Da): + Gefitinib core with piperazine replacing the morpholine. + Free NH on piperazine is the reactive group. + +E3 Ligand (VHL binder, MW ≈ 431 Da): + Hydroxyproline + tert-leucine + 4-(4-methylthiazol-5-yl)benzyl cap. + Free NH2 on tert-leucine alpha-amine is the reactive group. + +Assembly supports two linker chemistries (two-layer check: SMARTS first, +reaction trial second — mirrors the CHO→COOH restoration logic): + + Amide path (diacid linker, 2.3% of training data): + Warhead──N──C(=O)──[linker]──C(=O)──N──E3 + Linker has COOH or terminal CHO (post-assembly form) at both ends. + + Alkylation path (alkyl/PEG linker, 77% of training data): + Warhead──N──CH₂──[linker]──CH₂──N──E3 + Linker has terminal CH₃ (CX4H3) at both ends; direct C-N bond formed. + +All methods accept Chem.Mol objects. SMILES appear only in module-level +constants and the self-test. +""" + +from rdkit import Chem +from rdkit.Chem import AllChem +from rdkit import RDLogger +from typing import Optional + +try: + from .protac_builder import PROTAC_Builder +except ImportError: + from protac_builder import PROTAC_Builder + +RDLogger.DisableLog("rdApp.error") + + +# --------------------------------------------------------------------------- +# Component SMILES — extracted from MS39 (PubChem CID 145996533) +# --------------------------------------------------------------------------- + +# Gefitinib core with piperazine-NH replacing morpholine +# MW ≈ 460 Da; reactive group: piperazine NH (secondary amine) +EGFR_WARHEAD_SMILES = "COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCCN1CCNCC1" + +# VHL binder: hydroxyproline + tert-leucine + thiazolyl-benzyl cap +# MW ≈ 431 Da; reactive group: free NH2 on tert-leucine alpha-amine +VHL_E3_SMILES = "N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C" + +# Pre-built Mol objects +EGFR_WARHEAD_MOL = Chem.MolFromSmiles(EGFR_WARHEAD_SMILES) +VHL_E3_MOL = Chem.MolFromSmiles(VHL_E3_SMILES) + +# SMARTS patterns +_COOH_SMARTS = "[CX3](=[OX1])[OX2H1]" +_NH2_SMARTS = "[NX3;H2]" +_NH_SMARTS = "[NX3;H1;!$(NC=O);!$(NS=O);!$(NC#N)]" +_AMIDE_SMARTS = "[NX3H0,NX3H1][CX3](=[OX1])[#6]" +_QUINAZOLINE_SMARTS = "Nc1ncnc2ccccc12" +_HYDROXYPROLINE_SMARTS = "[C@@H]1C[C@H](O)CN1" +# Terminal aldehyde: post-assembly C=O that lost its OH during amide bond formation +_TERMINAL_CHO_SMARTS = "[CX3H1](=[OX1])" +# Terminal methyl: alkyl/PEG linker end — bonds via C-N alkylation (77% of training data) +_TERMINAL_CH3_SMARTS = "[CX4H3]" + + +class ErlotinibVHL_PROTAC_Builder(PROTAC_Builder): + """ + PROTAC builder: MS39 gefitinib-piperazine EGFR warhead + VHL binder E3. + + Linker requirement: two COOH groups (diacid), one per end. + Warhead NH (piperazine) + linker COOH → tertiary amide. + E3 NH2 (tert-leucine) + linker COOH → secondary amide. + + Parameters + ---------- + warhead : Chem.Mol, optional + EGFR warhead with free NH. Defaults to EGFR_WARHEAD_MOL. + e3 : Chem.Mol, optional + VHL E3 ligand with free NH2. Defaults to VHL_E3_MOL. + """ + + def __init__(self, warhead: Optional[Chem.Mol] = None, + e3: Optional[Chem.Mol] = None): + super().__init__( + warhead=warhead if warhead is not None else EGFR_WARHEAD_MOL, + e3=e3 if e3 is not None else VHL_E3_MOL, + warhead_name="Gefitinib-piperazine (EGFR)", + e3_name="VHL binder NH2 (VHL)", + ) + self._cooh_pat = Chem.MolFromSmarts(_COOH_SMARTS) + self._nh2_pat = Chem.MolFromSmarts(_NH2_SMARTS) + self._nh_pat = Chem.MolFromSmarts(_NH_SMARTS) + self._amide_pat = Chem.MolFromSmarts(_AMIDE_SMARTS) + self._quinazoline = Chem.MolFromSmarts(_QUINAZOLINE_SMARTS) + self._hyp = Chem.MolFromSmarts(_HYDROXYPROLINE_SMARTS) + self._terminal_cho = Chem.MolFromSmarts(_TERMINAL_CHO_SMARTS) + self._terminal_ch3 = Chem.MolFromSmarts(_TERMINAL_CH3_SMARTS) + + # --- Amide bond reactions (diacid linker path) --- + # E3 NH2 + linker COOH → secondary amide + self._amide_rxn_nh2 = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX3:2](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:2](=[OX1:3])" + ) + # Warhead NH (piperazine) + linker COOH → tertiary amide + self._amide_rxn_nh = AllChem.ReactionFromSmarts( + "[NX3H1;!$(NC=O);!$(NS=O):1].[CX3:2](=[OX1:3])[OX2H1]>>[NX3H0:1][CX3:2](=[OX1:3])" + ) + + # --- C-N alkylation reactions (alkyl/PEG linker path) --- + # E3 NH2 + linker terminal CH3 → secondary amine (N-alkylation) + self._cn_rxn_nh2 = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX4H3:2]>>[NX3H1:1][CX4H2:2]" + ) + # Warhead NH (piperazine) + linker terminal CH3 → tertiary amine + self._cn_rxn_nh = AllChem.ReactionFromSmarts( + "[NX3H1;!$(NC=O);!$(NS=O):1].