diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..bffb3dd0be0198ebadb88d59c92d002b7132e079 --- /dev/null +++ b/.gitignore @@ -0,0 +1,17 @@ +__pycache__/ +*.py[cod] +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ +.DS_Store +.idea/ +.vscode/ +.env +generation_logs/ +results/ +outputs/ +*.log + +# Hugging Face and PyTorch downloads should remain outside this repository. +.cache/ + diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000000000000000000000000000000000000..73fc80c40fad09949861bc81b5b7563ed45874f5 --- /dev/null +++ b/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/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d645695673349e3947e8e5ae42332d0ac3164cd7 --- /dev/null +++ b/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + 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/NOTICE b/NOTICE new file mode 100644 index 0000000000000000000000000000000000000000..e30383f27df6709614a1231d42187610f67863fb --- /dev/null +++ b/NOTICE @@ -0,0 +1,16 @@ +CupOFMoCA +Copyright 2026 CupOFMoCA authors + +The code in this repository is licensed under Apache License 2.0. Model +weights and third-party model downloads can be subject to their source model +licenses; users are responsible for reviewing those terms. + +third_party/SMILESToPeptide contains the exact peptide-to-SMILES conversion +helpers used in the submitted PDC experiments. The implementation is adapted +from p2smi by Aaron Feller, distributed under the MIT License. Its license is +included at third_party/SMILESToPeptide/LICENSE-p2smi. + +Runtime model dependencies include: + - facebook/esm2_t33_650M_UR50D + - aaronfeller/PeptideCLM-23M-all + diff --git a/README.md b/README.md index 4d3af4d95081c5b197ae333a5fbade9285608671..628731afe20644e25b93800c2b0b178b0afed3fd 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,226 @@ --- -license: cc-by-nc-nd-4.0 +license: apache-2.0 +library_name: pytorch +tags: + - chemistry + - drug-discovery + - molecular-generation + - peptide + - discrete-flow + - peptide-drug-conjugate --- + +# CupOFMoCA + +Code and PDC model artifacts for **CupOFMoCA: Coupled Objective-Guided Discrete Flows for +Molecular Conjugate Assembly** (NeurIPS 2026). + +CupOFMoCA performs coupled, objective-guided discrete-flow generation while enforcing chemical +assembly constraints. This release supports the paper's peptide-drug conjugate (PDC) experiments +across SMILES, natural amino-acid (AA), and unnatural amino-acid (UAA) representations. + +## Model overview + +CupOFMoCA is a research pipeline for generating peptide-drug conjugate candidates. It couples a +pretrained discrete-flow generator with property guidance and conjugation-validity checks during +sampling. This repository contains three generator checkpoints and the property scorers used by the +released PDC pipeline: + +| Checkpoint | Representation | Role | +|---|---|---| +| `SMILESReDi_base.ckpt` | peptide SMILES | pretrained SMILES discrete-flow generator | +| `PepReDi_base.pt` | canonical amino acids | pretrained AA discrete-flow generator | +| `UAAReDi_base.pt` | extended amino-acid vocabulary | pretrained UAA discrete-flow generator | + +The checkpoints are loaded by the repository's custom PyTorch code; this is not a Transformers +pipeline and does not provide a hosted-inference widget. + +### Intended uses + +- Reproduce and inspect the paper's PDC generation experiments. +- Generate computational PDC 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 + +- **AA-PepReDi:** peptides from PepNN and BioLip2, together with 6--49-residue sequences from + PPIRef, using an 80:10:10 train/validation/test split. +- **UAA-PepReDi:** 150,000 synthetic sequences constructed with `p2smi` across combinations of + non-canonical and D-amino-acid composition, using an 80:10:10 split. +- **SMILESReDi:** the released pretrained SMILESReDi weights are used without additional + CupOFMoCA fine-tuning. + +Architecture, optimization, data construction, and scorer details are reported in Appendix E of +the paper. Training datasets are not redistributed in this repository. + +### Evaluation + +The paper evaluates generated PDCs for assembly validity and predicted functional properties across +ERBB2, GNRHR, and NRP1, using SMILES, AA, and UAA representations. The reported results and +experimental protocol are in the paper; the submitted evaluation routines are included under +`smiles/evaluation/`. + +### 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 receptor targets and fixed target-specific conjugation + chemistries; performance outside this scope has not been established. +- The release contains the paper's PDC stack only. Its PROTAC experiments require separate code and + artifacts. +- Results can vary with hardware, CUDA/PyTorch versions, and revisions of models downloaded at + runtime. + +## Repository layout + +```text +checkpoints/ generator weights +configs/ paper settings and target sequences +docs/ model-artifact details and checksums +scripts/run_experiment.py unified experiment runner +scripts/verify_release.py release integrity checker +smiles/ + generation/ generation entry points + evaluation/ metrics and analyzers + models/ ReDi model and data code + conjugation/ PDC assembly builders + scoring/ property scorers + smiles_tokenizer/ tokenizer code and vocabulary +third_party/ peptide-to-SMILES conversion helper +``` + +The release currently covers the PDC implementation. The PROTAC experiments described in the paper +use a separate code and artifact stack that is not included here. + +## 1. Environment + +A CUDA GPU is strongly recommended. The tested environment used Python 3.9, PyTorch 2.5.1, and +CUDA 12.1. + +```bash +conda env create -f environment.yml +conda activate cupofmoca +``` + +Alternatively, install a PyTorch build appropriate for your CUDA driver and then install the pinned +packages: + +```bash +pip install -r requirements.txt +``` + +On first use, Transformers downloads `facebook/esm2_t33_650M_UR50D` and +`aaronfeller/PeptideCLM-23M-all` from Hugging Face. Set `HF_HOME` to a persistent cache if needed. + +Verify the bundled artifacts and source paths before running an experiment: + +```bash +python scripts/verify_release.py +``` + +## 2. Run PDC generation + +Run commands from the repository root. Preview a configured command without loading models: + +```bash +python scripts/run_experiment.py \ + --representation uaa \ + --variant cupofmoca \ + --method guided \ + --target NRP1 \ + --dry-run +``` + +Run the experiment: + +```bash +python scripts/run_experiment.py \ + --representation uaa \ + --variant cupofmoca \ + --method guided \ + --target NRP1 +``` + +Run a small smoke test: + +```bash +python scripts/run_experiment.py \ + --representation aa --variant baseline --method staged --target ERBB2 \ + --num-candidates 1 +``` + +Outputs are written under `outputs/` in separate run directories. Existing outputs are protected +unless `--force` is supplied. Arguments after `--` are passed to the underlying generator: + +```bash +python scripts/run_experiment.py \ + --representation smiles --variant cupofmoca --method guided --target GNRHR \ + -- --top_k 100 +``` + +### Configurations + +| Target | Protein | Conjugation builder | Default SMILES length | +|---|---|---|---:| +| ERBB2 | receptor tyrosine-protein kinase erbB-2 | `SSCpt` | 80 | +| GNRHR | gonadotropin-releasing hormone receptor | `AoaDau` | 80 | +| NRP1 | neuropilin-1 | `DSPASN38` | 100 | + +AA and UAA runs use length 10. Shared settings include 128 optimization steps, top-p 1.0, +objective weights `[1, 1]`, and acceptance sampling with beta 50 for CupOFMoCA. The machine-readable +configuration is in `configs/paper.json`; target sequences are in `configs/targets.fasta`. + +### Direct entry points + +The full argument surfaces remain available under `smiles/generation/`: + +```text +guided.py cupofmoca_guided.py +guided_aa.py cupofmoca_guided_aa.py +guided_uaa.py cupofmoca_guided_uaa.py +staged.py cupofmoca_staged.py +staged_aa.py cupofmoca_staged_aa.py +staged_uaa.py cupofmoca_staged_uaa.py +``` + +Run one directly as a module, for example: + +```bash +python -m smiles.generation.cupofmoca_guided_uaa --help +``` + +`smiles/evaluation/run_metrics.py` and `smiles/evaluation/run_metrics_uaa.py` contain the submitted evaluation routines. +Use `python --help` to inspect their arguments. + +## 3. Included checkpoints + +The release includes the SMILES, AA, and UAA generator weights together with the binding, +hemolysis, non-fouling, and solubility scorers needed by the PDC pipeline. Artifact roles, sizes, and +SHA-256 values are listed in [docs/ARTIFACTS.md](docs/ARTIFACTS.md). + +The AA-input affinity predictor is not included because the PDC generation configuration uses the +bundled SMILES-input scorer (`--affinity_type smiles`). + +Large `*.pt` and `*.ckpt` files are configured for Git LFS. The repository is approximately 1.9 GB +before environment and model-cache downloads. + +The checkpoint formats are loaded through PyTorch. As with any pickle-backed PyTorch artifact, +only load weights obtained from a source you trust. + +## 4. Notes + +- The paper reports NVIDIA RTX A6000 48 GB GPUs. Lower-memory devices may require smaller batches. +- Exact bitwise output can vary across CUDA, PyTorch, GPU, and downloaded model revisions. +- Generated structures are computational hypotheses that require experimental validation. Property + models are predictors, not clinical or safety guarantees. +- The peptide-to-SMILES helper used by the experiments is vendored under `third_party/` with its + upstream license notice. + +## 5. Citation and license + +Citation metadata is provided in `CITATION.cff`. The repository code is released under Apache-2.0; +see `LICENSE` and `NOTICE`. Third-party components and downloaded model artifacts retain their +respective terms. diff --git a/checkpoints/PepReDi_base.pt b/checkpoints/PepReDi_base.pt new file mode 100644 index 0000000000000000000000000000000000000000..8556143158eb3fb48da01d27fd67f5ffc78ce920 --- /dev/null +++ b/checkpoints/PepReDi_base.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b6437edee514bd7adb9aacea776f3dd97c59ebc7b4928b390eafdd87eaeb8c9 +size 344474053 diff --git a/checkpoints/SMILESReDi_base.ckpt b/checkpoints/SMILESReDi_base.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..5661438859ab23698ee873ca4d0a4e662f294377 --- /dev/null +++ b/checkpoints/SMILESReDi_base.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8b2a6049e7ce6c2ef2ab0da0987ee14a5c7e3df737c5c32202631f5c230a81df +size 1038182356 diff --git a/checkpoints/UAAReDi_base.pt b/checkpoints/UAAReDi_base.pt new file mode 100644 index 0000000000000000000000000000000000000000..731b20f54712b00e5de7136f4e23046f425fb1f1 --- /dev/null +++ b/checkpoints/UAAReDi_base.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa9c25692f308e87befee15cecdd4a24d9e2270e039b12d073023390632b2a8d +size 344824232 diff --git a/configs/paper.json b/configs/paper.json new file mode 100644 index 0000000000000000000000000000000000000000..dbc1ae47cf904d40965d00d649ec0f6443a02275 --- /dev/null +++ b/configs/paper.json @@ -0,0 +1,20 @@ +{ + "default_seeds": [42, 123, 456], + "num_candidates_per_seed": 100, + "num_samples_per_batch": 1, + "optimization_steps": 128, + "top_p": 1.0, + "top_k": 200, + "cascade_k": 8, + "beta_min": 50.0, + "beta_max": 50.0, + "weights": [1.0, 1.0], + "require_valid_x0": true, + "max_x0_retries": 10, + "targets": { + "ERBB2": {"builder": "SSCpt", "smiles_length": 80}, + "GNRHR": {"builder": "AoaDau", "smiles_length": 80}, + "NRP1": {"builder": "DSPASN38", "smiles_length": 100} + }, + "representation_lengths": {"aa": 10, "uaa": 10} +} diff --git a/configs/targets.fasta b/configs/targets.fasta new file mode 100644 index 0000000000000000000000000000000000000000..a9056ffe8ef5cfb2204c69e519e4ed5c6bd35ced --- /dev/null +++ b/configs/targets.fasta @@ -0,0 +1,6 @@ +>ERBB2 +MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV +>GNRHR +MANSASPEQNQNHCSAINNSIPLMQGNLPTLTLSGKIRVTVTFFLFLLSATFNASFLLKLQKWTQKKEKGKKLSRMKLLLKHLTLANLLETLIVMPLDGMWNITVQWYAGELLCKVLSYLKLFSMYAPAFMMVVISLDRSLAITRPLALKSNSKVGQSMVGLAWILSSVFAGPQLYIFRMIHLADSSGQTKVFSQCVTHCSFSQWWHQAFYNFFTFSCLFIIPLFIMLICNAKIIFTLTRVLHQDPHELQLNQSKNNIPRARLKTLKMTVAFATSFTVCWTPYYVLGIWYWFDPEMLNRLSDPVNHFFFLFAFLNPCFDPLIYGYFSL +>NRP1 +MERGLPLLCAVLALVLAPAGAFRNDKCGDTIKIESPGYLTSPGYPHSYHPSEKCEWLIQAPDPYQRIMINFNPHFDLEDRDCKYDYVEVFDGENENGHFRGKFCGKIAPPPVVSSGPFLFIKFVSDYETHGAGFSIRYEIFKRGPECSQNYTTPSGVIKSPGFPEKYPNSLECTYIVFVPKMSEIILEFESFDLEPDSNPPGGMFCRYDRLEIWDGFPDVGPHIGRYCGQKTPGRIRSSSGILSMVFYTDSAIAKEGFSANYSVLQSSVSEDFKCMEALGMESGEIHSDQITASSQYSTNWSAERSRLNYPENGWTPGEDSYREWIQVDLGLLRFVTAVGTQGAISKETKKKYYVKTYKIDVSSNGEDWITIKEGNKPVLFQGNTNPTDVVVAVFPKPLITRFVRIKPATWETGISMRFEVYGCKITDYPCSGMLGMVSGLISDSQITSSNQGDRNWMPENIRLVTSRSGWALPPAPHSYINEWLQIDLGEEKIVRGIIIQGGKHRENKVFMRKFKIGYSNNGSDWKMIMDDSKRKAKSFEGNNNYDTPELRTFPALSTRFIRIYPERATHGGLGLRMELLGCEVEAPTAGPTTPNGNLVDECDDDQANCHSGTGDDFQLTGGTTVLATEKPTVIDSTIQSEFPTYGFNCEFGWGSHKTFCHWEHDNHVQLKWSVLTSKTGPIQDHTGDGNFIYSQADENQKGKVARLVSPVVYSQNSAHCMTFWYHMSGSHVGTLRVKLRYQKPEEYDQLVWMAIGHQGDHWKEGRVLLHKSLKLYQVIFEGEIGKGNLGGIAVDDISINNHISQEDCAKPADLDKKNPEIKIDETGSTPGYEGEGEGDKNISRKPGNVLKTLDPILITIIAMSALGVLLGAVCGVVLYCACWHNGMSERNLSALENYNFELVDGVKLKKDKLNTQSTYSEA diff --git a/docs/ARTIFACTS.md b/docs/ARTIFACTS.md new file mode 100644 index 0000000000000000000000000000000000000000..7a11370d3fd4b155d583ee5f7d10a413dbd13e2a --- /dev/null +++ b/docs/ARTIFACTS.md @@ -0,0 +1,32 @@ +# Model artifacts + +The release includes every local checkpoint needed by the submitted PDC generation code. Run +`python scripts/verify_release.py` to verify byte counts, SHA-256 hashes, and private-path hygiene. +The machine-readable manifest is in `docs/artifacts.json`. + +| Artifact | Purpose | Size | SHA-256 | +|---|---|---:|---| +| `checkpoints/SMILESReDi_base.ckpt` | SMILES generator | 1,038,182,356 B | `8b2a6049e7ce6c2ef2ab0da0987ee14a5c7e3df737c5c32202631f5c230a81df` | +| `checkpoints/PepReDi_base.pt` | natural-AA generator | 344,474,053 B | `3b6437edee514bd7adb9aacea776f3dd97c59ebc7b4928b390eafdd87eaeb8c9` | +| `checkpoints/UAAReDi_base.pt` | UAA generator | 344,824,232 B | `fa9c25692f308e87befee15cecdd4a24d9e2270e039b12d073023390632b2a8d` | +| `smiles/scoring/checkpoints/binding-affinity.pt` | SMILES binding scorer | 132,489,414 B | `8daf2ce5ec84045e94bbe41b0a57669287ac937a2a51e97da1af36ae8dc5e16f` | +| `smiles/scoring/checkpoints/hemolysis-xgboost.json` | hemolysis scorer | 1,932,561 B | `69552199fa7722369211aa0e33adc9850816fb816f555ce9e207c73e0543b0ee` | +| `smiles/scoring/checkpoints/nonfouling-xgboost.json` | non-fouling scorer | 645,478 B | `08364879c255e236cf0a337505e99e36824f1a72090f5829d3f7870d4ca11644` | +| `smiles/scoring/checkpoints/solubility-xgboost.json` | solubility scorer | 196,887 B | `3c6a534db85550c947d864aa5cf0b1a2862333bc50eb4b6d5053074cc7d66f02` | + +ESM-2 (`facebook/esm2_t33_650M_UR50D`) and PeptideCLM +(`aaronfeller/PeptideCLM-23M-all`) are downloaded from Hugging Face on first use and therefore are +not duplicated here. + +The optional AA-input binding predictor was not used for the reported PDC runs, which used +`--affinity_type smiles`, and is intentionally excluded (it would add about 2.7 GB). The public +wrapper passes the reported SMILES-input setting explicitly. + +When publishing with `hf upload`, the Hugging Face Hub client handles large-file storage. If this +release is mirrored to a Git LFS remote: + +```bash +git lfs install +git lfs track "*.pt" "*.ckpt" +git add .gitattributes checkpoints smiles/scoring/checkpoints +``` diff --git a/docs/artifacts.json b/docs/artifacts.json new file mode 100644 index 0000000000000000000000000000000000000000..017cfcb1168339bedce8eb26058a374de3cd19be --- /dev/null +++ b/docs/artifacts.json @@ -0,0 +1,38 @@ +[ + { + "path": "checkpoints/SMILESReDi_base.ckpt", + "bytes": 1038182356, + "sha256": "8b2a6049e7ce6c2ef2ab0da0987ee14a5c7e3df737c5c32202631f5c230a81df" + }, + { + "path": "checkpoints/PepReDi_base.pt", + "bytes": 344474053, + "sha256": "3b6437edee514bd7adb9aacea776f3dd97c59ebc7b4928b390eafdd87eaeb8c9" + }, + { + "path": "checkpoints/UAAReDi_base.pt", + "bytes": 344824232, + "sha256": "fa9c25692f308e87befee15cecdd4a24d9e2270e039b12d073023390632b2a8d" + }, + { + "path": "smiles/scoring/checkpoints/binding-affinity.pt", + "bytes": 132489414, + "sha256": "8daf2ce5ec84045e94bbe41b0a57669287ac937a2a51e97da1af36ae8dc5e16f" + }, + { + "path": "smiles/scoring/checkpoints/hemolysis-xgboost.json", + "bytes": 1932561, + "sha256": "69552199fa7722369211aa0e33adc9850816fb816f555ce9e207c73e0543b0ee" + }, + { + "path": "smiles/scoring/checkpoints/nonfouling-xgboost.json", + "bytes": 645478, + "sha256": "08364879c255e236cf0a337505e99e36824f1a72090f5829d3f7870d4ca11644" + }, + { + "path": "smiles/scoring/checkpoints/solubility-xgboost.json", + "bytes": 196887, + "sha256": "3c6a534db85550c947d864aa5cf0b1a2862333bc50eb4b6d5053074cc7d66f02" + } +] + diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..a5b6b5e0b15fb87e0829d1c048fd81d908c11b35 --- /dev/null +++ b/environment.yml @@ -0,0 +1,28 @@ +name: cupofmoca +channels: + - pytorch + - nvidia + - conda-forge +dependencies: + - python=3.9 + - pip + - pytorch=2.5.1 + - pytorch-cuda=12.1 + - numpy=1.26.4 + - scipy=1.13.1 + - pandas=2.3.2 + - scikit-learn=1.6.1 + - xgboost=2.1.4 + - matplotlib=3.9.4 + - seaborn=0.13.2 + - pillow=11.3.0 + - rdkit=2025.03.6 + - pip: + - lightning==2.5.5 + - transformers==4.41.2 + - datasets==4.0.0 + - fair-esm==2.0.0 + - SmilesPE==0.0.3 + - easydict==1.13 + - tqdm==4.67.1 + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..036ed9c749c24f3ba1df2b1edce6c1ddcb3fed38 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,18 @@ +torch==2.5.1 +lightning==2.5.5 +transformers==4.41.2 +datasets==4.0.0 +fair-esm==2.0.0 +rdkit==2025.3.6 +SmilesPE==0.0.3 +numpy==1.26.4 +scipy==1.13.1 +pandas==2.3.2 +scikit-learn==1.6.1 +xgboost==2.1.4 +matplotlib==3.9.4 +seaborn==0.13.2 +pillow==11.3.0 +easydict==1.13 +tqdm==4.67.1 + diff --git a/scripts/run_experiment.py b/scripts/run_experiment.py new file mode 100644 index 0000000000000000000000000000000000000000..439838ba39bcd5b14fc8b37a4c714d98dff093c7 --- /dev/null +++ b/scripts/run_experiment.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Run reproducible CupOFMoCA PDC experiments. + +This wrapper keeps the submitted generation programs intact while providing one +portable interface, bundled checkpoint defaults, named targets, and collision- +safe outputs. +""" + +from __future__ import annotations + +import argparse +import json +import shlex +import subprocess +import sys +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +CONFIG = json.loads((ROOT / "configs" / "paper.json").read_text()) + +CHECKPOINTS = { + "smiles": ROOT / "checkpoints" / "SMILESReDi_base.ckpt", + "aa": ROOT / "checkpoints" / "PepReDi_base.pt", + "uaa": ROOT / "checkpoints" / "UAAReDi_base.pt", +} + + +def read_fasta(path: Path) -> dict[str, str]: + records: dict[str, str] = {} + name = None + chunks: list[str] = [] + for raw in path.read_text().splitlines(): + line = raw.strip() + if not line: + continue + if line.startswith(">"): + if name is not None: + records[name] = "".join(chunks) + name, chunks = line[1:].split()[0], [] + elif name is None: + raise ValueError(f"Sequence before FASTA header in {path}") + else: + chunks.append(line) + if name is not None: + records[name] = "".join(chunks) + return records + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--representation", choices=("smiles", "aa", "uaa"), required=True) + parser.add_argument("--variant", choices=("baseline", "cupofmoca"), default="cupofmoca") + parser.add_argument("--method", choices=("guided", "staged"), default="guided") + parser.add_argument("--target", choices=tuple(CONFIG["targets"]), required=True) + parser.add_argument( + "--seeds", type=int, nargs="+", default=CONFIG["default_seeds"], + help="Random seeds for generation.", + ) + parser.add_argument("--num-candidates", type=int, default=CONFIG["num_candidates_per_seed"]) + parser.add_argument("--gen-len", type=int, default=None) + parser.add_argument("--checkpoint", type=Path, default=None) + parser.add_argument("--output-root", type=Path, default=ROOT / "outputs") + parser.add_argument("--python", default=sys.executable) + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--force", action="store_true", help="Allow an existing output to be replaced.") + parser.add_argument( + "extra_args", nargs=argparse.REMAINDER, + help="Arguments after '--' are appended to the underlying generator command.", + ) + args = parser.parse_args() + if args.num_candidates < 1: + parser.error("--num-candidates must be positive") + return args + + +def build_command(args: argparse.Namespace, seed: int, target_seq: str, output: Path) -> list[str]: + suffix = "" if args.representation == "smiles" else f"_{args.representation}" + prefix = "cupofmoca_" if args.variant == "cupofmoca" else "" + program = f"smiles.generation.{prefix}{args.method}{suffix}" + checkpoint = (args.checkpoint or CHECKPOINTS[args.representation]).resolve() + target_cfg = CONFIG["targets"][args.target] + gen_len = args.gen_len or ( + target_cfg["smiles_length"] + if args.representation == "smiles" + else CONFIG["representation_lengths"][args.representation] + ) + + cmd = [ + args.python, "-u", "-m", program, + "--checkpoint", str(checkpoint), + "--target", target_seq, + "--num_samples", str(CONFIG["num_samples_per_batch"]), + "--num_batches", str(args.num_candidates), + "--gen_len", str(gen_len), + "--optimization_steps", str(CONFIG["optimization_steps"]), + "--weights", *map(str, CONFIG["weights"]), + "--top_p", str(CONFIG["top_p"]), + "--seed", str(seed), + "--output_file", str(output), + ] + + if args.representation in {"aa", "uaa"}: + cmd += ["--tokenizer_type", "amino"] + if args.representation == "aa": + # This is the scorer used by the PDC paper runs. The optional AA-input + # scorer is not needed and its multi-GB checkpoint is intentionally omitted. + cmd += ["--affinity_type", "smiles"] + + if args.method == "guided": + cmd += [ + "--builder_type", target_cfg["builder"], + "--require_valid_x0", + "--max_x0_retries", str(CONFIG["max_x0_retries"]), + "--output_dir", str(output.parent / "logs"), + ] + + if args.variant == "cupofmoca": + cmd += [ + "--use_acceptance", + "--beta_min", str(CONFIG["beta_min"]), + "--beta_max", str(CONFIG["beta_max"]), + ] + if args.method == "guided": + cmd += ["--guidance_method", "exp_tilt"] + if args.representation in {"aa", "uaa"}: + cmd += ["--cascade_k", str(CONFIG["cascade_k"])] + else: + cmd += ["--output_dir", str(output.parent / "logs")] + + extra = list(args.extra_args) + if extra and extra[0] == "--": + extra = extra[1:] + return cmd + extra + + +def main() -> int: + args = parse_args() + targets = read_fasta(ROOT / "configs" / "targets.fasta") + target_seq = targets[args.target] + failures = 0 + + for seed in args.seeds: + run_dir = ( + args.output_root.resolve() + / args.target + / args.representation + / f"{args.variant}_{args.method}" + / f"seed_{seed}" + ) + output = run_dir / "molecules.txt" + if output.exists() and not args.force and not args.dry_run: + print(f"Refusing to overwrite {output}; pass --force to replace it.", file=sys.stderr) + failures += 1 + continue + if output.exists() and args.force: + output.unlink() + command = build_command(args, seed, target_seq, output) + display = " ".join( + f"<{args.target} sequence, {len(target_seq)} aa>" if item == target_seq else shlex.quote(item) + for item in command + ) + print(display) + if args.dry_run: + continue + run_dir.mkdir(parents=True, exist_ok=True) + completed = subprocess.run(command, cwd=ROOT, check=False) + if completed.returncode: + failures += 1 + print(f"Seed {seed} failed with exit code {completed.returncode}.", file=sys.stderr) + + return 1 if failures else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) + diff --git a/scripts/verify_release.py b/scripts/verify_release.py new file mode 100644 index 0000000000000000000000000000000000000000..9396464b06578f40ec842191b452d7571ca789bb --- /dev/null +++ b/scripts/verify_release.py @@ -0,0 +1,52 @@ +#!/usr/bin/env python3 +"""Verify bundled PDC 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 path in list((ROOT / "smiles").rglob("*.py")) + list((ROOT / "scripts").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 + + 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/smiles/__init__.py b/smiles/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e1a4c2d302f733ad6923fb580336558af12290b7 --- /dev/null +++ b/smiles/__init__.py @@ -0,0 +1 @@ +"""CupOFMoCA peptide-drug conjugate implementation.""" diff --git a/smiles/conjugation/AoaDau.py b/smiles/conjugation/AoaDau.py new file mode 100644 index 0000000000000000000000000000000000000000..3a1893dfe38950091c37c57feb87525b27132525 --- /dev/null +++ b/smiles/conjugation/AoaDau.py @@ -0,0 +1,732 @@ +""" +AoaDaunorubicin PDC Builder + +Conjugation chemistry: Lysine ε-amine + Aoa-Daunorubicin (amide bond formation) + +Reference PDC: PDC_00208 (GnRH-III-[8Lys(Dau=Aoa)]) +- Receptor: GNRHR +- Linker: Aminooxyacetic acid (Aoa) +- Payload: Daunorubicin + +Architecture: + Peptide──Lys──NH──C(=O)──CH₂──O──N═C──Daunorubicin + │ │ + amide bond Aoa linker oxime bond (pre-formed) +""" + +from rdkit import Chem +from rdkit.Chem import RWMol, AllChem + +from .pdc_builder import PDC_Builder + + +# ============================================================================ +# Aoa-Daunorubicin PDC Builder +# ============================================================================ + +class AoaDaunorubicin_PDC_Builder(PDC_Builder): + """ + PDC builder for Aminooxyacetic acid (Aoa) - Daunorubicin conjugates + + Conjugation chemistry (amide bond formation): + - Peptide site: Lysine ε-amine (side chain -NH2) + - Linker-payload site: Carboxylic acid of Aoa (already conjugated to Dau via oxime) + - Reaction: Amide bond formation (Lys-NH2 + HOOC-Aoa-Dau → Lys-NH-CO-Aoa-Dau) + + Architecture: + Peptide──Lys──NH──C(=O)──CH₂──O──N═C(CH₃)──Daunorubicin + └── amide ──┘ └── oxime (pre-formed) ──┘ + + Reference: PDC_00208 (GnRH-III-[8Lys(Dau=Aoa)]) + """ + + # Default Aoa-Daunorubicin SMILES with free carboxylic acid + # Structure: HOOC-CH2-O-N=C(CH3)-Daunorubicin + # The oxime bond is already formed between Aoa and Dau ketone + DEFAULT_AOA_DAU_SMILES = ( + "OC(=O)CO/N=C(\\C)[C@@]1(O)Cc2c(O)c3c(c(O)c2[C@H](OC2C[C@@H](N)[C@@H](O)[C@@H](C)O2)C1)" + "C(=O)c1c(OC)cccc1C3=O" + ) + + def __init__(self, aoa_dau_smiles=None): + """ + Initialize Aoa-Daunorubicin builder + + Args: + aoa_dau_smiles: Optional custom Aoa-Dau SMILES. If None, uses default. + Must have free carboxylic acid for conjugation. + """ + if aoa_dau_smiles is None: + aoa_dau_smiles = self.DEFAULT_AOA_DAU_SMILES + + super().__init__(aoa_dau_smiles, component_name="Aoa-Daunorubicin (linker-payload)") + + def _verify_reactive_group(self): + """Verify carboxylic acid group is present for amide bond formation""" + # Pattern: Carboxylic acid (COOH) + carboxylic_acid_pattern = Chem.MolFromSmarts('[CX3](=O)[OX2H1]') + + if not self.linker_payload_mol.HasSubstructMatch(carboxylic_acid_pattern): + print(" Warning: No carboxylic acid group detected in Aoa-Dau") + else: + print(" Aoa-Daunorubicin loaded with carboxylic acid reactive group") + + # Also verify oxime bond is present (Aoa-Dau linkage) + oxime_pattern = Chem.MolFromSmarts('[#6]-[#8]-[#7]=[#6]') + if self.linker_payload_mol.HasSubstructMatch(oxime_pattern): + print(" Oxime bond (Aoa-Dau linkage) detected") + else: + print(" Warning: No oxime bond detected - check Aoa-Dau structure") + + def find_conjugation_sites(self, peptide_smiles): + """ + Find all lysine ε-amine groups in peptide + + Returns list of dicts with site information including atom index + """ + return self.find_peptide_lysines(peptide_smiles) + + def find_peptide_lysines(self, peptide_smiles): + """ + Find all lysine residues with free ε-amine in peptide + + Lysine side chain: -CH2-CH2-CH2-CH2-NH2 (CCCCN) + + Returns: + List of dicts with 'atom_idx' (nitrogen index) and metadata + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + lysines = [] + + # SMARTS pattern for Lysine ε-amine: 4 CH2 groups ending in NH2 + # This specifically matches side chain, not N-terminus + lys_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + matches = peptide_mol.GetSubstructMatches(lys_pattern) + + for match in matches: + # match indices: 0=CH2, 1=CH2, 2=CH2, 3=CH2, 4=NH2 + nitrogen_idx = match[4] + + lysines.append({ + 'atom_idx': nitrogen_idx, + 'side_chain_atoms': list(match), + 'type': 'Lys_epsilon_amine' + }) + + # Also count N-terminus for informational purposes + # N-terminus: NH2 attached to backbone carbon (CH for most AAs, CH2 for Gly) + n_terminus_pattern = Chem.MolFromSmarts('[NH2][CH,CH2][C](=O)') + n_term_matches = peptide_mol.GetSubstructMatches(n_terminus_pattern) + + if n_term_matches: + print(f" (Also found {len(n_term_matches)} N-terminus α-amine(s) - excluded by default)") + + return lysines + + def count_all_nh2_sites(self, peptide_smiles): + """ + Count all NH2 sites in peptide (both Lys ε-amines and N-terminus) + + Useful for understanding what sites are available. + + Returns: + Dict with counts: {'lys_epsilon': n, 'n_terminus': n, 'other': n, 'total': n} + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + counts = {'lys_epsilon': 0, 'n_terminus': 0, 'other': 0, 'total': 0} + + # Lys ε-amine: CH2-CH2-CH2-CH2-NH2 (4-carbon side chain) + lys_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + counts['lys_epsilon'] = len(peptide_mol.GetSubstructMatches(lys_pattern)) + + # N-terminus: NH2 attached to backbone carbon (either CH for most AAs, or CH2 for Gly) + # Pattern: NH2-C-C(=O) where C is part of backbone + n_term_pattern = Chem.MolFromSmarts('[NH2][CH,CH2][C](=O)') + counts['n_terminus'] = len(peptide_mol.GetSubstructMatches(n_term_pattern)) + + # Total NH2 (any primary amine) + all_nh2_pattern = Chem.MolFromSmarts('[NH2]') + total_nh2 = len(peptide_mol.GetSubstructMatches(all_nh2_pattern)) + + counts['other'] = total_nh2 - counts['lys_epsilon'] - counts['n_terminus'] + counts['total'] = total_nh2 + + return counts + + def _find_carboxylic_acid_atoms(self, mol): + """ + Find the carboxylic acid carbon (the reactive site for amide formation) + + Returns: (carbon_idx, oxygen_idx) of the C(=O)OH group + """ + # Pattern: C(=O)OH + cooh_pattern = Chem.MolFromSmarts('[CX3](=O)[OX2H1]') + matches = mol.GetSubstructMatches(cooh_pattern) + + if not matches: + return None, None + + # Return first match: (carbon_idx, hydroxyl_oxygen_idx) + match = matches[0] + return match[0], match[1] + + def _perform_conjugation(self, peptide_mol, site_info, exclude_n_terminus=True): + """ + Perform amide bond conjugation between NH2 groups and Aoa carboxylic acid + + Args: + peptide_mol: RDKit Mol object + site_info: Not used (kept for compatibility) + exclude_n_terminus: If True, skip N-terminus conjugation + + Returns: List of all possible conjugate SMILES + """ + return self._manual_conjugation(peptide_mol, site_info, exclude_n_terminus) + + def _manual_conjugation(self, peptide_mol, lysine_info, exclude_n_terminus=True): + """ + Create ALL possible conjugates by forming amide bond at NH2 sites + + Chemistry: + - Any-NH2 + HOOC-Aoa-Dau → Any-NH-C(=O)-Aoa-Dau + H2O + + Args: + peptide_mol: RDKit Mol object of peptide + lysine_info: Not used (kept for interface compatibility) + exclude_n_terminus: If True, only conjugate at Lys ε-amines (4-carbon chain), + not at N-terminus α-amine. Default: True + + Returns: + List of all possible conjugate SMILES + """ + if exclude_n_terminus: + # SMARTS that matches only Lys ε-amine: requires 4 CH2 chain before NH2 + # This includes the connection to the peptide backbone (*) to preserve the full peptide + # Pattern: *-CH2-CH2-CH2-CH2-NH2 (where * is the connection to the peptide) + amide_rxn = AllChem.ReactionFromSmarts( + '[*:5][CH2][CH2][CH2][CH2:4][NH2:1].[C:2](=[O:3])[OH]>>[*:5][CH2][CH2][CH2][CH2:4][NH:1][C:2](=[O:3])' + ) + else: + # Match ANY primary amine (includes N-terminus) + # Include wildcard connection to preserve the rest of the molecule + amide_rxn = AllChem.ReactionFromSmarts( + '[*:4][NH2:1].[C:2](=[O:3])[OH]>>[*:4][NH:1][C:2](=[O:3])' + ) + + # Run the reaction - returns all possible products + products = amide_rxn.RunReactants((peptide_mol, self.linker_payload_mol)) + + if not products: + raise ValueError("Amide bond formation reaction failed - no products generated") + + # Collect ALL valid conjugates + conjugates = [] + seen_smiles = set() # Deduplicate + + for product_tuple in products: + try: + conjugate_mol = product_tuple[0] + Chem.SanitizeMol(conjugate_mol) + conjugate_smiles = Chem.MolToSmiles(conjugate_mol) + + # Deduplicate (same SMILES = same molecule) + if conjugate_smiles not in seen_smiles: + seen_smiles.add(conjugate_smiles) + conjugates.append(conjugate_smiles) + except Exception as e: + # Skip invalid products + continue + + if not conjugates: + raise ValueError("All products failed sanitization") + + return conjugates + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate Aoa-Daunorubicin to peptide via amide bond at NH2 groups + + Args: + peptide_smiles: SMILES string of peptide (must contain NH2) + exclude_n_terminus: If True (default), only conjugate at Lys ε-amines, + not at N-terminus. Set to False to include N-terminus. + return_all: If True, return all possible conjugates. If False, return first only. + + Returns: + If return_all=True: List of all possible conjugate SMILES + If return_all=False: Single conjugate SMILES (first one) + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError(f"Invalid peptide SMILES: {peptide_smiles}") + + sites = self.find_conjugation_sites(peptide_smiles) + print(f"Found {len(sites)} Lys ε-amine site(s)") + + # Get all conjugates + conjugates = self._perform_conjugation(peptide_mol, None, exclude_n_terminus) + + site_type = "Lys ε-amine only" if exclude_n_terminus else "all NH2 (including N-terminus)" + print(f"Generated {len(conjugates)} unique conjugate(s) [{site_type}]") + + if return_all: + return conjugates + else: + return conjugates[0] if conjugates else None + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """ + Batch conjugate multiple peptides + + Args: + peptide_smiles_list: List of peptide SMILES strings + exclude_n_terminus: If True (default), only conjugate at Lys ε-amines + return_all: If True, return all possible conjugates per peptide + + Returns: + Dict with: + 'conjugates': List of lists (if return_all=True) or list of SMILES + 'successful': List of successful peptide indices + 'failed': List of (index, error) tuples + 'total_conjugates': Total number of conjugates generated + """ + results = { + 'conjugates': [], + 'successful': [], + 'failed': [], + 'total_conjugates': 0 + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + except Exception as e: + print(f" Peptide {i} failed: {e}") + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + + print(f"\n=== Summary ===") + print(f"Peptides: {len(peptide_smiles_list)}, Success: {len(results['successful'])}, Failed: {len(results['failed'])}") + print(f"Total conjugates generated: {results['total_conjugates']}") + + return results + + def flatten_batch_results(self, results): + """ + Flatten batch conjugation results into a single list of all conjugates + + Args: + results: Dict returned by batch_conjugate() + + Returns: + List of all conjugate SMILES (flattened) + """ + all_conjugates = [] + for conjugate_list in results['conjugates']: + if conjugate_list is not None: + if isinstance(conjugate_list, list): + all_conjugates.extend(conjugate_list) + else: + all_conjugates.append(conjugate_list) + return all_conjugates + + # ========================================================================= + # Attachment Site Compatibility Checks (Lys-amine + Aoa-COOH specific) + # ========================================================================= + + def has_aoa_amide_conjugation(self, mol): + """ + Check if molecule has the Aoa-amide conjugation pattern + + Pattern: N-C(=O)-CH2-O-N=C (amide-CH2-O-oxime linkage) + This is the signature of successful Lys-Aoa conjugation. + + Returns: + bool: True if Aoa conjugation pattern is present + """ + # Specific Aoa-conjugation pattern + # Looking for: N-C(=O)-C-O-N=C (amide-CH2-O-oxime linkage) + aoa_conjugation_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH2][OX2][NX2]=[#6]') + if aoa_conjugation_pattern: + return mol.HasSubstructMatch(aoa_conjugation_pattern) + return False + + def has_oxime_bond(self, mol): + """ + Check if molecule has oxime bond (Aoa-Dau linkage) + + Pattern: C-O-N=C + This confirms the Aoa-Dau linker-payload is intact. + """ + oxime_pattern = Chem.MolFromSmarts('[#6]-[#8]-[#7]=[#6]') + if oxime_pattern: + return mol.HasSubstructMatch(oxime_pattern) + return False + + def has_unreacted_aoa_carboxylic_acid(self, mol): + """ + Check if molecule has unreacted Aoa carboxylic acid + + The specific Aoa pattern: HOOC-CH2-O-N=C + If this is present, the Aoa hasn't been conjugated to the peptide. + + Note: We specifically check for the Aoa-COOH pattern, not just any COOH, + because peptides naturally have carboxylic acids (C-terminus, Asp, Glu). + + Returns: + bool: True if unreacted Aoa-COOH is present (bad) + """ + # Specific pattern: Free COOH attached to CH2-O-N=C (Aoa pattern) + unreacted_aoa_pattern = Chem.MolFromSmarts('[CX3](=[OX1])[OX2H1][CH2][OX2][NX2]=[#6]') + if unreacted_aoa_pattern: + return mol.HasSubstructMatch(unreacted_aoa_pattern) + return False + + def find_amide_conjugation_sites(self, mol): + """ + Find amide bonds that are part of the Aoa conjugation + + Specifically looks for: N-C(=O)-CH2-O-N=C pattern + + Returns: + List of (nitrogen_idx, carbonyl_carbon_idx) tuples + """ + aoa_conjugation_pattern = Chem.MolFromSmarts('[NX3:1][CX3:2](=[OX1])[CH2][OX2][NX2]=[#6]') + conjugation_sites = [] + + if aoa_conjugation_pattern: + matches = mol.GetSubstructMatches(aoa_conjugation_pattern) + for match in matches: + nitrogen_idx = match[0] + carbonyl_c_idx = match[1] + conjugation_sites.append((nitrogen_idx, carbonyl_c_idx)) + + return conjugation_sites + + def check_valency_at_conjugation_site(self, mol, nitrogen_idx): + """ + Check valency at amide nitrogen conjugation site + + For Lys-Aoa conjugation, the nitrogen should be: + - Amide nitrogen (bonded to carbonyl carbon) + - Secondary amine (2 bonds to heavy atoms) + - No charge + + Args: + mol: RDKit Mol object + nitrogen_idx: Index of the nitrogen atom to check + + Returns: + bool: True if valency is valid + """ + atom = mol.GetAtomWithIdx(nitrogen_idx) + + if atom.GetSymbol() != 'N': + return True # Not nitrogen, skip + + degree = atom.GetDegree() # Bonds to heavy atoms + total_hs = atom.GetTotalNumHs() + formal_charge = atom.GetFormalCharge() + + # Get neighbor information + neighbors = [mol.GetAtomWithIdx(n.GetIdx()) for n in atom.GetNeighbors()] + + # Count carbonyl carbons (C=O) among neighbors + carbonyl_neighbors = 0 + for neighbor in neighbors: + if neighbor.GetSymbol() == 'C': + # Check if this carbon has a double bond to oxygen + for n2 in neighbor.GetNeighbors(): + if n2.GetSymbol() == 'O': + bond = mol.GetBondBetweenAtoms(neighbor.GetIdx(), n2.GetIdx()) + if bond and bond.GetBondType() == Chem.BondType.DOUBLE: + carbonyl_neighbors += 1 + break + + # ========== Valid Amide Nitrogen States ========== + + # 1. Secondary amide: N bonded to 2 heavy atoms (one is carbonyl C), 1 H + # R-NH-C(=O)-R' + if degree == 2 and total_hs == 1 and formal_charge == 0 and carbonyl_neighbors >= 1: + return True + + # 2. Tertiary amide: N bonded to 3 heavy atoms (one is carbonyl C), 0 H + # R-N(R')-C(=O)-R'' + if degree == 3 and total_hs == 0 and formal_charge == 0 and carbonyl_neighbors >= 1: + return True + + # 3. Aromatic N in peptide backbone is okay + if atom.GetIsAromatic(): + return True + + # ========== Invalid States ========== + + # Quaternary nitrogen with positive charge (not typical for amide) + if degree >= 4 and formal_charge > 0: + return False + + # N with no carbonyl neighbor but expected to be amide + if carbonyl_neighbors == 0: + # This might be a free amine - need context to determine if that's okay + # For Aoa conjugation, we expect amide formation + pass # Allow for now, other checks will catch issues + + return True + + def check_attachment_site_compatibility(self, mol): + """ + Check if Lys-Aoa amide attachment site chemistry is valid + + For Aoa-Daunorubicin conjugation, validates: + 1. Has Aoa-amide conjugation pattern (N-C(=O)-CH2-O-N=C) + 2. Has oxime bond intact (Aoa-Dau linkage) + 3. No unreacted Aoa-COOH (conjugation must have happened) + 4. Valid valency at conjugation nitrogen sites + + Args: + mol: RDKit Mol object of the conjugate + + Returns: + bool: True if attachment site is valid, False otherwise + """ + # ===== CRITICAL CHECK: Must have Aoa-amide conjugation pattern ===== + if not self.has_aoa_amide_conjugation(mol): + return False + + # ===== Check oxime bond is intact ===== + if not self.has_oxime_bond(mol): + return False + + # ===== Check no unreacted Aoa-COOH ===== + # If the original Aoa carboxylic acid pattern is still present, + # conjugation didn't happen properly + if self.has_unreacted_aoa_carboxylic_acid(mol): + return False + + # ===== Check valency at conjugation sites ===== + conjugation_sites = self.find_amide_conjugation_sites(mol) + + for nitrogen_idx, carbonyl_c_idx in conjugation_sites: + if not self.check_valency_at_conjugation_site(mol, nitrogen_idx): + return False + + return True + + def _basic_validation(self, mol): + """ + Basic validation checks for Aoa-Daunorubicin conjugates + + Includes attachment site compatibility check with valency validation. + + Checks: + 1. Attachment site compatibility (Aoa-amide pattern, oxime, valency) + 2. Daunorubicin core present + 3. Aminosugar present (daunosamine) + + Note: We do NOT check for absence of COOH because peptides naturally + have carboxylic acids (C-terminus, Asp, Glu residues). + """ + checks = {} + + # Check 1: Has amide bond (general check) + amide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[#6]') + checks['has_amide_bond'] = mol.HasSubstructMatch(amide_pattern) + + # Check 2: Oxime bond present (Aoa-Dau linkage: C-O-N=C) + checks['has_oxime_bond'] = self.has_oxime_bond(mol) + + # Check 3: Specific Aoa-conjugation pattern + checks['has_aoa_conjugation'] = self.has_aoa_amide_conjugation(mol) + + # Check 4: No unreacted Aoa-COOH + checks['no_unreacted_aoa'] = not self.has_unreacted_aoa_carboxylic_acid(mol) + + # Check 5: Full attachment site compatibility (includes valency) + checks['attachment_site_valid'] = self.check_attachment_site_compatibility(mol) + + # Check 6: Daunorubicin core present (anthraquinone with specific substitution) + anthraquinone = Chem.MolFromSmarts('[#6]1[#6][#6][#6]2[#6](=[#8])[#6]3[#6][#6][#6][#6][#6]3[#6](=[#8])[#6]2[#6]1') + checks['has_daunorubicin_core'] = mol.HasSubstructMatch(anthraquinone) + + # Check 7: Has aminosugar (daunosamine - 6-membered ring with N) + aminosugar = Chem.MolFromSmarts('[OX2]1[#6][#6][#6]([NX3])[#6][#6]1') + checks['has_aminosugar'] = mol.HasSubstructMatch(aminosugar) + + # Find and report conjugation sites for diagnostics + conjugation_sites = self.find_amide_conjugation_sites(mol) + checks['conjugation_sites'] = conjugation_sites + + # Overall pass/fail - attachment_site_valid is the key check + checks['_passed'] = checks['attachment_site_valid'] + + return checks + + def validate_conjugate(self, conjugate_smiles): + """ + Validate that the conjugate is chemically reasonable + + Returns: + (passed: bool, checks: dict) + """ + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'error': 'Could not parse SMILES'} + + checks = self._basic_validation(mol) + checks['parseable'] = True + + passed = checks.get('_passed', True) + if '_passed' in checks: + del checks['_passed'] + + return passed, checks + + +# ============================================================================ +# Main - Test Examples +# ============================================================================ + +if __name__ == "__main__": + + print("=" * 70) + print("Aoa-Daunorubicin PDC Builder - Test") + print("=" * 70 + "\n") + + # Initialize builder + builder = AoaDaunorubicin_PDC_Builder() + + # Reference peptide: GnRH-III (PEP00168) + gnrh_iii_smiles = ( + "[H]NCCCC[C@H](NC(=O)[C@H](Cc1cn([H])c2ccccc12)NC(=O)[C@H](CC(=O)O)" + "NC(=O)[C@H](Cc1cn([H])cn1)NC(=O)[C@H](CO)NC(=O)[C@H](Cc1cn([H])c2ccccc12)" + "NC(=O)[C@H](Cc1cn([H])cn1)NC(=O)[C@@H]1CCC(=O)N1)C(=O)N1CCC[C@H]1C(=O)NCC(N)=O" + ) + + # Test peptides + test_peptides = [ + # Simple test: Gly-Lys-Gly (has N-terminus NH2 + Lys ε-amine = 2 NH2 sites) + "NCC(=O)N[C@@H](CCCCN)C(=O)NCC(=O)O", + # Lys-Arg (has N-terminus NH2 + Lys ε-amine = 2 NH2 sites) + "N[C@@H](CCCCN)C(=O)N[C@@H](CCCNC(=N)N)C(=O)O", + # GnRH-III + gnrh_iii_smiles, + ] + + print("\n" + "=" * 70) + print("Testing NH2 Site Detection") + print("=" * 70 + "\n") + + for i, pep in enumerate(test_peptides): + name = ["Gly-Lys-Gly", "Lys-Arg", "GnRH-III"][i] + counts = builder.count_all_nh2_sites(pep) + print(f"{name}:") + print(f" Lys ε-amines: {counts['lys_epsilon']}") + print(f" N-terminus: {counts['n_terminus']}") + print(f" Other NH2: {counts['other']}") + print(f" Total NH2: {counts['total']}") + print() + + print("\n" + "=" * 70) + print("Testing: exclude_n_terminus=True (DEFAULT)") + print("=" * 70 + "\n") + + peptide = test_peptides[0] # Gly-Lys-Gly + print(f"Input peptide (Gly-Lys-Gly): {peptide}") + + try: + conjugates = builder.conjugate_to_peptide(peptide, exclude_n_terminus=True) + print(f"\n SUCCESS!") + print(f"Number of conjugates: {len(conjugates)} (should be 1 - Lys only)") + + for i, conj in enumerate(conjugates): + valid, checks = builder.validate_conjugate(conj) + print(f" Conjugate {i+1}: {'PASSED' if valid else 'FAILED'}") + print(conj) + + except Exception as e: + print(f"\n FAILED: {e}") + import traceback + traceback.print_exc() + + print("\n" + "=" * 70) + print("Testing: exclude_n_terminus=False (include N-terminus)") + print("=" * 70 + "\n") + + print(f"Input peptide (Gly-Lys-Gly): {peptide}") + + try: + conjugates = builder.conjugate_to_peptide(peptide, exclude_n_terminus=False) + print(f"\n SUCCESS!") + print(f"Number of conjugates: {len(conjugates)} (should be 2 - Lys + N-terminus)") + + for i, conj in enumerate(conjugates): + valid, checks = builder.validate_conjugate(conj) + print(f" Conjugate {i+1}: {'PASSED' if valid else 'FAILED'}") + print(conj) + + except Exception as e: + print(f"\n FAILED: {e}") + import traceback + traceback.print_exc() + + print("\n" + "=" * 70) + print("Testing GnRH-III (has only 1 Lys at position 8)") + print("=" * 70 + "\n") + + print(f"Input peptide (GnRH-III): {gnrh_iii_smiles[:60]}...") + print("\nNote: GnRH-III sequence is Glp-His-Trp-Ser-His-Asp-Trp-Lys-Pro-Gly") + print(" Position 4 is Serine (not Lysine), so only 1 Lys at position 8") + + # First check the NH2 sites + counts = builder.count_all_nh2_sites(gnrh_iii_smiles) + print(f"\nNH2 site breakdown:") + print(f" Lys ε-amines: {counts['lys_epsilon']} (expected: 1 for Lys8)") + print(f" N-terminus: {counts['n_terminus']} (expected: 0 - Glp is cyclic)") + print(f" Other: {counts['other']}") + + try: + conjugates = builder.conjugate_to_peptide(gnrh_iii_smiles, exclude_n_terminus=True) + print(f"\n SUCCESS!") + print(f"Number of conjugates: {len(conjugates)}") + + for i, conj in enumerate(conjugates): + valid, checks = builder.validate_conjugate(conj) + status = "PASSED" if valid else "FAILED" + print(f" Conjugate {i+1}: {status} - {len(conj)} chars") + print(conj) + + except Exception as e: + print(f"\n FAILED: {e}") + import traceback + traceback.print_exc() + + print("\n" + "=" * 70) + print("Batch Conjugation Test (exclude_n_terminus=True)") + print("=" * 70 + "\n") + + results = builder.batch_conjugate(test_peptides, exclude_n_terminus=True) + + print(f"\nResults breakdown:") + for i in results['successful']: + conjugates = results['conjugates'][i] + num_valid = sum(1 for c in conjugates if builder.validate_conjugate(c)[0]) + name = ["Gly-Lys-Gly", "Lys-Arg", "GnRH-III"][i] + print(f" {name}: {len(conjugates)} conjugate(s), {num_valid} valid") \ No newline at end of file diff --git a/smiles/conjugation/CptFactory.py b/smiles/conjugation/CptFactory.py new file mode 100644 index 0000000000000000000000000000000000000000..4f2aaad70251f7b06f552f4d87d174e3d28a8298 --- /dev/null +++ b/smiles/conjugation/CptFactory.py @@ -0,0 +1,312 @@ +""" +Camptothecin PDC Factory - Drug-level builder with linker sampling + +Instead of specifying a particular linker-drug combo (e.g. SSCpt, DSPACpt), +the user specifies only the drug (Camptothecin) and a linker is sampled +according to ERBB2+Cpt entry frequencies from PDCdb. + +Linker frequencies (ERBB2 + Camptothecin): +=========================================== + Disulfide bond (SSCpt) - 7 entries + Maleimide-hexanoate (MalHexCpt) - 5 entries + 3-Disulfanylpropanoic acid (DSPACpt) - 5 entries + 2-(Disulfanyl)ethyl carbonate (DSECCpt) - 5 entries + Total: 22 entries + +Sampling probabilities: + SSCpt: 7/22 ≈ 0.318 + MalHexCpt: 5/22 ≈ 0.227 + DSPACpt: 5/22 ≈ 0.227 + DSECCpt: 5/22 ≈ 0.227 + +Usage: +====== + # Drug-only mode - linker is sampled per peptide + factory = CamptothecinPDC_Factory() + conjugates = factory.conjugate_to_peptide(peptide_smiles) + + # Fixed linker mode - use a specific linker + factory = CamptothecinPDC_Factory(linker='SSCpt') + conjugates = factory.conjugate_to_peptide(peptide_smiles) + + # All linkers mode - generate conjugates with every linker + factory = CamptothecinPDC_Factory(linker='all') + conjugates = factory.conjugate_to_peptide(peptide_smiles) +""" + +import numpy as np +import contextlib +import io + +from .SSCpt import DisulfideCamptothecin_PDC_Builder +from .DSPACpt import DisulfanylpropanoicAcidCpt_PDC_Builder +from .MalHexCpt import MaleimideHexanoateCpt_PDC_Builder +from .DSECCpt import DisulfanylethylCarbonateCpt_PDC_Builder + + +# ============================================================================ +# Linker registry: name → (builder_class, PDCdb frequency, description) +# ============================================================================ + +ERBB2_CPT_LINKER_REGISTRY = { + 'SSCpt': { + 'class': DisulfideCamptothecin_PDC_Builder, + 'frequency': 7, + 'linker_id': 'LIN00005', + 'description': 'Disulfide bond (ester + carbonate variants)', + 'pdc_refs': ['PDC_00294', 'PDC_00295'], + }, + 'MalHexCpt': { + 'class': MaleimideHexanoateCpt_PDC_Builder, + 'frequency': 5, + 'linker_id': 'LIN00031', + 'description': '4-yl 6-(2,5-dioxo-2,5-dihydro-1H-pyrrol-1-yl)hexanoate', + 'pdc_refs': ['PDC_00336'], + }, + 'DSPACpt': { + 'class': DisulfanylpropanoicAcidCpt_PDC_Builder, + 'frequency': 5, + 'linker_id': 'LIN00012', + 'description': '3-Disulfanylpropanoic acid', + 'pdc_refs': ['PDC_00335'], + }, + 'DSECCpt': { + 'class': DisulfanylethylCarbonateCpt_PDC_Builder, + 'frequency': 5, + 'linker_id': 'LIN00039', + 'description': '2-(Disulfanyl)ethyl hydrogen carbonate', + 'pdc_refs': ['PDC_00337'], + }, +} + + +def _compute_linker_probabilities(registry=None): + """Compute sampling probabilities from frequency counts.""" + if registry is None: + registry = ERBB2_CPT_LINKER_REGISTRY + names = list(registry.keys()) + freqs = np.array([registry[n]['frequency'] for n in names], dtype=np.float64) + probs = freqs / freqs.sum() + return names, probs + + +class CamptothecinPDC_Factory: + """ + Drug-level PDC factory for Camptothecin conjugates. + + Wraps all four ERBB2+Cpt linker builders and provides: + - 'sample' mode (default): randomly pick a linker per call according to + PDCdb entry frequencies + - 'all' mode: generate conjugates with every linker + - fixed linker mode: always use a specific linker (e.g. 