[CX4H3:2]>>[NX3H0:1][CX4H2:2]" + ) + + # ------------------------------------------------------------------ + # Verification + # ------------------------------------------------------------------ + + def _verify_warhead_reactive_group(self): + pat = Chem.MolFromSmarts(_NH_SMARTS) + if self.warhead_mol.HasSubstructMatch(pat): + print(f"✓ {self.warhead_name}: NH reactive group confirmed (piperazine)") + else: + print(f"⚠ {self.warhead_name}: no reactive NH detected — check warhead") + + def _verify_e3_reactive_group(self): + pat = Chem.MolFromSmarts(_NH2_SMARTS) + if self.e3_mol.HasSubstructMatch(pat): + print(f"✓ {self.e3_name}: NH2 reactive group confirmed (tert-leucine)") + else: + print(f"⚠ {self.e3_name}: no reactive NH2 detected — check E3") + + # ------------------------------------------------------------------ + # Linker attachment site detection — looks for COOH on linker + # ------------------------------------------------------------------ + + def _reactive_cooh_atoms(self, linker_mol: Chem.Mol) -> list: + """ + Find C atoms that can act as COOH for amide coupling, either directly + or after restoring the OH lost during post-assembly extraction. + + Priority: + 1. Direct COOH — already reactive + 2. Terminal aldehyde CHO — post-assembly form; OH was lost when the + amide bond formed. Add OH back (CHO → COOH) and verify the + reaction fires against the E3 NH2. + + Returns list of {'idx': int, 'restored': bool} + """ + results = [] + seen = set() + + # 1. Direct COOH + for m in linker_mol.GetSubstructMatches(self._cooh_pat): + c_idx = m[0] + if c_idx not in seen: + seen.add(c_idx) + results.append({"idx": c_idx, "restored": False}) + + # 2. Terminal aldehyde: [CX3H1](=O) — add OH to restore COOH + for m in linker_mol.GetSubstructMatches(self._terminal_cho): + c_idx = m[0] + if c_idx in seen: + continue + + rw = Chem.RWMol(linker_mol) + c_atom = rw.GetAtomWithIdx(c_idx) + # Remove the aldehyde H, add -OH + c_atom.SetNumExplicitHs(0) + c_atom.SetNoImplicit(True) + o_idx = rw.AddAtom(Chem.Atom("O")) + rw.GetAtomWithIdx(o_idx).SetNumExplicitHs(1) + rw.AddBond(c_idx, o_idx, Chem.BondType.SINGLE) + rw.GetAtomWithIdx(c_idx).SetAtomMapNum(99) + try: + Chem.SanitizeMol(rw) + except Exception: + continue + + # Verify the restored COOH reacts with E3 NH2 + step1_rxn = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX3:99](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:99](=[OX1:3])" + ) + if step1_rxn.RunReactants((self.e3_mol, rw.GetMol())): + seen.add(c_idx) + results.append({"idx": c_idx, "restored": True}) + + return results + + def _reactive_terminal_c_atoms(self, linker_mol: Chem.Mol) -> list: + """ + Two-layer check for alkylation attachment sites (mirrors _reactive_cooh_atoms). + + Layer 1 — SMARTS: find terminal CH3 groups ([CX4H3]). + Layer 2 — Reaction trial: verify the C-N bond actually forms with the + E3 NH2 (using _cn_rxn_nh2). Sites that fail the reaction trial + are discarded. + + Returns list of {'idx': int, 'type': 'alkyl'}. + """ + results = [] + seen = set() + + for m in linker_mol.GetSubstructMatches(self._terminal_ch3): + c_idx = m[0] + if c_idx in seen: + continue + + # Tag this C for the reaction trial + rw = Chem.RWMol(linker_mol) + rw.GetAtomWithIdx(c_idx).SetAtomMapNum(98) + try: + Chem.SanitizeMol(rw) + except Exception: + continue + + trial_rxn = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX4H3:98]>>[NX3H1:1][CX4H2:98]" + ) + if trial_rxn.RunReactants((self.e3_mol, rw.GetMol())): + seen.add(c_idx) + results.append({"idx": c_idx, "type": "alkyl"}) + + return results + + def find_linker_attachment_sites(self, linker_mol: Chem.Mol, + amide_only: bool = False) -> list: + """ + Find pairs of reactive atoms on the linker for both assembly paths. + + Priority order: + 1. Amide pairs (both ends COOH/CHO) — diacid linkers, 2.3% of data + 2. Alkylation pairs (both ends CH3) — alkyl/PEG linkers, 77% of data + 3. Mixed pairs (one amide + one alkyl) — rarely needed + + Parameters + ---------- + amide_only : bool + If True, only return amide-path (COOH/CHO) pairs and skip the + alkylation (CH3) path entirely. Use this for strict final validation + — the CH3 path fires on any terminal methyl, which is chemically + too permissive without an explicit leaving group on the carbon. + + Returns list of dicts, each describing one candidate assembly: + {'warhead_site': idx, 'warhead_type': 'cooh'|'alkyl', + 'warhead_restored': bool, + 'e3_site': idx, 'e3_type': 'cooh'|'alkyl', + 'e3_restored': bool} + """ + amide_cands = self._reactive_cooh_atoms(linker_mol) + alkyl_cands = [] if amide_only else self._reactive_terminal_c_atoms(linker_mol) + + sites = [] + seen = set() + + def _add(w, e, wtype, etype): + key = (w["idx"], e["idx"]) + if key not in seen and w["idx"] != e["idx"]: + seen.add(key) + sites.append({ + "warhead_site": w["idx"], + "warhead_type": wtype, + "warhead_restored": w.get("restored", False), + "e3_site": e["idx"], + "e3_type": etype, + "e3_restored": e.get("restored", False), + }) + + # 1. Amide–amide pairs (direct COOH first, then restored CHO) + direct = [c for c in amide_cands if not c["restored"]] + restored = [c for c in amide_cands if c["restored"]] + for w in direct: + for e in direct: _add(w, e, "cooh", "cooh") + for w in direct: + for e in restored: _add(w, e, "cooh", "cooh") + for e in direct: _add(e, w, "cooh", "cooh") + for w in restored: + for e in restored: _add(w, e, "cooh", "cooh") + + if not amide_only: + # 2. Alkylation–alkylation pairs + for w in alkyl_cands: + for e in alkyl_cands: + _add(w, e, "alkyl", "alkyl") + + # 3. Mixed pairs (amide one end, alkyl the other) + for w in amide_cands: + for e in alkyl_cands: + _add(w, e, "cooh", "alkyl") + _add(e, w, "alkyl", "cooh") + + return sites + + # ------------------------------------------------------------------ + # Assembly: two sequential amide bond formations + # ------------------------------------------------------------------ + + def _restore_cho_to_cooh(self, rw: Chem.RWMol, c_idx: int): + """In-place: convert aldehyde C at c_idx to COOH by adding -OH.""" + c_atom = rw.GetAtomWithIdx(c_idx) + c_atom.SetNumExplicitHs(0) + c_atom.SetNoImplicit(True) + o_idx = rw.AddAtom(Chem.Atom("O")) + rw.GetAtomWithIdx(o_idx).SetNumExplicitHs(1) + rw.AddBond(c_idx, o_idx, Chem.BondType.SINGLE) + + def _assemble_protac(self, linker_mol: Chem.Mol, warhead_site_idx: int, + e3_site_idx: int, + warhead_type: str = "cooh", e3_type: str = "cooh", + warhead_restored: bool = False, + e3_restored: bool = False) -> Optional[str]: + """ + Two-path assembly dispatched by warhead_type / e3_type. + + Amide path ('cooh'): + Step 1: E3 NH2 + tagged linker COOH → secondary amide (intermediate) + Step 2: warhead NH + intermediate COOH → tertiary amide (PROTAC) + If a site is 'restored', CHO is first converted to COOH (+OH). + + Alkylation path ('alkyl'): + Step 1: E3 NH2 + tagged linker CH3 → secondary amine (intermediate) + Step 2: warhead NH + intermediate CH3 → tertiary amine (PROTAC) + """ + rw = Chem.RWMol(linker_mol) + + # --- Prepare linker: restore CHO → COOH where needed (amide path) --- + if e3_type == "cooh" and e3_restored: + self._restore_cho_to_cooh(rw, e3_site_idx) + if warhead_type == "cooh" and warhead_restored: + self._restore_cho_to_cooh(rw, warhead_site_idx) + try: + Chem.SanitizeMol(rw) + except Exception: + return None + + # Tag the e3-side atom for step-1 reaction + rw.GetAtomWithIdx(e3_site_idx).SetAtomMapNum(99) + tagged_linker = rw.GetMol() + + # --- Step 1: E3 + e3-side linker atom --- + if e3_type == "cooh": + step1_rxn = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX3:99](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:99](=[OX1:3])" + ) + else: # alkyl + step1_rxn = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX4H3:99]>>[NX3H1:1][CX4H2:99]" + ) + + products1 = step1_rxn.RunReactants((self.e3_mol, tagged_linker)) + if not products1: + return None + + intermediate = None + for prod_tuple in products1: + try: + mol = prod_tuple[0] + Chem.SanitizeMol(mol) + intermediate = mol + break + except Exception: + continue + if intermediate is None: + return None + + # Clear tag + rw2 = Chem.RWMol(intermediate) + for atom in rw2.GetAtoms(): + if atom.GetAtomMapNum() == 99: + atom.SetAtomMapNum(0) + intermediate = rw2.GetMol() + + # --- Step 2: warhead + warhead-side linker atom --- + if warhead_type == "cooh": + step2_rxn = self._amide_rxn_nh + else: # alkyl + step2_rxn = self._cn_rxn_nh + + products2 = step2_rxn.RunReactants((self.warhead_mol, intermediate)) + if not products2: + return None + + for prod_tuple in products2: + try: + protac_mol = prod_tuple[0] + Chem.SanitizeMol(protac_mol) + return Chem.MolToSmiles(protac_mol) + except Exception: + continue + + return None + + # ------------------------------------------------------------------ + # Validation + # ------------------------------------------------------------------ + + def check_attachment_site_compatibility(self, protac_mol: Chem.Mol) -> bool: + """ + ≥2 linker bonds formed (amide or C-N) and no unreacted COOH remaining. + Amide path: ≥2 amide bonds + 0 COOH. + Alkylation path: warhead core + E3 core present (bonds implicit in graph). + """ + has_amide = len(protac_mol.GetSubstructMatches(self._amide_pat)) >= 2 + no_cooh = len(protac_mol.GetSubstructMatches(self._cooh_pat)) == 0 + has_cores = self.has_warhead_core(protac_mol) and self.has_e3_core(protac_mol) + return (has_amide and no_cooh) or has_cores + + def has_warhead_core(self, protac_mol: Chem.Mol) -> bool: + return protac_mol.HasSubstructMatch(self._quinazoline) + + def has_e3_core(self, protac_mol: Chem.Mol) -> bool: + return protac_mol.HasSubstructMatch(self._hyp) + + def has_both_amide_bonds(self, protac_mol: Chem.Mol) -> bool: + return len(protac_mol.GetSubstructMatches(self._amide_pat)) >= 2 + + def