'SSCpt') + + The interface mirrors the individual builder's `conjugate_to_peptide` so it + can be used as a drop-in replacement in the Conjugation classifier. + """ + + def __init__(self, linker='sample', rng_seed=None): + """ + Args: + linker: One of: + - 'sample' (default): sample linker per peptide according to + ERBB2+Cpt frequency weights + - 'all': use every linker and pool conjugates + - A specific linker name ('SSCpt', 'DSPACpt', 'MalHexCpt', 'DSECCpt') + rng_seed: Optional seed for reproducible linker sampling + """ + self.linker_mode = linker + self.registry = ERBB2_CPT_LINKER_REGISTRY + self.rng = np.random.default_rng(rng_seed) + + # Pre-compute probabilities + self._linker_names, self._linker_probs = _compute_linker_probabilities(self.registry) + + # Validate fixed linker + if linker not in ('sample', 'all') and linker not in self.registry: + raise ValueError( + f"Unknown linker '{linker}'. " + f"Choose from: 'sample', 'all', {list(self.registry.keys())}" + ) + + # Pre-instantiate builders (suppress verbose init output) + self._builders = {} + for name, entry in self.registry.items(): + with contextlib.redirect_stdout(io.StringIO()): + self._builders[name] = entry['class']() + + # Track which linker was last sampled (useful for logging) + self.last_sampled_linker = None + + # ------------------------------------------------------------------ + # Linker selection + # ------------------------------------------------------------------ + + def sample_linker(self): + """Sample a linker name according to frequency weights.""" + idx = self.rng.choice(len(self._linker_names), p=self._linker_probs) + return self._linker_names[idx] + + def get_builder(self, linker_name): + """Return the pre-instantiated builder for a given linker name.""" + return self._builders[linker_name] + + @property + def linker_probabilities(self): + """Return dict of linker name → sampling probability.""" + return dict(zip(self._linker_names, self._linker_probs.tolist())) + + # ------------------------------------------------------------------ + # Conjugation interface (mirrors individual builders) + # ------------------------------------------------------------------ + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate Camptothecin to peptide, selecting linker by mode. + + Args: + peptide_smiles: SMILES string of peptide (must contain Lys -NH2) + exclude_n_terminus: If True, only conjugate at Lys ε-amines + return_all: If True, return all positional conjugates + + Returns: + List of conjugate SMILES. + In 'sample' mode: conjugates from the sampled linker. + In 'all' mode: conjugates from every linker (pooled). + In fixed mode: conjugates from the specified linker. + """ + if self.linker_mode == 'all': + self.last_sampled_linker = 'all' + return self._conjugate_all_linkers(peptide_smiles, exclude_n_terminus, return_all) + elif self.linker_mode == 'sample': + chosen = self.sample_linker() + self.last_sampled_linker = chosen + return self._conjugate_with_linker(chosen, peptide_smiles, exclude_n_terminus, return_all) + else: + self.last_sampled_linker = self.linker_mode + return self._conjugate_with_linker(self.linker_mode, peptide_smiles, exclude_n_terminus, return_all) + + def _conjugate_with_linker(self, linker_name, peptide_smiles, exclude_n_terminus, return_all): + """Run conjugation with a single, specific linker.""" + builder = self._builders[linker_name] + with contextlib.redirect_stdout(io.StringIO()): + conjugates = builder.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all, + ) + # Normalize to list + if not isinstance(conjugates, list): + conjugates = [conjugates] if conjugates else [] + return conjugates + + def _conjugate_all_linkers(self, peptide_smiles, exclude_n_terminus, return_all): + """Generate conjugates with every linker and pool them.""" + all_conjugates = [] + seen = set() + for name in self._linker_names: + try: + conjs = self._conjugate_with_linker(name, peptide_smiles, exclude_n_terminus, return_all) + for c in conjs: + if c not in seen: + seen.add(c) + all_conjugates.append(c) + except Exception: + continue + if not all_conjugates: + raise ValueError("No conjugates generated with any linker") + return all_conjugates + + # ------------------------------------------------------------------ + # Validation (delegates to the appropriate builder) + # ------------------------------------------------------------------ + + def validate_conjugate(self, conjugate_smiles, linker_name=None): + """ + Validate a conjugate SMILES. + + If linker_name is given, validate with that builder. + Otherwise, try all builders and return the best result. + """ + if linker_name and linker_name in self._builders: + return self._builders[linker_name].validate_conjugate(conjugate_smiles) + + # Try each builder; return first passing result + for name, builder in self._builders.items(): + passed, checks = builder.validate_conjugate(conjugate_smiles) + if passed: + checks['matched_linker'] = name + return True, checks + + # None passed - return last failure + return False, checks + + def check_attachment_site_compatibility(self, conjugate_smiles, linker_name=None): + """Delegate to appropriate builder's attachment check.""" + if linker_name and linker_name in self._builders: + return self._builders[linker_name].check_attachment_site_compatibility(conjugate_smiles) + + for name, builder in self._builders.items(): + passed, details = builder.check_attachment_site_compatibility(conjugate_smiles) + if passed: + details['matched_linker'] = name + return True, details + return False, details + + # ------------------------------------------------------------------ + # Batch interface + # ------------------------------------------------------------------ + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """ + Batch conjugate multiple peptides. + + In 'sample' mode each peptide gets a freshly sampled linker. + + Returns: + Dict with 'conjugates', 'successful', 'failed', 'total_conjugates', + and 'linkers_used' (list of linker names, one per peptide). + """ + results = { + 'conjugates': [], + 'successful': [], + 'failed': [], + 'total_conjugates': 0, + 'linkers_used': [], + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all, + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + # Track which linker was used (only meaningful in sample/fixed mode) + results['linkers_used'].append( + self.linker_mode if self.linker_mode not in ('sample', 'all') else None + ) + except Exception as e: + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + results['linkers_used'].append(None) + + return results + + # ------------------------------------------------------------------ + # Informational + # ------------------------------------------------------------------ + + def __repr__(self): + probs = self.linker_probabilities + lines = [f"CamptothecinPDC_Factory(mode='{self.linker_mode}')"] + for name, prob in probs.items(): + desc = self.registry[name]['description'] + lines.append(f" {name:12s} p={prob:.3f} ({desc})") + return '\n'.join(lines) diff --git a/smiles/conjugation/DSEBSN38.py b/smiles/conjugation/DSEBSN38.py new file mode 100644 index 0000000000000000000000000000000000000000..c5ccff168ae5359ac992152b081d83055eeaafb7 --- /dev/null +++ b/smiles/conjugation/DSEBSN38.py @@ -0,0 +1,377 @@ +""" +4-(2-Disulfanylethoxy)-4-oxobutanoic Acid - SN-38 PDC Builder (DSEBSN38) + +Conjugation chemistry: Cysteine thiol + Linker thiol → Disulfide bond (S-S) + +Reference PDCs: +- PDC_02100 (SN38(10)-4-CRGDK) +- PDC_02103 (SN38(10)-4-(Ac)CGVQIRGDK) +- PDC_02105 (SN38(10)-4-(Ac)CRGDK) +- PDC_02108 (SN38(10)-4-CDRGDK) +- PDC_02109 (SN38(10)-4-(Ac)CDRGDK) +- PDC_02111 (SN38(20)-4-CRGDK) + +Receptor: NRP1 (Neuropilin-1) +Linker: LIN00168 - 4-(2-Disulfanylethoxy)-4-oxobutanoic acid +Payload: 7-Ethyl-10-hydroxycamptothecin (SN-38) +Architecture: Monomer, Cys thiol conjugation (disulfide bond) + +Chemistry Note: +=============== +LIN00168 provides a longer disulfide-ester linkage via succinic acid scaffold: + HOOC─CH₂─CH₂─C(═O)─O─CH₂─CH₂─S─SH + + butanoic acid ethoxy-disulfanyl + +Linker-to-Drug Connection (Ester at drug OH, thiol for peptide Cys): + SN-38(10-O)─C(═O)─CH₂─CH₂─C(═O)─O─CH₂─CH₂─SH + +Full PDC Architecture: + Peptide──Cys──S─S──CH₂─CH₂─O─C(═O)─CH₂─CH₂─C(═O)─O──SN-38 + └── disulfide ─┘ └── linker ──┘ └ ester ┘ + +Usage: +====== + builder = DisulfanylethOxyButanoicAcidSN38_PDC_Builder() + conjugates = builder.conjugate_to_peptide(peptide_smiles) +""" + +from rdkit import Chem +from rdkit.Chem import AllChem, Descriptors + +from .pdc_builder import PDC_Builder + + +# ============================================================================ +# 4-(2-Disulfanylethoxy)-4-oxobutanoic Acid - SN-38 PDC Builder +# ============================================================================ + +class DisulfanylethOxyButanoicAcidSN38_PDC_Builder(PDC_Builder): + """ + PDC builder for 4-(2-Disulfanylethoxy)-4-oxobutanoic Acid + SN-38 conjugates + + Conjugation chemistry (disulfide bond formation): + - Peptide site: Cysteine thiol (side chain -SH) + - Linker-payload site: Terminal thiol (-SH) on linker + - Reaction: Peptide-Cys-SH + HS-Linker-Drug → Peptide-Cys-S-S-Linker-Drug + + Linker: LIN00168 - 4-(2-Disulfanylethoxy)-4-oxobutanoic acid + IUPAC: 4-(2-(disulfanyl)ethoxy)-4-oxobutanoic acid + Connectivity: HOOC-CH2-CH2-C(=O)-O-CH2-CH2-S-SH + Succinic acid monoester with 2-(disulfanyl)ethanol + + Drug: 7-Ethyl-10-hydroxycamptothecin (SN-38) + Connection: Ester at C-10 hydroxyl (default) or C-20 hydroxyl + + Reference: PDC_02100, PDC_02111 (NRP1) + """ + + # ======================================================================== + # Linker-Drug SMILES Variants + # ======================================================================== + + # SN-38 with DSEB ester at C-10 (phenolic OH), free thiol + # Drug(10-O)-C(=O)-CH2-CH2-C(=O)-O-CH2-CH2-SH + POS10_LINKER_SN38_SMILES = ( + "CCC1(O)C(=O)OCc2c1cc1n(c2=O)Cc2c(CC)c3cc(OC(=O)CCC(=O)OCCS)ccc3nc2-1" + ) + + # SN-38 with DSEB ester at C-20 (tertiary OH), free thiol + POS20_LINKER_SN38_SMILES = ( + "CCC1(OC(=O)CCC(=O)OCCS)C(=O)OCc2c1cc1n(c2=O)Cc2c(CC)c3cc(O)ccc3nc2-1" + ) + + DEFAULT_LINKER_SN38_SMILES = POS10_LINKER_SN38_SMILES + + DRUG_POSITIONS = { + '10': POS10_LINKER_SN38_SMILES, + '20': POS20_LINKER_SN38_SMILES, + } + + def __init__(self, linker_drug_smiles=None, drug_position='all'): + """ + Initialize DSEB-SN38 builder + + Args: + linker_drug_smiles: Optional custom Linker-Drug SMILES. + drug_position: 'all' (default), '10', or '20'. + """ + self.drug_position = drug_position + + if linker_drug_smiles is not None: + self._variants = [linker_drug_smiles] + self._variant_names = ['custom'] + super().__init__( + linker_drug_smiles, + component_name="Custom-DSEB-SN38 (NRP1)" + ) + elif drug_position == 'all': + self._variants = [self.POS10_LINKER_SN38_SMILES, self.POS20_LINKER_SN38_SMILES] + self._variant_names = ['pos10', 'pos20'] + super().__init__( + self.POS10_LINKER_SN38_SMILES, + component_name="DSEB-SN38 (LIN00168, all positions)" + ) + else: + if drug_position not in self.DRUG_POSITIONS: + raise ValueError(f"Unknown drug_position '{drug_position}'. " + f"Choose from: 'all', '10', '20'") + smiles = self.DRUG_POSITIONS[drug_position] + self._variants = [smiles] + self._variant_names = [f'pos{drug_position}'] + super().__init__( + smiles, + component_name=f"DSEB-SN38 (LIN00168, C-{drug_position})" + ) + + def _verify_reactive_group(self): + """Verify thiol group is present for disulfide bond formation""" + for atom in self.linker_payload_mol.GetAtoms(): + if atom.GetAtomicNum() == 16: + if atom.GetTotalNumHs() >= 1 and atom.GetDegree() == 1: + print(" DSEB-SN38 loaded with free thiol reactive group") + break + else: + print(" Warning: No free thiol group detected in DSEB-SN38") + + ester_pattern = Chem.MolFromSmarts('[#6][CX3](=[OX1])[OX2][#6]') + if self.linker_payload_mol.HasSubstructMatch(ester_pattern): + print(" Ester bond (linker-to-drug) detected") + else: + print(" Warning: No ester bond detected") + + # ------------------------------------------------------------------ + # Conjugation site finding (Cysteine thiol) + # ------------------------------------------------------------------ + + def find_conjugation_sites(self, peptide_smiles): + """Find all free cysteine thiol groups in peptide""" + return self.find_peptide_cysteines(peptide_smiles) + + def find_peptide_cysteines(self, peptide_smiles): + """Find all free cysteine residues (thiol groups) in peptide""" + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + cysteines = [] + for atom in peptide_mol.GetAtoms(): + if atom.GetAtomicNum() == 16: + total_hs = atom.GetTotalNumHs() + degree = atom.GetDegree() + if total_hs >= 1 and degree == 1: + cysteines.append({ + 'atom_idx': atom.GetIdx(), + 'num_hydrogens': total_hs, + 'neighbors': [peptide_mol.GetAtomWithIdx(n.GetIdx()).GetSymbol() + for n in atom.GetNeighbors()], + 'type': 'Cys_thiol' + }) + return cysteines + + # ------------------------------------------------------------------ + # Conjugation reaction (disulfide bond formation) + # ------------------------------------------------------------------ + + def _perform_conjugation(self, peptide_mol, site_info, exclude_n_terminus=True): + """Form disulfide bond between peptide Cys thiol and linker-drug thiol.""" + all_conjugates = [] + seen = set() + + for variant_smiles in self._variants: + variant_mol = Chem.MolFromSmiles(variant_smiles) + if variant_mol is None: + continue + + disulfide_rxn = AllChem.ReactionFromSmarts( + '[C:1][SH:2].[SH:3][C:4]>>[C:1][S:2][S:3][C:4]' + ) + + products = disulfide_rxn.RunReactants((peptide_mol, variant_mol)) + if not products: + continue + + for product_tuple in products: + try: + conjugate_mol = product_tuple[0] + Chem.SanitizeMol(conjugate_mol) + conjugate_smiles = Chem.MolToSmiles(conjugate_mol) + if conjugate_smiles not in seen: + seen.add(conjugate_smiles) + all_conjugates.append(conjugate_smiles) + except Exception: + continue + + if not all_conjugates: + raise ValueError("Disulfide bond formation failed - no products generated") + + return all_conjugates + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate DSEB-SN38 to peptide via disulfide bond at Cys thiol. + + Args: + peptide_smiles: SMILES string of peptide (must contain Cys) + exclude_n_terminus: Not used for Cys conjugation (kept for interface) + return_all: If True, return all possible conjugates + + Returns: + List of all possible conjugate SMILES + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError(f"Invalid peptide SMILES: {peptide_smiles}") + + sites = self.find_conjugation_sites(peptide_smiles) + print(f"Found {len(sites)} Cys thiol site(s)") + + conjugates = self._perform_conjugation(peptide_mol, None, exclude_n_terminus) + + pos_label = f"C-{self.drug_position}" if self.drug_position != 'all' else "C-10 + C-20" + print(f"Generated {len(conjugates)} unique conjugate(s) [Cys disulfide] [DSEB {pos_label}]") + + if return_all: + return conjugates + else: + return conjugates[0] if conjugates else None + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """Batch conjugate multiple peptides.""" + results = { + 'conjugates': [], 'successful': [], 'failed': [], 'total_conjugates': 0 + } + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, exclude_n_terminus=exclude_n_terminus, return_all=return_all + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + except Exception as e: + print(f" Peptide {i} failed: {e}") + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + + print(f"\n=== Summary ===") + print(f"Peptides: {len(peptide_smiles_list)}, Success: {len(results['successful'])}, " + f"Failed: {len(results['failed'])}") + print(f"Total conjugates generated: {results['total_conjugates']}") + return results + + def flatten_batch_results(self, results): + """Flatten batch conjugation results into a single list""" + all_conjugates = [] + for conjugate_list in results['conjugates']: + if conjugate_list is not None: + if isinstance(conjugate_list, list): + all_conjugates.extend(conjugate_list) + else: + all_conjugates.append(conjugate_list) + return all_conjugates + + # ======================================================================== + # Validation + # ======================================================================== + + def has_disulfide_bond(self, mol): + """Check if molecule has disulfide bond (S-S)""" + disulfide_pattern = Chem.MolFromSmarts('[SX2]-[SX2]') + return mol.HasSubstructMatch(disulfide_pattern) + + def has_free_thiol(self, mol): + """Check if molecule has unconjugated (free) thiol groups""" + for atom in mol.GetAtoms(): + if atom.GetAtomicNum() == 16: + if atom.GetTotalNumHs() >= 1 and atom.GetDegree() == 1: + return True + return False + + def has_ester_bond_to_drug(self, mol): + """Check for ester bond connecting linker to drug""" + ester_pattern = Chem.MolFromSmarts('[#6][CX3](=[OX1])[OX2][#6]') + return mol.HasSubstructMatch(ester_pattern) + + def has_sn38_core(self, mol): + """Check for SN-38 core structure""" + cpt_core = Chem.MolFromSmarts('c1ccc2ncc3C(=O)OCc3c2c1') + return mol.HasSubstructMatch(cpt_core) + + def check_attachment_site_compatibility(self, conjugate_smiles): + """Check attachment site compatibility for Cys-SS-DSEB-SN38 conjugation.""" + mol = Chem.MolFromSmiles(conjugate_smiles) + if mol is None: + return False, {'parseable': False, 'error': 'Could not parse SMILES'} + + details = { + 'parseable': True, + 'validation_method': 'cys_disulfide_ester', + 'linker': 'LIN00168 (4-(2-Disulfanylethoxy)-4-oxobutanoic acid)', + 'drug': 'SN-38 (7-Ethyl-10-hydroxycamptothecin)', + } + details['has_disulfide_bond'] = self.has_disulfide_bond(mol) + details['no_free_thiol'] = not self.has_free_thiol(mol) + details['has_ester_bond'] = self.has_ester_bond_to_drug(mol) + + passed = all([details['has_disulfide_bond'], details['no_free_thiol']]) + return passed, details + + def _basic_validation(self, mol): + """Basic validation checks for DSEB-SN38 conjugates.""" + checks = {} + checks['has_disulfide_bond'] = mol.HasSubstructMatch(Chem.MolFromSmarts('[#16]-[#16]')) + checks['no_free_thiol'] = not self.has_free_thiol(mol) + checks['has_ester_bond'] = self.has_ester_bond_to_drug(mol) + checks['has_sn38_core'] = self.has_sn38_core(mol) + checks['_passed'] = all([checks['has_disulfide_bond'], checks['no_free_thiol']]) + return checks + + def validate_conjugate(self, conjugate_smiles): + """Validate that the conjugate is chemically reasonable.""" + mol = Chem.MolFromSmiles(conjugate_smiles) + if mol is None: + return False, {'error': 'Could not parse SMILES'} + checks = self._basic_validation(mol) + checks['parseable'] = True + passed = checks.get('_passed', True) + if '_passed' in checks: + del checks['_passed'] + return passed, checks + + +# ============================================================================ +# Main - Test Examples +# ============================================================================ + +if __name__ == "__main__": + + print("=" * 70) + print("DSEB-SN38 PDC Builder - Test") + print("Reference: NRP1 + LIN00168 + SN-38") + print("=" * 70 + "\n") + + test_peptides = [ + "NCC(=O)N[C@@H](CS)C(=O)NCC(=O)O", # Gly-Cys-Gly + ] + + builder = DisulfanylethOxyButanoicAcidSN38_PDC_Builder() + + for i, peptide in enumerate(test_peptides): + print(f"\n--- Test peptide {i+1} ---") + print(f"Input: {peptide}") + + try: + conjugates = builder.conjugate_to_peptide(peptide) + print(f" Generated {len(conjugates)} conjugate(s)") + for j, conj in enumerate(conjugates): + valid, checks = builder.validate_conjugate(conj) + print(f" Conjugate {j+1}: {'PASSED' if valid else 'FAILED'}") + except Exception as e: + print(f" FAILED: {e}") + import traceback + traceback.print_exc() diff --git a/smiles/conjugation/DSECCpt.py b/smiles/conjugation/DSECCpt.py new file mode 100644 index 0000000000000000000000000000000000000000..98a6caeba520b8344e4311e290a5bb0e24ba1b84 --- /dev/null +++ b/smiles/conjugation/DSECCpt.py @@ -0,0 +1,522 @@ +""" +Disulfanylethyl Carbonate - Camptothecin PDC Builder (DSECCpt) + +Conjugation chemistry: Lysine ε-amine + Cys-SS-Linker-Camptothecin (amide bond formation) + +Reference PDC: +- PDC_00337 (SMAC-P1 CPT conjugate 7) + +Receptor: ERBB2 (Receptor tyrosine-protein kinase erbB-2) +Linker: LIN00039 - 2-(Disulfanyl)ethyl hydrogen carbonate (disulfide + carbonate) +Payload: DRG00009 - Camptothecin +Architecture: Monomer, Lys side chain conjugation + +Chemistry Note: +=============== +LIN00039 (2-(Disulfanyl)ethyl hydrogen carbonate) provides a disulfide-carbonate linkage: + HS─S─CH₂─CH₂─O─C(═O)─OH + +Linker-to-Drug Connection (Carbonate): + Cys─S─S─CH₂─CH₂─O─C(═O)─O─Camptothecin + │ + carbonate bond (C-20 OH of CPT) + +Full PDC Architecture: + Peptide──Lys──NH──C(═O)──Cys──S─S──CH₂─CH₂─O──C(═O)─O──Camptothecin + └── amide ──┘ └── disulfide ──┘ └── carbonate ──┘ + +Note: This linker chemistry is equivalent to the SSCpt carbonate variant (Z8/PDC_00295). + +Usage: +====== + builder = DisulfanylethylCarbonateCpt_PDC_Builder() + conjugates = builder.conjugate_to_peptide(peptide_smiles) +""" + +from rdkit import Chem +from rdkit.Chem import AllChem, Descriptors + +from .pdc_builder import PDC_Builder + + +# ============================================================================ +# Disulfanylethyl Carbonate - Camptothecin PDC Builder +# ============================================================================ + +class DisulfanylethylCarbonateCpt_PDC_Builder(PDC_Builder): + """ + PDC builder for 2-(Disulfanyl)ethyl Carbonate + Camptothecin conjugates + + Conjugation chemistry (amide bond formation): + - Peptide site: Lysine ε-amine (side chain -NH2) + - Linker-payload site: Carboxylic acid of Cys (Cys-SS-Linker-Drug) + - Reaction: Lys-NH2 + HOOC-Cys-SS-Linker-Drug → Lys-NH-CO-Cys-SS-Linker-Drug + + Linker: LIN00039 - 2-(Disulfanyl)ethyl hydrogen carbonate + SMILES: C(CSS)OC(=O)O + IUPAC: 2-(disulfanyl)ethyl hydrogen carbonate + Connectivity: HS-S-CH2-CH2-O-C(=O)-OH + MW: 154.21, MF: C3H6O3S2 + + Drug: DRG00009 - Camptothecin + Canonical SMILES: CCC1(O)C(=O)OCc2c1cc1n(c2=O)Cc2cc3ccccc3nc2-1 + Connection: Carbonate at C-20 hydroxyl + + Reference: PDC_00337 (SMAC-P1 CPT conjugate 7, ERBB2) + """ + + # ======================================================================== + # Cys-SS-Linker-Drug SMILES + # ======================================================================== + # + # Pre-formed Cys-DisulfanylethylCarbonate-Camptothecin assembly: + # HOOC─Cys(NH₂)─CH₂─S─S─CH₂─CH₂─O─C(═O)─O─CPT(C-20) + # + # Cys provides COOH for amide bond with Lys ε-amine + # Disulfide bond (S-S) is cleavable inside cells + # Carbonate bond (O-C(=O)-O) connects linker to CPT C-20 hydroxyl + # + # ======================================================================== + + CYS_SS_LINKER_CPT_SMILES = ( + "N[C@H](CSSCCOC(=O)OC1(CC)C(=O)OCc2c1cc1n(c2=O)Cc2cc3ccccc3nc2-1)C(=O)O" + ) + + def __init__(self, cys_linker_drug_smiles=None): + """ + Initialize Disulfanylethyl Carbonate-Camptothecin builder + + Args: + cys_linker_drug_smiles: Optional custom Cys-SS-Linker-Drug SMILES. + If None, uses default LIN00039+CPT assembly. + Must have free carboxylic acid for conjugation. + """ + if cys_linker_drug_smiles is None: + cys_linker_drug_smiles = self.CYS_SS_LINKER_CPT_SMILES + + super().__init__( + cys_linker_drug_smiles, + component_name="Cys-SS-DisulfanylethylCarbonate-Camptothecin (LIN00039+DRG00009)" + ) + + def _verify_reactive_group(self): + """Verify carboxylic acid group is present for amide bond formation""" + # Pattern: Carboxylic acid (COOH) + carboxylic_acid_pattern = Chem.MolFromSmarts('[CX3](=O)[OX2H1]') + + if not self.linker_payload_mol.HasSubstructMatch(carboxylic_acid_pattern): + print(" Warning: No carboxylic acid group detected in Cys-SS-Linker-Drug") + else: + print(" Cys-SS-DSEC-Camptothecin loaded with carboxylic acid reactive group") + + # Verify disulfide bond is present + disulfide_pattern = Chem.MolFromSmarts('[#16]-[#16]') + if self.linker_payload_mol.HasSubstructMatch(disulfide_pattern): + print(" Disulfide bond (S-S) detected") + else: + print(" Warning: No disulfide bond detected - check structure") + + # Verify Cys α-amine is present + cys_amine_pattern = Chem.MolFromSmarts('[NH2][CH]([CH2]S)C(=O)') + if self.linker_payload_mol.HasSubstructMatch(cys_amine_pattern): + print(" Cys α-amine detected (will be free in conjugate)") + else: + print(" Warning: Cys α-amine pattern not detected") + + # Verify carbonate bond to drug (O-C(=O)-O pattern) + carbonate_pattern = Chem.MolFromSmarts('[OX2][CX3](=[OX1])[OX2]') + if self.linker_payload_mol.HasSubstructMatch(carbonate_pattern): + print(" Carbonate bond (linker-to-drug) detected") + else: + print(" Warning: No carbonate bond detected - check linker-drug connection") + + def find_conjugation_sites(self, peptide_smiles): + """ + Find all lysine ε-amine groups in peptide + + Returns list of dicts with site information including atom index + """ + return self.find_peptide_lysines(peptide_smiles) + + def find_peptide_lysines(self, peptide_smiles): + """ + Find all lysine residues with free ε-amine in peptide + + Lysine side chain: -CH2-CH2-CH2-CH2-NH2 (CCCCN) + + Returns: + List of dicts with 'atom_idx' (nitrogen index) and metadata + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + lysines = [] + + lys_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + matches = peptide_mol.GetSubstructMatches(lys_pattern) + + for match in matches: + nitrogen_idx = match[4] + lysines.append({ + 'atom_idx': nitrogen_idx, + 'side_chain_atoms': list(match), + 'type': 'Lys_epsilon_amine' + }) + + n_terminus_pattern = Chem.MolFromSmarts('[NH2][CH,CH2][C](=O)') + n_term_matches = peptide_mol.GetSubstructMatches(n_terminus_pattern) + + if n_term_matches: + print(f" (Also found {len(n_term_matches)} N-terminus α-amine(s) - excluded by default)") + + return lysines + + def _perform_conjugation(self, peptide_mol, site_info, exclude_n_terminus=True): + """ + Perform amide bond conjugation between NH2 groups and Cys carboxylic acid + + Args: + peptide_mol: RDKit Mol object + site_info: Not used (kept for compatibility) + exclude_n_terminus: If True, skip N-terminus conjugation + + Returns: List of all possible conjugate SMILES + """ + if exclude_n_terminus: + amide_rxn = AllChem.ReactionFromSmarts( + '[*:5][CH2][CH2][CH2][CH2:4][NH2:1].[C:2](=[O:3])[OH]>>[*:5][CH2][CH2][CH2][CH2:4][NH:1][C:2](=[O:3])' + ) + else: + amide_rxn = AllChem.ReactionFromSmarts( + '[*:4][NH2:1].[C:2](=[O:3])[OH]>>[*:4][NH:1][C:2](=[O:3])' + ) + + products = amide_rxn.RunReactants((peptide_mol, self.linker_payload_mol)) + + if not products: + raise ValueError("Amide bond formation reaction failed - no products generated") + + conjugates = [] + seen_smiles = set() + + for product_tuple in products: + try: + conjugate_mol = product_tuple[0] + Chem.SanitizeMol(conjugate_mol) + conjugate_smiles = Chem.MolToSmiles(conjugate_mol) + + if conjugate_smiles not in seen_smiles: + seen_smiles.add(conjugate_smiles) + conjugates.append(conjugate_smiles) + except Exception: + continue + + if not conjugates: + raise ValueError("All products failed sanitization") + + return conjugates + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate Cys-SS-DSEC-Camptothecin to peptide via amide bond at NH2 groups + + Args: + peptide_smiles: SMILES string of peptide (must contain Lys with NH2) + exclude_n_terminus: If True (default), only conjugate at Lys ε-amines + return_all: If True, return all possible conjugates + + Returns: + List of all possible conjugate SMILES + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError(f"Invalid peptide SMILES: {peptide_smiles}") + + sites = self.find_conjugation_sites(peptide_smiles) + print(f"Found {len(sites)} Lys ε-amine site(s)") + + conjugates = self._perform_conjugation(peptide_mol, None, exclude_n_terminus) + + site_type = "Lys ε-amine only" if exclude_n_terminus else "all NH2 (including N-terminus)" + print(f"Generated {len(conjugates)} unique conjugate(s) [{site_type}] [DSEC carbonate]") + + if return_all: + return conjugates + else: + return conjugates[0] if conjugates else None + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """ + Batch conjugate multiple peptides + + Args: + peptide_smiles_list: List of peptide SMILES strings + exclude_n_terminus: If True (default), only conjugate at Lys ε-amines + return_all: If True, return all possible conjugates per peptide + + Returns: + Dict with 'conjugates', 'successful', 'failed', 'total_conjugates' + """ + results = { + 'conjugates': [], + 'successful': [], + 'failed': [], + 'total_conjugates': 0 + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + except Exception as e: + print(f" Peptide {i} failed: {e}") + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + + print(f"\n=== Summary ===") + print(f"Peptides: {len(peptide_smiles_list)}, Success: {len(results['successful'])}, Failed: {len(results['failed'])}") + print(f"Total conjugates generated: {results['total_conjugates']}") + + return results + + def flatten_batch_results(self, results): + """Flatten batch conjugation results into a single list""" + all_conjugates = [] + for conjugate_list in results['conjugates']: + if conjugate_list is not None: + if isinstance(conjugate_list, list): + all_conjugates.extend(conjugate_list) + else: + all_conjugates.append(conjugate_list) + return all_conjugates + + # ======================================================================== + # Attachment Site Compatibility Checks + # ======================================================================== + + def has_lys_cys_amide_conjugation(self, mol): + """Check if molecule has Lys-Cys amide conjugation bond""" + lys_cys_amide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH]([CH2][SX2][SX2])[NX3]') + return mol.HasSubstructMatch(lys_cys_amide_pattern) + + def has_disulfide_bond(self, mol): + """Check if molecule has disulfide bond (S-S)""" + disulfide_pattern = Chem.MolFromSmarts('[SX2]-[SX2]') + return mol.HasSubstructMatch(disulfide_pattern) + + def has_carbonate_bond(self, mol): + """ + Check if molecule has carbonate bond (O-C(=O)-O) + + This specifically checks for the carbonate linkage between linker and drug. + """ + carbonate_pattern = Chem.MolFromSmarts('[OX2][CX3](=[OX1])[OX2]') + return mol.HasSubstructMatch(carbonate_pattern) + + def has_unreacted_lys_epsilon_amine(self, mol): + """Check for unreacted Lys ε-amine (free NH2 on 4-carbon side chain)""" + lys_amine_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + return mol.HasSubstructMatch(lys_amine_pattern) + + def has_unreacted_cys_carboxylic_acid(self, mol): + """Check for unreacted Cys carboxylic acid from Cys-SS-Linker-Drug""" + cys_cooh_pattern = Chem.MolFromSmarts('[NH2][CH]([CH2][SX2][SX2])[CX3](=[OX1])[OX2H1]') + return mol.HasSubstructMatch(cys_cooh_pattern) + + def find_amide_conjugation_sites(self, mol): + """Find all Lys-Cys amide conjugation sites in the molecule""" + lys_cys_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH]([CH2][SX2][SX2])[NX3]') + matches = mol.GetSubstructMatches(lys_cys_pattern) + + sites = [] + for match in matches: + nitrogen_idx = match[0] + carbonyl_c_idx = match[1] + cys_alpha_idx = match[3] + sites.append((nitrogen_idx, carbonyl_c_idx, cys_alpha_idx)) + + return sites + + def check_valency_at_conjugation_site(self, mol, site_indices): + """Check that valency is correct at Lys-Cys conjugation site""" + nitrogen_idx, carbonyl_c_idx, cys_alpha_idx = site_indices + + try: + n_atom = mol.GetAtomWithIdx(nitrogen_idx) + if n_atom.GetTotalDegree() != 3: + return False + + c_atom = mol.GetAtomWithIdx(carbonyl_c_idx) + if c_atom.GetTotalDegree() != 3: + return False + + cys_c_atom = mol.GetAtomWithIdx(cys_alpha_idx) + if cys_c_atom.GetTotalDegree() != 4: + return False + + return True + except Exception: + return False + + def check_attachment_site_compatibility(self, conjugate_smiles): + """ + Check attachment site compatibility for Lys-Cys-SS-DSEC-CPT conjugation + + Validates: + 1. Lys-Cys amide conjugation is present + 2. Disulfide bond (S-S) is intact + 3. Carbonate bond is present (linker-to-drug) + 4. Valency at conjugation sites is correct + 5. No unreacted Cys-COOH (unconjugated linker-drug) + + Returns: + Tuple of (passed: bool, details: dict) + """ + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'parseable': False, 'error': 'Could not parse SMILES'} + + details = { + 'parseable': True, + 'validation_method': 'lys_cys_amide_disulfide_carbonate', + 'linker': 'LIN00039 (2-(Disulfanyl)ethyl hydrogen carbonate)', + 'drug': 'DRG00009 (Camptothecin)', + 'pdc_ref': 'PDC_00337' + } + + details['has_lys_cys_amide'] = self.has_lys_cys_amide_conjugation(mol) + details['has_disulfide_bond'] = self.has_disulfide_bond(mol) + details['has_carbonate_bond'] = self.has_carbonate_bond(mol) + + conjugation_sites = self.find_amide_conjugation_sites(mol) + details['conjugation_sites'] = len(conjugation_sites) + + valency_ok = True + if conjugation_sites: + for site in conjugation_sites: + if not self.check_valency_at_conjugation_site(mol, site): + valency_ok = False + break + details['valency_correct'] = valency_ok + + details['no_unreacted_cys_cooh'] = not self.has_unreacted_cys_carboxylic_acid(mol) + details['has_unreacted_lys_amine'] = self.has_unreacted_lys_epsilon_amine(mol) + + passed = all([ + details['has_lys_cys_amide'], + details['has_disulfide_bond'], + details['has_carbonate_bond'], + details['valency_correct'], + details['no_unreacted_cys_cooh'] + ]) + + return passed, details + + def _basic_validation(self, mol): + """ + Basic validation checks for DSEC-Camptothecin conjugates + + Checks: + 1. Amide bond present (Lys-Cys conjugation) + 2. Disulfide bond present (S-S linker) + 3. Specific Lys-Cys-SS conjugation pattern + 4. Carbonate bond present (linker-to-drug) + 5. Camptothecin core present + """ + checks = {} + + amide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[#6]') + checks['has_amide_bond'] = mol.HasSubstructMatch(amide_pattern) + + disulfide_pattern = Chem.MolFromSmarts('[#16]-[#16]') + checks['has_disulfide_bond'] = mol.HasSubstructMatch(disulfide_pattern) + + lys_cys_ss_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH]([CH2][SX2][SX2])[NX3]') + checks['has_lys_cys_ss_conjugation'] = mol.HasSubstructMatch(lys_cys_ss_pattern) + + carbonate_pattern = Chem.MolFromSmarts('[OX2][CX3](=[OX1])[OX2]') + checks['has_carbonate_bond'] = mol.HasSubstructMatch(carbonate_pattern) + + camptothecin_core = Chem.MolFromSmarts('c1ccc2ncc3C(=O)OCc3c2c1') + checks['has_camptothecin_core'] = mol.HasSubstructMatch(camptothecin_core) + + checks['_passed'] = all([ + checks['has_amide_bond'], + checks['has_disulfide_bond'], + checks['has_lys_cys_ss_conjugation'], + checks['has_carbonate_bond'] + ]) + + return checks + + def validate_conjugate(self, conjugate_smiles): + """ + Validate that the conjugate is chemically reasonable + + Returns: + (passed: bool, checks: dict) + """ + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'error': 'Could not parse SMILES'} + + checks = self._basic_validation(mol) + checks['parseable'] = True + + passed = checks.get('_passed', True) + if '_passed' in checks: + del checks['_passed'] + + return passed, checks + + +# ============================================================================ +# Main - Test Examples +# ============================================================================ + +if __name__ == "__main__": + + print("=" * 70) + print("Disulfanylethyl Carbonate-Camptothecin PDC Builder - Test") + print("Reference: PDC_00337 (SMAC-P1 CPT conjugate 7, LIN00039)") + print("=" * 70 + "\n") + + # Test peptides + test_peptides = [ + # Gly-Lys-Gly (has N-terminus NH2 + Lys ε-amine) + "NCC(=O)N[C@@H](CCCCN)C(=O)NCC(=O)O", + # Lys-Arg + "N[C@@H](CCCCN)C(=O)N[C@@H](CCCNC(=N)N)C(=O)O", + ] + + builder = DisulfanylethylCarbonateCpt_PDC_Builder() + + for i, peptide in enumerate(test_peptides): + print(f"\n--- Test peptide {i+1} ---") + print(f"Input: {peptide}") + + try: + conjugates = builder.conjugate_to_peptide(peptide, exclude_n_terminus=True) + print(f" Generated {len(conjugates)} conjugate(s)") + + for j, conj in enumerate(conjugates): + valid, checks = builder.validate_conjugate(conj) + print(f" Conjugate {j+1}: {'PASSED' if valid else 'FAILED'} ({len(conj)} chars)") + + except Exception as e: + print(f" FAILED: {e}") + import traceback + traceback.print_exc() diff --git a/smiles/conjugation/DSEPSN38.py b/smiles/conjugation/DSEPSN38.py new file mode 100644 index 0000000000000000000000000000000000000000..d65842f4ff2cb93565b4c390a26b22b5b8b996f1 --- /dev/null +++ b/smiles/conjugation/DSEPSN38.py @@ -0,0 +1,373 @@ +""" +5-(2-Disulfanylethoxy)-5-oxopentanoic Acid - SN-38 PDC Builder (DSEPSN38) + +Conjugation chemistry: Cysteine thiol + Linker thiol → Disulfide bond (S-S) + +Reference PDCs: +- PDC_02101 (SN38(10)-5-CRGDK) +- PDC_02106 (SN38(10)-5-(Ac)CRGDK) + +Receptor: NRP1 (Neuropilin-1) +Linker: LIN00169 - 5-(2-Disulfanylethoxy)-5-oxopentanoic acid +Payload: 7-Ethyl-10-hydroxycamptothecin (SN-38) +Architecture: Monomer, Cys thiol conjugation (disulfide bond) + +Chemistry Note: +=============== +LIN00169 provides a glutaric acid-based disulfide-ester linkage: + HOOC─CH₂─CH₂─CH₂─C(═O)─O─CH₂─CH₂─S─SH + + pentanoic acid ethoxy-disulfanyl + +Linker-to-Drug Connection: + SN-38(10-O)─C(═O)─CH₂─CH₂─CH₂─C(═O)─O─CH₂─CH₂─SH + +Full PDC Architecture: + Peptide──Cys──S─S──CH₂─CH₂─O─C(═O)─CH₂─CH₂─CH₂─C(═O)─O──SN-38 + └── disulfide ─┘ └── linker ──────┘ └ ester ┘ + +Usage: +====== + builder = DisulfanylethOxyPentanoicAcidSN38_PDC_Builder() + conjugates = builder.conjugate_to_peptide(peptide_smiles) +""" + +from rdkit import Chem +from rdkit.Chem import AllChem, Descriptors + +from .pdc_builder import PDC_Builder + + +# ============================================================================ +# 5-(2-Disulfanylethoxy)-5-oxopentanoic Acid - SN-38 PDC Builder +# ============================================================================ + +class DisulfanylethOxyPentanoicAcidSN38_PDC_Builder(PDC_Builder): + """ + PDC builder for 5-(2-Disulfanylethoxy)-5-oxopentanoic Acid + SN-38 conjugates + + Conjugation chemistry (disulfide bond formation): + - Peptide site: Cysteine thiol (side chain -SH) + - Linker-payload site: Terminal thiol (-SH) on linker + - Reaction: Peptide-Cys-SH + HS-Linker-Drug → Peptide-Cys-S-S-Linker-Drug + + Linker: LIN00169 - 5-(2-Disulfanylethoxy)-5-oxopentanoic acid + IUPAC: 5-(2-(disulfanyl)ethoxy)-5-oxopentanoic acid + Connectivity: HOOC-CH2-CH2-CH2-C(=O)-O-CH2-CH2-S-SH + Glutaric acid monoester with 2-(disulfanyl)ethanol + + Drug: 7-Ethyl-10-hydroxycamptothecin (SN-38) + Connection: Ester at C-10 hydroxyl (default) or C-20 hydroxyl + + Reference: PDC_02101, PDC_02106 (NRP1) + """ + + # ======================================================================== + # Linker-Drug SMILES Variants + # ======================================================================== + + # SN-38 with DSEP ester at C-10 (phenolic OH), free thiol + # Drug(10-O)-C(=O)-CH2-CH2-CH2-C(=O)-O-CH2-CH2-SH + POS10_LINKER_SN38_SMILES = ( + "CCC1(O)C(=O)OCc2c1cc1n(c2=O)Cc2c(CC)c3cc(OC(=O)CCCC(=O)OCCS)ccc3nc2-1" + ) + + # SN-38 with DSEP ester at C-20 (tertiary OH), free thiol + POS20_LINKER_SN38_SMILES = ( + "CCC1(OC(=O)CCCC(=O)OCCS)C(=O)OCc2c1cc1n(c2=O)Cc2c(CC)c3cc(O)ccc3nc2-1" + ) + + DEFAULT_LINKER_SN38_SMILES = POS10_LINKER_SN38_SMILES + + DRUG_POSITIONS = { + '10': POS10_LINKER_SN38_SMILES, + '20': POS20_LINKER_SN38_SMILES, + } + + def __init__(self, linker_drug_smiles=None, drug_position='all'): + """ + Initialize DSEP-SN38 builder + + Args: + linker_drug_smiles: Optional custom Linker-Drug SMILES. + drug_position: 'all' (default), '10', or '20'. + """ + self.drug_position = drug_position + + if linker_drug_smiles is not None: + self._variants = [linker_drug_smiles] + self._variant_names = ['custom'] + super().__init__( + linker_drug_smiles, + component_name="Custom-DSEP-SN38 (NRP1)" + ) + elif drug_position == 'all': + self._variants = [self.POS10_LINKER_SN38_SMILES, self.POS20_LINKER_SN38_SMILES] + self._variant_names = ['pos10', 'pos20'] + super().__init__( + self.POS10_LINKER_SN38_SMILES, + component_name="DSEP-SN38 (LIN00169, all positions)" + ) + else: + if drug_position not in self.DRUG_POSITIONS: + raise ValueError(f"Unknown drug_position '{drug_position}'. " + f"Choose from: 'all', '10', '20'") + smiles = self.DRUG_POSITIONS[drug_position] + self._variants = [smiles] + self._variant_names = [f'pos{drug_position}'] + super().__init__( + smiles, + component_name=f"DSEP-SN38 (LIN00169, C-{drug_position})" + ) + + def _verify_reactive_group(self): + """Verify thiol group is present for disulfide bond formation""" + for atom in self.linker_payload_mol.GetAtoms(): + if atom.GetAtomicNum() == 16: + if atom.GetTotalNumHs() >= 1 and atom.GetDegree() == 1: + print(" DSEP-SN38 loaded with free thiol reactive group") + break + else: + print(" Warning: No free thiol group detected in DSEP-SN38") + + ester_pattern = Chem.MolFromSmarts('[#6][CX3](=[OX1])[OX2][#6]') + if self.linker_payload_mol.HasSubstructMatch(ester_pattern): + print(" Ester bond (linker-to-drug) detected") + else: + print(" Warning: No ester bond detected") + + # ------------------------------------------------------------------ + # Conjugation site finding (Cysteine thiol) + # ------------------------------------------------------------------ + + def find_conjugation_sites(self, peptide_smiles): + """Find all free cysteine thiol groups in peptide""" + return self.find_peptide_cysteines(peptide_smiles) + + def find_peptide_cysteines(self, peptide_smiles): + """Find all free cysteine residues (thiol groups) in peptide""" + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + cysteines = [] + for atom in peptide_mol.GetAtoms(): + if atom.GetAtomicNum() == 16: + total_hs = atom.GetTotalNumHs() + degree = atom.GetDegree() + if total_hs >= 1 and degree == 1: + cysteines.append({ + 'atom_idx': atom.GetIdx(), + 'num_hydrogens': total_hs, + 'neighbors': [peptide_mol.GetAtomWithIdx(n.GetIdx()).GetSymbol() + for n in atom.GetNeighbors()], + 'type': 'Cys_thiol' + }) + return cysteines + + # ------------------------------------------------------------------ + # Conjugation reaction (disulfide bond formation) + # ------------------------------------------------------------------ + + def _perform_conjugation(self, peptide_mol, site_info, exclude_n_terminus=True): + """Form disulfide bond between peptide Cys thiol and linker-drug thiol.""" + all_conjugates = [] + seen = set() + + for variant_smiles in self._variants: + variant_mol = Chem.MolFromSmiles(variant_smiles) + if variant_mol is None: + continue + + disulfide_rxn = AllChem.ReactionFromSmarts( + '[C:1][SH:2].[SH:3][C:4]>>[C:1][S:2][S:3][C:4]' + ) + + products = disulfide_rxn.RunReactants((peptide_mol, variant_mol)) + if not products: + continue + + for product_tuple in products: + try: + conjugate_mol = product_tuple[0] + Chem.SanitizeMol(conjugate_mol) + conjugate_smiles = Chem.MolToSmiles(conjugate_mol) + if conjugate_smiles not in seen: + seen.add(conjugate_smiles) + all_conjugates.append(conjugate_smiles) + except Exception: + continue + + if not all_conjugates: + raise ValueError("Disulfide bond formation failed - no products generated") + + return all_conjugates + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate DSEP-SN38 to peptide via disulfide bond at Cys thiol. + + Args: + peptide_smiles: SMILES string of peptide (must contain Cys) + exclude_n_terminus: Not used for Cys conjugation (kept for interface) + return_all: If True, return all possible conjugates + + Returns: + List of all possible conjugate SMILES + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError(f"Invalid peptide SMILES: {peptide_smiles}") + + sites = self.find_conjugation_sites(peptide_smiles) + print(f"Found {len(sites)} Cys thiol site(s)") + + conjugates = self._perform_conjugation(peptide_mol, None, exclude_n_terminus) + + pos_label = f"C-{self.drug_position}" if self.drug_position != 'all' else "C-10 + C-20" + print(f"Generated {len(conjugates)} unique conjugate(s) [Cys disulfide] [DSEP {pos_label}]") + + if return_all: + return conjugates + else: + return conjugates[0] if conjugates else None + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """Batch conjugate multiple peptides.""" + results = { + 'conjugates': [], 'successful': [], 'failed': [], 'total_conjugates': 0 + } + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, exclude_n_terminus=exclude_n_terminus, return_all=return_all + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + except Exception as e: + print(f" Peptide {i} failed: {e}") + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + + print(f"\n=== Summary ===") + print(f"Peptides: {len(peptide_smiles_list)}, Success: {len(results['successful'])}, " + f"Failed: {len(results['failed'])}") + print(f"Total conjugates generated: {results['total_conjugates']}") + return results + + def flatten_batch_results(self, results): + """Flatten batch conjugation results into a single list""" + all_conjugates = [] + for conjugate_list in results['conjugates']: + if conjugate_list is not None: + if isinstance(conjugate_list, list): + all_conjugates.extend(conjugate_list) + else: + all_conjugates.append(conjugate_list) + return all_conjugates + + # ======================================================================== + # Validation + # ======================================================================== + + def has_disulfide_bond(self, mol): + """Check if molecule has disulfide bond (S-S)""" + disulfide_pattern = Chem.MolFromSmarts('[SX2]-[SX2]') + return mol.HasSubstructMatch(disulfide_pattern) + + def has_free_thiol(self, mol): + """Check if molecule has unconjugated (free) thiol groups""" + for atom in mol.GetAtoms(): + if atom.GetAtomicNum() == 16: + if atom.GetTotalNumHs() >= 1 and atom.GetDegree() == 1: + return True + return False + + def has_ester_bond_to_drug(self, mol): + """Check for ester bond connecting linker to drug""" + ester_pattern = Chem.MolFromSmarts('[#6][CX3](=[OX1])[OX2][#6]') + return mol.HasSubstructMatch(ester_pattern) + + def has_sn38_core(self, mol): + """Check for SN-38 core structure""" + cpt_core = Chem.MolFromSmarts('c1ccc2ncc3C(=O)OCc3c2c1') + return mol.HasSubstructMatch(cpt_core) + + def check_attachment_site_compatibility(self, conjugate_smiles): + """Check attachment site compatibility for Cys-SS-DSEP-SN38 conjugation.""" + mol = Chem.MolFromSmiles(conjugate_smiles) + if mol is None: + return False, {'parseable': False, 'error': 'Could not parse SMILES'} + + details = { + 'parseable': True, + 'validation_method': 'cys_disulfide_ester', + 'linker': 'LIN00169 (5-(2-Disulfanylethoxy)-5-oxopentanoic acid)', + 'drug': 'SN-38 (7-Ethyl-10-hydroxycamptothecin)', + } + details['has_disulfide_bond'] = self.has_disulfide_bond(mol) + details['no_free_thiol'] = not self.has_free_thiol(mol) + details['has_ester_bond'] = self.has_ester_bond_to_drug(mol) + + passed = all([details['has_disulfide_bond'], details['no_free_thiol']]) + return passed, details + + def _basic_validation(self, mol): + """Basic validation checks for DSEP-SN38 conjugates.""" + checks = {} + checks['has_disulfide_bond'] = mol.HasSubstructMatch(Chem.MolFromSmarts('[#16]-[#16]')) + checks['no_free_thiol'] = not self.has_free_thiol(mol) + checks['has_ester_bond'] = self.has_ester_bond_to_drug(mol) + checks['has_sn38_core'] = self.has_sn38_core(mol) + checks['_passed'] = all([checks['has_disulfide_bond'], checks['no_free_thiol']]) + return checks + + def validate_conjugate(self, conjugate_smiles): + """Validate that the conjugate is chemically reasonable.""" + mol = Chem.MolFromSmiles(conjugate_smiles) + if mol is None: + return False, {'error': 'Could not parse SMILES'} + checks = self._basic_validation(mol) + checks['parseable'] = True + passed = checks.get('_passed', True) + if '_passed' in checks: + del checks['_passed'] + return passed, checks + + +# ============================================================================ +# Main - Test Examples +# ============================================================================ + +if __name__ == "__main__": + + print("=" * 70) + print("DSEP-SN38 PDC Builder - Test") + print("Reference: NRP1 + LIN00169 + SN-38") + print("=" * 70 + "\n") + + test_peptides = [ + "NCC(=O)N[C@@H](CS)C(=O)NCC(=O)O", # Gly-Cys-Gly + ] + + builder = DisulfanylethOxyPentanoicAcidSN38_PDC_Builder() + + for i, peptide in enumerate(test_peptides): + print(f"\n--- Test peptide {i+1} ---") + print(f"Input: {peptide}") + + try: + conjugates = builder.conjugate_to_peptide(peptide) + print(f" Generated {len(conjugates)} conjugate(s)") + for j, conj in enumerate(conjugates): + valid, checks = builder.validate_conjugate(conj) + print(f" Conjugate {j+1}: {'PASSED' if valid else 'FAILED'}") + except Exception as e: + print(f" FAILED: {e}") + import traceback + traceback.print_exc() diff --git a/smiles/conjugation/DSPACpt.py b/smiles/conjugation/DSPACpt.py new file mode 100644 index 0000000000000000000000000000000000000000..99f5a02e5150719ea58720859728f81d2af0084c --- /dev/null +++ b/smiles/conjugation/DSPACpt.py @@ -0,0 +1,517 @@ +""" +Disulfanylpropanoic Acid - Camptothecin PDC Builder (DSPACpt) + +Conjugation chemistry: Lysine ε-amine + Cys-SS-Linker-Camptothecin (amide bond formation) + +Reference PDC: +- PDC_00335 (SMAC-P1 CPT conjugate 5) + +Receptor: ERBB2 (Receptor tyrosine-protein kinase erbB-2) +Linker: LIN00012 - 3-Disulfanylpropanoic Acid (disulfide + ester) +Payload: DRG00009 - Camptothecin +Architecture: Monomer, Lys side chain conjugation + +Chemistry Note: +=============== +LIN00012 (3-Disulfanylpropanoic Acid) provides a disulfide-ester linkage: + HS─S─CH₂─CH₂─C(═O)─OH + +Linker-to-Drug Connection (Ester): + Cys─S─S─CH₂─CH₂─C(═O)─O─Camptothecin + │ + ester bond (C-20 OH of CPT) + +Full PDC Architecture: + Peptide──Lys──NH──C(═O)──Cys──S─S──CH₂─CH₂─C(═O)─O──Camptothecin + └── amide ──┘ └── disulfide ──┘ └── ester ──┘ + +Note: This linker chemistry is equivalent to the SSCpt ester variant (Z7/PDC_00294). + +Usage: +====== + builder = DisulfanylpropanoicAcidCpt_PDC_Builder() + conjugates = builder.conjugate_to_peptide(peptide_smiles) +""" + +from rdkit import Chem +from rdkit.Chem import AllChem, Descriptors + +from .pdc_builder import PDC_Builder + + +# ============================================================================ +# Disulfanylpropanoic Acid - Camptothecin PDC Builder +# ============================================================================ + +class DisulfanylpropanoicAcidCpt_PDC_Builder(PDC_Builder): + """ + PDC builder for 3-Disulfanylpropanoic Acid + Camptothecin conjugates + + Conjugation chemistry (amide bond formation): + - Peptide site: Lysine ε-amine (side chain -NH2) + - Linker-payload site: Carboxylic acid of Cys (Cys-SS-Linker-Drug) + - Reaction: Lys-NH2 + HOOC-Cys-SS-Linker-Drug → Lys-NH-CO-Cys-SS-Linker-Drug + + Linker: LIN00012 - 3-Disulfanylpropanoic Acid + SMILES: C(CSS)C(=O)O + IUPAC: 3-(disulfanyl)propanoic acid + Connectivity: HS-S-CH2-CH2-C(=O)-OH + MW: 138.21, MF: C3H6O2S2 + + Drug: DRG00009 - Camptothecin + Canonical SMILES: CCC1(O)C(=O)OCc2c1cc1n(c2=O)Cc2cc3ccccc3nc2-1 + Connection: Ester at C-20 hydroxyl + + Reference: PDC_00335 (SMAC-P1 CPT conjugate 5, ERBB2) + """ + + # ======================================================================== + # Cys-SS-Linker-Drug SMILES + # ======================================================================== + # + # Pre-formed Cys-Disulfanylpropanoic acid-Camptothecin assembly: + # HOOC─Cys(NH₂)─CH₂─S─S─CH₂─CH₂─C(═O)─O─CPT(C-20) + # + # Cys provides COOH for amide bond with Lys ε-amine + # Disulfide bond (S-S) is cleavable inside cells + # Ester bond connects linker to CPT C-20 hydroxyl + # + # ======================================================================== + + CYS_SS_LINKER_CPT_SMILES = ( + "N[C@H](CSSCCC(=O)OC1(CC)C(=O)OCc2c1cc1n(c2=O)Cc2cc3ccccc3nc2-1)C(=O)O" + ) + + def __init__(self, cys_linker_drug_smiles=None): + """ + Initialize Disulfanylpropanoic Acid-Camptothecin builder + + Args: + cys_linker_drug_smiles: Optional custom Cys-SS-Linker-Drug SMILES. + If None, uses default LIN00012+CPT assembly. + Must have free carboxylic acid for conjugation. + """ + if cys_linker_drug_smiles is None: + cys_linker_drug_smiles = self.CYS_SS_LINKER_CPT_SMILES + + super().