no_unreacted_cooh(self, protac_mol: Chem.Mol) -> bool: + return len(protac_mol.GetSubstructMatches(self._cooh_pat)) == 0 + + def _basic_validation(self, protac_mol: Chem.Mol) -> dict: + checks = { + "has_warhead_core": self.has_warhead_core(protac_mol), + "has_e3_core": self.has_e3_core(protac_mol), + "has_both_amide_bonds": self.has_both_amide_bonds(protac_mol), + "no_unreacted_cooh": self.no_unreacted_cooh(protac_mol), + "attachment_site_valid": self.check_attachment_site_compatibility(protac_mol), + } + checks["_passed"] = all([ + checks["has_warhead_core"], + checks["has_e3_core"], + checks["has_both_amide_bonds"], + checks["no_unreacted_cooh"], + ]) + return checks + + +# --------------------------------------------------------------------------- +# Standard interface aliases (used by defog_guided.py / batch_report.py) +# --------------------------------------------------------------------------- + +WARHEAD_MOL = EGFR_WARHEAD_MOL +E3_MOL = VHL_E3_MOL +WARHEAD_SMILES = EGFR_WARHEAD_SMILES +E3_SMILES = VHL_E3_SMILES +Builder = ErlotinibVHL_PROTAC_Builder +WH_REACTIVE_SMARTS = _NH_SMARTS +E3_REACTIVE_SMARTS = _NH2_SMARTS +WARHEAD_PAT = Chem.MolFromSmarts("c1cc2ncncc2cc1") # quinazoline core (EGFR) +E3_PAT = Chem.MolFromSmarts("N1CC(O)CC1") # hydroxyproline (VHL) + + +# --------------------------------------------------------------------------- +# Self-test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + from rdkit import RDLogger + from rdkit.Chem import Descriptors + RDLogger.DisableLog("rdApp.error") + + print("=" * 70) + print("MS39 PROTAC Builder — Self-Test") + print(f"Warhead : EGFR (gefitinib-piperazine, NH reactive)") + print(f"E3 : VHL (hydroxyproline/thiazolyl, NH2 reactive)") + print(f"Chemistry: diacid linker + 2× amide bond formation") + print("=" * 70) + + builder = ErlotinibVHL_PROTAC_Builder() + + test_linkers = [ + ("OC(=O)CCCCCCCCC(=O)O", "MS39 exact linker (MW match confirmed)"), + ("OC(=O)CCOCCOCCOC(=O)O", "PEG-diacid (valid)"), + ("OC(=O)CCOC(=O)O", "Short diacid (valid)"), + ("NCCOCCOCCN", "Diamine — no COOH, should fail"), + ("OC(=O)CCCC", "Single COOH — should fail"), + ] + + print() + for smiles, desc in test_linkers: + linker_mol = Chem.MolFromSmiles(smiles) + print(f"Linker : {desc}") + if linker_mol is None: + print(" [SKIP] unparseable SMILES") + print("-" * 70) + continue + + cooh_count = len(linker_mol.GetSubstructMatches(Chem.MolFromSmarts(_COOH_SMARTS))) + print(f"COOH sites: {cooh_count}") + + try: + protac_smi = builder.assemble(linker_mol, return_all=False) + protac_mol = Chem.MolFromSmiles(protac_smi) + passed, checks = builder.validate_protac(protac_mol) + tag = "VALID ✓" if passed else "ASSEMBLED but INVALID ✗" + print(f"Assembly: {tag}") + for k, v in checks.items(): + print(f" {'✓' if v else '✗'} {k}") + props = builder.get_protac_properties(protac_mol) + print(f" MW: {props['mw']} Da LogP: {props['logp']} " + f"HeavyAtoms: {props['heavy_atoms']}") + except Exception as e: + print(f"Assembly: FAILED — {e}") + + print("-" * 70) diff --git a/CupOFMoCA-PROTAC/src/protac/KRASVHL.py b/CupOFMoCA-PROTAC/src/protac/KRASVHL.py new file mode 100644 index 0000000000000000000000000000000000000000..d6e665c582e0e0a653c2b126209ff66cfb63fc62 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/protac/KRASVHL.py @@ -0,0 +1,421 @@ +""" +KRAS-VHL PROTAC Builder +Target: KRAS (aminocyanothiophene-tetrahydroquinoline-isoxazole-pyrimidine + warhead, ACBI3 family) +E3 Ligase: VHL (VH032 hydroxyproline/thiazolyl-benzyl binder) +Reference: ACBI3 (Astex/Boehringer Ingelheim); DegradMaster case_study_2 + +Warhead (ACBI3-derived KRAS binder, MW ≈ 478 Da): + Aminocyanothiophene fused to a tetrahydroquinoline; the thiophene-fused + ring is decorated with an isoxazole–pyrimidine. The pyrimidine bears a + 2-methyl-1,4-diazepane whose distal N-H is the linker attachment point + (analogous to the gefitinib piperazine NH in EGFR-VHL). + +E3 Ligand (VH032, MW ≈ 431 Da): + Hydroxyproline + tert-leucine + 4-(4-methylthiazol-5-yl)benzyl cap. + Free NH2 on tert-leucine alpha-amine is the reactive group. + +NOTE on chemistry: ACBI3 itself uses a click-derived 1,2,3-triazole linker +joining the warhead alkyl chain to a VHL-side azide-bearing fragment. For +generation/scoring purposes we mirror EGFR-VHL chemistry (diacid linker + +2× amide bond formation), since the diazepane NH and VH032 NH2 are both +direct amide-coupling sites and this preserves a single linker chemistry +across all three VHL targets in the study. +""" + +from rdkit import Chem +from rdkit.Chem import AllChem +from rdkit import RDLogger +from typing import Optional + +try: + from .protac_builder import PROTAC_Builder +except ImportError: + from protac_builder import PROTAC_Builder + +RDLogger.DisableLog("rdApp.error") + + +# --------------------------------------------------------------------------- +# Component SMILES — derived from ACBI3 (DegradMaster case_study_2, c0 reference) +# --------------------------------------------------------------------------- + +# ACBI3-derived KRAS warhead with diazepane-NH (linker stripped) +KRAS_WARHEAD_SMILES = ( + "C[C@H]1CNCCCN1c1nccc(-c2noc([C@@]3(C)CCCc4sc(N)c(C#N)c43)n2)n1" +) + +# VH032: same VHL ligand used by EGFR-VHL and BRD9-VHL modules +VHL_E3_SMILES = "N[C@H](C(=O)N1C[C@H](O)C[C@H]1C(=O)NCc1ccc(-c2scnc2C)cc1)C(C)(C)C" + +# Pre-built Mol objects +KRAS_WARHEAD_MOL = Chem.MolFromSmiles(KRAS_WARHEAD_SMILES) +VHL_E3_MOL = Chem.MolFromSmiles(VHL_E3_SMILES) + +# SMARTS patterns +_COOH_SMARTS = "[CX3](=[OX1])[OX2H1]" +_NH2_SMARTS = "[NX3;H2]" +_NH_SMARTS = "[NX3;H1;!$(NC=O);!$(NS=O);!$(NC#N)]" +_AMIDE_SMARTS = "[NX3H0,NX3H1][CX3](=[OX1])[#6]" +# Isoxazole–pyrimidine biaryl (signature of the ACBI3 KRAS warhead) +_ISOXAZOLE_PYRIMIDINE_SMARTS = "c1onc(n1)-c1ccnc(n1)" +_HYDROXYPROLINE_SMARTS = "[C@@H]1C[C@H](O)CN1" +_TERMINAL_CHO_SMARTS = "[CX3H1](=[OX1])" +_TERMINAL_CH3_SMARTS = "[CX4H3]" + + +class KRASVHL_PROTAC_Builder(PROTAC_Builder): + """ + PROTAC builder: ACBI3-derived KRAS warhead + VH032 VHL E3. + + Linker requirement: two COOH groups (diacid), one per end. + Warhead NH (diazepane) + linker COOH → tertiary amide. + E3 NH2 (tert-leucine) + linker COOH → secondary amide. + """ + + def __init__(self, warhead: Optional[Chem.Mol] = None, + e3: Optional[Chem.Mol] = None): + super().__init__( + warhead=warhead if warhead is not None else KRAS_WARHEAD_MOL, + e3=e3 if e3 is not None else VHL_E3_MOL, + warhead_name="ACBI3-diazepane (KRAS)", + e3_name="VHL binder NH2 (VH032)", + ) + self._cooh_pat = Chem.MolFromSmarts(_COOH_SMARTS) + self._nh2_pat = Chem.MolFromSmarts(_NH2_SMARTS) + self._nh_pat = Chem.MolFromSmarts(_NH_SMARTS) + self._amide_pat = Chem.MolFromSmarts(_AMIDE_SMARTS) + self._iso_pyrim = Chem.MolFromSmarts(_ISOXAZOLE_PYRIMIDINE_SMARTS) + self._hyp = Chem.MolFromSmarts(_HYDROXYPROLINE_SMARTS) + self._terminal_cho = Chem.MolFromSmarts(_TERMINAL_CHO_SMARTS) + self._terminal_ch3 = Chem.MolFromSmarts(_TERMINAL_CH3_SMARTS) + + # --- Amide bond reactions (diacid linker path) --- + self._amide_rxn_nh2 = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX3:2](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:2](=[OX1:3])" + ) + self._amide_rxn_nh = AllChem.ReactionFromSmarts( + "[NX3H1;!$(NC=O);!$(NS=O):1].[CX3:2](=[OX1:3])[OX2H1]>>[NX3H0:1][CX3:2](=[OX1:3])" + ) + + # --- C-N alkylation reactions (alkyl/PEG linker path) --- + self._cn_rxn_nh2 = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX4H3:2]>>[NX3H1:1][CX4H2:2]" + ) + self._cn_rxn_nh = AllChem.ReactionFromSmarts( + "[NX3H1;!$(NC=O);!$(NS=O):1].[CX4H3:2]>>[NX3H0:1][CX4H2:2]" + ) + + # ------------------------------------------------------------------ + # Verification + # ------------------------------------------------------------------ + + def _verify_warhead_reactive_group(self): + pat = Chem.MolFromSmarts(_NH_SMARTS) + if self.warhead_mol.HasSubstructMatch(pat): + print(f"✓ {self.warhead_name}: NH reactive group confirmed (diazepane)") + else: + print(f"⚠ {self.warhead_name}: no reactive NH detected — check warhead") + + def _verify_e3_reactive_group(self): + pat = Chem.MolFromSmarts(_NH2_SMARTS) + if self.e3_mol.HasSubstructMatch(pat): + print(f"✓ {self.e3_name}: NH2 reactive group confirmed (tert-leucine)") + else: + print(f"⚠ {self.e3_name}: no reactive NH2 detected — check E3") + + # ------------------------------------------------------------------ + # Linker attachment site detection + # ------------------------------------------------------------------ + + def _reactive_cooh_atoms(self, linker_mol: Chem.Mol) -> list: + results = [] + seen = set() + + for m in linker_mol.GetSubstructMatches(self._cooh_pat): + c_idx = m[0] + if c_idx not in seen: + seen.add(c_idx) + results.append({"idx": c_idx, "restored": False}) + + for m in linker_mol.GetSubstructMatches(self._terminal_cho): + c_idx = m[0] + if c_idx in seen: + continue + + rw = Chem.RWMol(linker_mol) + c_atom = rw.GetAtomWithIdx(c_idx) + c_atom.SetNumExplicitHs(0) + c_atom.SetNoImplicit(True) + o_idx = rw.AddAtom(Chem.Atom("O")) + rw.GetAtomWithIdx(o_idx).SetNumExplicitHs(1) + rw.AddBond(c_idx, o_idx, Chem.BondType.SINGLE) + rw.GetAtomWithIdx(c_idx).SetAtomMapNum(99) + try: + Chem.SanitizeMol(rw) + except Exception: + continue + + step1_rxn = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX3:99](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:99](=[OX1:3])" + ) + if step1_rxn.RunReactants((self.e3_mol, rw.GetMol())): + seen.add(c_idx) + results.append({"idx": c_idx, "restored": True}) + + return results + + def _reactive_terminal_c_atoms(self, linker_mol: Chem.Mol) -> list: + results = [] + seen = set() + + for m in linker_mol.GetSubstructMatches(self._terminal_ch3): + c_idx = m[0] + if c_idx in seen: + continue + + rw = Chem.RWMol(linker_mol) + rw.GetAtomWithIdx(c_idx).SetAtomMapNum(98) + try: + Chem.SanitizeMol(rw) + except Exception: + continue + + trial_rxn = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX4H3:98]>>[NX3H1:1][CX4H2:98]" + ) + if trial_rxn.RunReactants((self.e3_mol, rw.GetMol())): + seen.add(c_idx) + results.append({"idx": c_idx, "type": "alkyl"}) + + return results + + def find_linker_attachment_sites(self, linker_mol: Chem.Mol, + amide_only: bool = False) -> list: + amide_cands = self._reactive_cooh_atoms(linker_mol) + alkyl_cands = [] if amide_only else self._reactive_terminal_c_atoms(linker_mol) + + sites = [] + seen = set() + + def _add(w, e, wtype, etype): + key = (w["idx"], e["idx"]) + if key not in seen and w["idx"] != e["idx"]: + seen.add(key) + sites.append({ + "warhead_site": w["idx"], + "warhead_type": wtype, + "warhead_restored": w.get("restored", False), + "e3_site": e["idx"], + "e3_type": etype, + "e3_restored": e.get("restored", False), + }) + + direct = [c for c in amide_cands if not c["restored"]] + restored = [c for c in amide_cands if c["restored"]] + for w in direct: + for e in direct: _add(w, e, "cooh", "cooh") + for w in direct: + for e in restored: _add(w, e, "cooh", "cooh") + for e in direct: _add(e, w, "cooh", "cooh") + for w in restored: + for e in restored: _add(w, e, "cooh", "cooh") + + if not amide_only: + for w in alkyl_cands: + for e in alkyl_cands: + _add(w, e, "alkyl", "alkyl") + + for w in amide_cands: + for e in alkyl_cands: + _add(w, e, "cooh", "alkyl") + _add(e, w, "alkyl", "cooh") + + return sites + + # ------------------------------------------------------------------ + # Assembly + # ------------------------------------------------------------------ + + def _restore_cho_to_cooh(self, rw: Chem.RWMol, c_idx: int): + c_atom = rw.GetAtomWithIdx(c_idx) + c_atom.SetNumExplicitHs(0) + c_atom.SetNoImplicit(True) + o_idx = rw.AddAtom(Chem.Atom("O")) + rw.GetAtomWithIdx(o_idx).SetNumExplicitHs(1) + rw.AddBond(c_idx, o_idx, Chem.BondType.SINGLE) + + def _assemble_protac(self, linker_mol: Chem.Mol, warhead_site_idx: int, + e3_site_idx: int, + warhead_type: str = "cooh", e3_type: str = "cooh", + warhead_restored: bool = False, + e3_restored: bool = False) -> Optional[str]: + rw = Chem.RWMol(linker_mol) + + if e3_type == "cooh" and e3_restored: + self._restore_cho_to_cooh(rw, e3_site_idx) + if warhead_type == "cooh" and warhead_restored: + self._restore_cho_to_cooh(rw, warhead_site_idx) + try: + Chem.SanitizeMol(rw) + except Exception: + return None + + rw.GetAtomWithIdx(e3_site_idx).SetAtomMapNum(99) + tagged_linker = rw.GetMol() + + if e3_type == "cooh": + step1_rxn = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX3:99](=[OX1:3])[OX2H1]>>[NX3H1:1][CX3:99](=[OX1:3])" + ) + else: + step1_rxn = AllChem.ReactionFromSmarts( + "[NX3H2:1].[CX4H3:99]>>[NX3H1:1][CX4H2:99]" + ) + + products1 = step1_rxn.RunReactants((self.e3_mol, tagged_linker)) + if not products1: + return None + + intermediate = None + for prod_tuple in products1: + try: + mol = prod_tuple[0] + Chem.SanitizeMol(mol) + intermediate = mol + break + except Exception: + continue + if intermediate is None: + return None + + rw2 = Chem.RWMol(intermediate) + for atom in rw2.GetAtoms(): + if atom.GetAtomMapNum() == 99: + atom.SetAtomMapNum(0) + intermediate = rw2.GetMol() + + if warhead_type == "cooh": + step2_rxn = self._amide_rxn_nh + else: + step2_rxn = self._cn_rxn_nh + + products2 = step2_rxn.RunReactants((self.warhead_mol, intermediate)) + if not products2: + return None + + for prod_tuple in products2: + try: + protac_mol = prod_tuple[0] + Chem.SanitizeMol(protac_mol) + return Chem.MolToSmiles(protac_mol) + except Exception: + continue + + return None + + # ------------------------------------------------------------------ + # Validation + # ------------------------------------------------------------------ + + def check_attachment_site_compatibility(self, protac_mol: Chem.Mol) -> bool: + has_amide = len(protac_mol.GetSubstructMatches(self._amide_pat)) >= 2 + no_cooh = len(protac_mol.GetSubstructMatches(self._cooh_pat)) == 0 + has_cores = self.has_warhead_core(protac_mol) and self.has_e3_core(protac_mol) + return (has_amide and no_cooh) or has_cores + + def has_warhead_core(self, protac_mol: Chem.Mol) -> bool: + return protac_mol.HasSubstructMatch(self._iso_pyrim) + + def has_e3_core(self, protac_mol: Chem.Mol) -> bool: + return protac_mol.HasSubstructMatch(self._hyp) + + def has_both_amide_bonds(self, protac_mol: Chem.Mol) -> bool: + return len(protac_mol.GetSubstructMatches(self._amide_pat)) >= 2 + + def no_unreacted_cooh(self, protac_mol: Chem.Mol) -> bool: + return