__init__( + cys_linker_drug_smiles, + component_name="Cys-SS-DisulfanylpropanoicAcid-Camptothecin (LIN00012+DRG00009)" + ) + + def _verify_reactive_group(self): + """Verify carboxylic acid group is present for amide bond formation""" + # Pattern: Carboxylic acid (COOH) + carboxylic_acid_pattern = Chem.MolFromSmarts('[CX3](=O)[OX2H1]') + + if not self.linker_payload_mol.HasSubstructMatch(carboxylic_acid_pattern): + print(" Warning: No carboxylic acid group detected in Cys-SS-Linker-Drug") + else: + print(" Cys-SS-DSPA-Camptothecin loaded with carboxylic acid reactive group") + + # Verify disulfide bond is present + disulfide_pattern = Chem.MolFromSmarts('[#16]-[#16]') + if self.linker_payload_mol.HasSubstructMatch(disulfide_pattern): + print(" Disulfide bond (S-S) detected") + else: + print(" Warning: No disulfide bond detected - check structure") + + # Verify Cys α-amine is present + cys_amine_pattern = Chem.MolFromSmarts('[NH2][CH]([CH2]S)C(=O)') + if self.linker_payload_mol.HasSubstructMatch(cys_amine_pattern): + print(" Cys α-amine detected (will be free in conjugate)") + else: + print(" Warning: Cys α-amine pattern not detected") + + # Verify ester bond to drug + ester_pattern = Chem.MolFromSmarts('[#6][CX3](=[OX1])[OX2][#6]') + if self.linker_payload_mol.HasSubstructMatch(ester_pattern): + print(" Ester bond (linker-to-drug) detected") + else: + print(" Warning: No ester bond detected - check linker-drug connection") + + def find_conjugation_sites(self, peptide_smiles): + """ + Find all lysine ε-amine groups in peptide + + Returns list of dicts with site information including atom index + """ + return self.find_peptide_lysines(peptide_smiles) + + def find_peptide_lysines(self, peptide_smiles): + """ + Find all lysine residues with free ε-amine in peptide + + Lysine side chain: -CH2-CH2-CH2-CH2-NH2 (CCCCN) + + Returns: + List of dicts with 'atom_idx' (nitrogen index) and metadata + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + lysines = [] + + # SMARTS pattern for Lysine ε-amine: 4 CH2 groups ending in NH2 + lys_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + matches = peptide_mol.GetSubstructMatches(lys_pattern) + + for match in matches: + nitrogen_idx = match[4] + lysines.append({ + 'atom_idx': nitrogen_idx, + 'side_chain_atoms': list(match), + 'type': 'Lys_epsilon_amine' + }) + + # Count N-terminus for informational purposes + n_terminus_pattern = Chem.MolFromSmarts('[NH2][CH,CH2][C](=O)') + n_term_matches = peptide_mol.GetSubstructMatches(n_terminus_pattern) + + if n_term_matches: + print(f" (Also found {len(n_term_matches)} N-terminus α-amine(s) - excluded by default)") + + return lysines + + def _perform_conjugation(self, peptide_mol, site_info, exclude_n_terminus=True): + """ + Perform amide bond conjugation between NH2 groups and Cys carboxylic acid + + Args: + peptide_mol: RDKit Mol object + site_info: Not used (kept for compatibility) + exclude_n_terminus: If True, skip N-terminus conjugation + + Returns: List of all possible conjugate SMILES + """ + if exclude_n_terminus: + amide_rxn = AllChem.ReactionFromSmarts( + '[*:5][CH2][CH2][CH2][CH2:4][NH2:1].[C:2](=[O:3])[OH]>>[*:5][CH2][CH2][CH2][CH2:4][NH:1][C:2](=[O:3])' + ) + else: + amide_rxn = AllChem.ReactionFromSmarts( + '[*:4][NH2:1].[C:2](=[O:3])[OH]>>[*:4][NH:1][C:2](=[O:3])' + ) + + products = amide_rxn.RunReactants((peptide_mol, self.linker_payload_mol)) + + if not products: + raise ValueError("Amide bond formation reaction failed - no products generated") + + conjugates = [] + seen_smiles = set() + + for product_tuple in products: + try: + conjugate_mol = product_tuple[0] + Chem.SanitizeMol(conjugate_mol) + conjugate_smiles = Chem.MolToSmiles(conjugate_mol) + + if conjugate_smiles not in seen_smiles: + seen_smiles.add(conjugate_smiles) + conjugates.append(conjugate_smiles) + except Exception: + continue + + if not conjugates: + raise ValueError("All products failed sanitization") + + return conjugates + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate Cys-SS-DSPA-Camptothecin to peptide via amide bond at NH2 groups + + Args: + peptide_smiles: SMILES string of peptide (must contain Lys with NH2) + exclude_n_terminus: If True (default), only conjugate at Lys ε-amines + return_all: If True, return all possible conjugates + + Returns: + List of all possible conjugate SMILES + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError(f"Invalid peptide SMILES: {peptide_smiles}") + + sites = self.find_conjugation_sites(peptide_smiles) + print(f"Found {len(sites)} Lys ε-amine site(s)") + + conjugates = self._perform_conjugation(peptide_mol, None, exclude_n_terminus) + + site_type = "Lys ε-amine only" if exclude_n_terminus else "all NH2 (including N-terminus)" + print(f"Generated {len(conjugates)} unique conjugate(s) [{site_type}] [DSPA ester]") + + if return_all: + return conjugates + else: + return conjugates[0] if conjugates else None + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """ + Batch conjugate multiple peptides + + Args: + peptide_smiles_list: List of peptide SMILES strings + exclude_n_terminus: If True (default), only conjugate at Lys ε-amines + return_all: If True, return all possible conjugates per peptide + + Returns: + Dict with 'conjugates', 'successful', 'failed', 'total_conjugates' + """ + results = { + 'conjugates': [], + 'successful': [], + 'failed': [], + 'total_conjugates': 0 + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + except Exception as e: + print(f" Peptide {i} failed: {e}") + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + + print(f"\n=== Summary ===") + print(f"Peptides: {len(peptide_smiles_list)}, Success: {len(results['successful'])}, Failed: {len(results['failed'])}") + print(f"Total conjugates generated: {results['total_conjugates']}") + + return results + + def flatten_batch_results(self, results): + """Flatten batch conjugation results into a single list""" + all_conjugates = [] + for conjugate_list in results['conjugates']: + if conjugate_list is not None: + if isinstance(conjugate_list, list): + all_conjugates.extend(conjugate_list) + else: + all_conjugates.append(conjugate_list) + return all_conjugates + + # ======================================================================== + # Attachment Site Compatibility Checks + # ======================================================================== + + def has_lys_cys_amide_conjugation(self, mol): + """Check if molecule has Lys-Cys amide conjugation bond""" + lys_cys_amide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH]([CH2][SX2][SX2])[NX3]') + return mol.HasSubstructMatch(lys_cys_amide_pattern) + + def has_disulfide_bond(self, mol): + """Check if molecule has disulfide bond (S-S)""" + disulfide_pattern = Chem.MolFromSmarts('[SX2]-[SX2]') + return mol.HasSubstructMatch(disulfide_pattern) + + def has_unreacted_lys_epsilon_amine(self, mol): + """Check for unreacted Lys ε-amine (free NH2 on 4-carbon side chain)""" + lys_amine_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + return mol.HasSubstructMatch(lys_amine_pattern) + + def has_unreacted_cys_carboxylic_acid(self, mol): + """Check for unreacted Cys carboxylic acid from Cys-SS-Linker-Drug""" + cys_cooh_pattern = Chem.MolFromSmarts('[NH2][CH]([CH2][SX2][SX2])[CX3](=[OX1])[OX2H1]') + return mol.HasSubstructMatch(cys_cooh_pattern) + + def has_ester_bond_to_drug(self, mol): + """Check for ester bond connecting linker to drug""" + ester_pattern = Chem.MolFromSmarts('[SX2][SX2][CH2][CH2][CX3](=[OX1])[OX2][C]') + return mol.HasSubstructMatch(ester_pattern) + + def find_amide_conjugation_sites(self, mol): + """Find all Lys-Cys amide conjugation sites in the molecule""" + lys_cys_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH]([CH2][SX2][SX2])[NX3]') + matches = mol.GetSubstructMatches(lys_cys_pattern) + + sites = [] + for match in matches: + nitrogen_idx = match[0] + carbonyl_c_idx = match[1] + cys_alpha_idx = match[3] + sites.append((nitrogen_idx, carbonyl_c_idx, cys_alpha_idx)) + + return sites + + def check_valency_at_conjugation_site(self, mol, site_indices): + """Check that valency is correct at Lys-Cys conjugation site""" + nitrogen_idx, carbonyl_c_idx, cys_alpha_idx = site_indices + + try: + n_atom = mol.GetAtomWithIdx(nitrogen_idx) + if n_atom.GetTotalDegree() != 3: + return False + + c_atom = mol.GetAtomWithIdx(carbonyl_c_idx) + if c_atom.GetTotalDegree() != 3: + return False + + cys_c_atom = mol.GetAtomWithIdx(cys_alpha_idx) + if cys_c_atom.GetTotalDegree() != 4: + return False + + return True + except Exception: + return False + + def check_attachment_site_compatibility(self, conjugate_smiles): + """ + Check attachment site compatibility for Lys-Cys-SS-DSPA-CPT conjugation + + Validates: + 1. Lys-Cys amide conjugation is present + 2. Disulfide bond (S-S) is intact + 3. Valency at conjugation sites is correct + 4. No unreacted Cys-COOH (unconjugated linker-drug) + 5. Ester bond to drug is present + + Returns: + Tuple of (passed: bool, details: dict) + """ + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'parseable': False, 'error': 'Could not parse SMILES'} + + details = { + 'parseable': True, + 'validation_method': 'lys_cys_amide_disulfide_ester', + 'linker': 'LIN00012 (3-Disulfanylpropanoic Acid)', + 'drug': 'DRG00009 (Camptothecin)', + 'pdc_ref': 'PDC_00335' + } + + details['has_lys_cys_amide'] = self.has_lys_cys_amide_conjugation(mol) + details['has_disulfide_bond'] = self.has_disulfide_bond(mol) + + conjugation_sites = self.find_amide_conjugation_sites(mol) + details['conjugation_sites'] = len(conjugation_sites) + + valency_ok = True + if conjugation_sites: + for site in conjugation_sites: + if not self.check_valency_at_conjugation_site(mol, site): + valency_ok = False + break + details['valency_correct'] = valency_ok + + details['no_unreacted_cys_cooh'] = not self.has_unreacted_cys_carboxylic_acid(mol) + details['has_unreacted_lys_amine'] = self.has_unreacted_lys_epsilon_amine(mol) + + passed = all([ + details['has_lys_cys_amide'], + details['has_disulfide_bond'], + details['valency_correct'], + details['no_unreacted_cys_cooh'] + ]) + + return passed, details + + def _basic_validation(self, mol): + """ + Basic validation checks for DSPA-Camptothecin conjugates + + Checks: + 1. Amide bond present (Lys-Cys conjugation) + 2. Disulfide bond present (S-S linker) + 3. Specific Lys-Cys-SS conjugation pattern + 4. Camptothecin core present + 5. Ester bond to drug present + """ + checks = {} + + amide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[#6]') + checks['has_amide_bond'] = mol.HasSubstructMatch(amide_pattern) + + disulfide_pattern = Chem.MolFromSmarts('[#16]-[#16]') + checks['has_disulfide_bond'] = mol.HasSubstructMatch(disulfide_pattern) + + lys_cys_ss_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH]([CH2][SX2][SX2])[NX3]') + checks['has_lys_cys_ss_conjugation'] = mol.HasSubstructMatch(lys_cys_ss_pattern) + + camptothecin_core = Chem.MolFromSmarts('c1ccc2ncc3C(=O)OCc3c2c1') + checks['has_camptothecin_core'] = mol.HasSubstructMatch(camptothecin_core) + + ester_pattern = Chem.MolFromSmarts('[#6][CX3](=[OX1])[OX2][#6]') + checks['has_ester_bond'] = mol.HasSubstructMatch(ester_pattern) + + checks['_passed'] = all([ + checks['has_amide_bond'], + checks['has_disulfide_bond'], + checks['has_lys_cys_ss_conjugation'] + ]) + + return checks + + def validate_conjugate(self, conjugate_smiles): + """ + Validate that the conjugate is chemically reasonable + + Returns: + (passed: bool, checks: dict) + """ + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'error': 'Could not parse SMILES'} + + checks = self._basic_validation(mol) + checks['parseable'] = True + + passed = checks.get('_passed', True) + if '_passed' in checks: + del checks['_passed'] + + return passed, checks + + +# ============================================================================ +# Main - Test Examples +# ============================================================================ + +if __name__ == "__main__": + + print("=" * 70) + print("Disulfanylpropanoic Acid-Camptothecin PDC Builder - Test") + print("Reference: PDC_00335 (SMAC-P1 CPT conjugate 5, LIN00012)") + print("=" * 70 + "\n") + + # Test peptides + test_peptides = [ + # Gly-Lys-Gly (has N-terminus NH2 + Lys ε-amine) + "NCC(=O)N[C@@H](CCCCN)C(=O)NCC(=O)O", + # Lys-Arg + "N[C@@H](CCCCN)C(=O)N[C@@H](CCCNC(=N)N)C(=O)O", + ] + + builder = DisulfanylpropanoicAcidCpt_PDC_Builder() + + for i, peptide in enumerate(test_peptides): + print(f"\n--- Test peptide {i+1} ---") + print(f"Input: {peptide}") + + try: + conjugates = builder.conjugate_to_peptide(peptide, exclude_n_terminus=True) + print(f" Generated {len(conjugates)} conjugate(s)") + + for j, conj in enumerate(conjugates): + valid, checks = builder.validate_conjugate(conj) + print(f" Conjugate {j+1}: {'PASSED' if valid else 'FAILED'} ({len(conj)} chars)") + + except Exception as e: + print(f" FAILED: {e}") + import traceback + traceback.print_exc() diff --git a/smiles/conjugation/DSPASN38.py b/smiles/conjugation/DSPASN38.py new file mode 100644 index 0000000000000000000000000000000000000000..3976b29cd7aa258b484de55ddb1830ff550f47b3 --- /dev/null +++ b/smiles/conjugation/DSPASN38.py @@ -0,0 +1,515 @@ +""" +Disulfanylpropanoic Acid - SN-38 PDC Builder (DSPASN38) + +Conjugation chemistry: Cysteine thiol + Linker thiol → Disulfide bond (S-S) + +Reference PDCs: +- PDC_02099 (SN38(10)-CRGDK) +- PDC_02104 (SN38(10)-(Ac)CRGDK) +- PDC_02102 (SN38(10)-(Ac)CGVQIRGDK) +- PDC_02107 (SN38(10)-CRRGDK) +- PDC_02110 (SN38(10)-(Ac)CDRGDK) +- PDC_02112 (SN38(20)-CRGDK) +- PDC_02113 ((MOM)SN38(20)-CRGDK) +- PDC_02114 ((MOM)SN38(20)-(Ac)CRGDK) +- PDC_02115 ((MOM)SN38(20)-(Ac)CGVQIRGDK) + +Receptor: NRP1 (Neuropilin-1) +Linker: LIN00149 - 3-Disulfanylpropanoic Acid (disulfide + ester) +Payload: DRG - 7-Ethyl-10-hydroxycamptothecin (SN-38) +Architecture: Monomer, Cys thiol conjugation (disulfide bond) + +Chemistry Note: +=============== +LIN00149 (3-Disulfanylpropanoic Acid) connects via: + HS─CH₂─CH₂─C(═O)─OH + +Linker-to-Drug Connection (Ester): + SN-38(10-O)─C(═O)─CH₂─CH₂─SH (position 10, default) + SN-38(20-O)─C(═O)─CH₂─CH₂─SH (position 20) + +Full PDC Architecture: + Peptide──Cys──S─S──CH₂─CH₂─C(═O)─O──SN-38 + └── disulfide ──┘ └── ester ──┘ + +Usage: +====== + builder = DisulfanylpropanoicAcidSN38_PDC_Builder() + conjugates = builder.conjugate_to_peptide(peptide_smiles) +""" + +from rdkit import Chem +from rdkit.Chem import AllChem, Descriptors + +from .pdc_builder import PDC_Builder + + +# ============================================================================ +# Disulfanylpropanoic Acid - SN-38 PDC Builder +# ============================================================================ + +class DisulfanylpropanoicAcidSN38_PDC_Builder(PDC_Builder): + """ + PDC builder for 3-Disulfanylpropanoic Acid + SN-38 conjugates + + Conjugation chemistry (disulfide bond formation): + - Peptide site: Cysteine thiol (side chain -SH) + - Linker-payload site: Terminal thiol (-SH) on DSPA linker + - Reaction: Peptide-Cys-SH + HS-Linker-Drug → Peptide-Cys-S-S-Linker-Drug + + Linker: LIN00149 - 3-Disulfanylpropanoic Acid + SMILES: OC(=O)CCSS + IUPAC: 3-(disulfanyl)propanoic acid + Connectivity: HS-S-CH2-CH2-C(=O)-OH + + Drug: 7-Ethyl-10-hydroxycamptothecin (SN-38) + Canonical SMILES: CCC1(O)C(=O)OCc2c1cc1n(c2=O)Cc2c(CC)c3cc(O)ccc3nc2-1 + Connection: Ester at C-10 hydroxyl (default) or C-20 hydroxyl + + Supported drug conjugation positions: + - '10' (default): Ester at 10-OH (phenolic, most common in PDCdb) + - '20': Ester at 20-OH (tertiary aliphatic) + - 'all': Both positions, conjugates pooled + + Reference: PDC_02099, PDC_02112 (NRP1) + """ + + # ======================================================================== + # Linker-Drug SMILES Variants + # ======================================================================== + # + # DSPA linker ester to SN-38 with free terminal thiol for Cys conjugation + # + # Position 10 (phenolic OH): + # HS─CH₂─CH₂─C(═O)─O─SN38(10) + # + # Position 20 (tertiary OH): + # HS─CH₂─CH₂─C(═O)─O─SN38(20) + # + # ======================================================================== + + # SN-38 with DSPA ester at C-10 (phenolic OH), free thiol + POS10_LINKER_SN38_SMILES = ( + "CCC1(O)C(=O)OCc2c1cc1n(c2=O)Cc2c(CC)c3cc(OC(=O)CCS)ccc3nc2-1" + ) + + # SN-38 with DSPA ester at C-20 (tertiary OH), free thiol + POS20_LINKER_SN38_SMILES = ( + "CCC1(OC(=O)CCS)C(=O)OCc2c1cc1n(c2=O)Cc2c(CC)c3cc(O)ccc3nc2-1" + ) + + # Default: position 10 (most common) + DEFAULT_LINKER_SN38_SMILES = POS10_LINKER_SN38_SMILES + + # Available drug positions + DRUG_POSITIONS = { + '10': POS10_LINKER_SN38_SMILES, + '20': POS20_LINKER_SN38_SMILES, + } + + def __init__(self, linker_drug_smiles=None, drug_position='all'): + """ + Initialize DSPA-SN38 builder + + Args: + linker_drug_smiles: Optional custom Linker-Drug SMILES. + If provided, overrides drug_position. + Must have free thiol for Cys disulfide conjugation. + drug_position: 'all' (default), '10', or '20'. + '10': ester at C-10 phenolic OH (most common) + '20': ester at C-20 tertiary OH + 'all': both positions, conjugates pooled + Ignored if linker_drug_smiles is provided. + """ + self.drug_position = drug_position + + if linker_drug_smiles is not None: + self._variants = [linker_drug_smiles] + self._variant_names = ['custom'] + super().__init__( + linker_drug_smiles, + component_name="Custom-DSPA-SN38 (NRP1)" + ) + elif drug_position == 'all': + self._variants = [self.POS10_LINKER_SN38_SMILES, self.POS20_LINKER_SN38_SMILES] + self._variant_names = ['pos10', 'pos20'] + super().__init__( + self.POS10_LINKER_SN38_SMILES, + component_name="DSPA-SN38 (LIN00149, all positions)" + ) + else: + if drug_position not in self.DRUG_POSITIONS: + raise ValueError(f"Unknown drug_position '{drug_position}'. " + f"Choose from: 'all', '10', '20'") + smiles = self.DRUG_POSITIONS[drug_position] + self._variants = [smiles] + self._variant_names = [f'pos{drug_position}'] + super().__init__( + smiles, + component_name=f"DSPA-SN38 (LIN00149, C-{drug_position})" + ) + + def _verify_reactive_group(self): + """Verify thiol group is present for disulfide bond formation""" + # Check for free thiol (SH) + for atom in self.linker_payload_mol.GetAtoms(): + if atom.GetAtomicNum() == 16: # Sulfur + if atom.GetTotalNumHs() >= 1 and atom.GetDegree() == 1: + print(" DSPA-SN38 loaded with free thiol reactive group") + break + else: + print(" Warning: No free thiol group detected in DSPA-SN38") + + # Verify ester bond to drug + ester_pattern = Chem.MolFromSmarts('[#6][CX3](=[OX1])[OX2][c]') + if self.linker_payload_mol.HasSubstructMatch(ester_pattern): + print(" Ester bond (linker-to-drug) detected") + else: + # Try broader ester pattern + ester_broad = Chem.MolFromSmarts('[#6][CX3](=[OX1])[OX2][#6]') + if self.linker_payload_mol.HasSubstructMatch(ester_broad): + print(" Ester bond (linker-to-drug) detected") + else: + print(" Warning: No ester bond detected - check linker-drug connection") + + # ------------------------------------------------------------------ + # Conjugation site finding (Cysteine thiol) + # ------------------------------------------------------------------ + + def find_conjugation_sites(self, peptide_smiles): + """ + Find all free cysteine thiol groups in peptide + + Returns list of dicts with site information including atom index + """ + return self.find_peptide_cysteines(peptide_smiles) + + def find_peptide_cysteines(self, peptide_smiles): + """ + Find all free cysteine residues (thiol groups) in peptide + + Iterates over all atoms to find S with free H (thiol, not disulfide). + + Returns: + List of dicts with 'atom_idx' (sulfur index) and metadata + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + cysteines = [] + + for atom in peptide_mol.GetAtoms(): + if atom.GetAtomicNum() == 16: # Sulfur + total_hs = atom.GetTotalNumHs() + degree = atom.GetDegree() + + # Thiol: S with 1 H and bonded to 1 carbon (not disulfide S-S) + if total_hs >= 1 and degree == 1: + cysteines.append({ + 'atom_idx': atom.GetIdx(), + 'num_hydrogens': total_hs, + 'neighbors': [peptide_mol.GetAtomWithIdx(n.GetIdx()).GetSymbol() + for n in atom.GetNeighbors()], + 'type': 'Cys_thiol' + }) + + return cysteines + + # ------------------------------------------------------------------ + # Conjugation reaction (disulfide bond formation) + # ------------------------------------------------------------------ + + def _perform_conjugation(self, peptide_mol, site_info, exclude_n_terminus=True): + """ + Form disulfide bond between peptide Cys thiol and linker-drug thiol. + + Args: + peptide_mol: RDKit Mol object + site_info: Not used (kept for compatibility) + exclude_n_terminus: Not used for Cys conjugation (kept for interface) + + Returns: List of all possible conjugate SMILES + """ + all_conjugates = [] + seen = set() + + for variant_smiles in self._variants: + variant_mol = Chem.MolFromSmiles(variant_smiles) + if variant_mol is None: + continue + + # SMARTS: form disulfide between any two thiols + # Reactant 0 = peptide (Cys-CH2-SH), Reactant 1 = linker-drug (-CH2-SH) + disulfide_rxn = AllChem.ReactionFromSmarts( + '[C:1][SH:2].[SH:3][C:4]>>[C:1][S:2][S:3][C:4]' + ) + + products = disulfide_rxn.RunReactants((peptide_mol, variant_mol)) + + if not products: + continue + + for product_tuple in products: + try: + conjugate_mol = product_tuple[0] + Chem.SanitizeMol(conjugate_mol) + conjugate_smiles = Chem.MolToSmiles(conjugate_mol) + + if conjugate_smiles not in seen: + seen.add(conjugate_smiles) + all_conjugates.append(conjugate_smiles) + except Exception: + continue + + if not all_conjugates: + raise ValueError("Disulfide bond formation failed - no products generated") + + return all_conjugates + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate DSPA-SN38 to peptide via disulfide bond at Cys thiol. + + Args: + peptide_smiles: SMILES string of peptide (must contain Cys with free thiol) + exclude_n_terminus: Not used for Cys conjugation (kept for interface) + return_all: If True, return all possible conjugates + + Returns: + List of all possible conjugate SMILES + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError(f"Invalid peptide SMILES: {peptide_smiles}") + + sites = self.find_conjugation_sites(peptide_smiles) + print(f"Found {len(sites)} Cys thiol site(s)") + + conjugates = self._perform_conjugation(peptide_mol, None, exclude_n_terminus) + + pos_label = f"C-{self.drug_position}" if self.drug_position != 'all' else "C-10 + C-20" + print(f"Generated {len(conjugates)} unique conjugate(s) [Cys disulfide] [DSPA {pos_label}]") + + if return_all: + return conjugates + else: + return conjugates[0] if conjugates else None + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """ + Batch conjugate multiple peptides. + + Returns: + Dict with 'conjugates', 'successful', 'failed', 'total_conjugates' + """ + results = { + 'conjugates': [], + 'successful': [], + 'failed': [], + 'total_conjugates': 0 + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + except Exception as e: + print(f" Peptide {i} failed: {e}") + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + + print(f"\n=== Summary ===") + print(f"Peptides: {len(peptide_smiles_list)}, Success: {len(results['successful'])}, " + f"Failed: {len(results['failed'])}") + print(f"Total conjugates generated: {results['total_conjugates']}") + + return results + + def flatten_batch_results(self, results): + """Flatten batch conjugation results into a single list""" + all_conjugates = [] + for conjugate_list in results['conjugates']: + if conjugate_list is not None: + if isinstance(conjugate_list, list): + all_conjugates.extend(conjugate_list) + else: + all_conjugates.append(conjugate_list) + return all_conjugates + + # ======================================================================== + # Attachment Site Compatibility Checks (Cys disulfide) + # ======================================================================== + + def has_disulfide_bond(self, mol): + """Check if molecule has disulfide bond (S-S)""" + disulfide_pattern = Chem.MolFromSmarts('[SX2]-[SX2]') + return mol.HasSubstructMatch(disulfide_pattern) + + def has_free_thiol(self, mol): + """Check if molecule has unconjugated (free) thiol groups""" + for atom in mol.GetAtoms(): + if atom.GetAtomicNum() == 16: + if atom.GetTotalNumHs() >= 1 and atom.GetDegree() == 1: + return True + return False + + def has_ester_bond_to_drug(self, mol): + """Check for ester bond connecting linker to drug""" + ester_pattern = Chem.MolFromSmarts('[#6][CX3](=[OX1])[OX2][#6]') + return mol.HasSubstructMatch(ester_pattern) + + def has_sn38_core(self, mol): + """Check for SN-38 core structure (camptothecin with 7-ethyl)""" + # Camptothecin-like core: fused ring system with lactone + cpt_core = Chem.MolFromSmarts('c1ccc2ncc3C(=O)OCc3c2c1') + return mol.HasSubstructMatch(cpt_core) + + def check_attachment_site_compatibility(self, conjugate_smiles): + """ + Check attachment site compatibility for Cys-SS-DSPA-SN38 conjugation. + + Validates: + 1. Disulfide bond (S-S) is present + 2. No free thiol remaining + 3. Ester bond to drug is present + + Returns: + Tuple of (passed: bool, details: dict) + """ + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'parseable': False, 'error': 'Could not parse SMILES'} + + details = { + 'parseable': True, + 'validation_method': 'cys_disulfide_ester', + 'linker': 'LIN00149 (3-Disulfanylpropanoic Acid)', + 'drug': 'SN-38 (7-Ethyl-10-hydroxycamptothecin)', + } + + details['has_disulfide_bond'] = self.has_disulfide_bond(mol) + details['no_free_thiol'] = not self.has_free_thiol(mol) + details['has_ester_bond'] = self.has_ester_bond_to_drug(mol) + + passed = all([ + details['has_disulfide_bond'], + details['no_free_thiol'], + ]) + + return passed, details + + def _basic_validation(self, mol): + """ + Basic validation checks for DSPA-SN38 conjugates. + + Checks: + 1. Disulfide bond present (Cys-Linker conjugation) + 2. No free thiol remaining + 3. Ester bond to drug present + 4. SN-38 core present + """ + checks = {} + + disulfide_pattern = Chem.MolFromSmarts('[#16]-[#16]') + checks['has_disulfide_bond'] = mol.HasSubstructMatch(disulfide_pattern) + + checks['no_free_thiol'] = not self.has_free_thiol(mol) + + ester_pattern = Chem.MolFromSmarts('[#6][CX3](=[OX1])[OX2][#6]') + checks['has_ester_bond'] = mol.HasSubstructMatch(ester_pattern) + + checks['has_sn38_core'] = self.has_sn38_core(mol) + + checks['_passed'] = all([ + checks['has_disulfide_bond'], + checks['no_free_thiol'], + ]) + + return checks + + def validate_conjugate(self, conjugate_smiles): + """ + Validate that the conjugate is chemically reasonable. + + Returns: + (passed: bool, checks: dict) + """ + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'error': 'Could not parse SMILES'} + + checks = self._basic_validation(mol) + checks['parseable'] = True + + passed = checks.get('_passed', True) + if '_passed' in checks: + del checks['_passed'] + + return passed, checks + + +# ============================================================================ +# Main - Test Examples +# ============================================================================ + +if __name__ == "__main__": + + print("=" * 70) + print("DSPA-SN38 PDC Builder - Test") + print("Reference: NRP1 + LIN00149 + SN-38") + print("=" * 70 + "\n") + + # Test peptides with free cysteine (NRP1-targeting sequences) + test_peptides = [ + # Cys-Arg-Gly-Asp-Lys (CRGDK) - reference NRP1 peptide + "N[C@@H](CS)C(=O)N[C@@H](CCCNC(=N)N)C(=O)NCC(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCCCN)C(=O)O", + # Simple Gly-Cys-Gly + "NCC(=O)N[C@@H](CS)C(=O)NCC(=O)O", + ] + + print("Test 1: Default mode (all positions: C-10 + C-20)") + print("-" * 50) + + builder = DisulfanylpropanoicAcidSN38_PDC_Builder() + + for i, peptide in enumerate(test_peptides): + print(f"\n--- Test peptide {i+1} ---") + print(f"Input: {peptide[:60]}...") + + try: + conjugates = builder.conjugate_to_peptide(peptide) + print(f" Generated {len(conjugates)} conjugate(s)") + + for j, conj in enumerate(conjugates): + valid, checks = builder.validate_conjugate(conj) + print(f" Conjugate {j+1}: {'PASSED' if valid else 'FAILED'} " + f"({len(conj)} chars)") + + except Exception as e: + print(f" FAILED: {e}") + import traceback + traceback.print_exc() + + print("\n\nTest 2: Position-10 only") + print("-" * 50) + + builder_10 = DisulfanylpropanoicAcidSN38_PDC_Builder(drug_position='10') + + try: + conjugates = builder_10.conjugate_to_peptide(test_peptides[1]) + print(f" Generated {len(conjugates)} conjugate(s) [C-10 only]") + except Exception as e: + print(f" FAILED: {e}") diff --git a/smiles/conjugation/DauFactory.py b/smiles/conjugation/DauFactory.py new file mode 100644 index 0000000000000000000000000000000000000000..a4e0c4fb46363e1d1d016e46092a2ed387c0a47f --- /dev/null +++ b/smiles/conjugation/DauFactory.py @@ -0,0 +1,298 @@ +""" +Daunorubicin PDC Factory - Drug-level builder with linker sampling + +Instead of specifying a particular linker-drug combo (e.g. AoaDau, ValAlaDau), +the user specifies only the drug (Daunorubicin) and a linker is sampled +according to GNRHR+Dau entry frequencies from PDCdb. + +Linker frequencies (GNRHR + Daunorubicin): +=========================================== + Aminooxyacetic acid (Aoa) (AoaDau) - 21 entries + Val-Ala-PABC + glutaryl spacer (ValAlaDau) - 2 entries + Val-Cit-PABC + glutaryl spacer (ValCitDau) - 2 entries + Glutaric acid (GlutarylDau) - 2 entries + Total: 27 entries + +Sampling probabilities: + AoaDau: 21/27 ≈ 0.778 + ValAlaDau: 2/27 ≈ 0.074 + ValCitDau: 2/27 ≈ 0.074 + GlutarylDau: 2/27 ≈ 0.074 + +Usage: +====== + # Drug-only mode - linker is sampled per peptide + factory = DaunorubicinPDC_Factory() + conjugates = factory.conjugate_to_peptide(peptide_smiles) + + # Fixed linker mode - use a specific linker + factory = DaunorubicinPDC_Factory(linker='AoaDau') + conjugates = factory.conjugate_to_peptide(peptide_smiles) + + # All linkers mode - generate conjugates with every linker + factory = DaunorubicinPDC_Factory(linker='all') + conjugates = factory.conjugate_to_peptide(peptide_smiles) +""" + +import numpy as np +import contextlib +import io + +from .AoaDau import AoaDaunorubicin_PDC_Builder +from .ValAlaDau import ValAlaPABCDaunorubicin_PDC_Builder +from .ValCitDau import ValCitPABCDaunorubicin_PDC_Builder +from .GlutarylDau import GlutarylDaunorubicin_PDC_Builder + + +# ============================================================================ +# Linker registry: name → (builder_class, PDCdb frequency, description) +# ============================================================================ + +GNRHR_DAU_LINKER_REGISTRY = { + 'AoaDau': { + 'class': AoaDaunorubicin_PDC_Builder, + 'frequency': 21, + 'linker_id': 'LIN00019', + 'description': 'Aminooxyacetic acid (Aoa) - oxime bond to Dau ketone', + 'pdc_refs': ['PDC_00011', 'PDC_00208'], + }, + 'ValAlaDau': { + 'class': ValAlaPABCDaunorubicin_PDC_Builder, + 'frequency': 2, + 'linker_id': 'LIN00028', + 'description': 'Val-Ala dipeptide + PABC self-immolative + glutaryl spacer', + 'pdc_refs': ['PDC_00185', 'PDC_00193'], + }, + 'ValCitDau': { + 'class': ValCitPABCDaunorubicin_PDC_Builder, + 'frequency': 2, + 'linker_id': 'LIN00005', + 'description': 'Val-Cit dipeptide + PABC self-immolative + glutaryl spacer', + 'pdc_refs': ['PDC_00189', 'PDC_00194'], + }, + 'GlutarylDau': { + 'class': GlutarylDaunorubicin_PDC_Builder, + 'frequency': 2, + 'linker_id': 'LIN00033', + 'description': 'Glutaric acid - simple dicarboxylic acid spacer', + 'pdc_refs': ['PDC_00190', 'PDC_00195'], + }, +} + + +def _compute_linker_probabilities(registry=None): + """Compute sampling probabilities from frequency counts.""" + if registry is None: + registry = GNRHR_DAU_LINKER_REGISTRY + names = list(registry.keys()) + freqs = np.array([registry[n]['frequency'] for n in names], dtype=np.float64) + probs = freqs / freqs.sum() + return names, probs + + +class DaunorubicinPDC_Factory: + """ + Drug-level PDC factory for Daunorubicin conjugates. + + Wraps all four GNRHR+Dau linker builders and provides: + - 'sample' mode (default): randomly pick a linker per call according to + PDCdb entry frequencies + - 'all' mode: generate conjugates with every linker + - fixed linker mode: always use a specific linker (e.g. 'AoaDau') + + The interface mirrors the individual builder's `conjugate_to_peptide` so it + can be used as a drop-in replacement in the Conjugation classifier. + """ + + def __init__(self, linker='sample', rng_seed=None): + """ + Args: + linker: One of: + - 'sample' (default): sample linker per peptide according to + GNRHR+Dau frequency weights + - 'all': use every linker and pool conjugates + - A specific linker name ('AoaDau', 'ValAlaDau', 'ValCitDau', + 'GlutarylDau') + rng_seed: Optional seed for reproducible linker sampling + """ + self.linker_mode = linker + self.registry = GNRHR_DAU_LINKER_REGISTRY + self.rng = np.random.default_rng(rng_seed) + + # Pre-compute probabilities + self._linker_names, self._linker_probs = _compute_linker_probabilities(self.registry) + + # Validate fixed linker + if linker not in ('sample', 'all') and linker not in self.registry: + raise ValueError( + f"Unknown linker '{linker}'. " + f"Choose from: 'sample', 'all', {list(self.registry.keys())}" + ) + + # Pre-instantiate builders (suppress verbose init output) + self._builders = {} + for name, entry in self.registry.items(): + with contextlib.redirect_stdout(io.StringIO()): + self._builders[name] = entry['class']() + + # Track which linker was last sampled (useful for logging) + self.last_sampled_linker = None + + # ------------------------------------------------------------------ + # Linker selection + # ------------------------------------------------------------------ + + def sample_linker(self): + """Sample a linker name according to frequency weights.""" + idx = self.rng.choice(len(self._linker_names), p=self._linker_probs) + return self._linker_names[idx] + + def get_builder(self, linker_name): + """Return the pre-instantiated builder for a given linker name.""" + return self._builders[linker_name] + + @property + def linker_probabilities(self): + """Return dict of linker name → sampling probability.""" + return dict(zip(self._linker_names, self._linker_probs.tolist())) + + # ------------------------------------------------------------------ + # Conjugation interface (mirrors individual builders) + # ------------------------------------------------------------------ + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate Daunorubicin to peptide, selecting linker by mode. + + Args: + peptide_smiles: SMILES string of peptide (must contain Lys -NH2) + exclude_n_terminus: If True, only conjugate at Lys ε-amines + return_all: If True, return all positional conjugates + + Returns: + List of conjugate SMILES. + In 'sample' mode: conjugates from the sampled linker. + In 'all' mode: conjugates from every linker (pooled). + In fixed mode: conjugates from the specified linker. + """ + if self.linker_mode == 'all': + self.last_sampled_linker = 'all' + return self._conjugate_all_linkers(peptide_smiles, exclude_n_terminus, return_all) + elif self.linker_mode == 'sample': + chosen = self.sample_linker() + self.last_sampled_linker = chosen + return self._conjugate_with_linker(chosen, peptide_smiles, exclude_n_terminus, return_all) + else: + self.last_sampled_linker = self.linker_mode + return self._conjugate_with_linker(self.linker_mode, peptide_smiles, exclude_n_terminus, return_all) + + def _conjugate_with_linker(self, linker_name, peptide_smiles, exclude_n_terminus, return_all): + """Run conjugation with a single, specific linker.""" + builder = self._builders[linker_name] + with contextlib.redirect_stdout(io.StringIO()): + conjugates = builder.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all, + ) + # Normalize to list + if not isinstance(conjugates, list): + conjugates = [conjugates] if conjugates else [] + return conjugates + + def _conjugate_all_linkers(self, peptide_smiles, exclude_n_terminus, return_all): + """Generate conjugates with every linker and pool them.""" + all_conjugates = [] + seen = set() + for name in self._linker_names: + try: + conjs = self._conjugate_with_linker(name, peptide_smiles, exclude_n_terminus, return_all) + for c in conjs: + if c not in seen: + seen.add(c) + all_conjugates.append(c) + except Exception: + continue + if not all_conjugates: + raise ValueError("No conjugates generated with any linker") + return all_conjugates + + # ------------------------------------------------------------------ + # Validation (delegates to the appropriate builder) + # ------------------------------------------------------------------ + + def validate_conjugate(self, conjugate_smiles, linker_name=None): + """ + Validate a conjugate SMILES. + + If linker_name is given, validate with that builder. + Otherwise, try all builders and return the best result. + """ + if linker_name and linker_name in self._builders: + return self._builders[linker_name].validate_conjugate(conjugate_smiles) + + # Try each builder; return first passing result + for name, builder in self._builders.items(): + passed, checks = builder.validate_conjugate(conjugate_smiles) + if passed: + checks['matched_linker'] = name + return True, checks + + # None passed - return last failure + return False, checks + + def check_attachment_site_compatibility(self, conjugate_smiles, linker_name=None): + """Delegate to appropriate builder's attachment check.""" + if linker_name and linker_name in self._builders: + return self._builders[linker_name].check_attachment_site_compatibility(conjugate_smiles) + + for name, builder in self._builders.items(): + passed, details = builder.check_attachment_site_compatibility(conjugate_smiles) + if passed: + details['matched_linker'] = name + return True, details + return False, details + + # ------------------------------------------------------------------ + # Batch interface + # ------------------------------------------------------------------ + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """ + Batch conjugate multiple peptides. + + In 'sample' mode each peptide gets a freshly sampled linker. + + Returns: + Dict with 'conjugates', 'successful', 'failed', 'total_conjugates', + and 'linkers_used' (list of linker names, one per peptide). + """ + results = { + 'conjugates': [], + 'successful': [], + 'failed': [], + 'total_conjugates': 0, + 'linkers_used': [], + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all, + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + results['linkers_used'].append(self.last_sampled_linker) + except Exception as e: + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + results['linkers_used'].append(None) + + return results diff --git a/smiles/conjugation/GlutarylDau.py b/smiles/conjugation/GlutarylDau.py new file mode 100644 index 0000000000000000000000000000000000000000..ec954b885eda2a86e83869dce585d22f22d85603 --- /dev/null +++ b/smiles/conjugation/GlutarylDau.py @@ -0,0 +1,362 @@ +""" +Glutaryl-Daunorubicin PDC Builder + +Conjugation chemistry: Lysine ε-amine + Glutaryl-Daunorubicin (amide bond formation) + +Reference PDCs: +- PDC_00190 (GnRH-III-[2ΔHis-3D-Tic,8Lys(glutaryl-Val-Cit-PABC-diamine-PTX)]) +- PDC_00195 (GnRH-III-[2His-3Trp,8Lys(glutaryl-Val-Cit-PABC-diamine-PTX)]) +- Receptor: GNRHR +- Linker: Glutaric acid (LIN00033) +- Payload: Daunorubicin (DRG00010) + +Architecture: + Peptide──Lys──NH──C(=O)──(CH₂)₃──C(=O)──NH──Daunorubicin + │ │ + amide bond glutaryl spacer amide bond (pre-formed) + +The glutaric acid acts as a simple dicarboxylic acid spacer: +- One COOH forms amide with Dau 3'-amine (pre-formed in linker-payload) +- Other COOH forms amide with Lys ε-amine (conjugation reaction) +""" + +from rdkit import Chem +from rdkit.Chem import RWMol, AllChem + +from .pdc_builder import PDC_Builder + + +class GlutarylDaunorubicin_PDC_Builder(PDC_Builder): + """ + PDC builder for Glutaric acid (Glutaryl) - Daunorubicin conjugates + + Conjugation chemistry (amide bond formation): + - Peptide site: Lysine ε-amine (side chain -NH2) + - Linker-payload site: Carboxylic acid of glutaryl spacer (already conjugated + to Dau 3'-amine via amide) + - Reaction: Amide bond formation (Lys-NH2 + HOOC-glutaryl-Dau → Lys-NH-CO-glutaryl-Dau) + + Architecture: + Peptide──Lys──NH──C(=O)──(CH₂)₃──C(=O)──NH──Daunorubicin + └── amide ──┘ └── glutaryl ──┘ └── amide (pre-formed) ──┘ + + Reference: PDC_00190, PDC_00195 (GnRH-III conjugates, GNRHR) + """ + + # Glutaryl-Daunorubicin SMILES with free carboxylic acid + # Structure: HOOC-(CH2)3-CO-NH-Daunorubicin + # The amide bond between glutaric acid and Dau 3'-amine is pre-formed + DEFAULT_GLUTARYL_DAU_SMILES = ( + "C[C@H]1[C@H]([C@H](C[C@@H](O1)O[C@H]2C[C@@](CC3=C2C(=C4C(=C3O)" + "C(=O)C5=C(C4=O)C(=CC=C5)OC)O)(C(=O)C)O)NC(=O)CCCC(=O)O)O" + ) + + def __init__(self, glutaryl_dau_smiles=None): + """ + Initialize Glutaryl-Daunorubicin builder + + Args: + glutaryl_dau_smiles: Optional custom Glutaryl-Dau SMILES. + If None, uses default. + Must have free carboxylic acid for conjugation. + """ + if glutaryl_dau_smiles is None: + glutaryl_dau_smiles = self.DEFAULT_GLUTARYL_DAU_SMILES + + super().__init__(glutaryl_dau_smiles, component_name="Glutaryl-Daunorubicin (linker-payload)") + + def _verify_reactive_group(self): + """Verify carboxylic acid group is present for amide bond formation""" + carboxylic_acid_pattern = Chem.MolFromSmarts('[CX3](=O)[OX2H1]') + + if not self.linker_payload_mol.HasSubstructMatch(carboxylic_acid_pattern): + print(" Warning: No carboxylic acid group detected in Glutaryl-Dau") + else: + print(" Glutaryl-Daunorubicin loaded with carboxylic acid reactive group") + + # Verify Dau 3'-amide bond is present (glutaryl-Dau linkage) + amide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH2]') + if self.linker_payload_mol.HasSubstructMatch(amide_pattern): + print(" Amide bond (Glutaryl-Dau linkage) detected") + else: + print(" Warning: No amide bond detected - check Glutaryl-Dau structure") + + def find_conjugation_sites(self, peptide_smiles): + """ + Find all lysine ε-amine groups in peptide + + Returns list of dicts with site information including atom index + """ + return self._find_peptide_lysines(peptide_smiles) + + def _find_peptide_lysines(self, peptide_smiles): + """ + Find all lysine residues with free ε-amine in peptide + + Lysine side chain: -CH2-CH2-CH2-CH2-NH2 (CCCCN) + + Returns: + List of dicts with 'atom_idx' (nitrogen index) and metadata + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + lysines = [] + + lys_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + matches = peptide_mol.GetSubstructMatches(lys_pattern) + + for match in matches: + nitrogen_idx = match[4] + lysines.append({ + 'atom_idx': nitrogen_idx, + 'side_chain_atoms': list(match), + 'type': 'Lys_epsilon_amine' + }) + + n_terminus_pattern = Chem.MolFromSmarts('[NH2][CH,CH2][C](=O)') + n_term_matches = peptide_mol.GetSubstructMatches(n_terminus_pattern) + if n_term_matches: + print(f" (Also found {len(n_term_matches)} N-terminus α-amine(s) - excluded by default)") + + return lysines + + def _perform_conjugation(self, peptide_mol, site_info, exclude_n_terminus=True): + """ + Perform amide bond conjugation between NH2 groups and glutaryl carboxylic acid + + Args: + peptide_mol: RDKit Mol object + site_info: Not used (kept for compatibility) + exclude_n_terminus: If True, skip N-terminus conjugation + + Returns: List of all possible conjugate SMILES + """ + return self._manual_conjugation(peptide_mol, site_info, exclude_n_terminus) + + def _manual_conjugation(self, peptide_mol, lysine_info, exclude_n_terminus=True): + """ + Create ALL possible conjugates by forming amide bond at NH2 sites + + Chemistry: + - Any-NH2 + HOOC-glutaryl-Dau → Any-NH-C(=O)-glutaryl-Dau + H2O + """ + if exclude_n_terminus: + amide_rxn = AllChem.ReactionFromSmarts( + '[*:5][CH2][CH2][CH2][CH2:4][NH2:1].[C:2](=[O:3])[OH]>>' + '[*:5][CH2][CH2][CH2][CH2:4][NH:1][C:2](=[O:3])' + ) + else: + amide_rxn = AllChem.ReactionFromSmarts( + '[*:4][NH2:1].