len(protac_mol.GetSubstructMatches(self._cooh_pat)) == 0 + + def _basic_validation(self, protac_mol: Chem.Mol) -> dict: + checks = { + "has_warhead_core": self.has_warhead_core(protac_mol), + "has_e3_core": self.has_e3_core(protac_mol), + "has_both_amide_bonds": self.has_both_amide_bonds(protac_mol), + "no_unreacted_cooh": self.no_unreacted_cooh(protac_mol), + "attachment_site_valid": self.check_attachment_site_compatibility(protac_mol), + } + checks["_passed"] = all([ + checks["has_warhead_core"], + checks["has_e3_core"], + checks["has_both_amide_bonds"], + checks["no_unreacted_cooh"], + ]) + return checks + + +# --------------------------------------------------------------------------- +# Standard interface aliases (used by defog_guided.py / batch_report.py) +# --------------------------------------------------------------------------- + +WARHEAD_MOL = KRAS_WARHEAD_MOL +E3_MOL = VHL_E3_MOL +WARHEAD_SMILES = KRAS_WARHEAD_SMILES +E3_SMILES = VHL_E3_SMILES +Builder = KRASVHL_PROTAC_Builder +WH_REACTIVE_SMARTS = _NH_SMARTS +E3_REACTIVE_SMARTS = _NH2_SMARTS +WARHEAD_PAT = Chem.MolFromSmarts(_ISOXAZOLE_PYRIMIDINE_SMARTS) # KRAS warhead signature +E3_PAT = Chem.MolFromSmarts("N1CC(O)CC1") # hydroxyproline (VHL) + + +# --------------------------------------------------------------------------- +# Self-test +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + from rdkit import RDLogger + RDLogger.DisableLog("rdApp.error") + + print("=" * 70) + print("KRAS-VHL PROTAC Builder — Self-Test") + print(f"Warhead : KRAS (ACBI3-diazepane, NH reactive)") + print(f"E3 : VHL (VH032, NH2 reactive)") + print(f"Chemistry: diacid linker + 2× amide bond formation") + print("=" * 70) + + builder = KRASVHL_PROTAC_Builder() + + test_linkers = [ + ("OC(=O)CCCCCCCCC(=O)O", "Sebacic acid (C10 diacid)"), + ("OC(=O)CCOCCOCCOC(=O)O", "PEG-diacid (valid)"), + ("NCCOCCOCCN", "Diamine — no COOH, should fail"), + ("OC(=O)CCCC", "Single COOH — should fail"), + ] + + print() + for smiles, desc in test_linkers: + linker_mol = Chem.MolFromSmiles(smiles) + print(f"Linker : {desc}") + if linker_mol is None: + print(" [SKIP] unparseable SMILES") + print("-" * 70) + continue + + cooh_count = len(linker_mol.GetSubstructMatches(Chem.MolFromSmarts(_COOH_SMARTS))) + print(f"COOH sites: {cooh_count}") + + try: + protac_smi = builder.assemble(linker_mol, return_all=False) + protac_mol = Chem.MolFromSmiles(protac_smi) + passed, checks = builder.validate_protac(protac_mol) + tag = "VALID ✓" if passed else "ASSEMBLED but INVALID ✗" + print(f"Assembly: {tag}") + for k, v in checks.items(): + print(f" {'✓' if v else '✗'} {k}") + props = builder.get_protac_properties(protac_mol) + print(f" MW: {props['mw']} Da LogP: {props['logp']} " + f"HeavyAtoms: {props['heavy_atoms']}") + except Exception as e: + print(f"Assembly: FAILED — {e}") + + print("-" * 70) diff --git a/CupOFMoCA-PROTAC/src/protac/__init__.py b/CupOFMoCA-PROTAC/src/protac/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7b291cbb98494241463e04d8aee18bb652892745 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/protac/__init__.py @@ -0,0 +1,8 @@ +from .ARCRBN import ARCRBN_PROTAC_Builder +from .ARCRBNv2 import ARCRBNv2_PROTAC_Builder +from .BRD4CRBN import BRD4CRBN_PROTAC_Builder +from .CDK6CRBN import CDK6CRBN_PROTAC_Builder +from .ErlotinibVHL import ErlotinibVHL_PROTAC_Builder +from .BRD4VHL import BRD4VHL_PROTAC_Builder +from .BRD9VHL import BRD9VHL_PROTAC_Builder +from .KRASVHL import KRASVHL_PROTAC_Builder diff --git a/CupOFMoCA-PROTAC/src/protac/protac_builder.py b/CupOFMoCA-PROTAC/src/protac/protac_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..8dd21089ece1395947462cba84ecd552ee5dc937 --- /dev/null +++ b/CupOFMoCA-PROTAC/src/protac/protac_builder.py @@ -0,0 +1,185 @@ +""" +PROTAC Builder — Abstract Base Class + +Three-component assembly: warhead + linker + E3 ligand. + +All methods accept Chem.Mol objects. The DeFoG graph → Chem.Mol conversion +happens once upstream; this class never parses SMILES on method inputs. +Assembly output (assembled PROTAC) is returned as a SMILES string — this is +RDKit's native output from reaction SMARTS and is fine as an output format. +""" + +from abc import ABC, abstractmethod +from typing import Optional +from rdkit import Chem +from rdkit.Chem import AllChem, Descriptors + + +class PROTAC_Builder(ABC): + """ + Abstract base class for PROTAC builders. + + Subclasses encode one specific warhead + E3 ligand + linker chemistry + combination (e.g. erlotinib-COOH + VH032-COOH + diamine linker). + + Parameters + ---------- + warhead : Chem.Mol + Fixed warhead molecule. + e3 : Chem.Mol + Fixed E3 ligand molecule. + warhead_name / e3_name : str + Display names for logging. + """ + + def __init__(self, warhead: Chem.Mol, e3: Chem.Mol, + warhead_name: str = "Warhead", e3_name: str = "E3 Ligand"): + if warhead is None: + raise ValueError("warhead must be a Chem.Mol object") + if e3 is None: + raise ValueError("e3 must be a Chem.Mol object") + + self.warhead_mol = warhead + self.e3_mol = e3 + self.warhead_name = warhead_name + self.e3_name = e3_name + + # Canonical SMILES stored for display / serialization only + self.warhead_smiles = Chem.MolToSmiles(warhead) + self.e3_smiles = Chem.MolToSmiles(e3) + + self._verify_warhead_reactive_group() + self._verify_e3_reactive_group() + + # ------------------------------------------------------------------ + # Abstract interface + # ------------------------------------------------------------------ + + @abstractmethod + def _verify_warhead_reactive_group(self): + """Confirm warhead has the expected reactive group for linker attachment.""" + pass + + @abstractmethod + def _verify_e3_reactive_group(self): + """Confirm E3 ligand has the expected reactive group for linker attachment.""" + pass + + @abstractmethod + def find_linker_attachment_sites(self, linker_mol: Chem.Mol) -> list: + """ + Find attachment sites on the linker for both warhead and E3 ends. + + Returns list of dicts: + [{'warhead_site': atom_idx, 'e3_site': atom_idx, ...}] + """ + pass + + @abstractmethod + def _assemble_protac(self, linker_mol: Chem.Mol, warhead_site_idx: int, + e3_site_idx: int) -> Optional[str]: + """ + Perform the two bond-forming reactions. + Returns SMILES of the assembled PROTAC, or None on failure. + """ + pass + + @abstractmethod + def check_attachment_site_compatibility(self, protac_mol: Chem.Mol) -> bool: + """Verify assembled PROTAC has expected bond signatures at both ends.""" + pass + + @abstractmethod + def _basic_validation(self, protac_mol: Chem.Mol) -> dict: + """ + Run PROTAC-specific validation checks. + Must include '_passed': bool in the returned dict. + """ + pass + + # ------------------------------------------------------------------ + # Shared public interface + # ------------------------------------------------------------------ + + def assemble(self, linker_mol: Chem.Mol, return_all: bool = True, + amide_only: bool = False): + """ + Assemble warhead + linker + E3 into a full PROTAC. + + Parameters + ---------- + linker_mol : Chem.Mol + Generated linker as a molecular graph. + return_all : bool + True → return all unique assembled PROTACs (list[str]). + False → return first valid PROTAC (str). + amide_only : bool + If True, only attempt the amide (COOH/CHO) assembly path and + skip the CH3 alkylation path. Use for strict final validation. + + Returns + ------- + list[str] if return_all else str | None + """ + sites = self.find_linker_attachment_sites(linker_mol, amide_only=amide_only) + if not sites: + raise ValueError("No attachment sites found on linker") + + protacs = [] + seen = set() + for site in sites: + try: + smiles = self._assemble_protac( + linker_mol, site["warhead_site"], site["e3_site"], + warhead_type=site.get("warhead_type", "nh2"), + e3_type=site.get("e3_type", "nh2"), + warhead_restored=site.get("warhead_restored", False), + e3_restored=site.get("e3_restored", False), + ) + if smiles and smiles not in seen: + seen.add(smiles) + protacs.append(smiles) + except Exception: + continue + + if not protacs: + raise ValueError("Assembly failed at all linker sites") + + return protacs if return_all else protacs[0] + + def validate_protac(self, protac_mol: Chem.Mol): + """ + Returns (passed: bool, checks: dict) for an assembled PROTAC Mol. + """ + checks = self._basic_validation(protac_mol) + passed = checks.pop("_passed", False) + return passed, checks + + def get_protac_properties(self, protac_mol: Chem.Mol) -> dict: + """Compute molecular properties for an assembled PROTAC.""" + return { + "mw": round(Descriptors.MolWt(protac_mol), 2), + "logp": round(Descriptors.MolLogP(protac_mol), 2), + "tpsa": round(Descriptors.TPSA(protac_mol), 2), + "heavy_atoms": protac_mol.GetNumHeavyAtoms(), + "hbd": Descriptors.NumHDonors(protac_mol), + "hba": Descriptors.NumHAcceptors(protac_mol), + "rotbonds": Descriptors.NumRotatableBonds(protac_mol), + } + + def batch_assemble(self, linker_mols: list[Chem.Mol], + return_all: bool = True) -> dict: + """Assemble PROTACs for a list of linker Mol objects.""" + results = {"protacs": [], "successful": [], "failed": [], "total": 0} + for i, mol in enumerate(linker_mols): + try: + p = self.assemble(mol, return_all=return_all) + results["protacs"].append(p) + results["successful"].append(i) + results["total"] += len(p) if return_all else 1 + except Exception as e: + results["protacs"].append(None) + results["failed"].append((i, str(e))) + print(f"Assembled {results['total']} PROTAC(s) from " + f"{len(results['successful'])}/{len(linker_mols)} linkers") + return results \ No newline at end of file