[C:2](=[O:3])[OH]>>[*:4][NH:1][C:2](=[O:3])' + ) + + products = amide_rxn.RunReactants((peptide_mol, self.linker_payload_mol)) + + if not products: + raise ValueError("Amide bond formation reaction failed - no products generated") + + conjugates = [] + seen_smiles = set() + + for product_tuple in products: + try: + conjugate_mol = product_tuple[0] + Chem.SanitizeMol(conjugate_mol) + conjugate_smiles = Chem.MolToSmiles(conjugate_mol) + + if conjugate_smiles not in seen_smiles: + seen_smiles.add(conjugate_smiles) + conjugates.append(conjugate_smiles) + except Exception: + continue + + if not conjugates: + raise ValueError("All products failed sanitization") + + return conjugates + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate Glutaryl-Daunorubicin to peptide via amide bond at NH2 groups + + Args: + peptide_smiles: SMILES string of peptide (must contain NH2) + exclude_n_terminus: If True (default), only conjugate at Lys ε-amines + return_all: If True, return all possible conjugates + + Returns: + If return_all=True: List of all possible conjugate SMILES + If return_all=False: Single conjugate SMILES (first one) + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError(f"Invalid peptide SMILES: {peptide_smiles}") + + sites = self.find_conjugation_sites(peptide_smiles) + print(f"Found {len(sites)} Lys ε-amine site(s)") + + conjugates = self._perform_conjugation(peptide_mol, None, exclude_n_terminus) + + site_type = "Lys ε-amine only" if exclude_n_terminus else "all NH2 (including N-terminus)" + print(f"Generated {len(conjugates)} unique conjugate(s) [{site_type}]") + + if return_all: + return conjugates + else: + return conjugates[0] if conjugates else None + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """Batch conjugate multiple peptides""" + results = { + 'conjugates': [], + 'successful': [], + 'failed': [], + 'total_conjugates': 0 + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + except Exception as e: + print(f" Peptide {i} failed: {e}") + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + + return results + + # ========================================================================= + # Attachment Site Compatibility Checks + # ========================================================================= + + def has_glutaryl_amide_conjugation(self, mol): + """ + Check if molecule has the glutaryl conjugation pattern + + Pattern: N-C(=O)-(CH2)3-C(=O)-N (amide-glutaryl-amide linkage) + This is the signature of successful Lys-Glutaryl-Dau conjugation. + """ + glutaryl_pattern = Chem.MolFromSmarts( + '[NX3][CX3](=[OX1])[CH2][CH2][CH2][CX3](=[OX1])[NX3]' + ) + if glutaryl_pattern: + return mol.HasSubstructMatch(glutaryl_pattern) + return False + + def has_dau_amide_bond(self, mol): + """ + Check if molecule has the glutaryl-Dau amide bond intact + + The pre-formed amide: -(CH2)3-C(=O)-NH-Dau + """ + amide_pattern = Chem.MolFromSmarts('[CH2][CX3](=[OX1])[NX3]') + if amide_pattern: + return mol.HasSubstructMatch(amide_pattern) + return False + + def has_unreacted_glutaryl_cooh(self, mol): + """ + Check if molecule has unreacted glutaryl carboxylic acid + + Specific pattern: Free COOH on the glutaryl chain ending + """ + unreacted_pattern = Chem.MolFromSmarts( + '[CX3](=[OX1])[OX2H1][CH2][CH2][CH2][CX3](=[OX1])[NX3]' + ) + if unreacted_pattern: + return mol.HasSubstructMatch(unreacted_pattern) + return False + + def check_attachment_site_compatibility(self, mol): + """ + Check if Lys-Glutaryl-Dau attachment site chemistry is valid + + Validates: + 1. Has glutaryl bis-amide conjugation pattern + 2. Has Dau amide bond intact + 3. No unreacted glutaryl-COOH + """ + if not self.has_glutaryl_amide_conjugation(mol): + return False + + if not self.has_dau_amide_bond(mol): + return False + + if self.has_unreacted_glutaryl_cooh(mol): + return False + + return True + + def _basic_validation(self, mol): + """Basic validation checks for Glutaryl-Daunorubicin conjugates""" + checks = {} + + amide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[#6]') + checks['has_amide_bond'] = mol.HasSubstructMatch(amide_pattern) + + checks['has_glutaryl_conjugation'] = self.has_glutaryl_amide_conjugation(mol) + checks['has_dau_amide'] = self.has_dau_amide_bond(mol) + checks['no_unreacted_cooh'] = not self.has_unreacted_glutaryl_cooh(mol) + checks['attachment_site_valid'] = self.check_attachment_site_compatibility(mol) + + anthraquinone = Chem.MolFromSmarts( + '[#6]1[#6][#6][#6]2[#6](=[#8])[#6]3[#6][#6][#6][#6][#6]3[#6](=[#8])[#6]2[#6]1' + ) + checks['has_daunorubicin_core'] = mol.HasSubstructMatch(anthraquinone) + + aminosugar = Chem.MolFromSmarts('[OX2]1[#6][#6][#6]([NX3])[#6][#6]1') + checks['has_aminosugar'] = mol.HasSubstructMatch(aminosugar) + + checks['_passed'] = checks['attachment_site_valid'] + + return checks + + def validate_conjugate(self, conjugate_smiles): + """Validate that the conjugate is chemically reasonable""" + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'error': 'Could not parse SMILES'} + + checks = self._basic_validation(mol) + checks['parseable'] = True + + passed = checks.get('_passed', True) + if '_passed' in checks: + del checks['_passed'] + + return passed, checks + + +if __name__ == "__main__": + print("=" * 70) + print("Glutaryl-Daunorubicin PDC Builder - Test") + print("=" * 70 + "\n") + + builder = GlutarylDaunorubicin_PDC_Builder() + + test_peptides = [ + "NCC(=O)N[C@@H](CCCCN)C(=O)NCC(=O)O", # Gly-Lys-Gly + ] + + for pep in test_peptides: + try: + conjugates = builder.conjugate_to_peptide(pep, exclude_n_terminus=True) + print(f" {len(conjugates)} conjugate(s)") + for c in conjugates: + valid, checks = builder.validate_conjugate(c) + print(f" Valid: {valid}") + except Exception as e: + print(f" Failed: {e}") diff --git a/smiles/conjugation/MalHexCpt.py b/smiles/conjugation/MalHexCpt.py new file mode 100644 index 0000000000000000000000000000000000000000..fbcd82b761762ee4091911e559b3bbed4045dd19 --- /dev/null +++ b/smiles/conjugation/MalHexCpt.py @@ -0,0 +1,560 @@ +""" +Maleimide-Hexanoate-Camptothecin PDC Builder (MalHexCpt) + +Conjugation chemistry: Lysine ε-amine + Cys-S-Linker-Camptothecin (amide bond formation) + +Reference PDC: +- PDC_00336 (SMAC-P1 CPT conjugate 6) + +Receptor: ERBB2 (Receptor tyrosine-protein kinase erbB-2) +Linker: LIN00031 - 6-(2,5-dioxo-3-sulfanylpyrrolidin-1-yl)hexanoic acid +Payload: DRG00009 - Camptothecin +Architecture: Monomer, Lys side chain conjugation + +Chemistry Note: +=============== +LIN00031 is a maleimide-thiol adduct (succinimide-thioether) with a hexanoic acid spacer: + + Succinimide ring Hexanoic spacer + ┌──────────────┐ ┌─────────────────────┐ + O═C─CH₂─CH(SH)─C═O─N─CH₂─CH₂─CH₂─CH₂─CH₂─C(═O)─OH + └─────────────┘ + +Linker-to-Drug Connection (Ester via hexanoic acid): + Cys─S─C(succinimide)─N─(CH₂)₅─C(═O)─O─Camptothecin + │ + ester bond (C-20 OH of CPT) + +Full PDC Architecture: + Peptide──Lys──NH──C(═O)──Cys──S──[Succinimide]──N──(CH₂)₅──C(═O)─O──CPT + └── amide ──┘ └── thioether ──┘ └── hexanoic spacer ──┘ + +This is a NOVEL linker chemistry distinct from the simple disulfide-ester/carbonate +linkers in SSCpt. The maleimide reacts with Cys thiol via Michael addition to form +a thioether (C-S) bond. The succinimide ring provides structural rigidity and the +hexanoic spacer provides distance between the conjugation site and the drug. + +Usage: +====== + builder = MaleimideHexanoateCpt_PDC_Builder() + conjugates = builder.conjugate_to_peptide(peptide_smiles) +""" + +from rdkit import Chem +from rdkit.Chem import AllChem, Descriptors + +from .pdc_builder import PDC_Builder + + +# ============================================================================ +# Maleimide-Hexanoate-Camptothecin PDC Builder +# ============================================================================ + +class MaleimideHexanoateCpt_PDC_Builder(PDC_Builder): + """ + PDC builder for Maleimide-Hexanoate + Camptothecin conjugates + + Conjugation chemistry (amide bond formation): + - Peptide site: Lysine ε-amine (side chain -NH2) + - Linker-payload site: Carboxylic acid of Cys (Cys-S-Linker-Drug) + - Reaction: Lys-NH2 + HOOC-Cys-S-Linker-Drug → Lys-NH-CO-Cys-S-Linker-Drug + + Linker: LIN00031 - 6-(2,5-dioxo-3-sulfanylpyrrolidin-1-yl)hexanoic acid + SMILES: C1C(C(=O)N(C1=O)CCCCCC(=O)O)S + IUPAC: 6-(2,5-dioxo-3-sulfanylpyrrolidin-1-yl)hexanoic acid + MW: 245.3, MF: C10H15NO4S + Features: Succinimide ring (maleimide-thiol adduct) + hexanoic acid spacer + + Drug: DRG00009 - Camptothecin + Canonical SMILES: CCC1(O)C(=O)OCc2c1cc1n(c2=O)Cc2cc3ccccc3nc2-1 + Connection: Ester at C-20 hydroxyl + + Reference: PDC_00336 (SMAC-P1 CPT conjugate 6, ERBB2) + """ + + # ======================================================================== + # Cys-S-Linker-Drug SMILES (thioether via Michael addition) + # ======================================================================== + # + # Pre-formed Cys-MaleimideHexanoate-Camptothecin assembly: + # + # HOOC─Cys(NH₂)─CH₂─S─C(succinimide)─N─(CH₂)₅─C(═O)─O─CPT(C-20) + # + # Construction from components: + # Cys: N[C@H](CS...)C(=O)O + # S-C: ...S... (thioether, Michael addition of Cys-SH to maleimide) + # LIN00031 ring: ...C4CC(=O)N(...)C4=O (succinimide from maleimide-thiol adduct) + # Spacer: N(CCCCCC(=O)O...) (hexanoic acid, COOH → ester to CPT) + # CPT: ...OC1(CC)C(=O)OCc2c1cc1n(c2=O)Cc2cc3ccccc3nc2-1 + # + # ======================================================================== + + CYS_S_LINKER_CPT_SMILES = ( + "N[C@H](CSC4CC(=O)N(CCCCCC(=O)OC1(CC)C(=O)OCc2c1cc1n(c2=O)Cc2cc3ccccc3nc2-1)C4=O)C(=O)O" + ) + + def __init__(self, cys_linker_drug_smiles=None): + """ + Initialize Maleimide-Hexanoate-Camptothecin builder + + Args: + cys_linker_drug_smiles: Optional custom Cys-S-Linker-Drug SMILES. + If None, uses default LIN00031+CPT assembly. + Must have free carboxylic acid for conjugation. + """ + if cys_linker_drug_smiles is None: + cys_linker_drug_smiles = self.CYS_S_LINKER_CPT_SMILES + + super().__init__( + cys_linker_drug_smiles, + component_name="Cys-S-MaleimideHexanoate-Camptothecin (LIN00031+DRG00009)" + ) + + def _verify_reactive_group(self): + """Verify carboxylic acid group is present for amide bond formation""" + # Pattern: Carboxylic acid (COOH) + carboxylic_acid_pattern = Chem.MolFromSmarts('[CX3](=O)[OX2H1]') + + if not self.linker_payload_mol.HasSubstructMatch(carboxylic_acid_pattern): + print(" Warning: No carboxylic acid group detected in Cys-S-Linker-Drug") + else: + print(" Cys-S-MalHex-Camptothecin loaded with carboxylic acid reactive group") + + # Verify thioether bond is present (Cys-S-C, from Michael addition) + thioether_pattern = Chem.MolFromSmarts('[CH2][SX2][CH]') + if self.linker_payload_mol.HasSubstructMatch(thioether_pattern): + print(" Thioether bond (C-S-C) detected (maleimide-thiol Michael addition)") + else: + print(" Warning: No thioether bond detected - check structure") + + # Verify Cys α-amine is present + cys_amine_pattern = Chem.MolFromSmarts('[NH2][CH]([CH2][SX2])C(=O)') + if self.linker_payload_mol.HasSubstructMatch(cys_amine_pattern): + print(" Cys α-amine detected (will be free in conjugate)") + else: + print(" Warning: Cys α-amine pattern not detected") + + # Verify succinimide ring (maleimide-thiol adduct) + succinimide_pattern = Chem.MolFromSmarts('[CH2]C(=O)N(*)C(=O)') + if self.linker_payload_mol.HasSubstructMatch(succinimide_pattern): + print(" Succinimide ring (maleimide-thiol adduct) detected") + else: + print(" Warning: No succinimide ring detected - check linker structure") + + # Verify ester bond to drug + ester_pattern = Chem.MolFromSmarts('[#6][CX3](=[OX1])[OX2][#6]') + if self.linker_payload_mol.HasSubstructMatch(ester_pattern): + print(" Ester bond (linker-to-drug) detected") + else: + print(" Warning: No ester bond detected - check linker-drug connection") + + def find_conjugation_sites(self, peptide_smiles): + """ + Find all lysine ε-amine groups in peptide + + Returns list of dicts with site information including atom index + """ + return self.find_peptide_lysines(peptide_smiles) + + def find_peptide_lysines(self, peptide_smiles): + """ + Find all lysine residues with free ε-amine in peptide + + Lysine side chain: -CH2-CH2-CH2-CH2-NH2 (CCCCN) + + Returns: + List of dicts with 'atom_idx' (nitrogen index) and metadata + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + lysines = [] + + lys_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + matches = peptide_mol.GetSubstructMatches(lys_pattern) + + for match in matches: + nitrogen_idx = match[4] + lysines.append({ + 'atom_idx': nitrogen_idx, + 'side_chain_atoms': list(match), + 'type': 'Lys_epsilon_amine' + }) + + n_terminus_pattern = Chem.MolFromSmarts('[NH2][CH,CH2][C](=O)') + n_term_matches = peptide_mol.GetSubstructMatches(n_terminus_pattern) + + if n_term_matches: + print(f" (Also found {len(n_term_matches)} N-terminus α-amine(s) - excluded by default)") + + return lysines + + def _perform_conjugation(self, peptide_mol, site_info, exclude_n_terminus=True): + """ + Perform amide bond conjugation between NH2 groups and Cys carboxylic acid + + Args: + peptide_mol: RDKit Mol object + site_info: Not used (kept for compatibility) + exclude_n_terminus: If True, skip N-terminus conjugation + + Returns: List of all possible conjugate SMILES + """ + if exclude_n_terminus: + amide_rxn = AllChem.ReactionFromSmarts( + '[*:5][CH2][CH2][CH2][CH2:4][NH2:1].[C:2](=[O:3])[OH]>>[*:5][CH2][CH2][CH2][CH2:4][NH:1][C:2](=[O:3])' + ) + else: + amide_rxn = AllChem.ReactionFromSmarts( + '[*:4][NH2:1].[C:2](=[O:3])[OH]>>[*:4][NH:1][C:2](=[O:3])' + ) + + products = amide_rxn.RunReactants((peptide_mol, self.linker_payload_mol)) + + if not products: + raise ValueError("Amide bond formation reaction failed - no products generated") + + conjugates = [] + seen_smiles = set() + + for product_tuple in products: + try: + conjugate_mol = product_tuple[0] + Chem.SanitizeMol(conjugate_mol) + conjugate_smiles = Chem.MolToSmiles(conjugate_mol) + + if conjugate_smiles not in seen_smiles: + seen_smiles.add(conjugate_smiles) + conjugates.append(conjugate_smiles) + except Exception: + continue + + if not conjugates: + raise ValueError("All products failed sanitization") + + return conjugates + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate Cys-S-MalHex-Camptothecin to peptide via amide bond at NH2 groups + + Args: + peptide_smiles: SMILES string of peptide (must contain Lys with NH2) + exclude_n_terminus: If True (default), only conjugate at Lys ε-amines + return_all: If True, return all possible conjugates + + Returns: + List of all possible conjugate SMILES + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError(f"Invalid peptide SMILES: {peptide_smiles}") + + sites = self.find_conjugation_sites(peptide_smiles) + print(f"Found {len(sites)} Lys ε-amine site(s)") + + conjugates = self._perform_conjugation(peptide_mol, None, exclude_n_terminus) + + site_type = "Lys ε-amine only" if exclude_n_terminus else "all NH2 (including N-terminus)" + print(f"Generated {len(conjugates)} unique conjugate(s) [{site_type}] [maleimide-hexanoate ester]") + + if return_all: + return conjugates + else: + return conjugates[0] if conjugates else None + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """ + Batch conjugate multiple peptides + + Args: + peptide_smiles_list: List of peptide SMILES strings + exclude_n_terminus: If True (default), only conjugate at Lys ε-amines + return_all: If True, return all possible conjugates per peptide + + Returns: + Dict with 'conjugates', 'successful', 'failed', 'total_conjugates' + """ + results = { + 'conjugates': [], + 'successful': [], + 'failed': [], + 'total_conjugates': 0 + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + except Exception as e: + print(f" Peptide {i} failed: {e}") + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + + print(f"\n=== Summary ===") + print(f"Peptides: {len(peptide_smiles_list)}, Success: {len(results['successful'])}, Failed: {len(results['failed'])}") + print(f"Total conjugates generated: {results['total_conjugates']}") + + return results + + def flatten_batch_results(self, results): + """Flatten batch conjugation results into a single list""" + all_conjugates = [] + for conjugate_list in results['conjugates']: + if conjugate_list is not None: + if isinstance(conjugate_list, list): + all_conjugates.extend(conjugate_list) + else: + all_conjugates.append(conjugate_list) + return all_conjugates + + # ======================================================================== + # Attachment Site Compatibility Checks + # ======================================================================== + + def has_lys_cys_amide_conjugation(self, mol): + """Check if molecule has Lys-Cys amide conjugation bond""" + lys_cys_amide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH]([CH2][SX2])[NX3]') + return mol.HasSubstructMatch(lys_cys_amide_pattern) + + def has_thioether_bond(self, mol): + """Check if molecule has thioether bond (C-S-C, from maleimide-thiol Michael addition)""" + thioether_pattern = Chem.MolFromSmarts('[CH2][SX2][CH]') + return mol.HasSubstructMatch(thioether_pattern) + + def has_succinimide_ring(self, mol): + """ + Check if molecule has succinimide ring (maleimide-thiol adduct) + + The succinimide ring is the product of maleimide-thiol Michael addition. + Pattern: 5-membered ring with N and two C=O groups. + """ + succinimide_pattern = Chem.MolFromSmarts('C1CC(=O)N(*)C1=O') + return mol.HasSubstructMatch(succinimide_pattern) + + def has_hexanoic_spacer(self, mol): + """ + Check if molecule has hexanoic acid spacer chain + + Pattern: N-CH2-CH2-CH2-CH2-CH2-C(=O)-O (6-carbon chain from succinimide N) + """ + hexanoic_pattern = Chem.MolFromSmarts('[NX3]CCCCCC(=O)[OX2]') + return mol.HasSubstructMatch(hexanoic_pattern) + + def has_unreacted_lys_epsilon_amine(self, mol): + """Check for unreacted Lys ε-amine (free NH2 on 4-carbon side chain)""" + lys_amine_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + return mol.HasSubstructMatch(lys_amine_pattern) + + def has_unreacted_cys_carboxylic_acid(self, mol): + """Check for unreacted Cys carboxylic acid from Cys-S-Linker-Drug""" + cys_cooh_pattern = Chem.MolFromSmarts('[NH2][CH]([CH2][SX2])[CX3](=[OX1])[OX2H1]') + return mol.HasSubstructMatch(cys_cooh_pattern) + + def find_amide_conjugation_sites(self, mol): + """Find all Lys-Cys amide conjugation sites in the molecule""" + lys_cys_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH]([CH2][SX2])[NX3]') + matches = mol.GetSubstructMatches(lys_cys_pattern) + + sites = [] + for match in matches: + nitrogen_idx = match[0] + carbonyl_c_idx = match[1] + cys_alpha_idx = match[3] + sites.append((nitrogen_idx, carbonyl_c_idx, cys_alpha_idx)) + + return sites + + def check_valency_at_conjugation_site(self, mol, site_indices): + """Check that valency is correct at Lys-Cys conjugation site""" + nitrogen_idx, carbonyl_c_idx, cys_alpha_idx = site_indices + + try: + n_atom = mol.GetAtomWithIdx(nitrogen_idx) + if n_atom.GetTotalDegree() != 3: + return False + + c_atom = mol.GetAtomWithIdx(carbonyl_c_idx) + if c_atom.GetTotalDegree() != 3: + return False + + cys_c_atom = mol.GetAtomWithIdx(cys_alpha_idx) + if cys_c_atom.GetTotalDegree() != 4: + return False + + return True + except Exception: + return False + + def check_attachment_site_compatibility(self, conjugate_smiles): + """ + Check attachment site compatibility for Lys-Cys-S-MalHex-CPT conjugation + + Validates: + 1. Lys-Cys amide conjugation is present + 2. Thioether bond (C-S-C) is intact (maleimide-thiol Michael addition) + 3. Succinimide ring is present (maleimide-thiol adduct) + 4. Hexanoic spacer is present + 5. Valency at conjugation sites is correct + 6. No unreacted Cys-COOH (unconjugated linker-drug) + + Returns: + Tuple of (passed: bool, details: dict) + """ + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'parseable': False, 'error': 'Could not parse SMILES'} + + details = { + 'parseable': True, + 'validation_method': 'lys_cys_amide_thioether_maleimide_hexanoate_ester', + 'linker': 'LIN00031 (6-(2,5-dioxo-3-sulfanylpyrrolidin-1-yl)hexanoic acid)', + 'drug': 'DRG00009 (Camptothecin)', + 'pdc_ref': 'PDC_00336' + } + + details['has_lys_cys_amide'] = self.has_lys_cys_amide_conjugation(mol) + details['has_thioether_bond'] = self.has_thioether_bond(mol) + details['has_succinimide_ring'] = self.has_succinimide_ring(mol) + details['has_hexanoic_spacer'] = self.has_hexanoic_spacer(mol) + + conjugation_sites = self.find_amide_conjugation_sites(mol) + details['conjugation_sites'] = len(conjugation_sites) + + valency_ok = True + if conjugation_sites: + for site in conjugation_sites: + if not self.check_valency_at_conjugation_site(mol, site): + valency_ok = False + break + details['valency_correct'] = valency_ok + + details['no_unreacted_cys_cooh'] = not self.has_unreacted_cys_carboxylic_acid(mol) + details['has_unreacted_lys_amine'] = self.has_unreacted_lys_epsilon_amine(mol) + + passed = all([ + details['has_lys_cys_amide'], + details['has_thioether_bond'], + details['has_succinimide_ring'], + details['valency_correct'], + details['no_unreacted_cys_cooh'] + ]) + + return passed, details + + def _basic_validation(self, mol): + """ + Basic validation checks for MaleimideHexanoate-Camptothecin conjugates + + Checks: + 1. Amide bond present (Lys-Cys conjugation) + 2. Thioether bond present (C-S-C, maleimide-thiol Michael addition) + 3. Specific Lys-Cys-S conjugation pattern + 4. Succinimide ring present (maleimide-thiol adduct) + 5. Camptothecin core present + 6. Ester bond to drug present + """ + checks = {} + + amide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[#6]') + checks['has_amide_bond'] = mol.HasSubstructMatch(amide_pattern) + + thioether_pattern = Chem.MolFromSmarts('[CH2][SX2][CH]') + checks['has_thioether_bond'] = mol.HasSubstructMatch(thioether_pattern) + + lys_cys_s_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH]([CH2][SX2])[NX3]') + checks['has_lys_cys_s_conjugation'] = mol.HasSubstructMatch(lys_cys_s_pattern) + + succinimide_pattern = Chem.MolFromSmarts('C1CC(=O)N(*)C1=O') + checks['has_succinimide_ring'] = mol.HasSubstructMatch(succinimide_pattern) + + camptothecin_core = Chem.MolFromSmarts('c1ccc2ncc3C(=O)OCc3c2c1') + checks['has_camptothecin_core'] = mol.HasSubstructMatch(camptothecin_core) + + ester_pattern = Chem.MolFromSmarts('[#6][CX3](=[OX1])[OX2][#6]') + checks['has_ester_bond'] = mol.HasSubstructMatch(ester_pattern) + + checks['_passed'] = all([ + checks['has_amide_bond'], + checks['has_thioether_bond'], + checks['has_lys_cys_s_conjugation'], + checks['has_succinimide_ring'] + ]) + + return checks + + def validate_conjugate(self, conjugate_smiles): + """ + Validate that the conjugate is chemically reasonable + + Returns: + (passed: bool, checks: dict) + """ + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'error': 'Could not parse SMILES'} + + checks = self._basic_validation(mol) + checks['parseable'] = True + + passed = checks.get('_passed', True) + if '_passed' in checks: + del checks['_passed'] + + return passed, checks + + +# ============================================================================ +# Main - Test Examples +# ============================================================================ + +if __name__ == "__main__": + + print("=" * 70) + print("Maleimide-Hexanoate-Camptothecin PDC Builder - Test") + print("Reference: PDC_00336 (SMAC-P1 CPT conjugate 6, LIN00031)") + print("=" * 70 + "\n") + + # Test peptides + test_peptides = [ + # Gly-Lys-Gly (has N-terminus NH2 + Lys ε-amine) + "NCC(=O)N[C@@H](CCCCN)C(=O)NCC(=O)O", + # Lys-Arg + "N[C@@H](CCCCN)C(=O)N[C@@H](CCCNC(=N)N)C(=O)O", + ] + + builder = MaleimideHexanoateCpt_PDC_Builder() + + for i, peptide in enumerate(test_peptides): + print(f"\n--- Test peptide {i+1} ---") + print(f"Input: {peptide}") + + try: + conjugates = builder.conjugate_to_peptide(peptide, exclude_n_terminus=True) + print(f" Generated {len(conjugates)} conjugate(s)") + + for j, conj in enumerate(conjugates): + valid, checks = builder.validate_conjugate(conj) + print(f" Conjugate {j+1}: {'PASSED' if valid else 'FAILED'} ({len(conj)} chars)") + if not valid: + for k, v in checks.items(): + if v is False: + print(f" {k}") + + except Exception as e: + print(f" FAILED: {e}") + import traceback + traceback.print_exc() diff --git a/smiles/conjugation/SN38Factory.py b/smiles/conjugation/SN38Factory.py new file mode 100644 index 0000000000000000000000000000000000000000..ef396da4625e89ac713769334a5562ca8544461e --- /dev/null +++ b/smiles/conjugation/SN38Factory.py @@ -0,0 +1,308 @@ +""" +SN-38 PDC Factory - Drug-level builder with linker sampling + +Instead of specifying a particular linker-drug combo (e.g. DSPASN38, DSEBSN38), +the user specifies only the drug (SN-38) and a linker is sampled +according to NRP1+SN38 entry frequencies from PDCdb. + +Linker frequencies (NRP1 + SN-38): +=========================================== + 3-Disulfanylpropanoic acid (DSPASN38) - 9 entries + 4-(2-Disulfanylethoxy)-4-oxobutanoic acid (DSEBSN38) - 6 entries + 5-(2-Disulfanylethoxy)-5-oxopentanoic acid (DSEPSN38) - 2 entries + Total: 17 entries + +Sampling probabilities: + DSPASN38: 9/17 ≈ 0.529 + DSEBSN38: 6/17 ≈ 0.353 + DSEPSN38: 2/17 ≈ 0.118 + +Usage: +====== + # Drug-only mode - linker is sampled per peptide + factory = SN38PDC_Factory() + conjugates = factory.conjugate_to_peptide(peptide_smiles) + + # Fixed linker mode - use a specific linker + factory = SN38PDC_Factory(linker='DSPASN38') + conjugates = factory.conjugate_to_peptide(peptide_smiles) + + # All linkers mode - generate conjugates with every linker + factory = SN38PDC_Factory(linker='all') + conjugates = factory.conjugate_to_peptide(peptide_smiles) +""" + +import numpy as np +import contextlib +import io + +from .DSPASN38 import DisulfanylpropanoicAcidSN38_PDC_Builder +from .DSEBSN38 import DisulfanylethOxyButanoicAcidSN38_PDC_Builder +from .DSEPSN38 import DisulfanylethOxyPentanoicAcidSN38_PDC_Builder + + +# ============================================================================ +# Linker registry: name → (builder_class, PDCdb frequency, description) +# ============================================================================ + +NRP1_SN38_LINKER_REGISTRY = { + 'DSPASN38': { + 'class': DisulfanylpropanoicAcidSN38_PDC_Builder, + 'frequency': 9, + 'linker_id': 'LIN00149', + 'description': '3-Disulfanylpropanoic acid (disulfide + ester)', + 'pdc_refs': ['PDC_02099', 'PDC_02104', 'PDC_02102', 'PDC_02107', + 'PDC_02110', 'PDC_02112', 'PDC_02113', 'PDC_02114', 'PDC_02115'], + }, + 'DSEBSN38': { + 'class': DisulfanylethOxyButanoicAcidSN38_PDC_Builder, + 'frequency': 6, + 'linker_id': 'LIN00168', + 'description': '4-(2-Disulfanylethoxy)-4-oxobutanoic acid (succinic spacer)', + 'pdc_refs': ['PDC_02100', 'PDC_02103', 'PDC_02105', + 'PDC_02108', 'PDC_02109', 'PDC_02111'], + }, + 'DSEPSN38': { + 'class': DisulfanylethOxyPentanoicAcidSN38_PDC_Builder, + 'frequency': 2, + 'linker_id': 'LIN00169', + 'description': '5-(2-Disulfanylethoxy)-5-oxopentanoic acid (glutaric spacer)', + 'pdc_refs': ['PDC_02101', 'PDC_02106'], + }, +} + + +def _compute_linker_probabilities(registry=None): + """Compute sampling probabilities from frequency counts.""" + if registry is None: + registry = NRP1_SN38_LINKER_REGISTRY + names = list(registry.keys()) + freqs = np.array([registry[n]['frequency'] for n in names], dtype=np.float64) + probs = freqs / freqs.sum() + return names, probs + + +class SN38PDC_Factory: + """ + Drug-level PDC factory for SN-38 (7-Ethyl-10-hydroxycamptothecin) conjugates. + + Wraps all three NRP1+SN38 linker builders and provides: + - 'sample' mode (default): randomly pick a linker per call according to + PDCdb entry frequencies + - 'all' mode: generate conjugates with every linker + - fixed linker mode: always use a specific linker (e.g. 'DSPASN38') + + Conjugation chemistry: Cysteine thiol + Linker thiol → Disulfide bond + Target receptor: NRP1 (Neuropilin-1) + + The interface mirrors the individual builder's `conjugate_to_peptide` so it + can be used as a drop-in replacement in the Conjugation classifier. + """ + + def __init__(self, linker='sample', rng_seed=None): + """ + Args: + linker: One of: + - 'sample' (default): sample linker per peptide according to + NRP1+SN38 frequency weights + - 'all': use every linker and pool conjugates + - A specific linker name ('DSPASN38', 'DSEBSN38', 'DSEPSN38') + rng_seed: Optional seed for reproducible linker sampling + """ + self.linker_mode = linker + self.registry = NRP1_SN38_LINKER_REGISTRY + self.rng = np.random.default_rng(rng_seed) + + # Pre-compute probabilities + self._linker_names, self._linker_probs = _compute_linker_probabilities(self.registry) + + # Validate fixed linker + if linker not in ('sample', 'all') and linker not in self.registry: + raise ValueError( + f"Unknown linker '{linker}'. " + f"Choose from: 'sample', 'all', {list(self.registry.keys())}" + ) + + # Pre-instantiate builders (suppress verbose init output) + self._builders = {} + for name, entry in self.registry.items(): + with contextlib.redirect_stdout(io.StringIO()): + self._builders[name] = entry['class']() + + # Track which linker was last sampled (useful for logging) + self.last_sampled_linker = None + + # ------------------------------------------------------------------ + # Linker selection + # ------------------------------------------------------------------ + + def sample_linker(self): + """Sample a linker name according to frequency weights.""" + idx = self.rng.choice(len(self._linker_names), p=self._linker_probs) + return self._linker_names[idx] + + def get_builder(self, linker_name): + """Return the pre-instantiated builder for a given linker name.""" + return self._builders[linker_name] + + @property + def linker_probabilities(self): + """Return dict of linker name → sampling probability.""" + return dict(zip(self._linker_names, self._linker_probs.tolist())) + + # ------------------------------------------------------------------ + # Conjugation interface (mirrors individual builders) + # ------------------------------------------------------------------ + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate SN-38 to peptide, selecting linker by mode. + + Args: + peptide_smiles: SMILES string of peptide (must contain Cys thiol) + exclude_n_terminus: Not used for Cys conjugation (kept for interface) + return_all: If True, return all positional conjugates + + Returns: + List of conjugate SMILES. + In 'sample' mode: conjugates from the sampled linker. + In 'all' mode: conjugates from every linker (pooled). + In fixed mode: conjugates from the specified linker. + """ + if self.linker_mode == 'all': + self.last_sampled_linker = 'all' + return self._conjugate_all_linkers(peptide_smiles, exclude_n_terminus, return_all) + elif self.linker_mode == 'sample': + chosen = self.sample_linker() + self.last_sampled_linker = chosen + return self._conjugate_with_linker(chosen, peptide_smiles, exclude_n_terminus, return_all) + else: + self.last_sampled_linker = self.linker_mode + return self._conjugate_with_linker(self.linker_mode, peptide_smiles, exclude_n_terminus, return_all) + + def _conjugate_with_linker(self, linker_name, peptide_smiles, exclude_n_terminus, return_all): + """Run conjugation with a single, specific linker.""" + builder = self._builders[linker_name] + with contextlib.redirect_stdout(io.StringIO()): + conjugates = builder.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all, + ) + # Normalize to list + if not isinstance(conjugates, list): + conjugates = [conjugates] if conjugates else [] + return conjugates + + def _conjugate_all_linkers(self, peptide_smiles, exclude_n_terminus, return_all): + """Generate conjugates with every linker and pool them.""" + all_conjugates = [] + seen = set() + for name in self._linker_names: + try: + conjs = self._conjugate_with_linker(name, peptide_smiles, exclude_n_terminus, return_all) + for c in conjs: + if c not in seen: + seen.add(c) + all_conjugates.append(c) + except Exception: + continue + if not all_conjugates: + raise ValueError("No conjugates generated with any linker") + return all_conjugates + + # ------------------------------------------------------------------ + # Validation (delegates to the appropriate builder) + # ------------------------------------------------------------------ + + def validate_conjugate(self, conjugate_smiles, linker_name=None): + """ + Validate a conjugate SMILES. + + If linker_name is given, validate with that builder. + Otherwise, try all builders and return the best result. + """ + if linker_name and linker_name in self._builders: + return self._builders[linker_name].validate_conjugate(conjugate_smiles) + + # Try each builder; return first passing result + checks = None + for name, builder in self._builders.items(): + passed, checks = builder.validate_conjugate(conjugate_smiles) + if passed: + checks['matched_linker'] = name + return True, checks + + # None passed - return last failure + return False, checks + + def check_attachment_site_compatibility(self, conjugate_smiles, linker_name=None): + """Delegate to appropriate builder's attachment check.""" + if linker_name and linker_name in self._builders: + return self._builders[linker_name].check_attachment_site_compatibility(conjugate_smiles) + + details = None + for name, builder in self._builders.items(): + passed, details = builder.check_attachment_site_compatibility(conjugate_smiles) + if passed: + details['matched_linker'] = name + return True, details + return False, details + + # ------------------------------------------------------------------ + # Batch interface + # ------------------------------------------------------------------ + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """ + Batch conjugate multiple peptides. + + In 'sample' mode each peptide gets a freshly sampled linker. + + Returns: + Dict with 'conjugates', 'successful', 'failed', 'total_conjugates', + and 'linkers_used' (list of linker names, one per peptide). + """ + results = { + 'conjugates': [], + 'successful': [], + 'failed': [], + 'total_conjugates': 0, + 'linkers_used': [], + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all, + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + results['linkers_used'].append( + self.linker_mode if self.linker_mode not in ('sample', 'all') else None + ) + except Exception as e: + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + results['linkers_used'].append(None) + + return results + + # ------------------------------------------------------------------ + # Informational + # ------------------------------------------------------------------ + + def __repr__(self): + probs = self.linker_probabilities + lines = [f"SN38PDC_Factory(mode='{self.linker_mode}')"] + for name, prob in probs.items(): + desc = self.registry[name]['description'] + lines.append(f" {name:12s} p={prob:.3f} ({desc})") + return '\n'.join(lines) diff --git a/smiles/conjugation/SSCpt.py b/smiles/conjugation/SSCpt.py new file mode 100644 index 0000000000000000000000000000000000000000..f061f4f1f7d940f8f5650e3160beb33b992c9025 --- /dev/null +++ b/smiles/conjugation/SSCpt.py @@ -0,0 +1,891 @@ +""" +Disulfide-Camptothecin PDC Builder (SSCpt) + +Conjugation chemistry: Lysine ε-amine + Cys-SS-Linker-Camptothecin (amide bond formation) + +Reference PDCs: +- PDC_00294 (Z7): Ester linker chemistry +- PDC_00295 (Z8): Carbonate linker chemistry + +Receptor: ERBB2 +Linker: Disulfide bond (S-S) - cleavable inside cells +Payload: Camptothecin +Architecture: Monomer, Lys side chain conjugation + +Supported Linker-to-Drug Chemistries: +====================================== + +ESTER (Z7): + Cys─S─S─CH2─CH2─C(═O)─O─Camptothecin + │ + ester + +CARBONATE (Z8): + Cys─S─S─CH2─CH2─O─C(═O)─O─Camptothecin + │ + carbonate + +Usage: +====== + # DEFAULT: Both chemistries (returns combined list) + builder = DisulfideCamptothecin_PDC_Builder() + conjugates = builder.conjugate_to_peptide(peptide_smiles) + # Returns: [ester_conj1, ester_conj2, ..., carbonate_conj1, carbonate_conj2, ...] + + # Single chemistry only + builder = DisulfideCamptothecin_PDC_Builder(linker_chemistry='ester') + builder = DisulfideCamptothecin_PDC_Builder(linker_chemistry='carbonate') +""" + +from rdkit import Chem +from rdkit.Chem import AllChem, Descriptors + +from .pdc_builder import PDC_Builder + + +# ============================================================================ +# Disulfide-Camptothecin PDC Builder +# ============================================================================ + +class DisulfideCamptothecin_PDC_Builder(PDC_Builder): + """ + PDC builder for Disulfide-Camptothecin conjugates + + Conjugation chemistry (amide bond formation): + - Peptide site: Lysine ε-amine (side chain -NH2) + - Linker-payload site: Carboxylic acid of Cys (Cys-SS-Linker-Drug) + - Reaction: Lys-NH2 + HOOC-Cys-SS-Linker-Drug → Lys-NH-CO-Cys-SS-Linker-Drug + + Supported linker-to-drug chemistries: + - 'ester' (Z7, PDC_00294): Cys-S-S-CH2-CH2-C(=O)-O-CPT + - 'carbonate' (Z8, PDC_00295): Cys-S-S-CH2-CH2-O-C(=O)-O-CPT + + Architecture: + Peptide──Lys──NH──C(=O)──Cys──S─S──Linker──Camptothecin + └── amide ──┘ └── disulfide ──┘ + + Usage: + # Default: BOTH chemistries (returns combined list) + builder = DisulfideCamptothecin_PDC_Builder() + conjugates = builder.conjugate_to_peptide(peptide_smiles) + # For 1 Lys: returns 2 conjugates (1 ester + 1 carbonate) + + # Single chemistry only + builder = DisulfideCamptothecin_PDC_Builder(linker_chemistry='ester') + """ + + # ======================================================================== + # Linker Chemistry Variants + # ======================================================================== + # + # Two linker-to-drug chemistries are supported: + # + # ESTER (Z7, PDC_00294): + # Cys─S─S─CH2─CH2─C(═O)─O─Camptothecin + # │ + # ester + # + # CARBONATE (Z8, PDC_00295): + # Cys─S─S─CH2─CH2─O─C(═O)─O─Camptothecin + # │ + # carbonate + # + # ======================================================================== + + # Ester variant (Z7, PDC_00294) + # Structure: HOOC-Cys(α-amine)-CH2-S-S-CH2-CH2-C(=O)-O-Camptothecin + ESTER_CYS_SS_LINKER_CPT_SMILES = ( + "N[C@H](CSSCCC(=O)OC1(CC)C(=O)OCc2c1cc1n(c2=O)Cc2cc3ccccc3nc2-1)C(=O)O" + ) + + # Carbonate variant (Z8, PDC_00295) + # Structure: HOOC-Cys(α-amine)-CH2-S-S-CH2-CH2-O-C(=O)-O-Camptothecin + CARBONATE_CYS_SS_LINKER_CPT_SMILES = ( + "N[C@H](CSSCCOC(=O)OC1(CC)C(=O)OCc2c1cc1n(c2=O)Cc2cc3ccccc3nc2-1)C(=O)O" + ) + + # Default (backward compatibility) + DEFAULT_CYS_SS_LINKER_CPT_SMILES = ESTER_CYS_SS_LINKER_CPT_SMILES + + # Available linker chemistries + LINKER_CHEMISTRIES = { + 'ester': ESTER_CYS_SS_LINKER_CPT_SMILES, + 'carbonate': CARBONATE_CYS_SS_LINKER_CPT_SMILES, + } + + def __init__(self, cys_linker_drug_smiles=None, linker_chemistry='all'): + """ + Initialize Disulfide-Camptothecin builder + + Args: + cys_linker_drug_smiles: Optional custom Cys-SS-Linker-Drug SMILES. + If provided, overrides linker_chemistry. + Must have free carboxylic acid for conjugation. + linker_chemistry: 'all' (default), 'ester' (Z7), or 'carbonate' (Z8). + 'all' generates conjugates with both chemistries. + Ignored if cys_linker_drug_smiles is provided. + """ + self.linker_chemistry = linker_chemistry + + if cys_linker_drug_smiles is not None: + # Custom SMILES provided - use single chemistry mode + self.linker_chemistry = 'custom' + super().__init__(cys_linker_drug_smiles, component_name="Cys-SS-Linker-Camptothecin (custom)") + elif linker_chemistry == 'all': + # All chemistries mode - initialize with ester as base, but will use both + super().__init__(self.ESTER_CYS_SS_LINKER_CPT_SMILES, component_name="Cys-SS-Linker-Camptothecin (all)") + else: + # Single chemistry mode + if linker_chemistry not in self.LINKER_CHEMISTRIES: + raise ValueError(f"Unknown linker_chemistry: {linker_chemistry}. " + f"Choose from: 'all', {list(self.LINKER_CHEMISTRIES.keys())}") + cys_linker_drug_smiles = self.LINKER_CHEMISTRIES[linker_chemistry] + super().__init__(cys_linker_drug_smiles, component_name=f"Cys-SS-Linker-Camptothecin ({linker_chemistry})") + + @classmethod + def generate_all_chemistry_variants(cls, peptide_smiles, exclude_n_terminus=True): + """ + Generate conjugates with ALL available linker chemistries (Ester + Carbonate) + + This is useful for expanding candidate diversity. + + Args: + peptide_smiles: SMILES string of peptide + exclude_n_terminus: If True, only conjugate at Lys ε-amines + + Returns: + Dict with: + 'conjugates': List of all conjugate SMILES + 'by_chemistry': Dict mapping chemistry name to list of conjugates + 'by_site': Dict mapping site index to list of (chemistry, smiles) tuples + 'total': Total number of conjugates + """ + results = { + 'conjugates': [], + 'by_chemistry': {}, + 'by_site': {}, + 'total': 0 + } + + for chemistry_name in cls.LINKER_CHEMISTRIES.keys(): + builder = cls(linker_chemistry=chemistry_name) + + try: + conjugates = builder.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus + ) + + results['by_chemistry'][chemistry_name] = conjugates + + for i, conj in enumerate(conjugates): + results['conjugates'].append(conj) + + # Track by site + if i not in results['by_site']: + results['by_site'][i] = [] + results['by_site'][i].append((chemistry_name, conj)) + + except Exception as e: + print(f" {chemistry_name} variant failed: {e}") + results['by_chemistry'][chemistry_name] = [] + + results['total'] = len(results['conjugates']) + + print(f"\n=== All Chemistry Variants ===") + print(f"Total conjugates: {results['total']}") + for chem, conjs in results['by_chemistry'].items(): + print(f" {chem}: {len(conjs)} conjugate(s)") + + return results + + @classmethod + def batch_generate_all_variants(cls, peptide_smiles_list, exclude_n_terminus=True): + """ + Batch generate all chemistry variants for multiple peptides + + Args: + peptide_smiles_list: List of peptide SMILES strings + exclude_n_terminus: If True, only conjugate at Lys ε-amines + + Returns: + Dict with: + 'all_conjugates': Flat list of all conjugate SMILES + 'by_peptide': List of results dicts (one per peptide) + 'summary': Overall statistics + """ + results = { + 'all_conjugates': [], + 'by_peptide': [], + 'summary': { + 'peptides': len(peptide_smiles_list), + 'successful': 0, + 'failed': 0, + 'total_conjugates': 0 + } + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + print(f"\n--- Peptide {i+1}/{len(peptide_smiles_list)} ---") + try: + pep_results = cls.generate_all_chemistry_variants( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus + ) + results['by_peptide'].append(pep_results) + results['all_conjugates'].extend(pep_results['conjugates']) + results['summary']['successful'] += 1 + results['summary']['total_conjugates'] += pep_results['total'] + except Exception as e: + print(f" Peptide {i} failed completely: {e}") + results['by_peptide'].append(None) + results['summary']['failed'] += 1 + + print(f"\n=== Batch Summary ===") + print(f"Peptides: {results['summary']['peptides']}") + print(f"Successful: {results['summary']['successful']}") + print(f"Failed: {results['summary']['failed']}") + print(f"Total conjugates: {results['summary']['total_conjugates']}") + + return results + + def _verify_reactive_group(self): + """Verify carboxylic acid group is present for amide bond formation""" + # Pattern: Carboxylic acid (COOH) + carboxylic_acid_pattern = Chem.MolFromSmarts('[CX3](=O)[OX2H1]') + + if not self.linker_payload_mol.HasSubstructMatch(carboxylic_acid_pattern): + print(" Warning: No carboxylic acid group detected in Cys-SS-Linker-Drug") + else: + print(" Cys-SS-Linker-Camptothecin loaded with carboxylic acid reactive group") + + # Verify disulfide bond is present + disulfide_pattern = Chem.MolFromSmarts('[#16]-[#16]') + if self.linker_payload_mol.HasSubstructMatch(disulfide_pattern): + print(" Disulfide bond (S-S) detected") + else: + print(" Warning: No disulfide bond detected - check structure") + + # Verify Cys α-amine is present + cys_amine_pattern = Chem.MolFromSmarts('[NH2][CH]([CH2]S)C(=O)') + if self.linker_payload_mol.HasSubstructMatch(cys_amine_pattern): + print(" Cys α-amine detected (will be free in conjugate)") + else: + print(" Warning: Cys α-amine pattern not detected") + + def find_conjugation_sites(self, peptide_smiles): + """ + Find all lysine ε-amine groups in peptide + + Returns list of dicts with site information including atom index + """ + return self.find_peptide_lysines(peptide_smiles) + + def find_peptide_lysines(self, peptide_smiles): + """ + Find all lysine residues with free ε-amine in peptide + + Lysine side chain: -CH2-CH2-CH2-CH2-NH2 (CCCCN) + + Returns: + List of dicts with 'atom_idx' (nitrogen index) and metadata + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + lysines = [] + + # SMARTS pattern for Lysine ε-amine: 4 CH2 groups ending in NH2 + lys_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + matches = peptide_mol.GetSubstructMatches(lys_pattern) + + for match in matches: + nitrogen_idx = match[4] + + lysines.append({ + 'atom_idx': nitrogen_idx, + 'side_chain_atoms': list(match), + 'type': 'Lys_epsilon_amine' + }) + + # Also count N-terminus for informational purposes + n_terminus_pattern = Chem.MolFromSmarts('[NH2][CH,CH2][C](=O)') + n_term_matches = peptide_mol.GetSubstructMatches(n_terminus_pattern) + + if n_term_matches: + print(f" (Also found {len(n_term_matches)} N-terminus α-amine(s) - excluded by default)") + + return lysines + + def count_all_nh2_sites(self, peptide_smiles): + """ + Count all NH2 sites in peptide (both Lys ε-amines and N-terminus) + + Returns: + Dict with counts: {'lys_epsilon': n, 'n_terminus': n, 'other': n, 'total': n} + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + counts = {'lys_epsilon': 0, 'n_terminus': 0, 'other': 0, 'total': 0} + + # Lys ε-amine: CH2-CH2-CH2-CH2-NH2 (4-carbon side chain) + lys_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + counts['lys_epsilon'] = len(peptide_mol.GetSubstructMatches(lys_pattern)) + + # N-terminus: NH2 attached to backbone carbon + n_term_pattern = Chem.MolFromSmarts('[NH2][CH,CH2][C](=O)') + counts['n_terminus'] = len(peptide_mol.GetSubstructMatches(n_term_pattern)) + + # Total NH2 (any primary amine) + all_nh2_pattern = Chem.MolFromSmarts('[NH2]') + total_nh2 = len(peptide_mol.GetSubstructMatches(all_nh2_pattern)) + + counts['other'] = total_nh2 - counts['lys_epsilon'] - counts['n_terminus'] + counts['total'] = total_nh2 + + return counts + + def _perform_conjugation(self, peptide_mol, site_info, exclude_n_terminus=True): + """ + Perform amide bond conjugation between NH2 groups and Cys carboxylic acid + + Args: + peptide_mol: RDKit Mol object + site_info: Not used (kept for compatibility) + exclude_n_terminus: If True, skip N-terminus conjugation + + Returns: List of all possible conjugate SMILES + """ + return self._manual_conjugation(peptide_mol, site_info, exclude_n_terminus) + + def _manual_conjugation(self, peptide_mol, lysine_info, exclude_n_terminus=True): + """ + Create ALL possible conjugates by forming amide bond at NH2 sites + + Chemistry: + - Lys-NH2 + HOOC-Cys-SS-Linker-Drug → Lys-NH-C(=O)-Cys-SS-Linker-Drug + H2O + + Args: + peptide_mol: RDKit Mol object of peptide + lysine_info: Not used (kept for interface compatibility) + exclude_n_terminus: If True, only conjugate at Lys ε-amines (4-carbon chain), + not at N-terminus α-amine. Default: True + + Returns: + List of all possible conjugate SMILES + """ + if exclude_n_terminus: + # SMARTS that matches only Lys ε-amine: requires 4 CH2 chain before NH2 + # Include wildcard (*) to preserve connection to peptide backbone + amide_rxn = AllChem.ReactionFromSmarts( + '[*:5][CH2][CH2][CH2][CH2:4][NH2:1].[C:2](=[O:3])[OH]>>[*:5][CH2][CH2][CH2][CH2:4][NH:1][C:2](=[O:3])' + ) + else: + # Match ANY primary amine (includes N-terminus) + # Include wildcard to preserve the rest of the molecule + amide_rxn = AllChem.ReactionFromSmarts( + '[*:4][NH2:1].[C:2](=[O:3])[OH]>>[*:4][NH:1][C:2](=[O:3])' + ) + + # Run the reaction - returns all possible products + products = amide_rxn.RunReactants((peptide_mol, self.linker_payload_mol)) + + if not products: + raise ValueError("Amide bond formation reaction failed - no products generated") + + # Collect ALL valid conjugates + conjugates = [] + seen_smiles = set() # Deduplicate + + for product_tuple in products: + try: + conjugate_mol = product_tuple[0] + Chem.SanitizeMol(conjugate_mol) + conjugate_smiles = Chem.MolToSmiles(conjugate_mol) + + # Deduplicate (same SMILES = same molecule) + if conjugate_smiles not in seen_smiles: + seen_smiles.add(conjugate_smiles) + conjugates.append(conjugate_smiles) + except Exception as e: + # Skip invalid products + continue + + if not conjugates: + raise ValueError("All products failed sanitization") + + return conjugates + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate Cys-SS-Linker-Camptothecin to peptide via amide bond at NH2 groups + + If linker_chemistry='all' (default), generates conjugates with BOTH ester and + carbonate chemistries, returning a combined list. + + Args: + peptide_smiles: SMILES string of peptide (must contain Lys with NH2) + exclude_n_terminus: If True (default), only conjugate at Lys ε-amines, + not at N-terminus. Set to False to include N-terminus. + return_all: If True, return all possible conjugates. If False, return first only. + + Returns: + List of all possible conjugate SMILES (from all chemistries if mode='all') + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError(f"Invalid peptide SMILES: {peptide_smiles}") + + sites = self.find_conjugation_sites(peptide_smiles) + print(f"Found {len(sites)} Lys ε-amine site(s)") + + # If 'all' mode, generate conjugates with both chemistries + if self.linker_chemistry == 'all': + all_conjugates = [] + seen_smiles = set() + + for chem_name, chem_smiles in self.LINKER_CHEMISTRIES.items(): + # Temporarily set linker_payload_mol to this chemistry + temp_mol = Chem.MolFromSmiles(chem_smiles) + original_mol = self.linker_payload_mol + self.linker_payload_mol = temp_mol + + try: + conjugates = self._perform_conjugation(peptide_mol, None, exclude_n_terminus) + + for conj in conjugates: + if conj not in seen_smiles: + seen_smiles.add(conj) + all_conjugates.append(conj) + + print(f" {chem_name}: {len(conjugates)} conjugate(s)") + + except Exception as e: + print(f" {chem_name}: failed - {e}") + finally: + # Restore original + self.linker_payload_mol = original_mol + + site_type = "Lys ε-amine only" if exclude_n_terminus else "all NH2" + print(f"Generated {len(all_conjugates)} total unique conjugate(s) [{site_type}] [all chemistries]") + + if not all_conjugates: + raise ValueError("No conjugates generated - no valid conjugation sites found") + + if return_all: + return all_conjugates + else: + return all_conjugates[0] if all_conjugates else None + + else: + # Single chemistry mode + conjugates = self._perform_conjugation(peptide_mol, None, exclude_n_terminus) + + site_type = "Lys ε-amine only" if exclude_n_terminus else "all NH2 (including N-terminus)" + print(f"Generated {len(conjugates)} unique conjugate(s) [{site_type}] [{self.linker_chemistry}]") + + if return_all: + return conjugates + else: + return conjugates[0] if conjugates else None + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """ + Batch conjugate multiple peptides + + Args: + peptide_smiles_list: List of peptide SMILES strings + exclude_n_terminus: If True (default), only conjugate at Lys ε-amines + return_all: If True, return all possible conjugates per peptide + + Returns: + Dict with: + 'conjugates': List of lists (if return_all=True) or list of SMILES + 'successful': List of successful peptide indices + 'failed': List of (index, error) tuples + 'total_conjugates': Total number of conjugates generated + """ + results = { + 'conjugates': [], + 'successful': [], + 'failed': [], + 'total_conjugates': 0 + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + except Exception as e: + print(f" Peptide {i} failed: {e}") + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + + print(f"\n=== Summary ===") + print(f"Peptides: {len(peptide_smiles_list)}, Success: {len(results['successful'])}, Failed: {len(results['failed'])}") + print(f"Total conjugates generated: {results['total_conjugates']}") + + return results + + def flatten_batch_results(self, results): + """ + Flatten batch conjugation results into a single list of all conjugates + + Args: + results: Dict returned by batch_conjugate() + + Returns: + List of all conjugate SMILES (flattened) + """ + all_conjugates = [] + for conjugate_list in results['conjugates']: + if conjugate_list is not None: + if isinstance(conjugate_list, list): + all_conjugates.extend(conjugate_list) + else: + all_conjugates.append(conjugate_list) + return all_conjugates + + # ======================================================================== + # Attachment Site Compatibility Checks (Lys ε-amine + Cys-COOH amide bond) + # ======================================================================== + + def has_lys_cys_amide_conjugation(self, mol): + """ + Check if molecule has Lys-Cys amide conjugation bond + + Pattern: Lys-ε-N-C(=O)-Cα(Cys)-CH2-S-S + The amide bond connects Lys side chain to Cys α-carbon + + Returns: + bool: True if Lys-Cys amide conjugation is present + """ + # Pattern: N-C(=O)-C(N)(C-S-S) - amide to Cys with disulfide + lys_cys_amide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH]([CH2][SX2][SX2])[NX3]') + return mol.HasSubstructMatch(lys_cys_amide_pattern) + + def has_disulfide_bond(self, mol): + """ + Check if molecule has disulfide bond (S-S) + + Returns: + bool: True if disulfide bond is present + """ + disulfide_pattern = Chem.MolFromSmarts('[SX2]-[SX2]') + return mol.HasSubstructMatch(disulfide_pattern) + + def has_unreacted_lys_epsilon_amine(self, mol): + """ + Check for unreacted Lys ε-amine (free NH2 on 4-carbon side chain) + + In a fully conjugated PDC, all Lys ε-amines should be consumed. + This checks for remaining free NH2 groups on Lys side chains. + + Returns: + bool: True if unreacted Lys ε-amine is present (indicates incomplete conjugation) + """ + # Lys ε-amine: 4 CH2 chain ending in NH2 + lys_amine_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + return mol.HasSubstructMatch(lys_amine_pattern) + + def has_unreacted_cys_carboxylic_acid(self, mol): + """ + Check for unreacted Cys carboxylic acid (from Cys-SS-Linker-Drug) + + Pattern: Free COOH on Cys α-carbon adjacent to CH2-S-S + + Returns: + bool: True if unreacted Cys-COOH is present (indicates unconjugated linker-drug) + """ + # Cys with free COOH: H2N-CH(CH2-S-S)-COOH + cys_cooh_pattern = Chem.MolFromSmarts('[NH2][CH]([CH2][SX2][SX2])[CX3](=[OX1])[OX2H1]') + return mol.HasSubstructMatch(cys_cooh_pattern) + + def find_amide_conjugation_sites(self, mol): + """ + Find all Lys-Cys amide conjugation sites in the molecule + + Returns: + List of tuples: (nitrogen_idx, carbonyl_carbon_idx, cys_alpha_carbon_idx) + """ + # Pattern: N-C(=O)-C(N)(C-S-S) + lys_cys_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH]([CH2][SX2][SX2])[NX3]') + matches = mol.GetSubstructMatches(lys_cys_pattern) + + sites = [] + for match in matches: + nitrogen_idx = match[0] # Lys nitrogen + carbonyl_c_idx = match[1] # Carbonyl carbon + cys_alpha_idx = match[3] # Cys α-carbon + sites.append((nitrogen_idx, carbonyl_c_idx, cys_alpha_idx)) + + return sites + + def check_valency_at_conjugation_site(self, mol, site_indices): + """ + Check that valency is correct at Lys-Cys conjugation site + + Args: + mol: RDKit Mol object + site_indices: Tuple of (nitrogen_idx, carbonyl_c_idx, cys_alpha_idx) + + Returns: + bool: True if valency is correct + """ + nitrogen_idx, carbonyl_c_idx, cys_alpha_idx = site_indices + + try: + # Lys nitrogen should have 3 bonds (NH connected to amide) + n_atom = mol.GetAtomWithIdx(nitrogen_idx) + if n_atom.GetTotalDegree() != 3: + return False + + # Carbonyl carbon should have 3 bonds (C=O and two single bonds) + c_atom = mol.GetAtomWithIdx(carbonyl_c_idx) + if c_atom.GetTotalDegree() != 3: + return False + + # Cys α-carbon should have 4 bonds + cys_c_atom = mol.GetAtomWithIdx(cys_alpha_idx) + if cys_c_atom.GetTotalDegree() != 4: + return False + + return True + + except Exception: + return False + + def check_attachment_site_compatibility(self, conjugate_smiles): + """ + Check attachment site compatibility for Lys-Cys-SS-Linker-Drug conjugation + + Validates: + 1. Lys-Cys amide conjugation is present + 2. Disulfide bond (S-S) is intact + 3. Valency at conjugation sites is correct + 4. No unreacted Cys-COOH (unconjugated linker-drug) + + Args: + conjugate_smiles: SMILES string of the conjugate + + Returns: + Tuple of (passed: bool, details: dict) + """ + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, { + 'parseable': False, + 'error': 'Could not parse SMILES' + } + + details = { + 'parseable': True, + 'validation_method': 'lys_cys_amide_disulfide' + } + + # Check 1: Lys-Cys amide conjugation present + details['has_lys_cys_amide'] = self.has_lys_cys_amide_conjugation(mol) + + # Check 2: Disulfide bond intact + details['has_disulfide_bond'] = self.has_disulfide_bond(mol) + + # Check 3: Find conjugation sites and validate valency + conjugation_sites = self.find_amide_conjugation_sites(mol) + details['conjugation_sites'] = len(conjugation_sites) + + valency_ok = True + if conjugation_sites: + for site in conjugation_sites: + if not self.check_valency_at_conjugation_site(mol, site): + valency_ok = False + break + details['valency_correct'] = valency_ok + + # Check 4: No unreacted Cys-COOH (would indicate unconjugated linker-drug) + details['no_unreacted_cys_cooh'] = not self.has_unreacted_cys_carboxylic_acid(mol) + + # Optional info: Check for unreacted Lys ε-amines (for multi-Lys peptides) + details['has_unreacted_lys_amine'] = self.has_unreacted_lys_epsilon_amine(mol) + + # Overall pass/fail + passed = all([ + details['has_lys_cys_amide'], + details['has_disulfide_bond'], + details['valency_correct'], + details['no_unreacted_cys_cooh'] + ]) + + return passed, details + + def _basic_validation(self, mol): + """ + Basic validation checks for Disulfide-Camptothecin conjugates + + Checks: + 1. Amide bond present (Lys-Cys conjugation) + 2. Disulfide bond present (S-S linker) + 3. Specific Lys-Cys-SS conjugation pattern + 4. Camptothecin core present + 5. Ester bond to drug present + """ + checks = {} + + # Check 1: Has amide bond (general check) + amide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[#6]') + checks['has_amide_bond'] = mol.HasSubstructMatch(amide_pattern) + + # Check 2: Disulfide bond present (S-S) + disulfide_pattern = Chem.MolFromSmarts('[#16]-[#16]') + checks['has_disulfide_bond'] = mol.HasSubstructMatch(disulfide_pattern) + + # Check 3: Specific Lys-Cys-SS conjugation pattern + # Looking for: N-C(=O)-C-C-S-S (amide to Cys to disulfide) + lys_cys_ss_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH]([CH2][SX2][SX2])[NX3]') + checks['has_lys_cys_ss_conjugation'] = mol.HasSubstructMatch(lys_cys_ss_pattern) + + # Check 4: Camptothecin core (quinoline fused ring system) + # Simplified pattern for the fused ring system + camptothecin_core = Chem.MolFromSmarts('c1ccc2ncc3C(=O)OCc3c2c1') + checks['has_camptothecin_core'] = mol.HasSubstructMatch(camptothecin_core) + + # Check 5: Ester bond (linker to drug) + ester_pattern = Chem.MolFromSmarts('[#6][CX3](=[OX1])[OX2][#6]') + checks['has_ester_bond'] = mol.HasSubstructMatch(ester_pattern) + + # Overall pass/fail - based on essential conjugation checks + checks['_passed'] = all([ + checks['has_amide_bond'], + checks['has_disulfide_bond'], + checks['has_lys_cys_ss_conjugation'] + ]) + + return checks + + def validate_conjugate(self, conjugate_smiles): + """ + Validate that the conjugate is chemically reasonable + + Returns: + (passed: bool, checks: dict) + """ + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'error': 'Could not parse SMILES'} + + checks = self._basic_validation(mol) + checks['parseable'] = True + + passed = checks.get('_passed', True) + if '_passed' in checks: + del checks['_passed'] + + return passed, checks + + +# ============================================================================ +# Main - Test Examples +# ============================================================================ + +if __name__ == "__main__": + + print("=" * 70) + print("Disulfide-Camptothecin PDC Builder - Test") + print("Reference: PDC_00294 (Z7, Ester) & PDC_00295 (Z8, Carbonate)") + print("=" * 70 + "\n") + + # Test peptides + test_peptides = [ + # Simple test: Gly-Lys-Gly (has N-terminus NH2 + Lys ε-amine) + "NCC(=O)N[C@@H](CCCCN)C(=O)NCC(=O)O", + # Lys-Arg + "N[C@@H](CCCCN)C(=O)N[C@@H](CCCNC(=N)N)C(=O)O", + ] + + print("\n" + "=" * 70) + print("Test 1: DEFAULT MODE (all chemistries: Ester + Carbonate)") + print("=" * 70 + "\n") + + # Default: linker_chemistry='all' + builder = DisulfideCamptothecin_PDC_Builder() + + peptide = test_peptides[0] + print(f"Input peptide (Gly-Lys-Gly): {peptide}") + + try: + conjugates = builder.conjugate_to_peptide(peptide, exclude_n_terminus=True) + print(f"\n Total conjugates: {len(conjugates)} (should be 2: 1 ester + 1 carbonate)") + + for i, conj in enumerate(conjugates): + valid, checks = builder.validate_conjugate(conj) + print(f" Conjugate {i+1}: {'PASSED' if valid else 'FAILED'} ({len(conj)} chars)") + + except Exception as e: + print(f"\n FAILED: {e}") + import traceback + traceback.print_exc() + + print("\n" + "=" * 70) + print("Test 2: Single Chemistry Mode (Ester only)") + print("=" * 70 + "\n") + + builder_ester = DisulfideCamptothecin_PDC_Builder(linker_chemistry='ester') + + print(f"Input peptide (Gly-Lys-Gly): {peptide}") + + try: + conjugates = builder_ester.conjugate_to_peptide(peptide, exclude_n_terminus=True) + print(f"\n Ester only: {len(conjugates)} conjugate(s)") + + except Exception as e: + print(f"\n FAILED: {e}") + + print("\n" + "=" * 70) + print("Test 3: Single Chemistry Mode (Carbonate only)") + print("=" * 70 + "\n") + + builder_carbonate = DisulfideCamptothecin_PDC_Builder(linker_chemistry='carbonate') + + print(f"Input peptide (Gly-Lys-Gly): {peptide}") + + try: + conjugates = builder_carbonate.conjugate_to_peptide(peptide, exclude_n_terminus=True) + print(f"\n Carbonate only: {len(conjugates)} conjugate(s)") + + except Exception as e: + print(f"\n FAILED: {e}") + + print("\n" + "=" * 70) + print("Test 4: Batch Conjugation (default: all chemistries)") + print("=" * 70 + "\n") + + builder = DisulfideCamptothecin_PDC_Builder() # default: all + + try: + results = builder.batch_conjugate(test_peptides, exclude_n_terminus=True) + + print(f"\nResults breakdown:") + for i in results['successful']: + conjugates = results['conjugates'][i] + num_valid = sum(1 for c in conjugates if builder.validate_conjugate(c)[0]) + name = ["Gly-Lys-Gly", "Lys-Arg"][i] + print(f" {name}: {len(conjugates)} conjugate(s), {num_valid} valid") + + except Exception as e: + print(f"\n FAILED: {e}") + import traceback + traceback.print_exc() diff --git a/smiles/conjugation/ValAlaDau.py b/smiles/conjugation/ValAlaDau.py new file mode 100644 index 0000000000000000000000000000000000000000..ee66c57f0b595f9e489d7f947ff09e92cae174cf --- /dev/null +++ b/smiles/conjugation/ValAlaDau.py @@ -0,0 +1,348 @@ +""" +Val-Ala-PABC-Daunorubicin PDC Builder + +Conjugation chemistry: Lysine ε-amine + Glutaryl-Val-Ala-PABC-Daunorubicin (amide bond formation) + +Reference PDCs: +- PDC_00185 (GnRH-III-[2ΔHis-3D-Tic, 8Lys(glutaryl-Val-Ala-PABC-Dau)]) +- PDC_00193 (GnRH-III-[2His-3Trp,8Lys(glutaryl-Val-Ala-PABC-Dau)]) +- Receptor: GNRHR +- Linker: Val-Ala (LIN00028) +- Payload: Daunorubicin (DRG00010) + +Architecture: + Peptide──Lys──NH──C(=O)──(CH₂)₃──C(=O)──Val──Ala──PABC──O──C(=O)──NH──Dau + │ │ │ │ + amide bond glutaryl dipeptide carbamate + (reaction) spacer cleavable self-immolative + +The cleavable dipeptide Val-Ala is recognized by cathepsin B (lysosomal protease). +After cleavage, PABC self-immolation releases free Daunorubicin. +""" + +from rdkit import Chem +from rdkit.Chem import RWMol, AllChem + +from .pdc_builder import PDC_Builder + + +class ValAlaPABCDaunorubicin_PDC_Builder(PDC_Builder): + """ + PDC builder for Glutaryl-Val-Ala-PABC-Daunorubicin conjugates + + Conjugation chemistry (amide bond formation): + - Peptide site: Lysine ε-amine (side chain -NH2) + - Linker-payload site: Free carboxylic acid on glutaryl spacer + - Reaction: Amide bond (Lys-NH2 + HOOC-glutaryl-Val-Ala-PABC-Dau) + + Architecture: + Peptide──Lys──NH──CO──(CH₂)₃──CO──Val──Ala──PABC──OCO──NH──Dau + └── amide ──┘ └── cleavable ──┘ └─ carbamate ─┘ + + Reference: PDC_00185, PDC_00193 (GnRH-III conjugates, GNRHR) + """ + + # Val-Ala-PABC-Daunorubicin with free glutaryl COOH + # Structure: HOOC-(CH2)3-CO-Val-Ala-PABC-OCO-NH-Dau + # - Glutaryl spacer connects to Val N-terminus via amide + # - Val-Ala dipeptide (cathepsin B cleavable) + # - PABC (para-aminobenzylcarbamate) self-immolative spacer + # - Carbamate bond from PABC to Dau 3'-amine + DEFAULT_VALA_PABC_DAU_SMILES = ( + "C[C@H]1[C@H]([C@H](C[C@@H](O1)O[C@H]2C[C@@](CC3=C2C(=C4C(=C3O)" + "C(=O)C5=C(C4=O)C(=CC=C5)OC)O)(C(=O)C)O)NC(=O)OCc6ccc(NC(=O)" + "[C@@H](C)NC(=O)[C@@H](C(C)C)NC(=O)CCCC(=O)O)cc6)O" + ) + + def __init__(self, vala_pabc_dau_smiles=None): + """ + Initialize Val-Ala-PABC-Daunorubicin builder + + Args: + vala_pabc_dau_smiles: Optional custom SMILES. If None, uses default. + Must have free carboxylic acid for conjugation. + """ + if vala_pabc_dau_smiles is None: + vala_pabc_dau_smiles = self.DEFAULT_VALA_PABC_DAU_SMILES + + super().__init__(vala_pabc_dau_smiles, + component_name="Val-Ala-PABC-Daunorubicin (linker-payload)") + + def _verify_reactive_group(self): + """Verify carboxylic acid group is present for amide bond formation""" + carboxylic_acid_pattern = Chem.MolFromSmarts('[CX3](=O)[OX2H1]') + + if not self.linker_payload_mol.HasSubstructMatch(carboxylic_acid_pattern): + print(" Warning: No carboxylic acid group detected in Val-Ala-PABC-Dau") + else: + print(" Val-Ala-PABC-Daunorubicin loaded with carboxylic acid reactive group") + + # Verify PABC benzyl carbamate + pabc_pattern = Chem.MolFromSmarts('c1ccc(COC(=O)N)cc1') + if self.linker_payload_mol.HasSubstructMatch(pabc_pattern): + print(" PABC self-immolative spacer detected") + else: + print(" Warning: No PABC pattern detected") + + # Verify dipeptide amide bonds + dipeptide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH][NX3][CX3](=[OX1])') + if self.linker_payload_mol.HasSubstructMatch(dipeptide_pattern): + print(" Dipeptide (Val-Ala) linkage detected") + + def find_conjugation_sites(self, peptide_smiles): + """Find all lysine ε-amine groups in peptide""" + return self._find_peptide_lysines(peptide_smiles) + + def _find_peptide_lysines(self, peptide_smiles): + """Find all lysine residues with free ε-amine in peptide""" + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + lysines = [] + + lys_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + matches = peptide_mol.GetSubstructMatches(lys_pattern) + + for match in matches: + nitrogen_idx = match[4] + lysines.append({ + 'atom_idx': nitrogen_idx, + 'side_chain_atoms': list(match), + 'type': 'Lys_epsilon_amine' + }) + + n_terminus_pattern = Chem.MolFromSmarts('[NH2][CH,CH2][C](=O)') + n_term_matches = peptide_mol.GetSubstructMatches(n_terminus_pattern) + if n_term_matches: + print(f" (Also found {len(n_term_matches)} N-terminus α-amine(s) - excluded by default)") + + return lysines + + def _perform_conjugation(self, peptide_mol, site_info, exclude_n_terminus=True): + """Perform amide bond conjugation""" + return self._manual_conjugation(peptide_mol, site_info, exclude_n_terminus) + + def _manual_conjugation(self, peptide_mol, lysine_info, exclude_n_terminus=True): + """ + Create ALL possible conjugates by forming amide bond at NH2 sites + + Chemistry: + - Any-NH2 + HOOC-glutaryl-Val-Ala-PABC-Dau → amide bond + H2O + """ + if exclude_n_terminus: + amide_rxn = AllChem.ReactionFromSmarts( + '[*:5][CH2][CH2][CH2][CH2:4][NH2:1].[C:2](=[O:3])[OH]>>' + '[*:5][CH2][CH2][CH2][CH2:4][NH:1][C:2](=[O:3])' + ) + else: + amide_rxn = AllChem.ReactionFromSmarts( + '[*:4][NH2:1].[C:2](=[O:3])[OH]>>[*:4][NH:1][C:2](=[O:3])' + ) + + products = amide_rxn.RunReactants((peptide_mol, self.linker_payload_mol)) + + if not products: + raise ValueError("Amide bond formation reaction failed - no products generated") + + conjugates = [] + seen_smiles = set() + + for product_tuple in products: + try: + conjugate_mol = product_tuple[0] + Chem.SanitizeMol(conjugate_mol) + conjugate_smiles = Chem.MolToSmiles(conjugate_mol) + + if conjugate_smiles not in seen_smiles: + seen_smiles.add(conjugate_smiles) + conjugates.append(conjugate_smiles) + except Exception: + continue + + if not conjugates: + raise ValueError("All products failed sanitization") + + return conjugates + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate Val-Ala-PABC-Daunorubicin to peptide via amide bond + + Args: + peptide_smiles: SMILES string of peptide (must contain NH2) + exclude_n_terminus: If True (default), only conjugate at Lys ε-amines + return_all: If True, return all possible conjugates + + Returns: + If return_all=True: List of all possible conjugate SMILES + If return_all=False: Single conjugate SMILES + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError(f"Invalid peptide SMILES: {peptide_smiles}") + + sites = self.find_conjugation_sites(peptide_smiles) + print(f"Found {len(sites)} Lys ε-amine site(s)") + + conjugates = self._perform_conjugation(peptide_mol, None, exclude_n_terminus) + + site_type = "Lys ε-amine only" if exclude_n_terminus else "all NH2 (including N-terminus)" + print(f"Generated {len(conjugates)} unique conjugate(s) [{site_type}]") + + if return_all: + return conjugates + else: + return conjugates[0] if conjugates else None + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """Batch conjugate multiple peptides""" + results = { + 'conjugates': [], + 'successful': [], + 'failed': [], + 'total_conjugates': 0 + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + except Exception as e: + print(f" Peptide {i} failed: {e}") + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + + return results + + # ========================================================================= + # Attachment Site Compatibility Checks + # ========================================================================= + + def has_pabc_carbamate(self, mol): + """Check for PABC benzyl carbamate: Ar-CH2-O-C(=O)-N""" + pabc_pattern = Chem.MolFromSmarts('c1ccc(COC(=O)[NX3])cc1') + if pabc_pattern: + return mol.HasSubstructMatch(pabc_pattern) + return False + + def has_val_ala_dipeptide(self, mol): + """ + Check for Val-Ala dipeptide fragment + + Pattern: isopropyl-CH-C(=O)-NH-CH(CH3)-C(=O) + """ + # General dipeptide amide pattern + dipeptide = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[CH][NX3][CX3](=[OX1])') + if dipeptide: + return mol.HasSubstructMatch(dipeptide) + return False + + def has_glutaryl_spacer(self, mol): + """Check for glutaryl spacer: -C(=O)-(CH2)3-C(=O)-""" + glutaryl = Chem.MolFromSmarts('[CX3](=[OX1])[CH2][CH2][CH2][CX3](=[OX1])') + if glutaryl: + return mol.HasSubstructMatch(glutaryl) + return False + + def has_unreacted_glutaryl_cooh(self, mol): + """Check if free COOH on glutaryl end remains (should be consumed)""" + unreacted = Chem.MolFromSmarts( + '[OX2H1][CX3](=[OX1])[CH2][CH2][CH2][CX3](=[OX1])[NX3]' + ) + if unreacted: + return mol.HasSubstructMatch(unreacted) + return False + + def check_attachment_site_compatibility(self, mol): + """ + Check if attachment site chemistry is valid + + Validates: + 1. Has PABC carbamate (intact self-immolative spacer) + 2. Has glutaryl spacer + 3. No unreacted glutaryl-COOH + """ + if not self.has_pabc_carbamate(mol): + return False + + if not self.has_glutaryl_spacer(mol): + return False + + if self.has_unreacted_glutaryl_cooh(mol): + return False + + return True + + def _basic_validation(self, mol): + """Basic validation checks for Val-Ala-PABC-Daunorubicin conjugates""" + checks = {} + + amide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[#6]') + checks['has_amide_bond'] = mol.HasSubstructMatch(amide_pattern) + + checks['has_pabc_carbamate'] = self.has_pabc_carbamate(mol) + checks['has_val_ala_dipeptide'] = self.has_val_ala_dipeptide(mol) + checks['has_glutaryl_spacer'] = self.has_glutaryl_spacer(mol) + checks['no_unreacted_cooh'] = not self.has_unreacted_glutaryl_cooh(mol) + checks['attachment_site_valid'] = self.check_attachment_site_compatibility(mol) + + anthraquinone = Chem.MolFromSmarts( + '[#6]1[#6][#6][#6]2[#6](=[#8])[#6]3[#6][#6][#6][#6][#6]3[#6](=[#8])[#6]2[#6]1' + ) + checks['has_daunorubicin_core'] = mol.HasSubstructMatch(anthraquinone) + + aminosugar = Chem.MolFromSmarts('[OX2]1[#6][#6][#6]([NX3])[#6][#6]1') + checks['has_aminosugar'] = mol.HasSubstructMatch(aminosugar) + + checks['_passed'] = checks['attachment_site_valid'] + + return checks + + def validate_conjugate(self, conjugate_smiles): + """Validate that the conjugate is chemically reasonable""" + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'error': 'Could not parse SMILES'} + + checks = self._basic_validation(mol) + checks['parseable'] = True + + passed = checks.get('_passed', True) + if '_passed' in checks: + del checks['_passed'] + + return passed, checks + + +if __name__ == "__main__": + print("=" * 70) + print("Val-Ala-PABC-Daunorubicin PDC Builder - Test") + print("=" * 70 + "\n") + + builder = ValAlaPABCDaunorubicin_PDC_Builder() + + test_peptides = [ + "NCC(=O)N[C@@H](CCCCN)C(=O)NCC(=O)O", # Gly-Lys-Gly + ] + + for pep in test_peptides: + try: + conjugates = builder.conjugate_to_peptide(pep, exclude_n_terminus=True) + print(f" {len(conjugates)} conjugate(s)") + for c in conjugates: + valid, checks = builder.validate_conjugate(c) + print(f" Valid: {valid}") + except Exception as e: + print(f" Failed: {e}") diff --git a/smiles/conjugation/ValCitDau.py b/smiles/conjugation/ValCitDau.py new file mode 100644 index 0000000000000000000000000000000000000000..b300299e897c291e265458076f89ad48cd07344a --- /dev/null +++ b/smiles/conjugation/ValCitDau.py @@ -0,0 +1,358 @@ +""" +Val-Cit-PABC-Daunorubicin PDC Builder + +Conjugation chemistry: Lysine ε-amine + Glutaryl-Val-Cit-PABC-Daunorubicin (amide bond formation) + +Reference PDCs: +- PDC_00189 (GnRH-III-[2ΔHis-3D-Tic,8Lys(glutaryl-Val-Cit-PABC-Dau)]) +- PDC_00194 (GnRH-III-[2His-3Trp,8Lys(glutaryl-Val-Cit-PABC-Dau)]) +- Receptor: GNRHR +- Linker: Val-Cit (LIN00005) +- Payload: Daunorubicin (DRG00010) + +Architecture: + Peptide──Lys──NH──C(=O)──(CH₂)₃──C(=O)──Val──Cit──PABC──O──C(=O)──NH──Dau + │ │ │ │ + amide bond glutaryl dipeptide carbamate + (reaction) spacer cleavable self-immolative + +The cleavable dipeptide Val-Cit (valine-citrulline) is recognized by +cathepsin B (lysosomal protease). After cleavage, PABC self-immolation +releases free Daunorubicin. + +Citrulline (Cit) is a non-coded amino acid with a ureido side chain: + -CH₂-CH₂-CH₂-NH-C(=O)-NH₂ +""" + +from rdkit import Chem +from rdkit.Chem import RWMol, AllChem + +from .pdc_builder import PDC_Builder + + +class ValCitPABCDaunorubicin_PDC_Builder(PDC_Builder): + """ + PDC builder for Glutaryl-Val-Cit-PABC-Daunorubicin conjugates + + Conjugation chemistry (amide bond formation): + - Peptide site: Lysine ε-amine (side chain -NH2) + - Linker-payload site: Free carboxylic acid on glutaryl spacer + - Reaction: Amide bond (Lys-NH2 + HOOC-glutaryl-Val-Cit-PABC-Dau) + + Architecture: + Peptide──Lys──NH──CO──(CH₂)₃──CO──Val──Cit──PABC──OCO──NH──Dau + └── amide ──┘ └── cleavable ──┘ └─ carbamate ─┘ + + Reference: PDC_00189, PDC_00194 (GnRH-III conjugates, GNRHR) + """ + + # Val-Cit-PABC-Daunorubicin with free glutaryl COOH + # Structure: HOOC-(CH2)3-CO-Val-Cit-PABC-OCO-NH-Dau + # - Glutaryl spacer connects to Val N-terminus via amide + # - Val-Cit dipeptide (cathepsin B cleavable, Cit has ureido side chain) + # - PABC (para-aminobenzylcarbamate) self-immolative spacer + # - Carbamate bond from PABC to Dau 3'-amine + DEFAULT_VALCIT_PABC_DAU_SMILES = ( + "C[C@H]1[C@H]([C@H](C[C@@H](O1)O[C@H]2C[C@@](CC3=C2C(=C4C(=C3O)" + "C(=O)C5=C(C4=O)C(=CC=C5)OC)O)(C(=O)C)O)NC(=O)OCc6ccc(NC(=O)" + "[C@@H](CCCNC(=O)N)NC(=O)[C@@H](C(C)C)NC(=O)CCCC(=O)O)cc6)O" + ) + + def __init__(self, valcit_pabc_dau_smiles=None): + """ + Initialize Val-Cit-PABC-Daunorubicin builder + + Args: + valcit_pabc_dau_smiles: Optional custom SMILES. If None, uses default. + Must have free carboxylic acid for conjugation. + """ + if valcit_pabc_dau_smiles is None: + valcit_pabc_dau_smiles = self.DEFAULT_VALCIT_PABC_DAU_SMILES + + super().__init__(valcit_pabc_dau_smiles, + component_name="Val-Cit-PABC-Daunorubicin (linker-payload)") + + def _verify_reactive_group(self): + """Verify carboxylic acid group is present for amide bond formation""" + carboxylic_acid_pattern = Chem.MolFromSmarts('[CX3](=O)[OX2H1]') + + if not self.linker_payload_mol.HasSubstructMatch(carboxylic_acid_pattern): + print(" Warning: No carboxylic acid group detected in Val-Cit-PABC-Dau") + else: + print(" Val-Cit-PABC-Daunorubicin loaded with carboxylic acid reactive group") + + # Verify PABC benzyl carbamate + pabc_pattern = Chem.MolFromSmarts('c1ccc(COC(=O)N)cc1') + if self.linker_payload_mol.HasSubstructMatch(pabc_pattern): + print(" PABC self-immolative spacer detected") + else: + print(" Warning: No PABC pattern detected") + + # Verify citrulline ureido group + urea_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[NX3]') + if self.linker_payload_mol.HasSubstructMatch(urea_pattern): + print(" Citrulline ureido group detected") + else: + print(" Warning: No citrulline ureido group detected") + + def find_conjugation_sites(self, peptide_smiles): + """Find all lysine ε-amine groups in peptide""" + return self._find_peptide_lysines(peptide_smiles) + + def _find_peptide_lysines(self, peptide_smiles): + """Find all lysine residues with free ε-amine in peptide""" + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + lysines = [] + + lys_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + matches = peptide_mol.GetSubstructMatches(lys_pattern) + + for match in matches: + nitrogen_idx = match[4] + lysines.append({ + 'atom_idx': nitrogen_idx, + 'side_chain_atoms': list(match), + 'type': 'Lys_epsilon_amine' + }) + + n_terminus_pattern = Chem.MolFromSmarts('[NH2][CH,CH2][C](=O)') + n_term_matches = peptide_mol.GetSubstructMatches(n_terminus_pattern) + if n_term_matches: + print(f" (Also found {len(n_term_matches)} N-terminus α-amine(s) - excluded by default)") + + return lysines + + def _perform_conjugation(self, peptide_mol, site_info, exclude_n_terminus=True): + """Perform amide bond conjugation""" + return self._manual_conjugation(peptide_mol, site_info, exclude_n_terminus) + + def _manual_conjugation(self, peptide_mol, lysine_info, exclude_n_terminus=True): + """ + Create ALL possible conjugates by forming amide bond at NH2 sites + + Chemistry: + - Any-NH2 + HOOC-glutaryl-Val-Cit-PABC-Dau → amide bond + H2O + """ + if exclude_n_terminus: + amide_rxn = AllChem.ReactionFromSmarts( + '[*:5][CH2][CH2][CH2][CH2:4][NH2:1].[C:2](=[O:3])[OH]>>' + '[*:5][CH2][CH2][CH2][CH2:4][NH:1][C:2](=[O:3])' + ) + else: + amide_rxn = AllChem.ReactionFromSmarts( + '[*:4][NH2:1].[C:2](=[O:3])[OH]>>[*:4][NH:1][C:2](=[O:3])' + ) + + products = amide_rxn.RunReactants((peptide_mol, self.linker_payload_mol)) + + if not products: + raise ValueError("Amide bond formation reaction failed - no products generated") + + conjugates = [] + seen_smiles = set() + + for product_tuple in products: + try: + conjugate_mol = product_tuple[0] + Chem.SanitizeMol(conjugate_mol) + conjugate_smiles = Chem.MolToSmiles(conjugate_mol) + + if conjugate_smiles not in seen_smiles: + seen_smiles.add(conjugate_smiles) + conjugates.append(conjugate_smiles) + except Exception: + continue + + if not conjugates: + raise ValueError("All products failed sanitization") + + return conjugates + + def conjugate_to_peptide(self, peptide_smiles, exclude_n_terminus=True, return_all=True): + """ + Conjugate Val-Cit-PABC-Daunorubicin to peptide via amide bond + + Args: + peptide_smiles: SMILES string of peptide (must contain NH2) + exclude_n_terminus: If True (default), only conjugate at Lys ε-amines + return_all: If True, return all possible conjugates + + Returns: + If return_all=True: List of all possible conjugate SMILES + If return_all=False: Single conjugate SMILES + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError(f"Invalid peptide SMILES: {peptide_smiles}") + + sites = self.find_conjugation_sites(peptide_smiles) + print(f"Found {len(sites)} Lys ε-amine site(s)") + + conjugates = self._perform_conjugation(peptide_mol, None, exclude_n_terminus) + + site_type = "Lys ε-amine only" if exclude_n_terminus else "all NH2 (including N-terminus)" + print(f"Generated {len(conjugates)} unique conjugate(s) [{site_type}]") + + if return_all: + return conjugates + else: + return conjugates[0] if conjugates else None + + def batch_conjugate(self, peptide_smiles_list, exclude_n_terminus=True, return_all=True): + """Batch conjugate multiple peptides""" + results = { + 'conjugates': [], + 'successful': [], + 'failed': [], + 'total_conjugates': 0 + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugates = self.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus, + return_all=return_all + ) + if return_all: + results['conjugates'].append(conjugates) + results['total_conjugates'] += len(conjugates) + else: + results['conjugates'].append(conjugates) + results['total_conjugates'] += 1 + results['successful'].append(i) + except Exception as e: + print(f" Peptide {i} failed: {e}") + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + + return results + + # ========================================================================= + # Attachment Site Compatibility Checks + # ========================================================================= + + def has_pabc_carbamate(self, mol): + """Check for PABC benzyl carbamate: Ar-CH2-O-C(=O)-N""" + pabc_pattern = Chem.MolFromSmarts('c1ccc(COC(=O)[NX3])cc1') + if pabc_pattern: + return mol.HasSubstructMatch(pabc_pattern) + return False + + def has_val_cit_dipeptide(self, mol): + """ + Check for Val-Cit dipeptide fragment + + Citrulline has a ureido side chain: -CH₂CH₂CH₂-NH-C(=O)-NH₂ + """ + # Check for ureido (citrulline marker) + urea_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[NX3]') + if urea_pattern: + return mol.HasSubstructMatch(urea_pattern) + return False + + def has_glutaryl_spacer(self, mol): + """Check for glutaryl spacer: -C(=O)-(CH2)3-C(=O)-""" + glutaryl = Chem.MolFromSmarts('[CX3](=[OX1])[CH2][CH2][CH2][CX3](=[OX1])') + if glutaryl: + return mol.HasSubstructMatch(glutaryl) + return False + + def has_unreacted_glutaryl_cooh(self, mol): + """Check if free COOH on glutaryl end remains (should be consumed)""" + unreacted = Chem.MolFromSmarts( + '[OX2H1][CX3](=[OX1])[CH2][CH2][CH2][CX3](=[OX1])[NX3]' + ) + if unreacted: + return mol.HasSubstructMatch(unreacted) + return False + + def check_attachment_site_compatibility(self, mol): + """ + Check if attachment site chemistry is valid + + Validates: + 1. Has PABC carbamate + 2. Has glutaryl spacer + 3. Has citrulline ureido group + 4. No unreacted glutaryl-COOH + """ + if not self.has_pabc_carbamate(mol): + return False + + if not self.has_glutaryl_spacer(mol): + return False + + if not self.has_val_cit_dipeptide(mol): + return False + + if self.has_unreacted_glutaryl_cooh(mol): + return False + + return True + + def _basic_validation(self, mol): + """Basic validation checks for Val-Cit-PABC-Daunorubicin conjugates""" + checks = {} + + amide_pattern = Chem.MolFromSmarts('[NX3][CX3](=[OX1])[#6]') + checks['has_amide_bond'] = mol.HasSubstructMatch(amide_pattern) + + checks['has_pabc_carbamate'] = self.has_pabc_carbamate(mol) + checks['has_val_cit_dipeptide'] = self.has_val_cit_dipeptide(mol) + checks['has_glutaryl_spacer'] = self.has_glutaryl_spacer(mol) + checks['no_unreacted_cooh'] = not self.has_unreacted_glutaryl_cooh(mol) + checks['attachment_site_valid'] = self.check_attachment_site_compatibility(mol) + + anthraquinone = Chem.MolFromSmarts( + '[#6]1[#6][#6][#6]2[#6](=[#8])[#6]3[#6][#6][#6][#6][#6]3[#6](=[#8])[#6]2[#6]1' + ) + checks['has_daunorubicin_core'] = mol.HasSubstructMatch(anthraquinone) + + aminosugar = Chem.MolFromSmarts('[OX2]1[#6][#6][#6]([NX3])[#6][#6]1') + checks['has_aminosugar'] = mol.HasSubstructMatch(aminosugar) + + checks['_passed'] = checks['attachment_site_valid'] + + return checks + + def validate_conjugate(self, conjugate_smiles): + """Validate that the conjugate is chemically reasonable""" + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'error': 'Could not parse SMILES'} + + checks = self._basic_validation(mol) + checks['parseable'] = True + + passed = checks.get('_passed', True) + if '_passed' in checks: + del checks['_passed'] + + return passed, checks + + +if __name__ == "__main__": + print("=" * 70) + print("Val-Cit-PABC-Daunorubicin PDC Builder - Test") + print("=" * 70 + "\n") + + builder = ValCitPABCDaunorubicin_PDC_Builder() + + test_peptides = [ + "NCC(=O)N[C@@H](CCCCN)C(=O)NCC(=O)O", # Gly-Lys-Gly + ] + + for pep in test_peptides: + try: + conjugates = builder.conjugate_to_peptide(pep, exclude_n_terminus=True) + print(f" {len(conjugates)} conjugate(s)") + for c in conjugates: + valid, checks = builder.validate_conjugate(c) + print(f" Valid: {valid}") + except Exception as e: + print(f" Failed: {e}") diff --git a/smiles/conjugation/VcMMAE.py b/smiles/conjugation/VcMMAE.py new file mode 100644 index 0000000000000000000000000000000000000000..2d8332f0c2c855fae48d078f0296c4593c5b8ff7 --- /dev/null +++ b/smiles/conjugation/VcMMAE.py @@ -0,0 +1,457 @@ +from rdkit import Chem +from rdkit.Chem import RWMol + +from .pdc_builder import PDC_Builder + + +class VcMMAE_PDC_Builder(PDC_Builder): + """ + Specialized builder for vcMMAE (valine-citrulline-MMAE) conjugates + + vcMMAE consists of: + - Linker: Val-Cit-PABC (valine-citrulline-para-aminobenzylcarbamate) - cleavable dipeptide + - Payload: MMAE (monomethyl auristatin E) - cytotoxic drug + - Reactive group: Maleimide for cysteine conjugation + + Conjugation chemistry (thiol-maleimide Michael addition): + - Peptide site: Free cysteine thiol (-SH) + - vcMMAE site: Maleimide group (C=C in 5-membered ring) + - Reaction: Thiol adds to maleimide C=C, forming stable thioether bond + - Product: Peptide-S-vcMMAE conjugate + """ + + def __init__(self, vcmmae_smiles=None): + """ + Initialize vcMMAE builder + + Args: + vcmmae_smiles: Optional custom vcMMAE (linker-payload) SMILES. If None, uses default. + """ + # Default vcMMAE SMILES: maleimide-(6-carbon spacer)-Val-Cit-PABC-MMAE + # Structure: Maleimide-C6-Val-Cit-PABC-MMAE + if vcmmae_smiles is None: + vcmmae_smiles = "O[C@@H](C1=CC=CC=C1)[C@@H](C)NC([C@H](C)[C@H]([C@@]2([H])N(CCC2)C(C[C@@H](OC)[C@H]([C@@H](C)CC)N(C)C([C@H](C(C)C)NC([C@H](C(C)C)N(C)C(OCC3=CC=C(C=C3)NC([C@H](CCCNC(N)=O)NC([C@H](C(C)C)NC(CCCCCN4C(C=CC4=O)=O)=O)=O)=O)=O)=O)=O)=O)OC)=O" + + # Initialize base class + super().__init__(vcmmae_smiles, component_name="vcMMAE (linker-payload)") + + def _verify_reactive_group(self): + """Verify maleimide group is present""" + maleimide_pattern = Chem.MolFromSmarts('[#7]1[#6](=[#8])[#6]=[#6][#6]1=[#8]') + if not self.linker_payload_mol.HasSubstructMatch(maleimide_pattern): + print(" Warning: No maleimide group detected") + else: + print(" vcMMAE (linker-payload) loaded with maleimide reactive group") + + def find_conjugation_sites(self, peptide_smiles): + """ + Find all free cysteine residues (thiol groups) in peptide + + Returns list of dicts with site information including atom index + """ + return self.find_peptide_cysteines(peptide_smiles) + + def find_peptide_cysteines(self, peptide_smiles): + """ + Find all free cysteine residues in peptide + + FIX: Iterate over all atoms instead of using restrictive SMARTS + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError("Invalid peptide SMILES") + + cysteines = [] + + # FIX: Directly check each sulfur atom + for atom in peptide_mol.GetAtoms(): + if atom.GetAtomicNum() == 16: # Sulfur + # Check if it's a thiol (has hydrogen attached) + total_hs = atom.GetTotalNumHs() # Includes implicit H + degree = atom.GetDegree() # Number of bonds to other heavy atoms + + # Thiol: S with 1 H and bonded to 1 carbon + # (vs disulfide: S-S with no H) + if total_hs >= 1 and degree == 1: + cysteines.append({ + 'atom_idx': atom.GetIdx(), + 'num_hydrogens': total_hs, + 'neighbors': [peptide_mol.GetAtomWithIdx(n.GetIdx()).GetSymbol() + for n in atom.GetNeighbors()] + }) + + return cysteines + + def _find_maleimide_atoms(self, mol): + """ + Find the maleimide C=C double bond atoms + + Returns: (carbon1_idx, carbon2_idx) of the C=C in maleimide + """ + # Pattern: N in ring, with two C=O, and C=C + # O=C-C=C-C=O with N closing the ring + maleimide_pattern = Chem.MolFromSmarts('[#7]1[#6](=[#8])[#6]=[#6][#6]1=[#8]') + matches = mol.GetSubstructMatches(maleimide_pattern) + + if not matches: + return None, None + + # Pattern indices: 0=N, 1=C(=O), 2=C(of C=C), 3=C(of C=C), 4=C(=O) + match = matches[0] + return match[2], match[3] + + def _perform_conjugation(self, peptide_mol, site_info): + """ + Perform thiol-maleimide conjugation + + Uses manual bond formation for reliability with complex molecules + """ + return self._manual_conjugation(peptide_mol, site_info) + + def conjugate_to_peptide(self, peptide_smiles, cys_position=0): + """ + Conjugate vcMMAE to peptide via thiol-maleimide Michael addition + + Compatibility wrapper - calls parent class method with renamed parameter + """ + return super().conjugate_to_peptide(peptide_smiles, site_position=cys_position) + + def batch_conjugate(self, peptide_smiles_list, cys_position=0): + """ + Batch conjugate multiple peptides + + Compatibility wrapper - calls parent class method with renamed parameter + """ + return super().batch_conjugate(peptide_smiles_list, site_position=cys_position) + + # ========================================================================= + # Attachment Site Compatibility Checks (thiol-maleimide specific) + # ========================================================================= + + def has_free_thiol(self, mol): + """ + Check if molecule has unconjugated (free) thiol groups + + A valid PDC should NOT have free thiols - they should be conjugated + """ + # SMARTS pattern check + free_thiol_pattern = Chem.MolFromSmarts('[CH2][SH1]') + free_thiol_alt = Chem.MolFromSmarts('[CH2][S;H1;v2]') + + if free_thiol_pattern and mol.HasSubstructMatch(free_thiol_pattern): + return True + if free_thiol_alt and mol.HasSubstructMatch(free_thiol_alt): + return True + + # Also check by iterating atoms (more reliable) + for atom in mol.GetAtoms(): + if atom.GetAtomicNum() == 16: # Sulfur + if atom.GetTotalNumHs() >= 1 and atom.GetDegree() == 1: + # Sulfur with H and only one bond = free thiol + return True + + return False + + def has_unreacted_maleimide(self, mol): + """ + Check if molecule has unreacted maleimide (C=C in ring) + + After thiol-maleimide conjugation, the C=C should become C-C + """ + maleimide_unreacted = Chem.MolFromSmarts('[#7]1[#6](=[#8])[#6]=[#6][#6]1=[#8]') + if maleimide_unreacted: + return mol.HasSubstructMatch(maleimide_unreacted) + return False + + def find_thioether_conjugation_sites(self, mol): + """ + Identify thioether (C-S-C) conjugation sites in the molecule + + For thiol-maleimide conjugation, the sulfur should be bonded to: + - One carbon from the peptide cysteine + - One carbon from the (former) maleimide ring + + Returns list of sulfur atom indices that are conjugation sites + """ + thioether_pattern = Chem.MolFromSmarts('[#6][#16][#6]') + conjugation_sites = [] + + if thioether_pattern: + matches = mol.GetSubstructMatches(thioether_pattern) + for match in matches: + sulfur_idx = match[1] # S is the middle atom in C-S-C + sulfur_atom = mol.GetAtomWithIdx(sulfur_idx) + + # Check if one neighbor is in a 5-membered ring (succinimide) + neighbors = [mol.GetAtomWithIdx(n.GetIdx()) for n in sulfur_atom.GetNeighbors()] + for neighbor in neighbors: + if neighbor.IsInRingSize(5): + conjugation_sites.append(sulfur_idx) + break + else: + # Even if not clearly in succinimide, thioether is likely conjugation + conjugation_sites.append(sulfur_idx) + + return list(set(conjugation_sites)) # Remove duplicates + + def check_valency_at_conjugation_site(self, mol, conjugation_atom_idx): + """ + Check valency specifically at a sulfur conjugation site atom + + Valid thioether states for thiol-maleimide conjugation: + - Thioether: S with 2 single bonds to C, no H (C-S-C) + - NOT: free thiol, sulfonium, hypervalent sulfur + """ + atom = mol.GetAtomWithIdx(conjugation_atom_idx) + + if atom.GetSymbol() != 'S': + return True # Not sulfur, skip this check + + degree = atom.GetDegree() # Number of bonds to other heavy atoms + total_hs = atom.GetTotalNumHs() + formal_charge = atom.GetFormalCharge() + + # Get neighbor information + neighbors = [mol.GetAtomWithIdx(n.GetIdx()) for n in atom.GetNeighbors()] + neighbor_symbols = [n.GetSymbol() for n in neighbors] + + # Count double bonds to oxygen (for sulfoxide/sulfone) + double_bonded_oxygens = 0 + for neighbor in neighbors: + if neighbor.GetSymbol() == 'O': + bond = mol.GetBondBetweenAtoms(conjugation_atom_idx, neighbor.GetIdx()) + if bond and bond.GetBondType() == Chem.BondType.DOUBLE: + double_bonded_oxygens += 1 + + # ========== Valid Sulfur States ========== + + # 1. Thioether: S with 2 single bonds to C, no H + # C-S-C (degree=2, no O, no charge) + if degree == 2 and 'O' not in neighbor_symbols and formal_charge == 0: + if total_hs == 0: + return True # Valid thioether + else: + return False # Has H = still partially a thiol + + # 2. Sulfoxide: S with 1 double-bonded O + # C-S(=O)-C (degree=3, one =O) + if degree == 3 and double_bonded_oxygens == 1 and formal_charge == 0: + return True + + # 3. Sulfone: S with 2 double-bonded O + # C-S(=O)(=O)-C (degree=4, two =O) + if degree == 4 and double_bonded_oxygens == 2 and formal_charge == 0: + return True + + # ========== Invalid Sulfur States ========== + + # Sulfonium (positively charged S with 3+ bonds) - INVALID + if degree >= 3 and formal_charge > 0: + return False + + # Hypervalent sulfur without proper oxygen - INVALID + if degree > 2 and double_bonded_oxygens == 0: + return False + + # If none of the valid patterns matched, it's invalid + return False + + def check_attachment_site_compatibility(self, mol): + """ + Check if thiol-maleimide attachment site chemistry is valid + + For vcMMAE conjugation, validates: + 1. No free thiols (conjugation must have happened) + 2. Has thioether bond (C-S-C) + 3. No unreacted maleimide (C=C should be reduced) + 4. Valid valency at conjugation sites + + Args: + mol: RDKit Mol object of the conjugate + + Returns: + bool: True if attachment site is valid, False otherwise + """ + # ===== CRITICAL CHECK: Reject if free thiol present ===== + # A valid PDC should have the thiol CONSUMED by conjugation + if self.has_free_thiol(mol): + return False + + # ===== Check for thioether (C-S-C) ===== + thioether_pattern = Chem.MolFromSmarts('[#6][#16][#6]') + if not thioether_pattern or not mol.HasSubstructMatch(thioether_pattern): + return False # No thioether = no conjugation + + # ===== Check valency at conjugation sites ===== + conjugation_sites = self.find_thioether_conjugation_sites(mol) + + for site_idx in conjugation_sites: + if not self.check_valency_at_conjugation_site(mol, site_idx): + return False + + return True + + # ========================================================================= + # Manual Conjugation + # ========================================================================= + + def _manual_conjugation(self, peptide_mol, cysteine_info): + """ + Create conjugate by manually forming bonds + + Chemistry: + - Maleimide C=C becomes C-C (single bond) + - Sulfur bonds to one of these carbons + - Proton transfers (S loses H, C gains H) + + Result: Peptide-Cys-S-(linker-payload) + """ + + # Find maleimide C=C in vcMMAE linker-payload + maleimide_c1, maleimide_c2 = self._find_maleimide_atoms(self.linker_payload_mol) + + if maleimide_c1 is None: + raise ValueError("Could not locate maleimide in vcMMAE linker-payload") + + sulfur_idx = cysteine_info['atom_idx'] + + # Step 1: Combine molecules + combined = Chem.CombineMols(self.linker_payload_mol, peptide_mol) + combined_rw = RWMol(combined) + + # Step 2: Adjust indices (peptide atoms are shifted) + linker_payload_atom_count = self.linker_payload_mol.GetNumAtoms() + new_sulfur_idx = sulfur_idx + linker_payload_atom_count + + # Step 3: Change maleimide C=C to C-C + bond = combined_rw.GetBondBetweenAtoms(maleimide_c1, maleimide_c2) + if bond is not None: + bond.SetBondType(Chem.BondType.SINGLE) + + # Step 4: Add S-C bond (sulfur to one maleimide carbon) + combined_rw.AddBond(new_sulfur_idx, maleimide_c2, Chem.BondType.SINGLE) + + # Step 5: Sanitize and clean up + try: + mol = combined_rw.GetMol() + Chem.SanitizeMol(mol) + conjugate_smiles = Chem.MolToSmiles(mol) + return conjugate_smiles + + except Exception as e: + # Try alternative sanitization + try: + mol = combined_rw.GetMol() + mol = Chem.RemoveAllHs(mol) + Chem.SanitizeMol(mol) + conjugate_smiles = Chem.MolToSmiles(mol) + return conjugate_smiles + except Exception as e2: + raise ValueError(f"Sanitization failed: {e2}") + + def _basic_validation(self, mol): + """ + Basic validation checks for thiol-maleimide conjugates + + Includes attachment site compatibility check with valency validation. + + Returns dict of check_name: bool pairs + """ + checks = {} + + # Check 1: Has thioether (the new C-S-C bond) + thioether = Chem.MolFromSmarts('[#6]-[#16]-[#6]') + checks['has_thioether'] = mol.HasSubstructMatch(thioether) + + # Check 2: Maleimide C=C is gone (should be succinimide now) + checks['maleimide_consumed'] = not self.has_unreacted_maleimide(mol) + + # Check 3: Has succinimide (reduced maleimide) + succinimide = Chem.MolFromSmarts('[#7]1[#6](=[#8])[#6][#6][#6]1=[#8]') + checks['has_succinimide'] = mol.HasSubstructMatch(succinimide) + + # Check 4: No free thiol remaining + checks['no_free_thiol'] = not self.has_free_thiol(mol) + + # Check 5: Full attachment site compatibility (includes valency) + checks['attachment_site_valid'] = self.check_attachment_site_compatibility(mol) + + # Find and report conjugation sites for diagnostics + conjugation_sites = self.find_thioether_conjugation_sites(mol) + checks['conjugation_sites'] = conjugation_sites + + # Overall pass/fail - attachment_site_valid includes thioether and no_free_thiol checks + checks['_passed'] = checks['attachment_site_valid'] + + return checks + + + +# ============================================================================ +# Main - Test Examples +# ============================================================================ + +if __name__ == "__main__": + + print("=" * 70) + print("PDC Builder Framework - vcMMAE Example") + print("=" * 70 + "\n") + + builder = VcMMAE_PDC_Builder() + + # Test peptides with free cysteine + test_peptides = [ + "NCC(=O)N[C@@H](CS)C(=O)NCC(=O)O", # Gly-Cys-Gly + "N[C@@H](CS)C(=O)N[C@@H](CCCNC(=N)N)C(=O)O", # Cys-Arg + "N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CS)C(=O)N[C@@H](CC(C)C)C(=O)O", # Arg-Cys-Leu + ] + + print("\n" + "=" * 70) + print("Testing Single Conjugation") + print("=" * 70 + "\n") + + peptide = test_peptides[0] + print(f"Input peptide: {peptide}") + + try: + conjugate = builder.conjugate_to_peptide(peptide) + print(f"\n SUCCESS!") + print(f"Conjugate SMILES: {conjugate[:80]}...") + + valid, checks = builder.validate_conjugate(conjugate, use_analyzer=False) + print(f"\n--- Basic Validation ---") + print(f"Result: {'PASSED' if valid else 'FAILED'}") + for k, v in checks.items(): + print(f" {k}: {v}") + + except Exception as e: + print(f"\n FAILED: {e}") + import traceback + traceback.print_exc() + + print("\n" + "=" * 70) + print("Batch Conjugation & Validation") + print("=" * 70 + "\n") + + # Batch conjugate + results = builder.batch_conjugate(test_peptides) + + # Get successful conjugates + successful_conjugates = [c for c in results['conjugates'] if c is not None] + + if len(successful_conjugates) > 0: + print(f"\nValidating {len(successful_conjugates)} conjugates...") + + # Basic validation + print("\n--- Basic Validation ---") + basic_results = builder.batch_validate(successful_conjugates, use_analyzer=False) + + for i, (passed, checks) in enumerate(basic_results): + status = " VALID" if passed else " INVALID" + print(f" Conjugate {i+1}: {status}") + + if not passed: + # Show which checks failed + for check_name, check_val in checks.items(): + if check_name not in ['validation_method', 'parseable', 'conjugation_sites'] and not check_val: + print(f" Failed: {check_name}") \ No newline at end of file diff --git a/smiles/conjugation/__init__.py b/smiles/conjugation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..09fd7c40226f333555a7706455b098b56578a914 --- /dev/null +++ b/smiles/conjugation/__init__.py @@ -0,0 +1 @@ +"""PDC conjugation builders.""" diff --git a/smiles/conjugation/pdc_builder.py b/smiles/conjugation/pdc_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..aeb5d3c2a872f8b4826ff0b9dd9624ad6f3954a4 --- /dev/null +++ b/smiles/conjugation/pdc_builder.py @@ -0,0 +1,223 @@ +""" +PDC Builders - General and Specialized +""" + +from rdkit import Chem +from rdkit.Chem import Descriptors +from abc import ABC, abstractmethod + + +class PDC_Builder(ABC): + """ + Base class for Peptide-Drug Conjugate (PDC) builders + + This abstract class defines the interface for building PDCs with different + conjugation chemistries. Subclasses must implement specific conjugation rules. + + The conjugation component can be: + - Just a drug payload with reactive group + - A linker-payload combination (e.g., vcMMAE = Val-Cit linker + MMAE payload) + - A linker alone (for later payload attachment) + """ + + def __init__(self, linker_payload_smiles, component_name="Drug-Linker"): + """ + Initialize PDC builder with a linker-payload component + + Args: + linker_payload_smiles: SMILES string of the linker-payload component + component_name: Name of the component (for display) + """ + self.linker_payload_smiles = linker_payload_smiles + self.component_name = component_name + self.linker_payload_mol = Chem.MolFromSmiles(linker_payload_smiles) + + if self.linker_payload_mol is None: + raise ValueError(f"Could not parse {component_name} SMILES: {linker_payload_smiles}") + + # Verify reactive group is present + self._verify_reactive_group() + + @abstractmethod + def _verify_reactive_group(self): + """Verify that the linker-payload component has the expected reactive group""" + pass + + @abstractmethod + def find_conjugation_sites(self, peptide_smiles): + """ + Find conjugation sites in the peptide + + Args: + peptide_smiles: SMILES string of the peptide + + Returns: + List of dicts with conjugation site information + """ + pass + + @abstractmethod + def _perform_conjugation(self, peptide_mol, site_info): + """ + Perform the actual conjugation chemistry + + Args: + peptide_mol: RDKit Mol object of the peptide + site_info: Dict with conjugation site information + + Returns: + SMILES string of the conjugate + """ + pass + + @abstractmethod + def check_attachment_site_compatibility(self, mol): + """ + Check if attachment site chemistry is valid for this conjugation type + + This is conjugation-method specific and must be implemented by subclasses. + Checks should include: + - Presence of expected conjugation bond (e.g., thioether for thiol-maleimide) + - Absence of unreacted precursors (e.g., free thiol, unreacted maleimide) + - Valency at conjugation sites + + Args: + mol: RDKit Mol object of the conjugate + + Returns: + bool: True if attachment site is valid, False otherwise + """ + pass + + def conjugate_to_peptide(self, peptide_smiles, site_position=0): + """ + Conjugate linker-payload to peptide at specified conjugation site + + Args: + peptide_smiles: SMILES string of the peptide + site_position: Index of the conjugation site to use (default: 0) + + Returns: + SMILES string of the PDC conjugate + """ + peptide_mol = Chem.MolFromSmiles(peptide_smiles) + if peptide_mol is None: + raise ValueError(f"Invalid peptide SMILES: {peptide_smiles}") + + sites = self.find_conjugation_sites(peptide_smiles) + + if len(sites) == 0: + raise ValueError(f"No conjugation sites found in peptide") + + if site_position >= len(sites): + raise ValueError(f"Site position {site_position} out of range (found {len(sites)} sites)") + + print(f"Found {len(sites)} conjugation site(s) in peptide") + print(f"Conjugating at site position {site_position}") + + target_site = sites[site_position] + return self._perform_conjugation(peptide_mol, target_site) + + def batch_conjugate(self, peptide_smiles_list, site_position=0): + """ + Conjugate multiple peptides in batch + + Args: + peptide_smiles_list: List of peptide SMILES strings + site_position: Index of conjugation site to use + + Returns: + Dict with 'conjugates', 'successful', and 'failed' keys + """ + results = { + 'conjugates': [], + 'successful': [], + 'failed': [], + } + + for i, peptide_smiles in enumerate(peptide_smiles_list): + try: + conjugate = self.conjugate_to_peptide(peptide_smiles, site_position) + results['conjugates'].append(conjugate) + results['successful'].append(i) + except Exception as e: + print(f" Peptide {i} failed: {e}") + results['conjugates'].append(None) + results['failed'].append((i, str(e))) + + print(f"\n=== Summary ===") + print(f"Total: {len(peptide_smiles_list)}, Success: {len(results['successful'])}, Failed: {len(results['failed'])}") + + return results + + def get_conjugate_properties(self, conjugate_smiles): + """Calculate molecular properties of the conjugate""" + mol = Chem.MolFromSmiles(conjugate_smiles) + if mol is None: + return None + + return { + 'molecular_weight': Descriptors.MolWt(mol), + 'num_heavy_atoms': mol.GetNumHeavyAtoms(), + 'logp': Descriptors.MolLogP(mol), + 'tpsa': Descriptors.TPSA(mol), # Topological Polar Surface Area + } + + def validate_conjugate(self, conjugate_smiles): + """ + Validate that the conjugate is chemically reasonable + + Args: + conjugate_smiles: SMILES string of the conjugate + + Returns: + (passed: bool, checks: dict) + """ + mol = Chem.MolFromSmiles(conjugate_smiles) + + if mol is None: + return False, {'error': 'Could not parse SMILES'} + + # Basic validation (delegated to subclass) + checks = self._basic_validation(mol) + checks['validation_method'] = 'basic' + checks['parseable'] = True + + # Get pass/fail from subclass implementation + passed = checks.get('_passed', True) + if '_passed' in checks: + del checks['_passed'] # Remove internal flag from output + + return passed, checks + + @abstractmethod + def _basic_validation(self, mol): + """ + Perform basic validation checks specific to conjugation type + + Should include check_attachment_site_compatibility() and any other + conjugation-specific checks. + + Args: + mol: RDKit Mol object of the conjugate + + Returns: + Dict of check_name: bool pairs. Include '_passed': bool for overall result. + """ + pass + + def batch_validate(self, conjugate_smiles_list): + """Validate multiple conjugates""" + results = [] + for smiles in conjugate_smiles_list: + if smiles is None: + results.append((False, {'error': 'None SMILES'})) + else: + passed, checks = self.validate_conjugate(smiles) + results.append((passed, checks)) + + num_passed = sum(1 for passed, _ in results if passed) + print(f"\nValidation Summary: {num_passed}/{len(results)} passed") + + return results + diff --git a/smiles/evaluation/__init__.py b/smiles/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7f80841981cfe8c40a5e11383678c00ecb5f7265 --- /dev/null +++ b/smiles/evaluation/__init__.py @@ -0,0 +1 @@ +"""PDC evaluation and structure-analysis utilities.""" diff --git a/smiles/evaluation/metrics.py b/smiles/evaluation/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..5b0267fa16d8921d2d011b183d095dc96c78c856 --- /dev/null +++ b/smiles/evaluation/metrics.py @@ -0,0 +1,570 @@ +import torch +import warnings +import numpy as np +import pandas as pd +from rdkit import Chem, rdBase, DataStructs +from rdkit.Chem import AllChem +import os +import sys + +# Add current directory to path for imports +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + +# Import custom analyzers and conjugation builders +from smiles.evaluation.peptide_analyzer import PeptideAnalyzer +from smiles.evaluation.pdc_analyzer import PDCAnalyzer +from smiles.conjugation.AoaDau import AoaDaunorubicin_PDC_Builder +from smiles.conjugation.SSCpt import DisulfideCamptothecin_PDC_Builder +from smiles.conjugation.DSPASN38 import DisulfanylpropanoicAcidSN38_PDC_Builder +from smiles.scoring.classifiers import BindingAffinity + +rdBase.DisableLog('rdApp.error') +warnings.filterwarnings("ignore", category=DeprecationWarning) +warnings.filterwarnings("ignore", category=UserWarning) +warnings.filterwarnings("ignore", category=FutureWarning) + +def fingerprints_from_mol(molecule, radius=3, size=2048, hashed=False): + """ Create ECFP fingerprint of a molecule """ + if hashed: + fp_bits = AllChem.GetHashedMorganFingerprint(molecule, radius, nBits=size) + else: + fp_bits = AllChem.GetMorganFingerprintAsBitVect(molecule, radius, nBits=size) + fp_np = np.zeros((1,)) + DataStructs.ConvertToNumpyArray(fp_bits, fp_np) + return fp_np.reshape(1, -1) + +def average_agg_tanimoto(stock_vecs, gen_vecs, batch_size=5000, agg='max', device='cuda', p=1): + """ + For each molecule in gen_vecs finds closest molecule in stock_vecs. + Returns average tanimoto score for between these molecules + + Parameters: + stock_vecs: numpy array + gen_vecs: numpy array + agg: max or mean + p: power for averaging: (mean x^p)^(1/p) + """ + assert agg in ['max', 'mean'], "Can aggregate only max or mean" + agg_tanimoto = np.zeros(len(gen_vecs)) + total = np.zeros(len(gen_vecs)) + for j in range(0, stock_vecs.shape[0], batch_size): + x_stock = torch.tensor(stock_vecs[j:j + batch_size]).to(device).float() + for i in range(0, gen_vecs.shape[0], batch_size): + y_gen = torch.tensor(gen_vecs[i:i + batch_size]).to(device).float() + y_gen = y_gen.transpose(0, 1) + tp = torch.mm(x_stock, y_gen) + jac = (tp / (x_stock.sum(1, keepdim=True) + y_gen.sum(0, keepdim=True) - tp)).cpu().numpy() + jac[np.isnan(jac)] = 1 + if p != 1: + jac = jac**p + if agg == 'max': + agg_tanimoto[i:i + y_gen.shape[1]] = np.maximum(agg_tanimoto[i:i + y_gen.shape[1]], jac.max(0)) + elif agg == 'mean': + agg_tanimoto[i:i + y_gen.shape[1]] += jac.sum(0) + total[i:i + y_gen.shape[1]] += jac.shape[0] + if agg == 'mean': + agg_tanimoto /= total + if p != 1: + agg_tanimoto = (agg_tanimoto)**(1/p) + return np.mean(agg_tanimoto) + +def get_mol(smiles_or_mol): + ''' + Loads SMILES/molecule into RDKit's object + ''' + if isinstance(smiles_or_mol, str): + if len(smiles_or_mol) == 0: + return None + mol = Chem.MolFromSmiles(smiles_or_mol) + if mol is None: + return None + try: + Chem.SanitizeMol(mol) + except ValueError: + return None + return mol + return smiles_or_mol + +def canonic_smiles(smiles_or_mol): + mol = get_mol(smiles_or_mol) + if mol is None: + return None + return Chem.MolToSmiles(mol) + + +class Metrics: + + def __init__(self, prior_path='./data/smiles/30K_all.csv', n_jobs=100, input_type='smiles', device='cuda'): + self.device = device + self.input_type = input_type # 'smiles', 'peptide', or 'conjugate' + self.n_jobs = n_jobs + + # Initialize analyzers + self.peptide_analyzer = PeptideAnalyzer() + self.pdc_analyzer = PDCAnalyzer(device) + + # Initialize conjugation builders + self.aoa_dau_builder = AoaDaunorubicin_PDC_Builder() + self.ss_cpt_builder = DisulfideCamptothecin_PDC_Builder() + self.dspa_sn38_builder = DisulfanylpropanoicAcidSN38_PDC_Builder() + + # Load reference set for similarity calculations + train_set_cano_smi = pd.read_csv(prior_path)['SMILES'].astype(str).tolist() + + for smi in train_set_cano_smi: + mol = Chem.MolFromSmiles(smi) + + if mol is None: + print(f"Invalid SMILES: {smi}") + + self.train_set_cano_smi = train_set_cano_smi + + self.ref_fps = np.vstack([ + fingerprints_from_mol(Chem.MolFromSmiles(smi)) + for smi in train_set_cano_smi + if Chem.MolFromSmiles(smi) is not None + ]) + + def _determine_target_from_filename(self, generated_path): + """Determine target protein from filename""" + filename = os.path.basename(generated_path).upper() + if 'GNRHR' in filename: + return 'GNRHR' + elif 'ERBB2' in filename: + return 'ERBB2' + elif 'NRP1' in filename: + return 'NRP1' + else: + raise ValueError(f"Cannot determine target from filename: {filename}. Expected GNRHR / ERBB2 / NRP1 in filename.") + + def _get_target_sequence(self, target): + """Get target protein sequence for binding affinity calculation""" + # Define target sequences (you may need to update these) + target_sequences = { + 'GNRHR': 'MANSASPEQNQNHCSAINNSIPLMQGNLPTLTLSGKIRVTVTFFLFLLSATFNASFLLKLQKWTQKKEKGKKLSRMKLLLKHLTLANLLETLIVMPLDGMWNITVQWYAGELLCKVLSYLKLFSMYAPAFMMVVISLDRSLAITRPLALKSNSKVGQSMVGLAWILSSVFAGPQLYIFRMIHLADSSGQTKVFSQCVTHCSFSQWWHQAFYNFFTFSCLFIIPLFIMLICNAKIIFTLTRVLHQDPHELQLNQSKNNIPRARLKTLKMTVAFATSFTVCWTPYYVLGIWYWFDPEMLNRLSDPVNHFFFLFAFLNPCFDPLIYGYFSL', + 'ERBB2': 'MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV', + 'NRP1': 'MERGLPLLCAVLALVLAPAGAFRNDKCGDTIKIESPGYLTSPGYPHSYHPSEKCEWLIQAPDPYQRIMINFNPHFDLEDRDCKYDYVEVFDGENENGHFRGKFCGKIAPPPVVSSGPFLFIKFVSDYETHGAGFSIRYEIFKRGPECSQNYTTPSGVIKSPGFPEKYPNSLECTYIVFVPKMSEIILEFESFDLEPDSNPPGGMFCRYDRLEIWDGFPDVGPHIGRYCGQKTPGRIRSSSGILSMVFYTDSAIAKEGFSANYSVLQSSVSEDFKCMEALGMESGEIHSDQITASSQYSTNWSAERSRLNYPENGWTPGEDSYREWIQVDLGLLRFVTAVGTQGAISKETKKKYYVKTYKIDVSSNGEDWITIKEGNKPVLFQGNTNPTDVVVAVFPKPLITRFVRIKPATWETGISMRFEVYGCKITDYPCSGMLGMVSGLISDSQITSSNQGDRNWMPENIRLVTSRSGWALPPAPHSYINEWLQIDLGEEKIVRGIIIQGGKHRENKVFMRKFKIGYSNNGSDWKMIMDDSKRKAKSFEGNNNYDTPELRTFPALSTRFIRIYPERATHGGLGLRMELLGCEVEAPTAGPTTPNGNLVDECDDDQANCHSGTGDDFQLTGGTTVLATEKPTVIDSTIQSEFPTYGFNCEFGWGSHKTFCHWEHDNHVQLKWSVLTSKTGPIQDHTGDGNFIYSQADENQKGKVARLVSPVVYSQNSAHCMTFWYHMSGSHVGTLRVKLRYQKPEEYDQLVWMAIGHQGDHWKEGRVLLHKSLKLYQVIFEGEIGKGNLGGIAVDDISINNHISQEDCAKPADLDKKNPEIKIDETGSTPGYEGEGEGDKNISRKPGNVLKTLDPILITIIAMSALGVLLGAVCGVVLYCACWHNGMSERNLSALENYNFELVDGVKLKKDKLNTQSTYSEA' + } + return target_sequences.get(target, '') + + def get_metrics_for_peptides(self, generated_path, save_csv=True): + """ + Analyze peptide sequences with conjugation, validity, binding affinity, and drug-likeness + """ + print(f"Analyzing peptides from: {generated_path}") + + # Determine target from filename + target = self._determine_target_from_filename(generated_path) + print(f"Detected target: {target}") + + # Load data + df = pd.read_csv(generated_path) + if 'sequence' in df.columns: + peptide_sequences = df['sequence'].astype(str).tolist() + elif 'SMILES' in df.columns: + peptide_sequences = df['SMILES'].astype(str).tolist() + else: + raise ValueError("No 'sequence' or 'SMILES' column found in the input file") + + total_sequences = len(peptide_sequences) + print(f"Total sequences: {total_sequences}") + + # Step 1: Filter valid peptides + print("Step 1: Filtering valid peptides...") + valid_peptides = [] + valid_indices = [] + + for i, seq in enumerate(peptide_sequences): + if self.peptide_analyzer.is_peptide(seq): + valid_peptides.append(seq) + valid_indices.append(i) + + print(f"Valid peptides: {len(valid_peptides)}/{total_sequences}") + + if len(valid_peptides) == 0: + print("No valid peptides found!") + return { + 'total_sequences': total_sequences, + 'valid_peptides': 0, + 'validity_rate': 0.0, + 'conjugation_success_rate': 0.0, + 'binding_affinity_mean': 0.0, + 'binding_affinity_std': 0.0, + } + + # Step 2: Conjugate peptides + print("Step 2: Conjugating peptides...") + if target == 'GNRHR': + print("Using Aoa-Dau conjugation builder for GNRHR") + builder = self.aoa_dau_builder + elif target == 'ERBB2': + builder = self.ss_cpt_builder + elif target == 'NRP1': + builder = self.dspa_sn38_builder + + conjugate_results = [] + + for i, peptide in enumerate(valid_peptides): + try: + print(f" Conjugating peptide {i+1}/{len(valid_peptides)}: {peptide[:50]}...") + # Try different methods to get conjugates + try: + conjugates = builder.conjugate_to_peptide(peptide, exclude_n_terminus=True) + if conjugates and len(conjugates) > 0: + print(f" Success: Generated {len(conjugates)} conjugate(s)") + first_conjugate = conjugates[0] + print(f" DEBUG: First conjugate length: {len(first_conjugate)} chars") + print(f" DEBUG: First conjugate preview: {first_conjugate[:100]}...") + print(f" DEBUG: Expected peptide in conjugate: {peptide[:30] in first_conjugate}") + conjugate_results.append(first_conjugate) # Take first conjugate + else: + print(f" No conjugates generated") + conjugate_results.append(None) + except AttributeError: + # Fallback to batch_conjugate without return_all parameter + result = builder.batch_conjugate([peptide]) + if result and 'conjugates' in result and len(result['conjugates']) > 0: + conjugates = result['conjugates'][0] # First peptide's conjugates + if conjugates: + print(f" Success: Generated {len(conjugates)} conjugate(s)") + first_conjugate = conjugates[0] + print(f" DEBUG: Fallback conjugate length: {len(first_conjugate)} chars") + print(f" DEBUG: Fallback conjugate preview: {first_conjugate[:100]}...") + conjugate_results.append(first_conjugate) # Take first conjugate + else: + print(f" No conjugates generated") + conjugate_results.append(None) + else: + print(f" Batch conjugation failed") + conjugate_results.append(None) + + except Exception as e: + print(f" Conjugation failed for peptide {i+1}: {e}") + conjugate_results.append(None) + + print(f"Total conjugation attempts: {len(conjugate_results)}") + print(f"Successful conjugations: {sum(1 for c in conjugate_results if c is not None)}") + + # Step 3: Analyze conjugates with PDC analyzer + print("Step 3: Analyzing conjugates...") + valid_conjugates = [] + conjugate_properties = [] + + for i, conjugate in enumerate(conjugate_results): + if conjugate is not None: + print(f" Analyzing conjugate {i+1}: {conjugate[:60]}...") + # Check if SMILES is valid + mol = Chem.MolFromSmiles(conjugate) + if mol is not None: + print(f" Valid SMILES structure") + + # Try PDC analyzer validation + try: + linker_feasible = self.pdc_analyzer.check_linker_feasibility(mol) + repr_valid = self.pdc_analyzer.check_representational_validity(mol) + print(f" PDC checks - Linker feasible: {linker_feasible}, Repr valid: {repr_valid}") + + is_valid = linker_feasible and repr_valid + if not is_valid: + print(f" PDC validation failed, dropped this molecule") + is_valid = False + + except Exception as e: + print(f" PDC validation error: {e}, dropped this molecule") + is_valid = False + + if is_valid: + valid_conjugates.append(conjugate) + try: + # DEBUG: Check the molecule being analyzed + print(f" DEBUG: SMILES to analyze: {conjugate[:100]}...") + + # CRITICAL FIX: Create molecule from conjugate SMILES + conjugate_mol = Chem.MolFromSmiles(conjugate) + conjugate_formula = Chem.rdMolDescriptors.CalcMolFormula(conjugate_mol) + conjugate_mw = Chem.Descriptors.MolWt(conjugate_mol) + print(f" DEBUG: Analyzing molecule - Formula: {conjugate_formula}, MW: {conjugate_mw:.2f}") + print(f" DEBUG: SMILES length: {len(conjugate)} chars") + + # DEBUG: Double-check we're analyzing the right molecule + if conjugate_formula == 'C33H40N3O11': + print(f" WARNING: This is the linker-drug formula, not full conjugate!") + else: + print(f" This appears to be the full conjugate formula") + + # Use the correctly created molecule for property calculation + props = self.pdc_analyzer.calculate_molecular_properties(conjugate_mol) + # Ensure molecular weight is included + if 'molecular_weight' not in props: + from rdkit.Chem import Descriptors + props['molecular_weight'] = Descriptors.MolWt(conjugate_mol) + print(f" Calculated properties successfully: {props.get('mf', 'No MF')}, MW: {props.get('molecular_weight', 'N/A'):.2f}") + except Exception as e: + print(f" Property calculation failed: {e}, using basic properties") + # Fallback to basic RDKit properties - use conjugate molecule + conjugate_mol = Chem.MolFromSmiles(conjugate) + if conjugate_mol: + from rdkit.Chem import Descriptors + props = { + 'molecular_weight': Descriptors.MolWt(conjugate_mol), + 'logp': Descriptors.MolLogP(conjugate_mol), + 'num_atoms': conjugate_mol.GetNumHeavyAtoms(), + 'num_bonds': conjugate_mol.GetNumBonds() + } + else: + props = { + 'molecular_weight': 0, + 'logp': 0, + 'num_atoms': 0, + 'num_bonds': 0 + } + + # Get amino acid sequence from peptide analyzer + try: + aa_seq, seq_length = self.peptide_analyzer.analyze_structure(valid_peptides[i]) + except Exception as e: + print(f" Failed to analyze structure: {e}") + aa_seq, seq_length = None, None + + conjugate_properties.append({ + 'peptide_index': valid_indices[i], + 'peptide_smiles': valid_peptides[i], + 'aa_seq': aa_seq, + 'seq_length': seq_length, + 'conjugate_smiles': conjugate, + **props + }) + print(f" Added to valid conjugates") + else: + print(f" Failed validation") + else: + print(f" Invalid SMILES structure") + else: + print(f" Conjugate {i+1}: None (conjugation failed)") + + print(f"Valid conjugates: {len(valid_conjugates)}") + if len(valid_conjugates) > 0: + print(f"First few valid conjugates:") + for i, conj in enumerate(valid_conjugates[:3]): + print(f" {i+1}: {conj[:80]}...") + else: + print("No valid conjugates found!") + + # Step 4: Calculate binding affinity + print("Step 4: Calculating binding affinity...") + target_seq = self._get_target_sequence(target) + if not target_seq: + print(f"Warning: No target sequence found for {target}") + affinity_scores_all = [] + affinity_scores_conjugatable = [] + # Add None for binding affinity when no target sequence + for prop in conjugate_properties: + prop['binding_affinity'] = None + else: + print(f"Using target sequence (length: {len(target_seq)})") + try: + # Initialize binding affinity model + from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer + tokenizer = SMILES_SPE_Tokenizer('./smiles/smiles_tokenizer/new_vocab.txt', './smiles/smiles_tokenizer/new_splits.txt') + binding_model = BindingAffinity(target_seq, self.device) + print(f"Binding model initialized successfully") + + # Tokenize valid peptides + valid_peptide_tokens = [] + for peptide in valid_peptides: + tokens = tokenizer(peptide) + # Handle BatchEncoding object from tokenizer + if hasattr(tokens, 'input_ids'): + tokens = tokens.input_ids # Extract token IDs from BatchEncoding + if isinstance(tokens, list): + tokens = torch.tensor(tokens) + valid_peptide_tokens.append(tokens) + + print(f"Tokenized {len(valid_peptide_tokens)} valid peptides") + + # Calculate affinity for all valid peptides + if valid_peptide_tokens: + # Pad sequences + max_len = max(len(tokens) for tokens in valid_peptide_tokens) + padded_tokens = torch.zeros((len(valid_peptide_tokens), max_len), dtype=torch.long) + for i, tokens in enumerate(valid_peptide_tokens): + padded_tokens[i, :len(tokens)] = tokens + + print(f"Calculating affinity for {len(valid_peptide_tokens)} peptides...") + affinity_scores_all = binding_model(padded_tokens.to(self.device)).cpu().numpy() + print(f"Calculated {len(affinity_scores_all)} affinity scores") + else: + affinity_scores_all = [] + + # ===== NEW CODE STARTS HERE ===== + # Create mapping from valid_indices to affinity scores + affinity_map = {} + for i, valid_idx in enumerate(valid_indices): + if i < len(affinity_scores_all): + affinity_map[valid_idx] = float(affinity_scores_all[i]) + + # Add binding affinity to conjugate_properties + print(f"Adding binding affinity to conjugate properties...") + for prop in conjugate_properties: + peptide_idx = prop['peptide_index'] + if peptide_idx in affinity_map: + prop['binding_affinity'] = affinity_map[peptide_idx] + print(f" Added affinity {affinity_map[peptide_idx]:.4f} for peptide index {peptide_idx}") + else: + prop['binding_affinity'] = None + print(f" No affinity score for peptide index {peptide_idx}") + + # Calculate affinity for peptides with valid conjugates + # NOTE: pre-initialize so the variable always exists even when + # conjugate_properties is empty (zero valid conjugates). + # Also: this block runs ONCE here, not once per prop above. + affinity_scores_conjugatable = [] + conjugatable_indices = [prop['peptide_index'] for prop in conjugate_properties] + valid_conjugatable_indices = [ + valid_indices.index(idx) + for idx in conjugatable_indices + if idx in valid_indices + ] + + print(f"Found {len(valid_conjugatable_indices)} conjugatable peptides") + + if valid_conjugatable_indices: + conjugatable_tokens = [valid_peptide_tokens[i] for i in valid_conjugatable_indices] + max_len = max(len(tokens) for tokens in conjugatable_tokens) + padded_tokens = torch.zeros((len(conjugatable_tokens), max_len), dtype=torch.long) + for i, tokens in enumerate(conjugatable_tokens): + padded_tokens[i, :len(tokens)] = tokens + + affinity_scores_conjugatable = binding_model(padded_tokens.to(self.device)).cpu().numpy() + print(f"Calculated {len(affinity_scores_conjugatable)} conjugatable affinity scores") + + except Exception as e: + print(f"Binding affinity calculation failed: {e}") + import traceback + traceback.print_exc() + affinity_scores_all = [] + affinity_scores_conjugatable = [] + # ===== NEW CODE: Handle errors ===== + # Still add None values for binding affinity + for prop in conjugate_properties: + prop['binding_affinity'] = None + # ===== NEW CODE ENDS ===== + + + # Save detailed results to CSV + csv_path = None + if save_csv and conjugate_properties: + output_dir = os.path.dirname(generated_path) + base_name = os.path.splitext(os.path.basename(generated_path))[0] + csv_path = os.path.join(output_dir, f"{base_name}_conjugate_analysis.csv") + + df_results = pd.DataFrame(conjugate_properties) + df_results.to_csv(csv_path, index=False) + print(f"Detailed results saved to: {csv_path}") + + # Calculate summary statistics + validity_rate = len(valid_peptides) / total_sequences + conjugation_success_rate = len(valid_conjugates) / total_sequences + + results = { + 'total_sequences': total_sequences, + 'valid_peptides': len(valid_peptides), + 'validity_rate': validity_rate, + 'valid_conjugates': len(valid_conjugates), + 'conjugation_success_rate': conjugation_success_rate, + 'target': target, + 'csv_path': csv_path, # Add CSV path to results + } + + # Binding affinity statistics + if len(affinity_scores_all) > 0: + results.update({ + 'binding_affinity_all_mean': float(np.mean(affinity_scores_all)), + 'binding_affinity_all_std': float(np.std(affinity_scores_all)), + 'binding_affinity_all_min': float(np.min(affinity_scores_all)), + 'binding_affinity_all_max': float(np.max(affinity_scores_all)), + }) + + if len(affinity_scores_conjugatable) > 0: + results.update({ + 'binding_affinity_conjugatable_mean': float(np.mean(affinity_scores_conjugatable)), + 'binding_affinity_conjugatable_std': float(np.std(affinity_scores_conjugatable)), + 'binding_affinity_conjugatable_min': float(np.min(affinity_scores_conjugatable)), + 'binding_affinity_conjugatable_max': float(np.max(affinity_scores_conjugatable)), + }) + + + # Print summary + print("\n" + "="*60) + print("PEPTIDE ANALYSIS SUMMARY") + print("="*60) + print(f"Target: {target}") + print(f"Total sequences: {total_sequences}") + print(f"Valid peptides: {len(valid_peptides)} ({validity_rate:.1%})") + print(f"Valid conjugates: {len(valid_conjugates)} ({conjugation_success_rate:.1%})") + + print(f"\nBINDING AFFINITY ANALYSIS:") + if len(affinity_scores_all) > 0: + print(f" All valid peptides ({len(affinity_scores_all)} peptides):") + print(f" Mean: {np.mean(affinity_scores_all):.4f} ± {np.std(affinity_scores_all):.4f}") + print(f" Range: {np.min(affinity_scores_all):.4f} to {np.max(affinity_scores_all):.4f}") + else: + print(f" All valid peptides: No scores calculated (binding affinity model failed)") + + if len(affinity_scores_conjugatable) > 0: + print(f" Conjugatable peptides ({len(affinity_scores_conjugatable)} peptides):") + print(f" Mean: {np.mean(affinity_scores_conjugatable):.4f} ± {np.std(affinity_scores_conjugatable):.4f}") + print(f" Range: {np.min(affinity_scores_conjugatable):.4f} to {np.max(affinity_scores_conjugatable):.4f}") + else: + print(f" Conjugatable peptides: No scores calculated") + + return results + + def get_metrics(self, generated_path): + """Main method to get metrics based on input type""" + if self.input_type == 'peptide': + return self.get_metrics_for_peptides(generated_path) + elif self.input_type == 'conjugate': + # Placeholder for future conjugate analysis + print("Conjugate analysis not yet implemented") + return self._get_basic_metrics(generated_path) + else: + # Default SMILES analysis + return self._get_basic_metrics(generated_path) + + def _get_basic_metrics(self, generated_path): + """Original metrics calculation for general SMILES""" + generated_smi = pd.read_csv(generated_path)['SMILES'].astype(str).tolist() + #generated_smi = pd.read_csv(generated_path, usecols=['Generated SMILES'])['Generated SMILES'].tolist() + #mols = [Chem.MolFromSmiles(smi) if smi else None for smi in generated_smi] + mols = [Chem.MolFromSmiles(smi) if smi else None for smi in generated_smi] + is_valid = [1 if mol else 0 for mol in mols] + validity = sum(is_valid) / len(is_valid) + + valid_canon_smiles = [Chem.MolToSmiles(mol) for mol in mols if mol] + uniqueness = len(set(valid_canon_smiles)) / len(valid_canon_smiles) + + uniq_smis = list(set(valid_canon_smiles)) + uniq_mols = [Chem.MolFromSmiles(smi) for smi in uniq_smis] + + fps = np.vstack([fingerprints_from_mol(mol) for mol in uniq_mols]) + diversity = 1 - (average_agg_tanimoto(fps, fps, agg='mean', p=1)).mean() + + snn = average_agg_tanimoto(self.ref_fps, fps, agg='max', p=1) + + # gen_smiles = mapper(self.n_jobs)(canonic_smiles, valid_canon_smiles) + # gen_smiles_set = set(gen_smiles) - {None} + # train_set = set(self.train_set_cano_smi) + # novelty = len(gen_smiles_set - train_set) / len(gen_smiles_set) + + # print(f"validity\tuniqueness\tdiversity\tsnn\tnovelty") + # print(f"{validity:.3f}\t{uniqueness:.3f}\t{diversity:.3f}\t{snn:.3f}\t{novelty:.3f}") + print(f"validity\tuniqueness\tdiversity\tsnn") + print(f"{validity:.3f}\t{uniqueness:.3f}\t{diversity:.3f}\t{snn:.3f}") + return { + "validity": validity, + "uniqueness": uniqueness, + "diversity": diversity, + "snn": snn, # "structural novelty" + # "novelty": novelty, + } diff --git a/smiles/evaluation/pdc_analyzer.py b/smiles/evaluation/pdc_analyzer.py new file mode 100644 index 0000000000000000000000000000000000000000..da39cc75302e6054fb6d7192ca1b21c94dde456f --- /dev/null +++ b/smiles/evaluation/pdc_analyzer.py @@ -0,0 +1,431 @@ +""" +PDC Analyzer +Analyzes Peptide-Drug Conjugates (PDCs) for structural validity + +Hard constraints (validity = 1 if all pass, 0 if any fails): +1. Linker feasibility: ring-closure constraints, long carbon chains +2. Representational validity: SMILES parsing, sanitization, chemistry problems + +Note: Attachment/conjugation site checks are conjugation-method specific +and should be handled in the conjugation PDC builder (e.g., pdc_builder.py) +""" + +import torch +from rdkit import Chem +from rdkit.Chem import Descriptors, AllChem, rdMolDescriptors + + +class PDCAnalyzer: + """ + Analyzes Peptide-Drug Conjugates (PDCs) for structural validity + + Hard constraint checks (validity = 1 if all pass, 0 if any fails): + 1. Linker feasibility: ring-closure constraints, long carbon chains + 2. Representational validity: SMILES parsing, sanitization, chemistry problems + + Optional molecular properties (toggle on/off): + - mf: molecular formula + - polararea: topological polar surface area + - complexity: molecular complexity (BertzCT) + - xlogp: calculated LogP (Wildman-Crippen) + - heavycnt: heavy atom count + - hbonddonor: hydrogen bond donors + - hbondacc: hydrogen bond acceptors + - rotbonds: rotatable bonds + + Note: Attachment/conjugation site checks are conjugation-method specific + and should be handled in the conjugation PDC builder. + """ + + def __init__(self, device, + max_linker_chain_length=16, + calculate_properties=True): + self.device = device + self.max_linker_chain_length = max_linker_chain_length + self.calculate_properties = calculate_properties + + # Property calculation flags (all on by default) + self.property_flags = { + 'mf': True, # molecular formula + 'polararea': True, # topological polar surface area + 'complexity': True, # BertzCT complexity + 'xlogp': True, # Wildman-Crippen LogP + 'heavycnt': True, # heavy atom count + 'hbonddonor': True, # H-bond donors + 'hbondacc': True, # H-bond acceptors + 'rotbonds': True, # rotatable bonds + } + + # Pre-compile SMARTS patterns for efficiency + self._compile_patterns() + + def set_property_flags(self, **kwargs): + """ + Enable/disable specific property calculations + + Args: + **kwargs: property_name=True/False pairs + + Example: + analyzer.set_property_flags(mf=False, xlogp=True, rotbonds=False) + """ + for key, value in kwargs.items(): + if key in self.property_flags: + self.property_flags[key] = bool(value) + else: + raise ValueError(f"Unknown property: {key}. Valid properties: {list(self.property_flags.keys())}") + + def enable_all_properties(self): + """Enable calculation of all optional properties""" + for key in self.property_flags: + self.property_flags[key] = True + + def disable_all_properties(self): + """Disable calculation of all optional properties""" + for key in self.property_flags: + self.property_flags[key] = False + + def _compile_patterns(self): + """Pre-compile SMARTS patterns used frequently""" + self.patterns = { + # Long carbon chain (indicates potentially problematic linker) + # 16 consecutive CH2 groups + 'long_chain': Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][CH2][CH2][CH2][CH2][CH2][CH2][CH2][CH2][CH2][CH2][CH2][CH2]'), + } + + def find_linker_rings(self, mol): + """ + Identify rings that are part of the linker (not peptide backbone or drug core) + + Linker rings typically include: + - Succinimide (from maleimide after thiol addition) + - Small saturated rings used as spacers + - NOT aromatic rings (usually part of drug) + """ + ring_info = mol.GetRingInfo() + atom_rings = ring_info.AtomRings() + + linker_rings = [] + + for ring in atom_rings: + ring_size = len(ring) + ring_atoms = [mol.GetAtomWithIdx(idx) for idx in ring] + ring_symbols = [atom.GetSymbol() for atom in ring_atoms] + + # Skip aromatic rings (likely drug component) + if all(atom.GetIsAromatic() for atom in ring_atoms): + continue + + # Check for succinimide (5-membered, N + 2 C=O) + if ring_size == 5 and 'N' in ring_symbols: + carbonyl_count = 0 + for idx in ring: + atom = mol.GetAtomWithIdx(idx) + if atom.GetSymbol() == 'C': + for neighbor in atom.GetNeighbors(): + if neighbor.GetSymbol() == 'O' and neighbor.GetIdx() not in ring: + bond = mol.GetBondBetweenAtoms(idx, neighbor.GetIdx()) + if bond and bond.GetBondType() == Chem.BondType.DOUBLE: + carbonyl_count += 1 + + if carbonyl_count >= 2: + # This is a succinimide - valid linker ring + linker_rings.append(ring) + continue + + # Check for small saturated rings (potential linker spacers) + if ring_size <= 6: + is_saturated = not any(atom.GetIsAromatic() for atom in ring_atoms) + if is_saturated: + linker_rings.append(ring) + + # Flag very large non-aromatic rings (unusual in linkers) + if ring_size > 12: + linker_rings.append(ring) # Will be checked for validity + + return linker_rings + + def check_ring_closure_at_linker(self, mol): + """ + Check ring-closure constraints in the linker region + """ + linker_rings = self.find_linker_rings(mol) + + if len(linker_rings) == 0: + return True # No linker rings is fine + + for ring in linker_rings: + ring_size = len(ring) + + # Impossible rings + if ring_size < 3: + return False + + # 3-membered rings: check for severe strain + if ring_size == 3: + for atom_idx in ring: + atom = mol.GetAtomWithIdx(atom_idx) + # sp2 carbon in 3-ring is very strained (except epoxides/aziridines) + if atom.GetSymbol() == 'C': + if atom.GetHybridization() == Chem.HybridizationType.SP2: + # Check if it's part of a C=C (bad) vs just geometry + for neighbor in atom.GetNeighbors(): + bond = mol.GetBondBetweenAtoms(atom_idx, neighbor.GetIdx()) + if bond and bond.GetBondType() == Chem.BondType.DOUBLE: + if neighbor.GetSymbol() == 'C': + return False # C=C in 3-ring is invalid + + # 4-membered rings: check for excessive sp2 character + if ring_size == 4: + sp2_count = sum(1 for idx in ring + if mol.GetAtomWithIdx(idx).GetHybridization() == Chem.HybridizationType.SP2) + if sp2_count > 2: + return False + + # Very large rings in linker are suspicious + if ring_size > 12: + return False + + return True + + def check_linker_feasibility(self, mol): + """ + Check if linker properties are reasonable + + Hard constraints only: + - Ring-closure constraints + - Long carbon chain check + """ + # Check ring-closure constraints + if not self.check_ring_closure_at_linker(mol): + return False + + # Check for excessively long carbon chains + if self.patterns['long_chain'] and mol.HasSubstructMatch(self.patterns['long_chain']): + return False + + return True + + def check_representational_validity(self, mol): + """ + Check if molecule can be properly represented + """ + # Sanitization + try: + Chem.SanitizeMol(mol) + except: + return False + + # Round-trip SMILES + try: + smiles = Chem.MolToSmiles(mol) + mol_reconstructed = Chem.MolFromSmiles(smiles) + if mol_reconstructed is None: + return False + except: + return False + + # Check for chemistry problems + try: + problems = Chem.DetectChemistryProblems(mol) + for problem in problems: + problem_type = problem.GetType() + if problem_type in ['AtomValenceException', 'KekulizeException']: + return False + except: + return False + + # 2D coordinates (basic structural validity) + try: + AllChem.Compute2DCoords(mol) + except: + return False + + return True + + def calculate_molecular_properties(self, mol): + """ + Calculate optional molecular properties based on property_flags + + Returns dict of property_name: value for enabled properties + """ + properties = {} + + if not self.calculate_properties: + return properties + + if self.property_flags.get('mf'): + properties['mf'] = rdMolDescriptors.CalcMolFormula(mol) + + if self.property_flags.get('polararea'): + properties['polararea'] = round(Descriptors.TPSA(mol), 2) + + if self.property_flags.get('complexity'): + properties['complexity'] = round(Descriptors.BertzCT(mol), 2) + + if self.property_flags.get('xlogp'): + properties['xlogp'] = round(Descriptors.MolLogP(mol), 2) + + if self.property_flags.get('heavycnt'): + properties['heavycnt'] = mol.GetNumHeavyAtoms() + + if self.property_flags.get('hbonddonor'): + properties['hbonddonor'] = Descriptors.NumHDonors(mol) + + if self.property_flags.get('hbondacc'): + properties['hbondacc'] = Descriptors.NumHAcceptors(mol) + + if self.property_flags.get('rotbonds'): + properties['rotbonds'] = Descriptors.NumRotatableBonds(mol) + + return properties + + def get_scores(self, input_seqs_list): + """ + Validate PDC structures based on hard constraints + + Hard constraints: + 1. Linker feasibility (ring-closure, long chains) + 2. Representational validity (SMILES, sanitization, chemistry) + + Returns list of 0/1 scores + """ + results = [] + + for candidates in input_seqs_list: + for smiles in candidates: + mol = Chem.MolFromSmiles(smiles) + if mol is None: + continue + + # Run hard constraint checks only + linker_ok = self.check_linker_feasibility(mol) + representational_ok = self.check_representational_validity(mol) + + is_valid = linker_ok and representational_ok + if is_valid: + results.append(1) + break + else: + results.append(0) + + return results + + def get_detailed_scores(self, input_seqs_list): + """ + Return detailed validation results with optional properties + """ + detailed_results = [] + + for candidates in input_seqs_list: + candidate_results = [] + + for smiles in candidates: + mol = Chem.MolFromSmiles(smiles) + if mol is None: + candidate_results.append({ + 'valid': 0, + 'smiles': smiles, + 'error': 'Cannot parse SMILES' + }) + continue + + # Run hard constraint checks + checks = { + 'linker_feasible': self.check_linker_feasibility(mol), + 'representational': self.check_representational_validity(mol) + } + + is_valid = all(checks.values()) + + # Calculate optional properties if enabled + properties = self.calculate_molecular_properties(mol) + + # Add linker ring info for diagnostics + linker_rings = self.find_linker_rings(mol) + checks['linker_ring_sizes'] = [len(ring) for ring in linker_rings] + + candidate_results.append({ + 'valid': 1 if is_valid else 0, + 'smiles': smiles, + 'checks': checks, + 'properties': properties + }) + + detailed_results.append(candidate_results) + + return detailed_results + + def __call__(self, input_seqs): + scores = self.get_scores(input_seqs) + return torch.tensor(scores) + + +# ============================================================================ +# Testing +# ============================================================================ + +if __name__ == "__main__": + device = 'cpu' + pdc_analyzer = PDCAnalyzer(device) + + print("=" * 70) + print("PDC Analyzer - Structural Validity Checker") + print("=" * 70) + print("\nHard constraints checked:") + print(" 1. Linker feasibility (ring-closure, long carbon chains)") + print(" 2. Representational validity (SMILES, sanitization)") + print("\nNote: Conjugation site checks are method-specific") + print(" and should be in the conjugation PDC builder.\n") + + # Test property flag toggling + print("Testing property flags:") + print(f" Default flags: {pdc_analyzer.property_flags}") + + pdc_analyzer.set_property_flags(mf=False, complexity=False) + print(f" After disabling mf, complexity: {pdc_analyzer.property_flags}") + + pdc_analyzer.enable_all_properties() + print(f" After enable_all: {pdc_analyzer.property_flags}") + + print("\n" + "=" * 70) + + test_smiles = [ + # Test 1: Valid structure + ("NCC(=O)N[C@@H](CSC1CC(=O)N(CCOCCOCCOCCOCCN)C1=O)C(=O)NCC(=O)O", + "Valid structure with succinimide ring"), + + # Test 2: Invalid - strained 3-membered ring with C=C + ("NCC(=O)N[C@@H](CSC1=CC1CCCCN)C(=O)NCC(=O)O", + "Invalid: Strained 3-membered ring with sp2 carbon"), + + # Test 3: Simple valid peptide + ("NCC(=O)N[C@@H](C)C(=O)NCC(=O)O", + "Simple valid peptide structure"), + ] + + print("\nTest Results") + print("=" * 70 + "\n") + + for i, (smiles, description) in enumerate(test_smiles): + print(f"Test {i+1}: {description}") + print(f"SMILES: {smiles[:60]}{'...' if len(smiles) > 60 else ''}") + + detailed = pdc_analyzer.get_detailed_scores([smiles])[0] + + valid_str = " VALID" if detailed['valid'] == 1 else " INVALID" + print(f"Result: {valid_str}") + + if 'checks' in detailed: + print("\nHard constraint checks:") + for check_name in ['linker_feasible', 'representational']: + if check_name in detailed['checks']: + status = "" if detailed['checks'][check_name] else "" + print(f" {status} {check_name}") + + if 'properties' in detailed and detailed['properties']: + print("\nMolecular properties:") + for prop, value in detailed['properties'].items(): + print(f" {prop}: {value}") + + print("\n" + "-" * 70 + "\n") diff --git a/smiles/evaluation/peptide_analyzer.py b/smiles/evaluation/peptide_analyzer.py new file mode 100644 index 0000000000000000000000000000000000000000..fc6d0046661f6ad0608169747e43514a726c442c --- /dev/null +++ b/smiles/evaluation/peptide_analyzer.py @@ -0,0 +1,1259 @@ +import os +import re +import pandas as pd +from io import StringIO +import rdkit +from rdkit import Chem +from rdkit.Chem import AllChem, Draw +import numpy as np +from PIL import Image, ImageDraw, ImageFont +import matplotlib.pyplot as plt +import matplotlib.patches as patches +from io import BytesIO +import tempfile +from rdkit import Chem, RDLogger +RDLogger.DisableLog('rdApp.*') + +import pdb + +class PeptideAnalyzer: + def __init__(self): + self.bond_patterns = [ + (r'OC\(=O\)', 'ester'), # Ester bond + (r'N\(C\)C\(=O\)', 'n_methyl'), # N-methylated peptide bond + (r'N[0-9]C\(=O\)', 'proline'), # Proline peptide bond + (r'NC\(=O\)', 'peptide'), # Standard peptide bond + (r'C\(=O\)N\(C\)', 'n_methyl_reverse'), # Reverse N-methylated + (r'C\(=O\)N[12]?', 'peptide_reverse') # Reverse peptide bond + ] + # Three to one letter code mapping + self.three_to_one = { + 'Ala': 'A', 'Cys': 'C', 'Asp': 'D', 'Glu': 'E', + 'Phe': 'F', 'Gly': 'G', 'His': 'H', 'Ile': 'I', + 'Lys': 'K', 'Leu': 'L', 'Met': 'M', 'Asn': 'N', + 'Pro': 'P', 'Gln': 'Q', 'Arg': 'R', 'Ser': 'S', + 'Thr': 'T', 'Val': 'V', 'Trp': 'W', 'Tyr': 'Y' + } + + def is_peptide(self, smiles): + """Check if the SMILES represents a peptide structure""" + # pdb.set_trace() + mol = Chem.MolFromSmiles(smiles) + if mol is None: + return False + + # Look for peptide bonds: NC(=O) pattern + peptide_bond_pattern = Chem.MolFromSmarts('[NH][C](=O)') + if mol.HasSubstructMatch(peptide_bond_pattern): + return True + + # Look for N-methylated peptide bonds: N(C)C(=O) pattern + n_methyl_pattern = Chem.MolFromSmarts('[N;H0;$(NC)](C)[C](=O)') + if mol.HasSubstructMatch(n_methyl_pattern): + return True + + return False + + def is_cyclic(self, smiles): + """Improved cyclic peptide detection""" + # Check for C-terminal carboxyl + if smiles.endswith('C(=O)O'): + return False, [], [] + + # Find all numbers used in ring closures + ring_numbers = re.findall(r'(?:^|[^c])[0-9](?=[A-Z@\(\)])', smiles) + + # Find aromatic ring numbers + aromatic_matches = re.findall(r'c[0-9](?:ccccc|c\[nH\]c)[0-9]', smiles) + aromatic_cycles = [] + for match in aromatic_matches: + numbers = re.findall(r'[0-9]', match) + aromatic_cycles.extend(numbers) + + # Numbers that aren't part of aromatic rings are peptide cycles + peptide_cycles = [n for n in ring_numbers if n not in aromatic_cycles] + + is_cyclic = len(peptide_cycles) > 0 and not smiles.endswith('C(=O)O') + return is_cyclic, peptide_cycles, aromatic_cycles + + def split_on_bonds(self, smiles): + """Split SMILES into segments with simplified Pro handling""" + positions = [] + used = set() + + # Find Gly pattern first + gly_pattern = r'NCC\(=O\)' + for match in re.finditer(gly_pattern, smiles): + if not any(p in range(match.start(), match.end()) for p in used): + positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': 'gly', + 'pattern': match.group() + }) + used.update(range(match.start(), match.end())) + + for pattern, bond_type in self.bond_patterns: + for match in re.finditer(pattern, smiles): + if not any(p in range(match.start(), match.end()) for p in used): + positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': bond_type, + 'pattern': match.group() + }) + used.update(range(match.start(), match.end())) + + # Sort by position + positions.sort(key=lambda x: x['start']) + + # Create segments + segments = [] + + if positions: + # First segment + if positions[0]['start'] > 0: + segments.append({ + 'content': smiles[0:positions[0]['start']], + 'bond_after': positions[0]['pattern'] + }) + + # Process segments + for i in range(len(positions)-1): + current = positions[i] + next_pos = positions[i+1] + + if current['type'] == 'gly': + segments.append({ + 'content': 'NCC(=O)', + 'bond_before': positions[i-1]['pattern'] if i > 0 else None, + 'bond_after': next_pos['pattern'] + }) + else: + content = smiles[current['end']:next_pos['start']] + if content: + segments.append({ + 'content': content, + 'bond_before': current['pattern'], + 'bond_after': next_pos['pattern'] + }) + + # Last segment + if positions[-1]['end'] < len(smiles): + segments.append({ + 'content': smiles[positions[-1]['end']:], + 'bond_before': positions[-1]['pattern'] + }) + + return segments + + def clean_terminal_carboxyl(self, segment): + """Remove C-terminal carboxyl only if it's the true terminus""" + content = segment['content'] + + # Only clean if: + # 1. Contains C(=O)O + # 2. No bond_after exists (meaning it's the last segment) + # 3. C(=O)O is at the end of the content + if 'C(=O)O' in content and not segment.get('bond_after'): + print('recognized?') + # Remove C(=O)O pattern regardless of position + cleaned = re.sub(r'\(C\(=O\)O\)', '', content) + # Remove any leftover empty parentheses + cleaned = re.sub(r'\(\)', '', cleaned) + print(cleaned) + return cleaned + return content + + def identify_residue(self, segment): + """Identify residue with Pro reconstruction""" + # Only clean terminal carboxyl if this is the last segment + content = self.clean_terminal_carboxyl(segment) + mods = self.get_modifications(segment) + + # UAA pattern matching section - before regular residues + # Phenylglycine and derivatives + if 'c1ccccc1' in content: + if '[C@@H](c1ccccc1)' in content or '[C@H](c1ccccc1)' in content: + return '4', mods # Base phenylglycine + + # 4-substituted phenylalanines + if 'Cc1ccc' in content: + if 'OMe' in content or 'OCc1ccc' in content: + return '0A1', mods # 4-methoxy-Phenylalanine + elif 'Clc1ccc' in content: + return '200', mods # 4-chloro-Phenylalanine + elif 'Brc1ccc' in content: + return '4BF', mods # 4-Bromo-phenylalanine + elif 'C#Nc1ccc' in content: + return '4CF', mods # 4-cyano-phenylalanine + elif 'Ic1ccc' in content: + return 'PHI', mods # 4-Iodo-phenylalanine + elif 'Fc1ccc' in content: + return 'PFF', mods # 4-Fluoro-phenylalanine + + # Modified tryptophans + if 'c[nH]c2' in content: + if 'Oc2cccc2' in content: + return '0AF', mods # 7-hydroxy-tryptophan + elif 'Fc2cccc2' in content: + return '4FW', mods # 4-fluoro-tryptophan + elif 'Clc2cccc2' in content: + return '6CW', mods # 6-chloro-tryptophan + elif 'Brc2cccc2' in content: + return 'BTR', mods # 6-bromo-tryptophan + elif 'COc2cccc2' in content: + return 'MOT5', mods # 5-Methoxy-tryptophan + elif 'Cc2cccc2' in content: + return 'MTR5', mods # 5-Methyl-tryptophan + + # Special amino acids + if 'CC(C)(C)[C@@H]' in content or 'CC(C)(C)[C@H]' in content: + return 'BUG', mods # Tertleucine + + if 'CCCNC(=N)N' in content: + return 'CIR', mods # Citrulline + + if '[SeH]' in content: + return 'CSE', mods # Selenocysteine + + if '[NH3]CC[C@@H]' in content or '[NH3]CC[C@H]' in content: + return 'DAB', mods # Diaminobutyric acid + + if 'C1CCCCC1' in content: + if 'C1CCCCC1[C@@H]' in content or 'C1CCCCC1[C@H]' in content: + return 'CHG', mods # Cyclohexylglycine + elif 'C1CCCCC1C[C@@H]' in content or 'C1CCCCC1C[C@H]' in content: + return 'ALC', mods # 3-cyclohexyl-alanine + + # Naphthalene derivatives + if 'c1cccc2c1cccc2' in content: + if 'c1cccc2c1cccc2[C@@H]' in content or 'c1cccc2c1cccc2[C@H]' in content: + return 'NAL', mods # 2-Naphthyl-alanine + + # Heteroaromatic derivatives + if 'c1cncc' in content: + return 'PYR4', mods # 3-(4-Pyridyl)-alanine + if 'c1cscc' in content: + return 'THA3', mods # 3-(3-thienyl)-alanine + if 'c1nnc' in content: + return 'TRZ4', mods # 3-(1,2,4-Triazol-1-yl)-alanine + + # Modified serines and threonines + if 'OP(O)(O)O' in content: + if '[C@@H](COP' in content or '[C@H](COP' in content: + return 'SEP', mods # phosphoserine + elif '[C@@H](OP' in content or '[C@H](OP' in content: + return 'TPO', mods # phosphothreonine + + # Specialized ring systems + if 'c1c2ccccc2cc2c1cccc2' in content: + return 'ANTH', mods # 3-(9-anthryl)-alanine + if 'c1csc2c1cccc2' in content: + return 'BTH3', mods # 3-(3-benzothienyl)-alanine + if '[C@]12C[C@H]3C[C@@H](C2)C[C@@H](C1)C3' in content: + return 'ADAM', mods # Adamanthane + + # Fluorinated derivatives + if 'FC(F)(F)' in content: + if 'CC(F)(F)F' in content: + return 'FLA', mods # Trifluoro-alanine + if 'C(F)(F)F)c1' in content: + if 'c1ccccc1C(F)(F)F' in content: + return 'TFG2', mods # 2-(Trifluoromethyl)-phenylglycine + if 'c1cccc(c1)C(F)(F)F' in content: + return 'TFG3', mods # 3-(Trifluoromethyl)-phenylglycine + if 'c1ccc(cc1)C(F)(F)F' in content: + return 'TFG4', mods # 4-(Trifluoromethyl)-phenylglycine + + # Multiple halogen patterns + if 'F' in content and 'c1' in content: + if 'c1ccc(c(c1)F)F' in content: + return 'F2F', mods # 3,4-Difluoro-phenylalanine + if 'cc(F)cc(c1)F' in content: + return 'WFP', mods # 3,5-Difluoro-phenylalanine + if 'Cl' in content and 'c1' in content: + if 'c1ccc(cc1Cl)Cl' in content: + return 'CP24', mods # 2,4-dichloro-phenylalanine + if 'c1ccc(c(c1)Cl)Cl' in content: + return 'CP34', mods # 3,4-dichloro-phenylalanine + + # Hydroxy and amino derivatives + if 'O' in content and 'c1' in content: + if 'c1cc(O)cc(c1)O' in content: + return '3FG', mods # (2s)-amino(3,5-dihydroxyphenyl)-ethanoic acid + if 'c1ccc(c(c1)O)O' in content: + return 'DAH', mods # 3,4-Dihydroxy-phenylalanine + + # Cyclic amino acids + if 'C1CCCC1' in content: + return 'CPA3', mods # 3-Cyclopentyl-alanine + if 'C1CCCCC1' in content: + if 'CC1CCCCC1' in content: + return 'ALC', mods # 3-cyclohexyl-alanine + else: + return 'CHG', mods # Cyclohexylglycine + + # Chain-length variants + if 'CCC[C@@H]' in content or 'CCC[C@H]' in content: + return 'NLE', mods # Norleucine + if 'CC[C@@H]' in content or 'CC[C@H]' in content: + if not any(x in content for x in ['CC(C)', 'COC', 'CN(']): + return 'ABA', mods # 2-Aminobutyric acid + + # Modified histidines + if 'c1cnc' in content: + if '[C@@H]1CN[C@@H](N1)F' in content: + return '2HF', mods # 2-fluoro-l-histidine + if 'c1cnc([nH]1)F' in content: + return '2HF1', mods # 2-fluoro-l-histidine variant + if 'c1c[nH]c(n1)F' in content: + return '2HF2', mods # 2-fluoro-l-histidine variant + + # Sulfur and selenium containing + if '[SeH]' in content: + return 'CSE', mods # Selenocysteine + if 'S' in content: + if 'CSCc1ccccc1' in content: + return 'BCS', mods # benzylcysteine + if 'CCSC' in content: + return 'ESC', mods # Ethionine + if 'CCS' in content: + return 'HCS', mods # homocysteine + + # Additional modifications + if 'CN=[N]=N' in content: + return 'AZDA', mods # azido-alanine + if '[NH]=[C](=[NH2])=[NH2]' in content: + if 'CCC[NH]=' in content: + return 'AGM', mods # 5-methyl-arginine + if 'CC[NH]=' in content: + return 'GDPR', mods # 2-Amino-3-guanidinopropionic acid + + if 'CCON' in content: + return 'CAN', mods # canaline + if '[C@@H]1C=C[C@@H](C=C1)' in content: + return 'ACZ', mods # cis-amiclenomycin + if 'CCC(=O)[NH3]' in content: + return 'ONL', mods # 5-oxo-l-norleucine + if 'c1ccncc1' in content: + return 'PYR4', mods # 3-(4-Pyridyl)-alanine + if 'c1ccco1' in content: + return 'FUA2', mods # (2-furyl)-alanine + + if 'c1ccc' in content: + if 'c1ccc(cc1)c1ccccc1' in content: + return 'BIF', mods # 4,4-biphenylalanine + if 'c1ccc(cc1)C(=O)c1ccccc1' in content: + return 'PBF', mods # 4-benzoyl-phenylalanine + if 'c1ccc(cc1)C(C)(C)C' in content: + return 'TBP4', mods # 4-tert-butyl-phenylalanine + if 'c1ccc(cc1)[C](=[NH2])=[NH2]' in content: + return '0BN', mods # 4-carbamimidoyl-l-phenylalanine + if 'c1cccc(c1)[C](=[NH2])=[NH2]' in content: + return 'APM', mods # m-amidinophenyl-3-alanine + + # Multiple hydroxy patterns + if 'O' in content: + if '[C@H]([C@H](C)O)O' in content: + return 'ILX', mods # 4,5-dihydroxy-isoleucine + if '[C@H]([C@@H](C)O)O' in content: + return 'ALO', mods # Allo-threonine + if '[C@H](COP(O)(O)O)' in content: + return 'SEP', mods # phosphoserine + if '[C@H]([C@@H](C)OP(O)(O)O)' in content: + return 'TPO', mods # phosphothreonine + if '[C@H](c1ccc(O)cc1)O' in content: + return 'OMX', mods # (betar)-beta-hydroxy-l-tyrosine + if '[C@H](c1ccc(c(Cl)c1)O)O' in content: + return 'OMY', mods # (betar)-3-chloro-beta-hydroxy-l-tyrosine + + # Heterocyclic patterns + if 'n1' in content: + if 'n1cccn1' in content: + return 'PYZ1', mods # 3-(1-Pyrazolyl)-alanine + if 'n1nncn1' in content: + return 'TEZA', mods # 3-(2-Tetrazolyl)-alanine + if 'c2c(n1)cccc2' in content: + return 'QU32', mods # 3-(2-Quinolyl)-alanine + if 'c1cnc2c(c1)cccc2' in content: + return 'QU33', mods # 3-(3-quinolyl)-alanine + if 'c1ccnc2c1cccc2' in content: + return 'QU34', mods # 3-(4-quinolyl)-alanine + if 'c1ccc2c(c1)nccc2' in content: + return 'QU35', mods # 3-(5-Quinolyl)-alanine + if 'c1ccc2c(c1)cncc2' in content: + return 'QU36', mods # 3-(6-Quinolyl)-alanine + if 'c1cnc2c(n1)cccc2' in content: + return 'QX32', mods # 3-(2-quinoxalyl)-alanine + + # Multiple nitrogen patterns + if 'N' in content: + if '[NH3]CC[C@@H]' in content: + return 'DAB', mods # Diaminobutyric acid + if '[NH3]C[C@@H]' in content: + return 'DPP', mods # 2,3-Diaminopropanoic acid + if '[NH3]CCCCCC[C@@H]' in content: + return 'HHK', mods # (2s)-2,8-diaminooctanoic acid + if 'CCC[NH]=[C](=[NH2])=[NH2]' in content: + return 'GBUT', mods # 2-Amino-4-guanidinobutryric acid + if '[NH]=[C](=S)=[NH2]' in content: + return 'THIC', mods # Thio-citrulline + + # Chain modified amino acids + if 'CC' in content: + if 'CCCC[C@@H]' in content: + return 'AHP', mods # 2-Aminoheptanoic acid + if 'CCC([C@@H])(C)C' in content: + return 'I2M', mods # 3-methyl-l-alloisoleucine + if 'CC[C@H]([C@@H])C' in content: + return 'IIL', mods # Allo-Isoleucine + if '[C@H](CCC(C)C)' in content: + return 'HLEU', mods # Homoleucine + if '[C@@H]([C@@H](C)O)C' in content: + return 'HLU', mods # beta-hydroxyleucine + + # Modified glutamate/aspartate patterns + if '[C@@H]' in content: + if '[C@@H](C[C@@H](F))' in content: + return 'FGA4', mods # 4-Fluoro-glutamic acid + if '[C@@H](C[C@@H](O))' in content: + return '3GL', mods # 4-hydroxy-glutamic-acid + if '[C@@H](C[C@H](C))' in content: + return 'LME', mods # (3r)-3-methyl-l-glutamic acid + if '[C@@H](CC[C@H](C))' in content: + return 'MEG', mods # (3s)-3-methyl-l-glutamic acid + + # Sulfur and selenium modifications + if 'S' in content: + if 'SCC[C@@H]' in content: + return 'HSER', mods # homoserine + if 'SCCN' in content: + return 'SLZ', mods # thialysine + if 'SC(=O)' in content: + return 'CSA', mods # s-acetonylcysteine + if '[S@@](=O)' in content: + return 'SME', mods # Methionine sulfoxide + if 'S(=O)(=O)' in content: + return 'OMT', mods # Methionine sulfone + + # Double bond containing + if 'C=' in content: + if 'C=C[C@@H]' in content: + return '2AG', mods # 2-Allyl-glycine + if 'C=C[C@@H]' in content: + return 'LVG', mods # vinylglycine + if 'C=Cc1ccccc1' in content: + return 'STYA', mods # Styrylalanine + + # Special cases + if '[C@@H]1Cc2c(C1)cccc2' in content: + return 'IGL', mods # alpha-amino-2-indanacetic acid + if '[C](=[C](=O)=O)=O' in content: + return '26P', mods # 2-amino-6-oxopimelic acid + if '[C](=[C](=O)=O)=C' in content: + return '2NP', mods # l-2-amino-6-methylene-pimelic acid + if 'c2cnc[nH]2' in content: + return 'HIS', mods # histidine core + if 'c1cccc2c1cc(O)cc2' in content: + return 'NAO1', mods # 5-hydroxy-1-naphthalene + if 'c1ccc2c(c1)cc(O)cc2' in content: + return 'NAO2', mods # 6-hydroxy-2-naphthalene + + # Proline (P) - flexible ring numbers + if any([ + # Check for any ring number in bond patterns + (segment.get('bond_after', '').startswith(f'N{n}C(=O)') and 'CCC' in content and + any(f'[C@@H]{n}' in content or f'[C@H]{n}' in content for n in '123456789')) + for n in '123456789' + ]) or any([ + # Check ending patterns with any ring number + (f'CCCN{n}' in content and content.endswith('=O') and + any(f'[C@@H]{n}' in content or f'[C@H]{n}' in content for n in '123456789')) + for n in '123456789' + ]) or any([ + # Handle CCC[C@H]n patterns + (content == f'CCC[C@H]{n}' and segment.get('bond_before', '').startswith(f'C(=O)N{n}')) or + (content == f'CCC[C@@H]{n}' and segment.get('bond_before', '').startswith(f'C(=O)N{n}')) or + # N-terminal Pro with any ring number + (f'N{n}CCC[C@H]{n}' in content) or + (f'N{n}CCC[C@@H]{n}' in content) + for n in '123456789' + ]): + return 'Pro', mods + + # Tryptophan (W) - more specific indole pattern + if re.search(r'c[0-9]c\[nH\]c[0-9]ccccc[0-9][0-9]', content) and \ + 'c[nH]c' in content.replace(' ', ''): + return 'Trp', mods + + # Lysine (K) - both patterns + if '[C@@H](CCCCN)' in content or '[C@H](CCCCN)' in content: + return 'Lys', mods + + # Arginine (R) - both patterns + if '[C@@H](CCCNC(=N)N)' in content or '[C@H](CCCNC(=N)N)' in content: + return 'Arg', mods + + if ('C[C@H](CCCC)' in content or 'C[C@@H](CCCC)' in content) and 'CC(C)' not in content: + return 'Nle', mods + + # Ornithine (Orn) - 3-carbon chain with NH2 + if ('C[C@H](CCCN)' in content or 'C[C@@H](CCCN)' in content) and 'CC(C)' not in content: + return 'Orn', mods + + # 2-Naphthylalanine (2Nal) - distinct from Phe pattern + if ('Cc3cc2ccccc2c3' in content) and ('C[C@H]' in content or 'C[C@@H]' in content): + return '2Nal', mods + + # Cyclohexylalanine (Cha) - already in your code but moved here for clarity + if 'N2CCCCC2' in content or 'CCCCC2' in content: + return 'Cha', mods + + # Aminobutyric acid (Abu) - 2-carbon chain + if ('C[C@H](CC)' in content or 'C[C@@H](CC)' in content) and not any(p in content for p in ['CC(C)', 'CCCC', 'CCC(C)']): + return 'Abu', mods + + # Pipecolic acid (Pip) - 6-membered ring like Pro + if ('N3CCCCC3' in content or 'CCCCC3' in content) and ('C[C@H]' in content or 'C[C@@H]' in content): + return 'Pip', mods + + # Cyclohexylglycine (Chg) - direct cyclohexyl without CH2 + if ('C[C@H](C1CCCCC1)' in content or 'C[C@@H](C1CCCCC1)' in content): + return 'Chg', mods + + # 4-Fluorophenylalanine (4F-Phe) + if ('Cc2ccc(F)cc2' in content) and ('C[C@H]' in content or 'C[C@@H]' in content): + return '4F-Phe', mods + + # Regular residue identification + if ('NCC(=O)' in content) or (content == 'C'): + # Middle case - between bonds + if segment.get('bond_before') and segment.get('bond_after'): + if ('C(=O)N' in segment['bond_before'] or 'C(=O)N(C)' in segment['bond_before']): + return 'Gly', mods + # Terminal case - at the end + elif segment.get('bond_before') and segment.get('bond_before').startswith('C(=O)N'): + return 'Gly', mods + + if 'CC(C)C[C@H]' in content or 'CC(C)C[C@@H]' in content: + return 'Leu', mods + if '[C@@H](CC(C)C)' in content or '[C@H](CC(C)C)' in content: + return 'Leu', mods + + if '[C@@H]([C@@H](C)O)' in content or '[C@H]([C@H](C)O)' in content: + return 'Thr', mods + + if '[C@H](Cc2ccccc2)' in content or '[C@@H](Cc2ccccc2)' in content: + return 'Phe', mods + + if ('[C@H](C(C)C)' in content or # With outer parentheses + '[C@@H](C(C)C)' in content or # With outer parentheses + '[C@H]C(C)C' in content or # Without outer parentheses + '[C@@H]C(C)C' in content): # Without outer parentheses + if not any(p in content for p in ['CC(C)C[C@H]', 'CC(C)C[C@@H]']): # Still check not Leu + return 'Val', mods + + if '[C@H](COC(C)(C)C)' in content or '[C@@H](COC(C)(C)C)' in content: + return 'O-tBu', mods + + if any([ + 'CC[C@H](C)' in content, + 'CC[C@@H](C)' in content, + 'C(C)C[C@H]' in content and 'CC(C)C' not in content, + 'C(C)C[C@@H]' in content and 'CC(C)C' not in content + ]): + return 'Ile', mods + + if ('[C@H](C)' in content or '[C@@H](C)' in content): + if not any(p in content for p in ['C(C)C', 'COC', 'CN(', 'C(C)O', 'CC[C@H]', 'CC[C@@H]']): + return 'Ala', mods + + # Tyrosine (Tyr) - 4-hydroxybenzyl side chain + if re.search(r'Cc[0-9]ccc\(O\)cc[0-9]', content): + return 'Tyr', mods + + + # Serine (Ser) - Hydroxymethyl side chain + if '[C@H](CO)' in content or '[C@@H](CO)' in content: + if not ('C(C)O' in content or 'COC' in content): + return 'Ser', mods + + # Threonine (Thr) - 1-hydroxyethyl side chain + if '[C@@H]([C@@H](C)O)' in content or '[C@H]([C@H](C)O)' in content or '[C@@H](C)O' in content or '[C@H](C)O' in content: + return 'Thr', mods + + # Cysteine (Cys) - Thiol side chain + if '[C@H](CS)' in content or '[C@@H](CS)' in content: + return 'Cys', mods + + # Methionine (Met) - Methylthioethyl side chain + if ('C[C@H](CCSC)' in content or 'C[C@@H](CCSC)' in content): + return 'Met', mods + + # Asparagine (Asn) - Carbamoylmethyl side chain + if ('CC(=O)N' in content) and ('C[C@H]' in content or 'C[C@@H]' in content): + return 'Asn', mods + + # Glutamine (Gln) - Carbamoylethyl side chain + if ('CCC(=O)N' in content) and ('C[C@H]' in content or 'C[C@@H]' in content): + return 'Gln', mods + + # Aspartic acid (Asp) - Carboxymethyl side chain + if ('CC(=O)O' in content) and ('C[C@H]' in content or 'C[C@@H]' in content): + return 'Asp', mods + + # Glutamic acid (Glu) - Carboxyethyl side chainCC(C(=O)O)N + if ('CCC(=O)O' in content) and ('C[C@H]' in content or 'C[C@@H]' in content): + return 'Glu', mods + + # Arginine (Arg) - 3-guanidinopropyl side chain + if ('CCCNC(=N)N' in content) and ('C[C@H]' in content or 'C[C@@H]' in content): + return 'Arg', mods + + # Histidine (His) - Imidazole side chain + if ('Cc2cnc[nH]2' in content) and ('C[C@H]' in content or 'C[C@@H]' in content): + return 'His', mods + + return None, mods + + def get_modifications(self, segment): + """Get modifications based on bond types""" + mods = [] + if segment.get('bond_after'): + if 'N(C)' in segment['bond_after'] or segment['bond_after'].startswith('C(=O)N(C)'): + mods.append('N-Me') + if 'OC(=O)' in segment['bond_after']: + mods.append('O-linked') + return mods + + def analyze_structure(self, smiles): + """Main analysis function with debug output""" + print("\nAnalyzing structure:", smiles) + + # Split into segments + segments = self.split_on_bonds(smiles) + + print("\nSegment Analysis:") + sequence = [] + for i, segment in enumerate(segments): + print(f"\nSegment {i}:") + print(f"Content: {segment['content']}") + print(f"Bond before: {segment.get('bond_before', 'None')}") + print(f"Bond after: {segment.get('bond_after', 'None')}") + + residue, mods = self.identify_residue(segment) + if residue: + if mods: + sequence.append(f"{residue}({','.join(mods)})") + else: + sequence.append(residue) + print(f"Identified as: {residue}") + print(f"Modifications: {mods}") + else: + print(f"Warning: Could not identify residue in segment: {segment['content']}") + + # Check if cyclic + is_cyclic, peptide_cycles, aromatic_cycles = self.is_cyclic(smiles) + three_letter = '-'.join(sequence) + one_letter = ''.join(self.three_to_one.get(aa.split('(')[0], 'X') for aa in sequence) + + if is_cyclic: + three_letter = f"cyclo({three_letter})" + one_letter = f"cyclo({one_letter})" + + print(f"\nFinal sequence: {three_letter}") + print(f"One-letter code: {one_letter}") + print(f"Is cyclic: {is_cyclic}") + #print(f"Peptide cycles: {peptide_cycles}") + #print(f"Aromatic cycles: {aromatic_cycles}") + + return three_letter, len(segments) + """return { + 'three_letter': three_letter, + #'one_letter': one_letter, + 'is_cyclic': is_cyclic + }""" + + def return_sequence(self, smiles): + """Main analysis function with debug output""" + print("\nAnalyzing structure:", smiles) + + # Split into segments + segments = self.split_on_bonds(smiles) + + print("\nSegment Analysis:") + sequence = [] + for i, segment in enumerate(segments): + print(f"\nSegment {i}:") + print(f"Content: {segment['content']}") + print(f"Bond before: {segment.get('bond_before', 'None')}") + print(f"Bond after: {segment.get('bond_after', 'None')}") + + residue, mods = self.identify_residue(segment) + if residue: + if mods: + sequence.append(f"{residue}({','.join(mods)})") + else: + sequence.append(residue) + print(f"Identified as: {residue}") + print(f"Modifications: {mods}") + else: + print(f"Warning: Could not identify residue in segment: {segment['content']}") + + return sequence + +""" +def annotate_cyclic_structure(mol, sequence): + '''Create annotated 2D structure with clear, non-overlapping residue labels''' + # Generate 2D coordinates + # Generate 2D coordinates + AllChem.Compute2DCoords(mol) + + # Create drawer with larger size for annotations + drawer = Draw.rdMolDraw2D.MolDraw2DCairo(2000, 2000) # Even larger size + + # Get residue list and reverse it to match structural representation + if sequence.startswith('cyclo('): + residues = sequence[6:-1].split('-') + else: + residues = sequence.split('-') + residues = list(reversed(residues)) # Reverse the sequence + + # Draw molecule first to get its bounds + drawer.drawOptions().addAtomIndices = False + drawer.DrawMolecule(mol) + drawer.FinishDrawing() + + # Convert to PIL Image + img = Image.open(BytesIO(drawer.GetDrawingText())) + draw = ImageDraw.Draw(img) + + try: + # Try to use DejaVuSans as it's commonly available on Linux systems + font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 60) + small_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 60) + except OSError: + try: + # Fallback to Arial if available (common on Windows) + font = ImageFont.truetype("arial.ttf", 60) + small_font = ImageFont.truetype("arial.ttf", 60) + except OSError: + # If no TrueType fonts are available, fall back to default + print("Warning: TrueType fonts not available, using default font") + font = ImageFont.load_default() + small_font = ImageFont.load_default() + # Get molecule bounds + conf = mol.GetConformer() + positions = [] + for i in range(mol.GetNumAtoms()): + pos = conf.GetAtomPosition(i) + positions.append((pos.x, pos.y)) + + x_coords = [p[0] for p in positions] + y_coords = [p[1] for p in positions] + min_x, max_x = min(x_coords), max(x_coords) + min_y, max_y = min(y_coords), max(y_coords) + + # Calculate scaling factors + scale = 150 # Increased scale factor + center_x = 1000 # Image center + center_y = 1000 + + # Add residue labels in a circular arrangement around the structure + n_residues = len(residues) + radius = 700 # Distance of labels from center + + # Start from the rightmost point (3 o'clock position) and go counterclockwise + # Offset by -3 positions to align with structure + offset = 0 # Adjust this value to match the structure alignment + for i, residue in enumerate(residues): + # Calculate position in a circle around the structure + # Start from 0 (3 o'clock) and go counterclockwise + angle = -(2 * np.pi * ((i + offset) % n_residues) / n_residues) + + # Calculate label position + label_x = center_x + radius * np.cos(angle) + label_y = center_y + radius * np.sin(angle) + + # Draw residue label + text = f"{i+1}. {residue}" + bbox = draw.textbbox((label_x, label_y), text, font=font) + padding = 10 + draw.rectangle([bbox[0]-padding, bbox[1]-padding, + bbox[2]+padding, bbox[3]+padding], + fill='white', outline='white') + draw.text((label_x, label_y), text, + font=font, fill='black', anchor="mm") + + # Add sequence at the top with white background + seq_text = f"Sequence: {sequence}" + bbox = draw.textbbox((center_x, 100), seq_text, font=small_font) + padding = 10 + draw.rectangle([bbox[0]-padding, bbox[1]-padding, + bbox[2]+padding, bbox[3]+padding], + fill='white', outline='white') + draw.text((center_x, 100), seq_text, + font=small_font, fill='black', anchor="mm") + + return img +""" +def annotate_cyclic_structure(mol, sequence): + """Create structure visualization with just the sequence header""" + # Generate 2D coordinates + AllChem.Compute2DCoords(mol) + + # Create drawer with larger size for annotations + drawer = Draw.rdMolDraw2D.MolDraw2DCairo(2000, 2000) + + # Draw molecule first + drawer.drawOptions().addAtomIndices = False + drawer.DrawMolecule(mol) + drawer.FinishDrawing() + + # Convert to PIL Image + img = Image.open(BytesIO(drawer.GetDrawingText())) + draw = ImageDraw.Draw(img) + try: + small_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 60) + except OSError: + try: + small_font = ImageFont.truetype("arial.ttf", 60) + except OSError: + print("Warning: TrueType fonts not available, using default font") + small_font = ImageFont.load_default() + + # Add just the sequence header at the top + seq_text = f"Sequence: {sequence}" + bbox = draw.textbbox((1000, 100), seq_text, font=small_font) + padding = 10 + draw.rectangle([bbox[0]-padding, bbox[1]-padding, + bbox[2]+padding, bbox[3]+padding], + fill='white', outline='white') + draw.text((1000, 100), seq_text, + font=small_font, fill='black', anchor="mm") + + return img + +def create_enhanced_linear_viz(sequence, smiles): + """Create an enhanced linear representation using PeptideAnalyzer""" + analyzer = PeptideAnalyzer() # Create analyzer instance + + # Create figure with two subplots + fig = plt.figure(figsize=(15, 10)) + gs = fig.add_gridspec(2, 1, height_ratios=[1, 2]) + ax_struct = fig.add_subplot(gs[0]) + ax_detail = fig.add_subplot(gs[1]) + + # Parse sequence and get residues + if sequence.startswith('cyclo('): + residues = sequence[6:-1].split('-') + else: + residues = sequence.split('-') + + # Get segments using analyzer + segments = analyzer.split_on_bonds(smiles) + + # Debug print + print(f"Number of residues: {len(residues)}") + print(f"Number of segments: {len(segments)}") + + # Top subplot - Basic structure + ax_struct.set_xlim(0, 10) + ax_struct.set_ylim(0, 2) + + num_residues = len(residues) + spacing = 9.0 / (num_residues - 1) if num_residues > 1 else 9.0 + + # Draw basic structure + y_pos = 1.5 + for i in range(num_residues): + x_pos = 0.5 + i * spacing + + # Draw amino acid box + rect = patches.Rectangle((x_pos-0.3, y_pos-0.2), 0.6, 0.4, + facecolor='lightblue', edgecolor='black') + ax_struct.add_patch(rect) + + # Draw connecting bonds if not the last residue + if i < num_residues - 1: + segment = segments[i] if i < len(segments) else None + if segment: + # Determine bond type from segment info + bond_type = 'ester' if 'O-linked' in segment.get('bond_after', '') else 'peptide' + is_n_methylated = 'N-Me' in segment.get('bond_after', '') + + bond_color = 'red' if bond_type == 'ester' else 'black' + linestyle = '--' if bond_type == 'ester' else '-' + + # Draw bond line + ax_struct.plot([x_pos+0.3, x_pos+spacing-0.3], [y_pos, y_pos], + color=bond_color, linestyle=linestyle, linewidth=2) + + # Add bond type label + mid_x = x_pos + spacing/2 + bond_label = f"{bond_type}" + if is_n_methylated: + bond_label += "\n(N-Me)" + ax_struct.text(mid_x, y_pos+0.1, bond_label, + ha='center', va='bottom', fontsize=10, + color=bond_color) + + # Add residue label + ax_struct.text(x_pos, y_pos-0.5, residues[i], + ha='center', va='top', fontsize=14) + + # Bottom subplot - Detailed breakdown + ax_detail.set_ylim(0, len(segments)+1) + ax_detail.set_xlim(0, 1) + + # Create detailed breakdown + segment_y = len(segments) # Start from top + for i, segment in enumerate(segments): + y = segment_y - i + + # Check if this is a bond or residue + residue, mods = analyzer.identify_residue(segment) + if residue: + text = f"Residue {i+1}: {residue}" + if mods: + text += f" ({', '.join(mods)})" + color = 'blue' + else: + # Must be a bond + text = f"Bond {i}: " + if 'O-linked' in segment.get('bond_after', ''): + text += "ester" + elif 'N-Me' in segment.get('bond_after', ''): + text += "peptide (N-methylated)" + else: + text += "peptide" + color = 'red' + + # Add segment analysis + ax_detail.text(0.05, y, text, fontsize=12, color=color) + ax_detail.text(0.5, y, f"SMILES: {segment.get('content', '')}", fontsize=10, color='gray') + + # If cyclic, add connection indicator + if sequence.startswith('cyclo('): + ax_struct.annotate('', xy=(9.5, y_pos), xytext=(0.5, y_pos), + arrowprops=dict(arrowstyle='<->', color='red', lw=2)) + ax_struct.text(5, y_pos+0.3, 'Cyclic Connection', + ha='center', color='red', fontsize=14) + + # Add titles and adjust layout + ax_struct.set_title("Peptide Structure Overview", pad=20) + ax_detail.set_title("Segment Analysis Breakdown", pad=20) + + # Remove axes + for ax in [ax_struct, ax_detail]: + ax.set_xticks([]) + ax.set_yticks([]) + ax.axis('off') + + plt.tight_layout() + return fig + +class PeptideStructureGenerator: + """A class to generate 3D structures of peptides using different embedding methods""" + + @staticmethod + def prepare_molecule(smiles): + """Prepare molecule with proper hydrogen handling""" + mol = Chem.MolFromSmiles(smiles, sanitize=False) + if mol is None: + raise ValueError("Failed to create molecule from SMILES") + + # Calculate valence for each atom + for atom in mol.GetAtoms(): + atom.UpdatePropertyCache(strict=False) + + # Sanitize with reduced requirements + Chem.SanitizeMol(mol, + sanitizeOps=Chem.SANITIZE_FINDRADICALS| + Chem.SANITIZE_KEKULIZE| + Chem.SANITIZE_SETAROMATICITY| + Chem.SANITIZE_SETCONJUGATION| + Chem.SANITIZE_SETHYBRIDIZATION| + Chem.SANITIZE_CLEANUPCHIRALITY) + + mol = Chem.AddHs(mol) + return mol + + @staticmethod + def get_etkdg_params(attempt=0): + """Get ETKDG parameters with optional modifications based on attempt number""" + params = AllChem.ETKDGv3() + params.randomSeed = -1 + params.maxIterations = 200 + params.numThreads = 4 # Reduced for web interface + params.useBasicKnowledge = True + params.enforceChirality = True + params.useExpTorsionAnglePrefs = True + params.useSmallRingTorsions = True + params.useMacrocycleTorsions = True + params.ETversion = 2 + params.pruneRmsThresh = -1 + params.embedRmsThresh = 0.5 + + if attempt > 10: + params.bondLength = 1.5 + (attempt - 10) * 0.02 + params.useExpTorsionAnglePrefs = False + + return params + + def generate_structure_etkdg(self, smiles, max_attempts=20): + """Generate 3D structure using ETKDG without UFF optimization""" + success = False + mol = None + + for attempt in range(max_attempts): + try: + mol = self.prepare_molecule(smiles) + params = self.get_etkdg_params(attempt) + + if AllChem.EmbedMolecule(mol, params) == 0: + success = True + break + except Exception as e: + continue + + if not success: + raise ValueError("Failed to generate structure with ETKDG") + + return mol + + def generate_structure_uff(self, smiles, max_attempts=20): + """Generate 3D structure using ETKDG followed by UFF optimization""" + best_mol = None + lowest_energy = float('inf') + + for attempt in range(max_attempts): + try: + test_mol = self.prepare_molecule(smiles) + params = self.get_etkdg_params(attempt) + + if AllChem.EmbedMolecule(test_mol, params) == 0: + res = AllChem.UFFOptimizeMolecule(test_mol, maxIters=2000, + vdwThresh=10.0, confId=0, + ignoreInterfragInteractions=True) + + if res == 0: + ff = AllChem.UFFGetMoleculeForceField(test_mol) + if ff: + current_energy = ff.CalcEnergy() + if current_energy < lowest_energy: + lowest_energy = current_energy + best_mol = Chem.Mol(test_mol) + except Exception: + continue + + if best_mol is None: + raise ValueError("Failed to generate optimized structure") + + return best_mol + + @staticmethod + def mol_to_sdf_bytes(mol): + """Convert RDKit molecule to SDF file bytes""" + # First write to StringIO in text mode + sio = StringIO() + writer = Chem.SDWriter(sio) + writer.write(mol) + writer.close() + + # Convert the string to bytes + return sio.getvalue().encode('utf-8') + +def process_input(smiles_input=None, file_obj=None, show_linear=False, + show_segment_details=False, generate_3d=False, use_uff=False): + """Process input and create visualizations using PeptideAnalyzer""" + analyzer = PeptideAnalyzer() + temp_dir = tempfile.mkdtemp() if generate_3d else None + structure_files = [] + + # Handle direct SMILES input + if smiles_input: + smiles = smiles_input.strip() + + # First check if it's a peptide using analyzer's method + if not analyzer.is_peptide(smiles): + return "Error: Input SMILES does not appear to be a peptide structure.", None, None + + try: + # Create molecule + mol = Chem.MolFromSmiles(smiles) + if mol is None: + return "Error: Invalid SMILES notation.", None, None + + # Generate 3D structures if requested + if generate_3d: + generator = PeptideStructureGenerator() + + try: + # Generate ETKDG structure + mol_etkdg = generator.generate_structure_etkdg(smiles) + etkdg_path = os.path.join(temp_dir, "structure_etkdg.sdf") + writer = Chem.SDWriter(etkdg_path) + writer.write(mol_etkdg) + writer.close() + structure_files.append(etkdg_path) + + # Generate UFF structure if requested + if use_uff: + mol_uff = generator.generate_structure_uff(smiles) + uff_path = os.path.join(temp_dir, "structure_uff.sdf") + writer = Chem.SDWriter(uff_path) + writer.write(mol_uff) + writer.close() + structure_files.append(uff_path) + + except Exception as e: + return f"Error generating 3D structures: {str(e)}", None, None, None + + # Use analyzer to get sequence + segments = analyzer.split_on_bonds(smiles) + + # Process segments and build sequence + sequence_parts = [] + output_text = "" + + # Only include segment analysis in output if requested + if show_segment_details: + output_text += "Segment Analysis:\n" + for i, segment in enumerate(segments): + output_text += f"\nSegment {i}:\n" + output_text += f"Content: {segment['content']}\n" + output_text += f"Bond before: {segment.get('bond_before', 'None')}\n" + output_text += f"Bond after: {segment.get('bond_after', 'None')}\n" + + residue, mods = analyzer.identify_residue(segment) + if residue: + if mods: + sequence_parts.append(f"{residue}({','.join(mods)})") + else: + sequence_parts.append(residue) + output_text += f"Identified as: {residue}\n" + output_text += f"Modifications: {mods}\n" + else: + output_text += f"Warning: Could not identify residue in segment: {segment['content']}\n" + output_text += "\n" + else: + # Just build sequence without detailed analysis in output + for segment in segments: + residue, mods = analyzer.identify_residue(segment) + if residue: + if mods: + sequence_parts.append(f"{residue}({','.join(mods)})") + else: + sequence_parts.append(residue) + + # Check if cyclic using analyzer's method + is_cyclic, peptide_cycles, aromatic_cycles = analyzer.is_cyclic(smiles) + three_letter = '-'.join(sequence_parts) + one_letter = ''.join(analyzer.three_to_one.get(aa.split('(')[0], 'X') for aa in sequence_parts) + + if is_cyclic: + three_letter = f"cyclo({three_letter})" + one_letter = f"cyclo({one_letter})" + + # Create cyclic structure visualization + img_cyclic = annotate_cyclic_structure(mol, three_letter) + + # Create linear representation if requested + img_linear = None + if show_linear: + fig_linear = create_enhanced_linear_viz(three_letter, smiles) + buf = BytesIO() + fig_linear.savefig(buf, format='png', bbox_inches='tight', dpi=300) + buf.seek(0) + img_linear = Image.open(buf) + plt.close(fig_linear) + + # Add summary to output + summary = "Summary:\n" + summary += f"Sequence: {three_letter}\n" + summary += f"One-letter code: {one_letter}\n" + summary += f"Is Cyclic: {'Yes' if is_cyclic else 'No'}\n" + #if is_cyclic: + #summary += f"Peptide Cycles: {', '.join(peptide_cycles)}\n" + #summary += f"Aromatic Cycles: {', '.join(aromatic_cycles)}\n" + + if structure_files: + summary += "\n3D Structures Generated:\n" + for filepath in structure_files: + summary += f"- {os.path.basename(filepath)}\n" + + return summary + output_text, img_cyclic, img_linear, structure_files if structure_files else None + + except Exception as e: + return f"Error processing SMILES: {str(e)}", None, None, None + + # Handle file input + if file_obj is not None: + try: + # Handle file content + if hasattr(file_obj, 'name'): + with open(file_obj.name, 'r') as f: + content = f.read() + else: + content = file_obj.decode('utf-8') if isinstance(file_obj, bytes) else str(file_obj) + + output_text = "" + for line in content.splitlines(): + smiles = line.strip() + if smiles: + # Check if it's a peptide + if not analyzer.is_peptide(smiles): + output_text += f"Skipping non-peptide SMILES: {smiles}\n" + continue + + # Process this SMILES + segments = analyzer.split_on_bonds(smiles) + sequence_parts = [] + + # Add segment details if requested + if show_segment_details: + output_text += f"\nSegment Analysis for SMILES: {smiles}\n" + for i, segment in enumerate(segments): + output_text += f"\nSegment {i}:\n" + output_text += f"Content: {segment['content']}\n" + output_text += f"Bond before: {segment.get('bond_before', 'None')}\n" + output_text += f"Bond after: {segment.get('bond_after', 'None')}\n" + residue, mods = analyzer.identify_residue(segment) + if residue: + if mods: + sequence_parts.append(f"{residue}({','.join(mods)})") + else: + sequence_parts.append(residue) + output_text += f"Identified as: {residue}\n" + output_text += f"Modifications: {mods}\n" + else: + for segment in segments: + residue, mods = analyzer.identify_residue(segment) + if residue: + if mods: + sequence_parts.append(f"{residue}({','.join(mods)})") + else: + sequence_parts.append(residue) + + # Get cyclicity and create sequence + is_cyclic, peptide_cycles, aromatic_cycles = analyzer.is_cyclic(smiles) + sequence = f"cyclo({'-'.join(sequence_parts)})" if is_cyclic else '-'.join(sequence_parts) + + output_text += f"\nSummary for SMILES: {smiles}\n" + output_text += f"Sequence: {sequence}\n" + output_text += f"Is Cyclic: {'Yes' if is_cyclic else 'No'}\n" + if is_cyclic: + output_text += f"Peptide Cycles: {', '.join(peptide_cycles)}\n" + #output_text += f"Aromatic Cycles: {', '.join(aromatic_cycles)}\n" + output_text += "-" * 50 + "\n" + + return output_text, None, None + + except Exception as e: + return f"Error processing file: {str(e)}", None, None + + return "No input provided.", None, None + + diff --git a/smiles/evaluation/run_metrics.py b/smiles/evaluation/run_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..7dcaaa7c0b971d0c4564d2ff4ddbdb62326eb4f2 --- /dev/null +++ b/smiles/evaluation/run_metrics.py @@ -0,0 +1,282 @@ +#!/usr/bin/env python3 +""" +Script to run peptide metrics analysis on SMILES text files + +Usage: + python run_metrics.py input.txt [--target TARGET] [--output OUTPUT_DIR] [--device DEVICE] + +Arguments: + input.txt: Text file containing SMILES sequences (one per line) + --target: Target protein (GNRHR / ERBB2 / NRP1). If not specified, will try to detect from filename + --output: Output directory for results (default: same as input file directory) + --device: Device for computation (cuda or cpu, default: cuda if available) +""" + +import argparse +import os +import sys +import pandas as pd +import tempfile +import torch +import shutil +from pathlib import Path + +# Add current directory to path for imports +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + +from smiles.evaluation.metrics import Metrics + +def read_smiles_file(input_path): + """ + Read SMILES sequences from a text file + + Args: + input_path: Path to input text file + + Returns: + List of SMILES strings + """ + smiles_list = [] + + with open(input_path, 'r') as f: + for line_num, line in enumerate(f, 1): + line = line.strip() + if line and not line.startswith('#'): # Skip empty lines and comments + smiles_list.append(line) + + print(f"Read {len(smiles_list)} SMILES sequences from {input_path}") + return smiles_list + +def create_temp_csv(smiles_list, target=None): + """ + Create a temporary CSV file with SMILES data + + Args: + smiles_list: List of SMILES strings + target: Target protein name (for filename) + + Returns: + Path to temporary CSV file + """ + # Create DataFrame + df = pd.DataFrame({'SMILES': smiles_list}) + + # Create temporary file + suffix = f"_{target}" if target else "" + temp_file = tempfile.NamedTemporaryFile( + mode='w', + suffix=f"{suffix}_smiles.csv", + delete=False + ) + + # Save to CSV + df.to_csv(temp_file.name, index=False) + temp_file.close() + + print(f"Created temporary CSV: {temp_file.name}") + return temp_file.name + +def determine_target(input_path, target_arg): + """ + Determine target protein from filename or argument + + Args: + input_path: Path to input file + target_arg: Target specified via command line argument + + Returns: + Target protein name or None + """ + if target_arg: + target = target_arg.upper() + if target not in ['GNRHR', 'ERBB2', 'NRP1']: + print(f"Warning: Unknown target '{target}'. Supported targets: GNRHR, ERBB2, NRP1") + return None + return target + + # Try to detect from filename + filename = os.path.basename(input_path).upper() + if 'GNRHR' in filename: + return 'GNRHR' + elif 'ERBB2' in filename: + return 'ERBB2' + elif 'NRP1' in filename: + return 'NRP1' + else: + print("Warning: Could not determine target from filename. Specify with --target") + print("Supported targets: GNRHR, ERBB2, NRP1") + return None + +def save_results(results, output_path): + """ + Save analysis results to a text file + + Args: + results: Dictionary of analysis results + output_path: Path to output file + """ + with open(output_path, 'w') as f: + f.write("PEPTIDE METRICS ANALYSIS RESULTS\n") + f.write("=" * 50 + "\n\n") + + # Basic statistics + f.write("BASIC STATISTICS:\n") + f.write(f" Total sequences: {results.get('total_sequences', 0)}\n") + f.write(f" Valid peptides: {results.get('valid_peptides', 0)}\n") + f.write(f" Validity rate: {results.get('validity_rate', 0):.1%}\n") + f.write(f" Valid conjugates: {results.get('valid_conjugates', 0)}\n") + f.write(f" Conjugation success rate: {results.get('conjugation_success_rate', 0):.1%}\n") + f.write(f" Target protein: {results.get('target', 'Unknown')}\n\n") + + # Binding affinity + if 'binding_affinity_all_mean' in results: + f.write("BINDING AFFINITY (All valid peptides):\n") + f.write(f" Mean: {results['binding_affinity_all_mean']:.4f}\n") + f.write(f" Std: {results['binding_affinity_all_std']:.4f}\n") + f.write(f" Min: {results['binding_affinity_all_min']:.4f}\n") + f.write(f" Max: {results['binding_affinity_all_max']:.4f}\n\n") + + if 'binding_affinity_conjugatable_mean' in results: + f.write("BINDING AFFINITY (Conjugatable peptides):\n") + f.write(f" Mean: {results['binding_affinity_conjugatable_mean']:.4f}\n") + f.write(f" Std: {results['binding_affinity_conjugatable_std']:.4f}\n") + f.write(f" Min: {results['binding_affinity_conjugatable_min']:.4f}\n") + f.write(f" Max: {results['binding_affinity_conjugatable_max']:.4f}\n\n") + + # Raw results (for programmatic access) + f.write("RAW RESULTS (JSON format):\n") + f.write(str(results)) + +def main(): + parser = argparse.ArgumentParser( + description="Run peptide metrics analysis on SMILES text files", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + python run_metrics.py peptides.txt + python run_metrics.py gnrhr_peptides.txt --target GNRHR + python run_metrics.py peptides.txt --target ERBB2 --output ./results + python run_metrics.py peptides.txt --target NRP1 --device cpu + """ + ) + + parser.add_argument('input_file', type=str, + help='Text file containing SMILES sequences (one per line)') + parser.add_argument('--target', type=str, choices=['GNRHR', 'ERBB2', 'NRP1'], default=None, + help='Target protein (GNRHR, ERBB2, NRP1). If not specified, will try to detect from filename') + parser.add_argument('--output', type=str, default=None, + help='Output directory for results (default: same as input file directory)') + parser.add_argument('--device', type=str, default='auto', + help='Device for computation (cuda, cpu, or auto)') + parser.add_argument('--no-csv', action='store_true', + help='Skip saving detailed CSV results') + + args = parser.parse_args() + + # Validate input file + if not os.path.exists(args.input_file): + print(f"Error: Input file '{args.input_file}' does not exist") + return 1 + + # Determine device + if args.device == 'auto': + device = 'cuda' if torch.cuda.is_available() else 'cpu' + else: + device = args.device + + print(f"Using device: {device}") + + # Set up output directory + if args.output: + output_dir = args.output + os.makedirs(output_dir, exist_ok=True) + else: + output_dir = os.path.dirname(os.path.abspath(args.input_file)) + + # Generate output filenames + base_name = Path(args.input_file).stem + results_file = os.path.join(output_dir, f"{base_name}_metrics.txt") + + try: + # Read SMILES sequences + print("Reading SMILES sequences...") + smiles_list = read_smiles_file(args.input_file) + + if not smiles_list: + print("Error: No SMILES sequences found in input file") + return 1 + + # Determine target + target = determine_target(args.input_file, args.target) + if not target: + print("Error: Could not determine target protein. Use --target to specify.") + return 1 + + # Create temporary CSV file + temp_csv_path = create_temp_csv(smiles_list, target) + + try: + # Initialize metrics calculator + print("Initializing metrics calculator...") + metrics_calculator = Metrics( + input_type='peptide', + device=device + ) + + # Run analysis + print("Running peptide analysis...") + results = metrics_calculator.get_metrics_for_peptides( + temp_csv_path, + save_csv=(not args.no_csv) + ) + + # Save results + save_results(results, results_file) + print(f"\nResults saved to: {results_file}") + + # Handle detailed CSV if it was created + if not args.no_csv and results.get('csv_path'): + source_csv = results['csv_path'] + + if os.path.exists(source_csv): + final_csv = os.path.join(output_dir, f"{base_name}_conjugate_analysis.csv") + + # If output_dir is different from source directory, move the file + if os.path.abspath(os.path.dirname(source_csv)) != os.path.abspath(output_dir): + try: + shutil.move(source_csv, final_csv) + print(f"Detailed CSV moved to: {final_csv}") + except Exception as e: + # Fallback: copy then delete + try: + shutil.copy2(source_csv, final_csv) + os.remove(source_csv) + print(f"Detailed CSV copied to: {final_csv}") + except Exception as e2: + print(f"Warning: Failed to move CSV file: {e2}") + print(f"CSV remains at: {source_csv}") + else: + print(f"Detailed CSV already saved at: {source_csv}") + else: + print(f"Warning: Expected detailed CSV not found at: {source_csv}") + elif not args.no_csv: + print("Warning: No detailed CSV was created (possibly no valid conjugates found)") + + finally: + # Clean up temporary CSV + if os.path.exists(temp_csv_path): + os.unlink(temp_csv_path) + print(f"Cleaned up temporary file: {temp_csv_path}") + + print("\nAnalysis complete!") + return 0 + + except Exception as e: + print(f"Error during analysis: {e}") + import traceback + traceback.print_exc() + return 1 + +if __name__ == "__main__": + exit_code = main() + sys.exit(exit_code) \ No newline at end of file diff --git a/smiles/evaluation/run_metrics_uaa.py b/smiles/evaluation/run_metrics_uaa.py new file mode 100644 index 0000000000000000000000000000000000000000..d398f84a85dd6700b4e964272eeee05dfab9cd72 --- /dev/null +++ b/smiles/evaluation/run_metrics_uaa.py @@ -0,0 +1,590 @@ +#!/usr/bin/env python3 +""" +run_metrics_uaa.py +================== + +Re-evaluates already-generated UAA peptide-drug conjugates after stripping +Fmoc-strategy side-chain protecting groups, and reports protected-vs-deprotected +correlation for the binding-affinity oracle. + +This script does NOT re-run guidance / generation. It works entirely on the +existing `*_conjugate_analysis.csv` files produced by run_metrics.py. + +Pipeline per input CSV +---------------------- + 1. Load CSV (must contain `peptide_smiles`, `conjugate_smiles`, `binding_affinity`). + 2. Strip protecting groups from `peptide_smiles` and `conjugate_smiles` + using a small library of RDKit reactions (Mmt, Trt, Boc, OtBu ester, + OtBu/OBn ether, OBn ester, OBn-phosphate, Pbf). + 3. Recompute molecular properties (MW, LogP, TPSA, HBD/HBA, rotbonds, + heavy atoms) on the deprotected conjugate. + 4. Re-tokenize the deprotected peptide SMILES and re-score with the same + `BindingAffinity` model used originally. (Skip with --no-rescore.) + 5. Optionally pair each row with the matching NCAA sequence string from the + original `.txt` file (--ncaa-txt or auto-detect). + 6. Save `_deprot.csv` with both protected and deprotected columns, + plus a `uaa_deprotection_summary.csv` aggregating across all inputs: + - n + - mean / std of binding (×10 pKd) protected and deprotected + - Pearson and Spearman correlation across the two + - per-rule strip counts on conjugates + +Usage +----- + python run_metrics_uaa.py INPUT_CSV [INPUT_CSV ...] \ + [--output OUTPUT_DIR] [--target {ERBB2,GNRHR,NRP1}] \ + [--device {cuda,cpu,auto}] [--no-rescore] \ + [--vocab PATH] [--splits PATH] [--ncaa-txt-dir PATH] + +Examples +-------- + # Re-evaluate one CSV + python -m smiles.evaluation.run_metrics_uaa \ + smiles/results/uaa_results/cupofmoca_guided_uaa_ERBB2_SSCpt_k8_len10_smiles_conjugate_analysis.csv \ + --output smiles/results/uaa_results_deprot + + # Re-evaluate everything (see scripts/analyze_smiles_uaa_deprotect.sh) +""" + +import argparse +import os +import sys +from pathlib import Path + +import numpy as np +import pandas as pd + +from rdkit import Chem, RDLogger +from rdkit.Chem import AllChem, Descriptors + +sys.path.append(os.path.dirname(os.path.abspath(__file__))) + +RDLogger.DisableLog("rdApp.*") + + +# --------------------------------------------------------------------------- +# Target protein sequences (must match metrics.py - copied here so this script +# is self-contained and doesn't trigger the full Metrics(...) construction +# which loads a 30K reference SMILES set we don't need.) +# --------------------------------------------------------------------------- +TARGET_SEQUENCES = { + 'GNRHR': ( + 'MANSASPEQNQNHCSAINNSIPLMQGNLPTLTLSGKIRVTVTFFLFLLSATFNASFLLKLQKWTQKKEKGKK' + 'LSRMKLLLKHLTLANLLETLIVMPLDGMWNITVQWYAGELLCKVLSYLKLFSMYAPAFMMVVISLDRSLAIT' + 'RPLALKSNSKVGQSMVGLAWILSSVFAGPQLYIFRMIHLADSSGQTKVFSQCVTHCSFSQWWHQAFYNFFTF' + 'SCLFIIPLFIMLICNAKIIFTLTRVLHQDPHELQLNQSKNNIPRARLKTLKMTVAFATSFTVCWTPYYVLGI' + 'WYWFDPEMLNRLSDPVNHFFFLFAFLNPCFDPLIYGYFSL' + ), + 'ERBB2': ( + 'MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLS' + 'FLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRS' + 'LTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLT' + 'RTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGR' + 'YTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSAN' + 'IQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIR' + 'GRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDEC' + 'VGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTC' + 'FGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRA' + 'SPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETEL' + 'RKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTS' + 'TVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDF' + 'GLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLE' + 'KGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSL' + 'LEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEG' + 'AGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSP' + 'REGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWD' + 'QDPPERGAPPSTFKGTPTAENPEYLGLDVPV' + ), + 'NRP1': ( + 'MERGLPLLCAVLALVLAPAGAFRNDKCGDTIKIESPGYLTSPGYPHSYHPSEKCEWLIQAPDPYQRIMINFN' + 'PHFDLEDRDCKYDYVEVFDGENENGHFRGKFCGKIAPPPVVSSGPFLFIKFVSDYETHGAGFSIRYEIFKRG' + 'PECSQNYTTPSGVIKSPGFPEKYPNSLECTYIVFVPKMSEIILEFESFDLEPDSNPPGGMFCRYDRLEIWDG' + 'FPDVGPHIGRYCGQKTPGRIRSSSGILSMVFYTDSAIAKEGFSANYSVLQSSVSEDFKCMEALGMESGEIHS' + 'DQITASSQYSTNWSAERSRLNYPENGWTPGEDSYREWIQVDLGLLRFVTAVGTQGAISKETKKKYYVKTYKI' + 'DVSSNGEDWITIKEGNKPVLFQGNTNPTDVVVAVFPKPLITRFVRIKPATWETGISMRFEVYGCKITDYPCS' + 'GMLGMVSGLISDSQITSSNQGDRNWMPENIRLVTSRSGWALPPAPHSYINEWLQIDLGEEKIVRGIIIQGGK' + 'HRENKVFMRKFKIGYSNNGSDWKMIMDDSKRKAKSFEGNNNYDTPELRTFPALSTRFIRIYPERATHGGLGL' + 'RMELLGCEVEAPTAGPTTPNGNLVDECDDDQANCHSGTGDDFQLTGGTTVLATEKPTVIDSTIQSEFPTYGF' + 'NCEFGWGSHKTFCHWEHDNHVQLKWSVLTSKTGPIQDHTGDGNFIYSQADENQKGKVARLVSPVVYSQNSAH' + 'CMTFWYHMSGSHVGTLRVKLRYQKPEEYDQLVWMAIGHQGDHWKEGRVLLHKSLKLYQVIFEGEIGKGNLGG' + 'IAVDDISINNHISQEDCAKPADLDKKNPEIKIDETGSTPGYEGEGEGDKNISRKPGNVLKTLDPILITIIAM' + 'SALGVLLGAVCGVVLYCACWHNGMSERNLSALENYNFELVDGVKLKKDKLNTQSTYSEA' + ), +} + + +# --------------------------------------------------------------------------- +# Deprotection rules (Fmoc SPPS side-chain protecting groups). +# Each rule is (label, reaction-SMARTS). Reactions are applied iteratively +# until no rule matches; on each match we re-sanitize the molecule. +# Patterns are intentionally specific so they don't accidentally rewrite +# atoms in the linker/payload (Aoa, SS, DSPA, Cpt, Dau, SN-38 do not contain +# tBu, trityl, or benzyl-phosphate groups). +# --------------------------------------------------------------------------- +DEPROTECTION_RULES = [ + # 4-methoxytrityl on Cys: -S-C(Ph)(Ph)(p-anisyl) -> -SH + ("Mmt-S", + "[#16:1][C](-c1ccccc1)(-c2ccccc2)-c3ccc(O[CH3])cc3>>[#16:1]"), + + # Trityl on Cys / His-N: -S-C(Ph)3 -> -SH ; -N-C(Ph)3 -> -NH + ("Trt-S/N", + "[#16,#7:1][C](-c1ccccc1)(-c2ccccc2)-c3ccccc3>>[*:1]"), + + # Boc on amine: -N-C(=O)-O-C(CH3)3 -> -NH + ("Boc-N", + "[N:1][C](=O)O[C]([CH3])([CH3])[CH3]>>[N:1]"), + + # OtBu ester on COOH: -C(=O)-O-C(CH3)3 -> -C(=O)OH + ("OtBu-ester", + "[C:1](=O)O[C]([CH3])([CH3])[CH3]>>[C:1](=O)O"), + + # OtBu ether on Ser/Thr/Tyr: -O-C(CH3)3 -> -OH + ("OtBu-ether", + "[O:1][C]([CH3])([CH3])[CH3]>>[O:1]"), + + # Bn on phosphate / phosphonate: -O-P(=O)(O)-O-CH2-Ph -> -O-P(=O)(O)-OH + ("Bn-phosphate", + "[#15:1](=O)([O])O[CH2]c1ccccc1>>[#15:1](=O)([O])O"), + + # Bn ester: -C(=O)-O-CH2-Ph -> -C(=O)OH + ("Bn-ester", + "[C:1](=O)O[CH2]c1ccccc1>>[C:1](=O)O"), + + # Bn ether (catch-all, after phosphate/ester): -O-CH2-Ph -> -OH + ("Bn-ether", + "[O:1][CH2]c1ccccc1>>[O:1]"), + + # Pbf on Arg: -N-S(=O)(=O)-pentamethyldihydrobenzofuran -> -N + ("Pbf-N", + "[N:1][S](=O)(=O)c1c([CH3])c2c(c1)O[C]([CH3])([CH3])[C]2([CH3])[CH3]>>[N:1]"), + + # 2,4-dimethoxybenzyl (Dmb) amide / amine cap: -N-CH2-Ar(2,4-OMe) -> -NH + ("Dmb-N", + "[N:1][CH2]c1cc(O[CH3])cc(O[CH3])c1>>[N:1]"), + ("Dmb-N-alt", + "[N:1][CH2]c1c(O[CH3])cc(O[CH3])cc1>>[N:1]"), +] + + +def deprotect_mol(mol, rules, max_iters=30): + """Apply deprotection reactions until convergence. + + Returns + ------- + final_mol : Chem.Mol or None + counts : dict {label: int} -- how many times each rule fired. + """ + counts = {label: 0 for label, _ in rules} + if mol is None: + return None, counts + + # Pre-compile reactions + compiled = [] + for label, rxn_sm in rules: + try: + rxn = AllChem.ReactionFromSmarts(rxn_sm) + if rxn.GetNumReactantTemplates() == 1: + compiled.append((label, rxn)) + except Exception: + pass + + for _ in range(max_iters): + changed = False + for label, rxn in compiled: + try: + products = rxn.RunReactants((mol,)) + except Exception: + continue + if not products: + continue + new_mol = products[0][0] + try: + Chem.SanitizeMol(new_mol) + except Exception: + continue + try: + if Chem.MolToSmiles(new_mol) == Chem.MolToSmiles(mol): + continue + except Exception: + continue + mol = new_mol + counts[label] += 1 + changed = True + break # restart rule list from the top + if not changed: + break + return mol, counts + + +def deprotect_smiles(smi, rules): + """Convenience wrapper: SMILES -> (deprot_smi, counts).""" + empty = {label: 0 for label, _ in rules} + if not isinstance(smi, str) or not smi: + return None, empty + mol = Chem.MolFromSmiles(smi) + if mol is None: + return None, empty + final_mol, counts = deprotect_mol(mol, rules) + if final_mol is None: + return None, counts + try: + return Chem.MolToSmiles(final_mol), counts + except Exception: + return None, counts + + +def compute_props(smi): + if not isinstance(smi, str) or not smi: + return None + mol = Chem.MolFromSmiles(smi) + if mol is None: + return None + return { + 'mw_dep': float(Descriptors.MolWt(mol)), + 'logp_dep': float(Descriptors.MolLogP(mol)), + 'tpsa_dep': float(Descriptors.TPSA(mol)), + 'hbd_dep': int(Descriptors.NumHDonors(mol)), + 'hba_dep': int(Descriptors.NumHAcceptors(mol)), + 'rotbonds_dep': int(Descriptors.NumRotatableBonds(mol)), + 'heavy_dep': int(mol.GetNumHeavyAtoms()), + } + + +def determine_target(input_path, target_arg): + if target_arg: + t = target_arg.upper() + if t in TARGET_SEQUENCES: + return t + fn = os.path.basename(input_path).upper() + for t in TARGET_SEQUENCES: + if t in fn: + return t + return None + + +def score_binding_smiles(model, tokenizer, smi_list, device, batch_size=32): + """Score a list of peptide SMILES with the BindingAffinity model. + + Returns a numpy array of length len(smi_list); failed entries are NaN. + """ + import torch + out = np.full(len(smi_list), np.nan, dtype=float) + if not smi_list: + return out + + valid_idx = [] + valid_tok = [] + for i, s in enumerate(smi_list): + if not isinstance(s, str) or not s: + continue + try: + t = tokenizer(s) + except Exception: + continue + if hasattr(t, 'input_ids'): + t = t.input_ids + if isinstance(t, list): + t = torch.tensor(t) + if not torch.is_tensor(t): + continue + if t.dim() == 2: + t = t.squeeze(0) + if t.dim() != 1 or t.numel() == 0: + continue + valid_idx.append(i) + valid_tok.append(t.long()) + + if not valid_tok: + return out + + for j in range(0, len(valid_tok), batch_size): + chunk = valid_tok[j:j + batch_size] + max_len = max(int(t.numel()) for t in chunk) + pad = torch.zeros((len(chunk), max_len), dtype=torch.long) + for k, t in enumerate(chunk): + pad[k, :t.numel()] = t + try: + with torch.no_grad(): + scores = model(pad.to(device)).cpu().numpy().reshape(-1) + except Exception as e: + print(f" [score_binding] batch {j}: {e}") + continue + for k, idx in enumerate(valid_idx[j:j + batch_size]): + out[idx] = float(scores[k]) + return out + + +def maybe_load_ncaa_sequences(csv_path, ncaa_dir): + """Pair rows of the CSV with NCAA sequence strings from the matching + `.txt` two-column file (NCAASMILES). Returns a list of + NCAA strings (None for unmatched rows) aligned to the CSV rows. + + Lookup strategy: + 1. Read all (NCAA, SMILES) pairs from the .txt. + 2. For each row in the CSV, match by `peptide_smiles`. + """ + base = Path(csv_path).stem + if base.endswith('_conjugate_analysis'): + base = base[: -len('_conjugate_analysis')] + candidates = [] + if ncaa_dir: + candidates.append(os.path.join(ncaa_dir, base + '.txt')) + csv_dir = os.path.dirname(os.path.abspath(csv_path)) + candidates.extend([ + os.path.join(csv_dir, base + '.txt'), + os.path.join(csv_dir, '..', base + '.txt'), + os.path.join(os.path.dirname(csv_dir), base + '.txt'), + ]) + # also try stripping _smiles suffix used by some files + if base.endswith('_smiles'): + b2 = base[: -len('_smiles')] + for d in [ncaa_dir, csv_dir, os.path.join(csv_dir, '..'), os.path.dirname(csv_dir)]: + if d: + candidates.append(os.path.join(d, b2 + '.txt')) + + for path in candidates: + if path and os.path.exists(path): + try: + pairs = {} + with open(path, 'r', encoding='utf-8', errors='replace') as f: + for line in f: + line = line.rstrip('\n').rstrip('\r') + if not line: + continue + if '\t' in line: + ncaa, smi = line.split('\t', 1) + else: + # one-column SMILES file, no NCAA available + return None + pairs[smi] = ncaa + return path, pairs + except Exception: + continue + return None + + +def parse_args(): + p = argparse.ArgumentParser( + description="UAA peptide-drug conjugate deprotection + re-scoring", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + p.add_argument('inputs', nargs='+', + help='UAA *_conjugate_analysis.csv file(s) produced by run_metrics.py') + p.add_argument('--output', '-o', default=None, + help='Output directory for *_deprot.csv files and summary ' + '(default: same directory as each input)') + p.add_argument('--target', default=None, choices=list(TARGET_SEQUENCES), + help='Target protein (default: auto-detect from filename)') + p.add_argument('--device', default='auto', + help='cuda / cpu / auto (default: auto)') + p.add_argument('--no-rescore', action='store_true', + help='Skip binding rescoring (deprotect + recompute properties only)') + p.add_argument('--vocab', default='./smiles/smiles_tokenizer/new_vocab.txt') + p.add_argument('--splits', default='./smiles/smiles_tokenizer/new_splits.txt') + p.add_argument('--ncaa-txt-dir', default=None, + help='Directory holding the original NCAA .txt files ' + '(NCAASMILES). Default: same dir as the CSV.') + return p.parse_args() + + +def main(): + args = parse_args() + + # Lazy heavy imports (only if needed) + if not args.no_rescore: + import torch + if args.device == 'auto': + device = 'cuda' if torch.cuda.is_available() else 'cpu' + else: + device = args.device + print(f"Device: {device}") + + # Load tokenizer + binding model lazily + try: + from smiles.scoring.classifiers import BindingAffinity + from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer + except Exception as e: + print(f"Cannot import binding-affinity oracle modules: {e}") + print("Falling back to --no-rescore.") + args.no_rescore = True + + if not args.no_rescore: + if not (os.path.exists(args.vocab) and os.path.exists(args.splits)): + print(f"Tokenizer vocab/splits not found " + f"(--vocab={args.vocab}, --splits={args.splits}).") + print("Run from MoCA root, or pass --vocab/--splits. Skipping rescoring.") + args.no_rescore = True + else: + tokenizer = SMILES_SPE_Tokenizer(args.vocab, args.splits) + else: + device = 'cpu' + + # Print rule list + print("Deprotection rules (in order, applied iteratively):") + for label, _ in DEPROTECTION_RULES: + print(f" - {label}") + + binding_models = {} + summary_rows = [] + overall_strip_counts = {label: 0 for label, _ in DEPROTECTION_RULES} + + for in_csv in args.inputs: + print(f"\n>>> {in_csv}") + if not os.path.exists(in_csv): + print(f" missing, skipping") + continue + df = pd.read_csv(in_csv) + if df.empty: + print(f" empty CSV, skipping") + continue + target = determine_target(in_csv, args.target) + if target is None: + print(f" cannot determine target, skipping") + continue + print(f" target = {target}, n = {len(df)}") + + # Try to attach NCAA sequence strings from the matching .txt + ncaa_seq_col = [None] * len(df) + ncaa_lookup = maybe_load_ncaa_sequences(in_csv, args.ncaa_txt_dir) + if ncaa_lookup is not None: + ncaa_path, pairs = ncaa_lookup + n_matched = 0 + for i, row in df.iterrows(): + smi = row.get('peptide_smiles') + if isinstance(smi, str) and smi in pairs: + ncaa_seq_col[i] = pairs[smi] + n_matched += 1 + print(f" paired {n_matched}/{len(df)} rows with NCAA sequences from {ncaa_path}") + df['ncaa_seq'] = ncaa_seq_col + + # ---- Deprotection ---- + prot_pept = df['peptide_smiles'].fillna('').astype(str).tolist() + prot_conj = df['conjugate_smiles'].fillna('').astype(str).tolist() \ + if 'conjugate_smiles' in df.columns else [''] * len(df) + + deprot_pept = [] + deprot_conj = [] + rule_counts_pept = {label: 0 for label, _ in DEPROTECTION_RULES} + rule_counts_conj = {label: 0 for label, _ in DEPROTECTION_RULES} + n_strips_pept = [] + n_strips_conj = [] + for sp, sc in zip(prot_pept, prot_conj): + dp, cp = deprotect_smiles(sp, DEPROTECTION_RULES) + dc, cc = deprotect_smiles(sc, DEPROTECTION_RULES) + deprot_pept.append(dp) + deprot_conj.append(dc) + for k, v in cp.items(): + rule_counts_pept[k] += v + for k, v in cc.items(): + rule_counts_conj[k] += v + overall_strip_counts[k] += v + n_strips_pept.append(sum(cp.values())) + n_strips_conj.append(sum(cc.values())) + + df['peptide_smiles_deprot'] = deprot_pept + df['conjugate_smiles_deprot'] = deprot_conj + df['n_strip_pept'] = n_strips_pept + df['n_strip_conj'] = n_strips_conj + + # ---- Properties on deprotected conjugate ---- + props_list = [compute_props(s) for s in deprot_conj] + for k in ('mw_dep', 'logp_dep', 'tpsa_dep', 'hbd_dep', + 'hba_dep', 'rotbonds_dep', 'heavy_dep'): + df[k] = [p[k] if p else np.nan for p in props_list] + + # ---- Binding rescoring ---- + df['binding_protected'] = df.get('binding_affinity', np.nan) + if args.no_rescore: + df['binding_deprotected'] = np.nan + else: + if target not in binding_models: + print(f" loading binding-affinity model for {target}...") + seq = TARGET_SEQUENCES[target] + binding_models[target] = BindingAffinity(seq, device=device) + model = binding_models[target] + print(f" scoring {len(deprot_pept)} deprotected peptide SMILES...") + df['binding_deprotected'] = score_binding_smiles( + model, tokenizer, deprot_pept, device + ) + + # Convenience pKd columns (×10) + df['binding_protected_pKd'] = df['binding_protected'] * 10 + df['binding_deprotected_pKd'] = df['binding_deprotected'] * 10 + + # ---- Save per-CSV output ---- + out_dir = args.output or os.path.dirname(os.path.abspath(in_csv)) + os.makedirs(out_dir, exist_ok=True) + out_csv = os.path.join(out_dir, Path(in_csv).stem + '_deprot.csv') + df.to_csv(out_csv, index=False) + print(f" -> {out_csv}") + + # ---- Per-CSV summary ---- + bp = pd.to_numeric(df['binding_protected'], errors='coerce').values + bd = pd.to_numeric(df['binding_deprotected'], errors='coerce').values + mask = np.isfinite(bp) & np.isfinite(bd) + n_pair = int(mask.sum()) + if n_pair > 1: + try: + from scipy.stats import pearsonr, spearmanr + pr = float(pearsonr(bp[mask], bd[mask])[0]) + sr = float(spearmanr(bp[mask], bd[mask]).correlation) + except Exception: + bp_z = bp[mask] - bp[mask].mean() + bd_z = bd[mask] - bd[mask].mean() + denom = (np.linalg.norm(bp_z) * np.linalg.norm(bd_z)) + pr = float((bp_z * bd_z).sum() / denom) if denom else float('nan') + sr = float('nan') + else: + pr = float('nan') + sr = float('nan') + + bp_v = bp[np.isfinite(bp)] + bd_v = bd[np.isfinite(bd)] + row = { + 'file': os.path.basename(in_csv), + 'target': target, + 'n': len(df), + 'n_with_both': n_pair, + 'protect_avg_strips_per_conj': float(np.mean(n_strips_conj)) if n_strips_conj else 0.0, + 'binding_prot_pKd_mean': float(bp_v.mean() * 10) if bp_v.size else float('nan'), + 'binding_prot_pKd_std': float(bp_v.std() * 10) if bp_v.size else float('nan'), + 'binding_dep_pKd_mean': float(bd_v.mean() * 10) if bd_v.size else float('nan'), + 'binding_dep_pKd_std': float(bd_v.std() * 10) if bd_v.size else float('nan'), + 'pearson_r': pr, + 'spearman_r': sr, + } + for label, _ in DEPROTECTION_RULES: + row[f'strips_conj__{label}'] = rule_counts_conj[label] + summary_rows.append(row) + + # Print + print(f"\n --- {os.path.basename(in_csv)} ---") + print(f" binding (pKd) protected : {row['binding_prot_pKd_mean']:.3f} ± {row['binding_prot_pKd_std']:.3f} (n={int(np.isfinite(bp).sum())})") + print(f" binding (pKd) deprotected : {row['binding_dep_pKd_mean']:.3f} ± {row['binding_dep_pKd_std']:.3f} (n={int(np.isfinite(bd).sum())})") + print(f" Pearson r (prot vs deprot, paired n={n_pair}): {pr:.3f}") + print(f" Spearman r (prot vs deprot, paired n={n_pair}): {sr:.3f}") + avg_per = float(np.mean(n_strips_conj)) if n_strips_conj else 0.0 + print(f" Total strip events on conjugates: {sum(n_strips_conj)} ({avg_per:.2f} per molecule)") + for label, _ in DEPROTECTION_RULES: + v = rule_counts_conj[label] + if v: + print(f" {label:<14s} {v}") + + # ---- Aggregate summary ---- + if summary_rows: + out_dir = args.output or os.path.dirname(os.path.abspath(args.inputs[0])) + os.makedirs(out_dir, exist_ok=True) + summary_csv = os.path.join(out_dir, 'uaa_deprotection_summary.csv') + pd.DataFrame(summary_rows).to_csv(summary_csv, index=False) + print(f"\nAggregate summary -> {summary_csv}") + print("\nOverall strip events on conjugates across all inputs:") + for label, _ in DEPROTECTION_RULES: + v = overall_strip_counts[label] + if v: + print(f" {label:<14s} {v}") + + return 0 + + +if __name__ == '__main__': + sys.exit(main() or 0) diff --git a/smiles/generation/__init__.py b/smiles/generation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a127cb09ddd7ea68c92b770cc7a4cd7a21c32445 --- /dev/null +++ b/smiles/generation/__init__.py @@ -0,0 +1 @@ +"""PDC generation entry points.""" diff --git a/smiles/generation/cupofmoca_guided.py b/smiles/generation/cupofmoca_guided.py new file mode 100644 index 0000000000000000000000000000000000000000..3dc6641bf1f1db468a6422ebe83dabdb6cdb01a2 --- /dev/null +++ b/smiles/generation/cupofmoca_guided.py @@ -0,0 +1,996 @@ +""" +CupOfMoCA-style Guided Generation with Alternative Guidance Methods + +This implements Algorithm 2 (Sampling with CupOfMoCA) with different guidance strategies: +1. Exponential Tilting (ExpTilt) - Direct implementation of Algorithm 2 +2. Classifier Guidance (CG) - Using external scores to adjust logits +3. Best-of-K Selection (BoK) - Sample K candidates and select best + +Key differences from the Barker-based method in guided.py: +- ExpTilt: Uses p'(z') ∝ p(z') · exp(β · S(z')) directly without Barker balancing +- CG: Adjusts base logits using external classifier/score signals: logits = base + w * score_logits +- BoK: Samples multiple candidates per step and selects the one with highest score + +Note on Classifier Guidance vs Classifier-Free Guidance: +- Classifier Guidance (what we implement here): External scorers modify the generation distribution +- Classifier-Free Guidance (NOT implemented): Would require model trained with conditioning dropout, + and extrapolates between conditional and unconditional model outputs without external classifiers +""" + +import argparse +import re +import random +from collections import Counter +import csv +import json +import os +from datetime import datetime +from enum import Enum +from typing import List, Optional, Tuple, Dict, Any + +import torch +import torch.nn as nn +import torch.nn.functional as F +from tqdm import tqdm +import numpy as np + +# Import from the original codebase +from smiles.models.train import MDLMLightningModule +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +from smiles.evaluation.peptide_analyzer import PeptideAnalyzer +from smiles.evaluation.pdc_analyzer import PDCAnalyzer +from smiles.scoring.classifiers import Conjugation, BindingAffinity + + +class GuidanceMethod(Enum): + """Available guidance methods""" + EXPONENTIAL_TILT = "exp_tilt" # Direct exponential tilting (Algorithm 2) + CLASSIFIER_GUIDANCE = "cg" # Classifier/score-based logit adjustment + BEST_OF_K = "bok" # Best-of-K selection + HYBRID = "hybrid" # Combination of methods + + +class GenerationTracker: + """ + Tracks detailed validity information during generation + """ + 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): + """Log information for a generation step""" + step_info = { + 'step': step, + 'scores': scores.cpu().numpy().tolist() if isinstance(scores, torch.Tensor) else scores, + 'num_valid': int(sum(1 for s in scores if s > 0)) if hasattr(scores, '__iter__') else 0, + 'improved_indices': improved_indices or [], + 'guidance_info': guidance_info or {} + } + + if detailed_results: + step_info['detailed'] = [] + for i, result in enumerate(detailed_results): + step_info['detailed'].append({ + 'sample_idx': i, + 'is_valid_peptide': result.get('is_valid_peptide', False), + 'has_conjugation_site': result.get('has_conjugation_site', False), + 'conjugation_success': result.get('conjugation_success', False), + 'pdc_valid': result.get('pdc_valid', False), + 'num_conjugates': len(result.get('conjugates', [])), + 'error': result.get('error') + }) + + self.step_logs.append(step_info) + + def log_final(self, sequences, detailed_results_with_props): + """Log final results with properties""" + for i, (seq, result) in enumerate(zip(sequences, detailed_results_with_props)): + final_info = { + 'sample_idx': i, + 'peptide_smiles': seq, + 'is_valid_peptide': result.get('is_valid_peptide', False), + 'has_conjugation_site': result.get('has_conjugation_site', False), + 'num_conjugates': len(result.get('conjugates', [])), + 'num_valid_pdcs': len(result.get('valid_pdcs', [])), + 'best_pdc': None, + 'linker_used': result.get('linker_used', ''), + 'all_conjugates': result.get('conjugates', []), + 'properties': result.get('properties', {}) + } + + if result.get('best_pdc'): + final_info['best_pdc'] = { + 'smiles': result['best_pdc'].get('smiles', ''), + 'checks': result['best_pdc'].get('checks', {}) + } + + self.final_results.append(final_info) + + def save(self, filename_prefix='generation'): + """Save all logs to files""" + step_log_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_steps.json") + with open(step_log_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', 'peptide_smiles', 'is_valid_peptide', 'has_conjugation_site', + 'linker_used', 'num_conjugates', 'num_valid_pdcs', 'best_pdc_smiles', + 'mf', 'polararea', 'xlogp', 'heavycnt', 'rotbonds' + ]) + for result in self.final_results: + props = result.get('properties', {}) + writer.writerow([ + result['sample_idx'], + result['peptide_smiles'], + result['is_valid_peptide'], + result['has_conjugation_site'], + result.get('linker_used', ''), + result['num_conjugates'], + result['num_valid_pdcs'], + result['best_pdc']['smiles'] if result.get('best_pdc') else '', + props.get('mf', ''), + props.get('polararea', ''), + props.get('xlogp', ''), + props.get('heavycnt', ''), + props.get('rotbonds', '') + ]) + + # Save dedicated PDC output: peptide → linker → all conjugate SMILES + pdc_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_pdcs.csv") + with open(pdc_path, 'w', newline='') as f: + writer = csv.writer(f) + writer.writerow(['sample_idx', 'peptide_smiles', 'linker_used', 'conjugate_idx', 'pdc_smiles']) + for result in self.final_results: + for j, conj in enumerate(result.get('all_conjugates', [])): + writer.writerow([ + result['sample_idx'], + result['peptide_smiles'], + result.get('linker_used', ''), + j, + conj + ]) + + print(f"\n=== Generation logs saved ===") + print(f" Step logs: {step_log_path}") + print(f" Final results: {final_path}") + print(f" Summary CSV: {csv_path}") + print(f" PDC conjugates: {pdc_path}") + + return step_log_path, final_path, csv_path, pdc_path + + def print_step_summary(self, step, scores, detailed_results=None, guidance_method=None): + """Print a summary for the current step""" + if isinstance(scores, torch.Tensor): + scores_list = scores.cpu().numpy().tolist() + else: + scores_list = list(scores) + + num_valid = sum(1 for s in scores_list if s > 0) + + print(f"\n--- Step {step} Summary ({guidance_method or 'unknown'}) ---") + print(f" Valid: {num_valid}/{len(scores_list)}") + + if detailed_results: + valid_peptide = sum(1 for r in detailed_results if r.get('is_valid_peptide')) + has_site = sum(1 for r in detailed_results if r.get('has_conjugation_site')) + conj_success = sum(1 for r in detailed_results if r.get('conjugation_success')) + pdc_valid = sum(1 for r in detailed_results if r.get('pdc_valid')) + + print(f" Breakdown:") + print(f" Valid peptides: {valid_peptide}") + print(f" Has conjugation site: {has_site}") + print(f" Conjugation success: {conj_success}") + print(f" PDC valid: {pdc_valid}") + + def print_final_summary(self): + """Print final summary""" + print("\n" + "=" * 60) + print("FINAL GENERATION SUMMARY") + print("=" * 60) + + total = len(self.final_results) + valid_pdcs = sum(1 for r in self.final_results if r.get('num_valid_pdcs', 0) > 0) + + print(f"\nTotal sequences: {total}") + print(f"With valid PDCs: {valid_pdcs}") + + print("\nDetailed results:") + for result in self.final_results: + status = "" if result.get('num_valid_pdcs', 0) > 0 else "" + print(f"\n [{status}] Sample {result['sample_idx']}:") + print(f" Peptide: {result['peptide_smiles'][:60]}...") + print(f" Valid peptide: {result['is_valid_peptide']}") + print(f" Has conjugation site: {result['has_conjugation_site']}") + print(f" Conjugates: {result['num_conjugates']}") + print(f" Valid PDCs: {result['num_valid_pdcs']}") + + +class CupOfMoCAGenerator: + """ + CupOfMoCA-style generator implementing Algorithm 2 with multiple guidance strategies. + + Key differences from Barker-based approach: + 1. ExpTilt: Direct exponential tilting without balancing function + 2. CFG: Classifier-free guidance by mixing logits + 3. BoK: Best-of-K selection strategy + """ + + def __init__(self, model, device, objectives, args, tracker=None): + self.model = model + self.device = device + self.objectives = objectives + self.args = args + self.num_objectives = len(objectives) + self.peptide_analyzer = PeptideAnalyzer() + self.pdc_analyzer = PDCAnalyzer(device) + self.invalid_tokens = [0, 1, 2, 3, 4, 585, 586] # Special tokens to avoid + self.tracker = tracker + + # Parse guidance method + self.guidance_method = GuidanceMethod(args.guidance_method) + + # Find the Conjugation objective for detailed tracking + self.conjugation_obj = None + for obj in objectives: + if isinstance(obj, Conjugation): + self.conjugation_obj = obj + break + + # ========================================================================= + # Initial Generation (x0 from prior μ₀) + # ========================================================================= + + def _generate_single_x0(self, n_steps: int = 16, temperature: float = 1.0) -> torch.Tensor: + """ + Generate initial sequences using flow matching (Algorithm 2, Line 6: Sample Z_0 ~ μ_0) + This follows the flow from noise to data without guidance. + """ + self.model.eval() + + # Start with random tokens (noise at t=0) + x = torch.randint( + 0, self.model.tokenizer.vocab_size, + (self.args.num_samples, self.args.gen_len), + device=self.device + ) + + # Time schedule: t_0 = 0 to t_K = T (Line 5 of Algorithm 2) + time_steps = torch.linspace(0.0, 1.0, n_steps + 1, device=self.device) + + with torch.no_grad(): + for i in tqdm(range(n_steps), desc="Initial Flow Matching", leave=False): + t_curr = time_steps[i] + t_next = time_steps[i + 1] + + t_tensor = torch.full((self.args.num_samples,), t_curr, device=self.device) + + # Get predicted distribution (Line 8: p_{k+1} ← Φ_{θ*}^{t_k→t_{k+1}}(Z_k)) + logits = self.model(x, t_tensor) + logits = logits / temperature + logits[:, :, 586] = -1000 # Mask invalid token + + pred_x1 = torch.argmax(logits, dim=-1) + + if i == n_steps - 1: + x = pred_x1 + break + + # Interpolate between noise and prediction + noise_prob = 1.0 - t_next + mask = torch.rand(x.shape, device=self.device) < noise_prob + noise = torch.randint(0, self.model.tokenizer.vocab_size, x.shape, device=self.device) + x = torch.where(mask, noise, pred_x1) + + return x + + def _check_conjugation_validity(self, x: torch.Tensor) -> torch.Tensor: + """Check if sequences can be conjugated (validity indicator 1_valid in Algorithm 2)""" + if self.conjugation_obj is None: + sequences = self.model.tokenizer.batch_decode(x) + return torch.tensor([self.peptide_analyzer.is_peptide(seq) for seq in sequences], + device=self.device) + scores = self.conjugation_obj(x) + return scores > 0 + + def generate_x0(self, n_steps: int = 16, temperature: float = 1.0, + require_valid: bool = False, max_retries: int = 10) -> torch.Tensor: + """ + Generate initial x0 sequences with optional validity enforcement. + Implements the constraint that x0 should satisfy validity before guidance. + """ + print("Generating initial sequences (x0 from prior)...") + + x = self._generate_single_x0(n_steps, temperature) + + if require_valid: + print("Enforcing initial validity constraint...") + + for retry in range(max_retries): + validity = self._check_conjugation_validity(x) + 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 are valid!") + break + + print(f" Retry {retry + 1}/{max_retries}: {num_valid}/{self.args.num_samples} valid") + + invalid_indices = torch.where(~validity)[0] + original_num_samples = self.args.num_samples + self.args.num_samples = num_invalid + new_x = self._generate_single_x0(n_steps, temperature) + self.args.num_samples = original_num_samples + x[invalid_indices] = new_x + + final_validity = self._check_conjugation_validity(x) + if final_validity.sum().item() < self.args.num_samples: + print(f"⚠ After {max_retries} retries, {final_validity.sum().item()}/{self.args.num_samples} valid") + + generated_sequences = self.model.tokenizer.batch_decode(x) + for seq in generated_sequences: + print(f" {seq[:80]}...") + + return x + + # ========================================================================= + # Score Functions + # ========================================================================= + + def _get_scores(self, x_batch: torch.Tensor) -> torch.Tensor: + """Calculate scores for all objectives: S(z) in Algorithm 2""" + scores = [] + for obj_func in self.objectives: + scores.append(obj_func(x_batch.to(self.device))) + return torch.stack(scores, dim=0).to(self.device) + + def _compute_aggregated_score(self, scores: torch.Tensor, weights: torch.Tensor) -> torch.Tensor: + """Compute weighted aggregated score S(z) for guidance""" + # Weighted sum or min depending on aggregation strategy + if self.args.score_aggregation == "weighted_sum": + return (weights * scores).sum(dim=0) + elif self.args.score_aggregation == "weighted_min": + return torch.min(weights * scores, dim=0).values + else: # default: weighted sum + return (weights * scores).sum(dim=0) + + # ========================================================================= + # Guidance Methods + # ========================================================================= + + def _exponential_tilt_guidance( + self, + x: torch.Tensor, + pos_probs: torch.Tensor, + candidates: torch.Tensor, + mut_idx: int, + sample_idx: int, + beta: float, + weights: torch.Tensor, + current_score: float = None + ) -> Tuple[int, Dict]: + """ + Exponential Tilting Guidance (Algorithm 2, Lines 10-12) with acceptance criterion + + p'(z') ∝ p_{k+1}(z') · exp(β · S(z')) · 1_valid(z') + + Added: Only accept if new_score >= current_score (prevents regression) + """ + num_candidates = len(candidates) + current_token = x[sample_idx, mut_idx].item() + + if num_candidates == 0: + return current_token, {'method': 'exp_tilt', 'fallback': True, 'accepted': False} + + # Limit candidates for efficiency + if num_candidates > self.args.top_k: + candidates = candidates[:self.args.top_k] + num_candidates = self.args.top_k + + # Create batch of proposals + x_proposals = x[sample_idx].unsqueeze(0).repeat(num_candidates, 1) + x_proposals[:, mut_idx] = candidates + + # Compute scores S(z') for all candidates + proposal_scores = self._get_scores(x_proposals) # [num_objectives, num_candidates] + aggregated_scores = self._compute_aggregated_score(proposal_scores, weights) # [num_candidates] + + # Validity mask: 1_valid(z') + validity_mask = (proposal_scores[0] > 0).float() # Using first objective (conjugation) as validity + + # Get base probabilities from the model + base_probs = pos_probs[sample_idx, candidates] + + # Apply exponential tilting with numerical stability: + # p'(z') ∝ p(z') · exp(β · S(z')) · 1_valid(z') + # + # To avoid overflow, we use log-space computation: + # log(p') = log(p) + β·S + log(1_valid) + # Then normalize in log-space before exp + + log_base_probs = torch.log(base_probs + 1e-10) + log_exp_scores = beta * aggregated_scores + log_validity = torch.log(validity_mask + 1e-10) # -inf for invalid + + log_tilted = log_base_probs + log_exp_scores + log_validity + + # Subtract max for numerical stability (log-sum-exp trick) + log_tilted_max = log_tilted.max() + if torch.isinf(log_tilted_max) or torch.isnan(log_tilted_max): + # All candidates are invalid or have zero probability + # Fallback to base distribution + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + else: + log_tilted_stable = log_tilted - log_tilted_max + tilted_probs = torch.exp(log_tilted_stable) + + # Normalize + prob_sum = tilted_probs.sum() + if prob_sum < 1e-10 or torch.isnan(prob_sum) or torch.isinf(prob_sum): + # Fallback to base distribution + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + else: + fallback = False + final_probs = tilted_probs / prob_sum + + # Final safety check + if torch.any(torch.isnan(final_probs)) or torch.any(torch.isinf(final_probs)): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + + # Ensure no negative or zero probabilities for multinomial + final_probs = torch.clamp(final_probs, min=1e-10) + final_probs = final_probs / final_probs.sum() + + selected_idx = torch.multinomial(final_probs, 1).item() + selected_token = candidates[selected_idx].item() + selected_score = aggregated_scores[selected_idx].item() + + # ACCEPTANCE CRITERION: Only accept if score improves or stays same + accepted = True + if self.args.use_acceptance and current_score is not None: + if selected_score < current_score: + # Reject: keep current token + selected_token = current_token + accepted = False + + info = { + 'method': 'exp_tilt', + 'beta': beta, + 'num_candidates': num_candidates, + 'proposal_score': aggregated_scores[selected_idx].item(), + 'selected_score': selected_score if accepted else current_score, + 'fallback': fallback, + 'validity_rate': validity_mask.mean().item(), + 'accepted': accepted + } + + return selected_token, info + + def _classifier_guidance( + self, + x: torch.Tensor, + logits: torch.Tensor, + mut_idx: int, + sample_idx: int, + guidance_weight: float, + weights: torch.Tensor + ) -> Tuple[int, Dict]: + """ + Classifier Guidance (Score-based Logit Adjustment) + + Uses external score functions to adjust the base model's logits: + logits_guided = logits_base + guidance_weight * score_logits + + This differs from Classifier-Free Guidance (CFG) which requires: + - A model trained with conditioning dropout + - Extrapolation between conditional and unconditional model outputs + + Here we use external classifiers/scorers to provide guidance signal. + """ + base_logits = logits[sample_idx, mut_idx, :] + + # Get top-k candidates from base distribution + sorted_logits, sorted_indices = torch.sort(base_logits, descending=True) + candidates = sorted_indices[:self.args.top_k] + + # Filter invalid tokens + valid_mask = ~torch.isin(candidates, torch.tensor(self.invalid_tokens, device=self.device)) + candidates = candidates[valid_mask] + + if len(candidates) == 0: + return x[sample_idx, mut_idx].item(), {'method': 'cg', 'fallback': True} + + # Create proposals and score them + x_proposals = x[sample_idx].unsqueeze(0).repeat(len(candidates), 1) + x_proposals[:, mut_idx] = candidates + + proposal_scores = self._get_scores(x_proposals) + aggregated_scores = self._compute_aggregated_score(proposal_scores, weights) + + # Create score-based logits adjustment + # Higher score -> higher probability + score_logits = aggregated_scores * self.args.score_temperature + + # Base logits for candidates + base_candidate_logits = base_logits[candidates] + + # Classifier guidance: guided = base + w * score_adjustment + guided_logits = base_candidate_logits + guidance_weight * score_logits + + # Convert to probabilities + guided_probs = F.softmax(guided_logits, dim=-1) + + # Sample + selected_idx = torch.multinomial(guided_probs, 1).item() + selected_token = candidates[selected_idx].item() + + info = { + 'method': 'cg', + 'guidance_weight': guidance_weight, + 'num_candidates': len(candidates), + 'selected_score': aggregated_scores[selected_idx].item(), + 'fallback': False + } + + return selected_token, info + + def _best_of_k_guidance( + self, + x: torch.Tensor, + pos_probs: torch.Tensor, + candidates: torch.Tensor, + mut_idx: int, + sample_idx: int, + k: int, + weights: torch.Tensor + ) -> Tuple[int, Dict]: + """ + Best-of-K Selection + + 1. Sample K candidates from the base distribution + 2. Score all K candidates + 3. Select the one with the highest score + + This is a simple but effective strategy that doesn't require + probability manipulation. + """ + num_candidates = len(candidates) + if num_candidates == 0: + return x[sample_idx, mut_idx].item(), {'method': 'bok', 'fallback': True} + + # Limit k to available candidates + k = min(k, num_candidates) + + # Get probabilities for candidates + candidate_probs = pos_probs[sample_idx, candidates] + candidate_probs = candidate_probs / (candidate_probs.sum() + 1e-9) + + # Sample K candidates + if k >= num_candidates: + sampled_indices = torch.arange(num_candidates, device=self.device) + else: + sampled_indices = torch.multinomial(candidate_probs, k, replacement=False) + + sampled_tokens = candidates[sampled_indices] + + # Create proposals for all K samples + x_proposals = x[sample_idx].unsqueeze(0).repeat(len(sampled_tokens), 1) + x_proposals[:, mut_idx] = sampled_tokens + + # Score all proposals + proposal_scores = self._get_scores(x_proposals) + aggregated_scores = self._compute_aggregated_score(proposal_scores, weights) + + # Select best + best_idx = torch.argmax(aggregated_scores).item() + selected_token = sampled_tokens[best_idx].item() + + info = { + 'method': 'bok', + 'k': k, + 'num_candidates': num_candidates, + 'selected_score': aggregated_scores[best_idx].item(), + 'mean_score': aggregated_scores.mean().item(), + 'max_score': aggregated_scores.max().item(), + 'fallback': False + } + + return selected_token, info + + def _hybrid_guidance( + self, + x: torch.Tensor, + pos_probs: torch.Tensor, + logits: torch.Tensor, + candidates: torch.Tensor, + mut_idx: int, + sample_idx: int, + step: int, + beta: float, + weights: torch.Tensor + ) -> Tuple[int, Dict]: + """ + Hybrid Guidance: Combines multiple methods adaptively + + Strategy: + - Early steps: Use Best-of-K for exploration + - Middle steps: Use Exponential Tilting for balanced guidance + - Late steps: Use CFG for refinement + """ + total_steps = self.args.optimization_steps + progress = step / max(total_steps - 1, 1) + + if progress < 0.3: + # Early: Best-of-K for exploration + return self._best_of_k_guidance( + x, pos_probs, candidates, mut_idx, sample_idx, + k=self.args.bok_k, weights=weights + ) + elif progress < 0.7: + # Middle: Exponential tilting + return self._exponential_tilt_guidance( + x, pos_probs, candidates, mut_idx, sample_idx, + beta=beta, weights=weights + ) + else: + # Late: Classifier guidance for refinement + return self._classifier_guidance( + x, logits, mut_idx, sample_idx, + guidance_weight=self.args.cg_weight, weights=weights + ) + + # ========================================================================= + # Main Generation Loop (Algorithm 2) + # ========================================================================= + + def generate(self) -> torch.Tensor: + """ + Main generation loop implementing Algorithm 2 with the selected guidance method. + + Lines 7-17 of Algorithm 2: + for k = 0 to K-1 do + p_{k+1} ← Φ_{θ*}^{t_k→t_{k+1}}(Z_k) + if guidance is active (β > 0) then + for each candidate z' ∈ TopK(p_{k+1}) do + p'(z') ∝ p_{k+1}(z') · exp(β · S(z')) · 1_valid(z') + Sample Z_{k+1} ~ p' + else + Sample Z_{k+1} ~ p_{k+1} + return Z_K + """ + # Generate initial x0 with validity constraint + x = self.generate_x0( + require_valid=self.args.require_valid_x0, + max_retries=self.args.max_x0_retries + ) + + # Setup weights + if self.args.weights is None: + weights = torch.tensor( + [1.0] + [1.0 / (self.num_objectives - 1)] * (self.num_objectives - 1), + device=self.device + ).view(-1, 1) + else: + weights = torch.tensor(self.args.weights, device=self.device).view(-1, 1) + if len(weights) != self.num_objectives: + raise ValueError("Number of weights must match number of objectives.") + + print(f"\nGuidance Method: {self.guidance_method.value}") + print(f"Weights: {weights.squeeze().tolist()}") + print(f"β range: [{self.args.beta_min}, {self.args.beta_max}]") + print(f"Acceptance criterion: {'ENABLED' if self.args.use_acceptance else 'DISABLED'}") + + # Main optimization loop (Line 7: for k = 0 to K-1) + with torch.no_grad(): + for step in tqdm(range(self.args.optimization_steps), desc="Guided Generation"): + improved_indices = [] + + # Anneal guidance strength β (from beta_min to beta_max) + progress = step / max(self.args.optimization_steps - 1, 1) + beta = self.args.beta_min + (self.args.beta_max - self.args.beta_min) * progress + + # Choose position to modify (can be random or sequential) + if self.args.position_strategy == "random": + mut_idx = random.randint(1, self.args.gen_len - 2) + else: # sequential + mut_idx = (step % (self.args.gen_len - 2)) + 1 + + # Get model prediction (Line 8: p_{k+1} ← Φ_{θ*}^{t_k→t_{k+1}}(Z_k)) + time_t = torch.full( + (self.args.num_samples,), + step / self.args.optimization_steps, + device=self.device + ) + + logits = self.model(x, time_t) + probs = F.softmax(logits, dim=-1) + pos_probs = probs[:, mut_idx, :] + + # Zero out current tokens and invalid tokens + pos_probs[:, x[:, mut_idx]] = 0 + pos_probs[:, self.invalid_tokens] = 0 + + # Get TopK candidates (Line 10: for each candidate z' ∈ TopK(p_{k+1})) + sorted_probs, sorted_indices = torch.sort(pos_probs, descending=True) + + # Get current scores for comparison + current_scores = self._get_scores(x) + current_aggregated = self._compute_aggregated_score(current_scores, weights) + + if step == 0: + print(f"\nInitial Scores: {current_scores.cpu().numpy()}") + if self.tracker and self.conjugation_obj: + detailed = self.conjugation_obj.get_last_detailed_results() + self.tracker.log_step(step, current_scores[0], detailed) + self.tracker.print_step_summary(step, current_scores[0], detailed, + self.guidance_method.value) + + # Process each sample + final_tokens = [] + guidance_infos = [] + + for i in range(self.args.num_samples): + # Get candidates for this sample + sample_probs = sorted_probs[i] + sample_indices = sorted_indices[i] + + # Top-p filtering + cumsum = torch.cumsum(sample_probs, dim=0) + mask = cumsum <= self.args.top_p + mask[0] = True # Always include at least one + candidates = sample_indices[mask] + + # Current score for this sample (for acceptance criterion) + current_sample_score = current_aggregated[i].item() + + # Apply selected guidance method (Lines 9-15) + if beta > 0: # Guidance active (Line 9) + if self.guidance_method == GuidanceMethod.EXPONENTIAL_TILT: + token, info = self._exponential_tilt_guidance( + x, pos_probs, candidates, mut_idx, i, beta, weights, + current_score=current_sample_score + ) + elif self.guidance_method == GuidanceMethod.CLASSIFIER_GUIDANCE: + token, info = self._classifier_guidance( + x, logits, mut_idx, i, self.args.cg_weight, weights + ) + elif self.guidance_method == GuidanceMethod.BEST_OF_K: + token, info = self._best_of_k_guidance( + x, pos_probs, candidates, mut_idx, i, self.args.bok_k, weights + ) + elif self.guidance_method == GuidanceMethod.HYBRID: + token, info = self._hybrid_guidance( + x, pos_probs, logits, candidates, mut_idx, i, step, beta, weights + ) + else: + raise ValueError(f"Unknown guidance method: {self.guidance_method}") + else: + # No guidance: sample from p_{k+1} directly (Line 15) + candidate_probs = pos_probs[i, candidates] + candidate_probs = candidate_probs / (candidate_probs.sum() + 1e-9) + idx = torch.multinomial(candidate_probs, 1).item() + token = candidates[idx].item() + info = {'method': 'unguided'} + + final_tokens.append(token) + guidance_infos.append(info) + + # Update sequences with new tokens + x[:, mut_idx] = torch.tensor(final_tokens, device=self.device) + + # Check for improvements + new_scores = self._get_scores(x) + new_aggregated = self._compute_aggregated_score(new_scores, weights) + + for i in range(self.args.num_samples): + if new_aggregated[i] > current_aggregated[i]: + improved_indices.append(i) + + if improved_indices: + print(f"\nStep {step}: Improved samples: {improved_indices}") + for i in improved_indices: + print(f" Sample {i}:") + # print(f" Previous score: {current_aggregated[i].item():.4f} | Per-objective: {current_scores[:, i].cpu().numpy()}") + print(f" New score: {new_aggregated[i].item():.4f} | Per-objective: {new_scores[:, i].cpu().numpy()}") + print(f" Sequences: {self.model.tokenizer.batch_decode(x[improved_indices])}") + + # Logging + if self.tracker and self.conjugation_obj and (step % 4 == 0 or step == self.args.optimization_steps - 1): + step_scores = self._get_scores(x) + detailed = self.conjugation_obj.get_last_detailed_results() + self.tracker.log_step( + step, step_scores[0], detailed, improved_indices, + guidance_info={'method': self.guidance_method.value, 'beta': beta} + ) + + if step == self.args.optimization_steps - 1: + self.tracker.print_step_summary(step, step_scores[0], detailed, + self.guidance_method.value) + + return x + + +# ============================================================================= +# Main Execution +# ============================================================================= + +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + print(f"Guidance method: {args.guidance_method}") + + tokenizer = SMILES_SPE_Tokenizer( + './smiles/smiles_tokenizer/new_vocab.txt', + './smiles/smiles_tokenizer/new_splits.txt' + ) + + # Initialize objectives + conjugation_model = Conjugation(tokenizer, device=device, builder_type=args.builder_type) + affinity_model = BindingAffinity(args.target, device) + OBJECTIVE_FUNCTIONS = [conjugation_model, affinity_model] + + # Initialize tracker + tracker = GenerationTracker(output_dir=args.output_dir) + + # Set random seeds + 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) + print(f"Random seed: {args.seed}") + + # Load model + print(f"Loading model from: {args.checkpoint}") + checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False) + model_hparams = checkpoint["hyper_parameters"]["args"] + + model = MDLMLightningModule.load_from_checkpoint( + args.checkpoint, + args=model_hparams, + tokenizer=tokenizer, + map_location=device, + strict=False + ) + model.to(device) + print("Model loaded successfully.") + + # Create generator + generator = CupOfMoCAGenerator(model, device, OBJECTIVE_FUNCTIONS, args, tracker=tracker) + + all_sequences = [] + all_final_results = [] + + for batch_idx in range(args.num_batches): + print(f"\n{'=' * 60}") + print(f"BATCH {batch_idx + 1}/{args.num_batches}") + print(f"{'=' * 60}") + + generated_tokens = generator.generate() + final_scores = generator._get_scores(generated_tokens.detach()).detach().cpu().numpy() + sequence_str = tokenizer.batch_decode(generated_tokens) + + print("\n--- Batch Results ---") + print(f"Sequences: {sequence_str}") + print(f"Scores: {final_scores}") + + all_sequences.extend(sequence_str) + + # Get detailed results + detailed_with_props = conjugation_model.get_detailed_scores_with_properties(generated_tokens) + tracker.log_final(sequence_str, detailed_with_props) + all_final_results.extend(detailed_with_props) + + # Save results + tracker.print_final_summary() + tracker.save(filename_prefix=f'cupofmoca_{args.guidance_method}') + + with open(args.output_file, 'w') as f: + for seq in all_sequences: + f.write(seq + '\n') + print(f"\nSequences saved to: {args.output_file}") + print("\nGeneration complete.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="CupOfMoCA-style Guided Generation with Alternative Guidance Methods" + ) + + # Model and data + parser.add_argument("--checkpoint", type=str, required=True, + help="Path to trained model checkpoint") + parser.add_argument("--target", type=str, required=True, + help="Target protein sequence for binding affinity") + + # Generation settings + parser.add_argument("--num_samples", type=int, default=10, + help="Number of samples per batch") + parser.add_argument("--num_batches", type=int, default=10, + help="Number of batches to generate") + parser.add_argument("--gen_len", type=int, default=50, + help="Length of sequences to generate") + parser.add_argument("--optimization_steps", type=int, default=16, + help="Number of guided optimization steps (K in Algorithm 2)") + + # Guidance method selection + parser.add_argument("--guidance_method", type=str, default="exp_tilt", + choices=["exp_tilt", "cg", "bok", "hybrid"], + help="Guidance method: exp_tilt (exponential tilting), " + "cg (classifier/score guidance), bok (best-of-k), hybrid") + + # Guidance parameters + parser.add_argument("--beta_min", type=float, default=1.0, + help="Minimum guidance strength β") + parser.add_argument("--beta_max", type=float, default=20.0, + help="Maximum guidance strength β") + parser.add_argument("--cg_weight", type=float, default=2.0, + help="Classifier guidance weight (for cg method)") + parser.add_argument("--use_acceptance", action="store_true", + help="Use acceptance criterion: only accept if new_score >= current_score (prevents regression)") + parser.add_argument("--bok_k", type=int, default=8, + help="Number of candidates for best-of-k selection") + parser.add_argument("--top_k", type=int, default=200, + help="Top-K candidates to consider for guidance") + parser.add_argument("--top_p", type=float, default=0.9, + help="Top-p threshold for candidate filtering") + parser.add_argument("--score_temperature", type=float, default=1.0, + help="Temperature for score-based logit adjustment") + parser.add_argument("--score_aggregation", type=str, default="weighted_sum", + choices=["weighted_sum", "weighted_min"], + help="How to aggregate multi-objective scores") + + # Weights + parser.add_argument("--weights", type=float, nargs='+', default=None, + help="Weights for objectives (e.g., 0.5 0.5)") + + # Position selection + parser.add_argument("--position_strategy", type=str, default="random", + choices=["random", "sequential"], + help="Strategy for selecting mutation positions") + + # Initial generation + parser.add_argument("--require_valid_x0", action="store_true", + help="Regenerate x0 until valid conjugatable peptides") + parser.add_argument("--max_x0_retries", type=int, default=10, + help="Max retries for valid x0 generation") + + # PDC builder + parser.add_argument("--builder_type", type=str, default="AoaDau", + choices=["AoaDau", "ValAlaDau", "ValCitDau", "GlutarylDau", "Dau", + "SSCpt", "VcMMAE", "DSPACpt", "MalHexCpt", "DSECCpt", "Cpt", + "DSPASN38", "DSEBSN38", "DSEPSN38", "SN38"], + help="PDC builder type for conjugation. " + "'Dau' samples linker by GNRHR+Dau frequency. " + "'Cpt' samples linker by ERBB2+Cpt frequency. " + "'SN38' samples linker by NRP1+SN38 frequency.") + + # Output + parser.add_argument("--output_file", type=str, default="./smiles.txt", + help="File to save generated sequences") + parser.add_argument("--output_dir", type=str, default="./generation_logs", + help="Directory for detailed logs") + + # Reproducibility + parser.add_argument("--seed", type=int, default=None, + help="Random seed") + + args = parser.parse_args() + main(args) \ No newline at end of file diff --git a/smiles/generation/cupofmoca_guided_aa.py b/smiles/generation/cupofmoca_guided_aa.py new file mode 100644 index 0000000000000000000000000000000000000000..e331e221e7347e6cec9d768b9af5728f958324d0 --- /dev/null +++ b/smiles/generation/cupofmoca_guided_aa.py @@ -0,0 +1,1126 @@ +"""CupOfMoCA-style guided generation with the natural-AA foundation model.""" + +import argparse +import csv +import json +import os +import random +import sys +import time +from collections import defaultdict +from concurrent.futures import ThreadPoolExecutor +from contextlib import contextmanager +from datetime import datetime +from enum import Enum +from typing import Dict, Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from easydict import EasyDict as edict +from tqdm import tqdm + +from smiles.models.train import MDLMLightningModule +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +from smiles.evaluation.peptide_analyzer import PeptideAnalyzer +from smiles.evaluation.pdc_analyzer import PDCAnalyzer +from smiles.scoring.classifiers import Conjugation, BindingAffinity + +# AA-mode shared utilities +from smiles.models.aa_helpers import ( + ESM2AminoTokenizer as _AAEsmTokenizer, + MDLM_PepReDi as _AAMDLM, + make_affinity, + DEFAULT_AA_AFFINITY_CKPT, + INVALID_AA_TOKEN_IDS as _AA_INVALID_IDS, +) + +from third_party.SMILESToPeptide.peptide_to_smiles import aa_to_smiles +from third_party.SMILESToPeptide.aminoacid_selective import one_letter_to_three_letter + + +# ============================================================================= +# Amino Acid Foundation Model (DiT-based MDLM) +# ============================================================================= + +# --- AA-mode Model + Tokenizer (imported from aa_helpers) --- +MDLM = _AAMDLM +AminoTokenizer = _AAEsmTokenizer + + +# ============================================================================= +# Profiler +# ============================================================================= + +class Profiler: + """Aggregates wall-clock time per named section. Optionally syncs CUDA. + + `reset()` clears all state (use between batches for self-contained summaries). + `print_summary()` shows cumulative totals since the last reset, plus a + delta column showing time since the previous `print_summary()` call. + """ + + def __init__(self, enabled=False, cuda_sync=True): + self.enabled = enabled + self.cuda_sync = cuda_sync and torch.cuda.is_available() + self.totals = defaultdict(float) + self.counts = defaultdict(int) + self._last_totals = defaultdict(float) + self._last_counts = defaultdict(int) + + @contextmanager + def section(self, name): + if not self.enabled: + yield + return + if self.cuda_sync: + torch.cuda.synchronize() + t0 = time.perf_counter() + try: + yield + finally: + if self.cuda_sync: + torch.cuda.synchronize() + self.totals[name] += time.perf_counter() - t0 + self.counts[name] += 1 + + def reset(self): + self.totals.clear() + self.counts.clear() + self._last_totals.clear() + self._last_counts.clear() + + def print_summary(self, header=""): + if not self.enabled or not self.totals: + return + cumulative_total = sum(self.totals.values()) + delta_totals = {k: self.totals[k] - self._last_totals.get(k, 0.0) + for k in self.totals} + delta_counts = {k: self.counts[k] - self._last_counts.get(k, 0) + for k in self.counts} + interval_total = sum(delta_totals.values()) + + print(f"\n=== Profiler summary {header} ===") + print(f" (cumulative since reset | interval since previous summary)") + print(f" {'section':<34} " + f"{'calls':>6} {'cum(s)':>8} {'cum_avg':>8} " + f"{'Δcalls':>6} {'Δ(s)':>7} {'Δavg':>7} {'%Δ':>5}") + for name, tot in sorted(self.totals.items(), key=lambda kv: -kv[1]): + n = self.counts[name] + cum_avg = (tot / n) * 1000.0 if n else 0.0 + dt = delta_totals[name] + dn = delta_counts[name] + d_avg = (dt / dn) * 1000.0 if dn else 0.0 + d_pct = (dt / interval_total) * 100.0 if interval_total > 0 else 0.0 + print(f" {name:<34} " + f"{n:>6d} {tot:>8.2f} {cum_avg:>7.1f}ms " + f"{dn:>6d} {dt:>7.2f} {d_avg:>6.1f}ms {d_pct:>4.1f}%") + print(f" {'-' * 34} {'-' * 6} {'-' * 8} {'-' * 8} " + f"{'-' * 6} {'-' * 7} {'-' * 7} {'-' * 5}") + print(f" {'TOTAL':<34} {'':>6} {cumulative_total:>8.2f} {'':>8} " + f"{'':>6} {interval_total:>7.2f}") + + self._last_totals = dict(self.totals) + self._last_counts = dict(self.counts) + + +# ============================================================================= +# Guidance Method Enum +# ============================================================================= + +class GuidanceMethod(Enum): + EXPONENTIAL_TILT = "exp_tilt" + CLASSIFIER_GUIDANCE = "cg" + BEST_OF_K = "bok" + HYBRID = "hybrid" + + +# ============================================================================= +# Generation Tracker +# ============================================================================= + +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, 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(sum(1 for s in scores if s > 0)) if hasattr(scores, '__iter__') else 0, + 'improved_indices': improved_indices or [], + 'guidance_info': guidance_info or {} + } + if detailed_results: + step_info['detailed'] = [] + for i, result in enumerate(detailed_results): + step_info['detailed'].append({ + 'sample_idx': i, + 'is_valid_peptide': result.get('is_valid_peptide', False), + 'has_conjugation_site': result.get('has_conjugation_site', False), + 'conjugation_success': result.get('conjugation_success', False), + 'pdc_valid': result.get('pdc_valid', False), + 'num_conjugates': len(result.get('conjugates', [])), + 'error': result.get('error') + }) + self.step_logs.append(step_info) + + def log_final(self, sequences, detailed_results_with_props, smiles_list=None): + for i, (seq, result) in enumerate(zip(sequences, detailed_results_with_props)): + final_info = { + 'sample_idx': i, + 'aa_sequence': seq, + 'peptide_smiles': smiles_list[i] if smiles_list else seq, + 'is_valid_peptide': result.get('is_valid_peptide', False), + 'has_conjugation_site': result.get('has_conjugation_site', False), + 'num_conjugates': len(result.get('conjugates', [])), + 'num_valid_pdcs': len(result.get('valid_pdcs', [])), + 'best_pdc': None, + 'linker_used': result.get('linker_used', ''), + 'all_conjugates': result.get('conjugates', []), + 'properties': result.get('properties', {}) + } + if result.get('best_pdc'): + final_info['best_pdc'] = { + 'smiles': result['best_pdc'].get('smiles', ''), + 'checks': result['best_pdc'].get('checks', {}) + } + self.final_results.append(final_info) + + def save(self, filename_prefix='generation'): + step_log_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_steps.json") + with open(step_log_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', 'aa_sequence', 'peptide_smiles', 'is_valid_peptide', + 'has_conjugation_site', 'linker_used', 'num_conjugates', 'num_valid_pdcs', + 'best_pdc_smiles', 'mf', 'polararea', 'xlogp', 'heavycnt', 'rotbonds' + ]) + for result in self.final_results: + props = result.get('properties', {}) + writer.writerow([ + result['sample_idx'], + result.get('aa_sequence', ''), + result['peptide_smiles'], + result['is_valid_peptide'], + result['has_conjugation_site'], + result.get('linker_used', ''), + result['num_conjugates'], + result['num_valid_pdcs'], + result['best_pdc']['smiles'] if result.get('best_pdc') else '', + props.get('mf', ''), + props.get('polararea', ''), + props.get('xlogp', ''), + props.get('heavycnt', ''), + props.get('rotbonds', '') + ]) + + pdc_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_pdcs.csv") + with open(pdc_path, 'w', newline='') as f: + writer = csv.writer(f) + writer.writerow(['sample_idx', 'aa_sequence', 'peptide_smiles', 'linker_used', + 'conjugate_idx', 'pdc_smiles']) + for result in self.final_results: + for j, conj in enumerate(result.get('all_conjugates', [])): + writer.writerow([ + result['sample_idx'], + result.get('aa_sequence', ''), + result['peptide_smiles'], + result.get('linker_used', ''), + j, + conj + ]) + + print(f"\n=== Generation logs saved ===") + print(f" Step logs: {step_log_path}") + print(f" Final results: {final_path}") + print(f" Summary CSV: {csv_path}") + print(f" PDC conjugates: {pdc_path}") + return step_log_path, final_path, csv_path, pdc_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) + num_valid = sum(1 for s in scores_list if s > 0) + print(f"\n--- Step {step} Summary ({guidance_method or 'unknown'}) ---") + print(f" Valid: {num_valid}/{len(scores_list)}") + if detailed_results: + print(f" Breakdown:") + print(f" Valid peptides: {sum(1 for r in detailed_results if r.get('is_valid_peptide'))}") + print(f" Has conjugation site: {sum(1 for r in detailed_results if r.get('has_conjugation_site'))}") + print(f" Conjugation success: {sum(1 for r in detailed_results if r.get('conjugation_success'))}") + print(f" PDC valid: {sum(1 for r in detailed_results if r.get('pdc_valid'))}") + + def print_final_summary(self): + print("\n" + "=" * 60) + print("FINAL GENERATION SUMMARY") + print("=" * 60) + total = len(self.final_results) + valid_pdcs = sum(1 for r in self.final_results if r.get('num_valid_pdcs', 0) > 0) + print(f"\nTotal sequences: {total}") + print(f"With valid PDCs: {valid_pdcs}") + for result in self.final_results: + status = "" if result.get('num_valid_pdcs', 0) > 0 else "" + print(f"\n [{status}] Sample {result['sample_idx']}:") + print(f" AA seq: {result.get('aa_sequence', '')}") + print(f" Peptide SMILES: {result['peptide_smiles'][:60]}...") + print(f" Valid peptide: {result['is_valid_peptide']}") + print(f" Has conjugation site: {result['has_conjugation_site']}") + print(f" Conjugates: {result['num_conjugates']}") + print(f" Valid PDCs: {result['num_valid_pdcs']}") + + +# ============================================================================= +# CupOfMoCA Generator (UAA-aware) +# ============================================================================= + +class CupOfMoCAGeneratorUAA: + """CupOfMoCA Algorithm 2 generator that operates in AA token space. + + AA tokens are converted to SMILES on the fly for scoring with the + Conjugation and BindingAffinity objectives. + """ + + def __init__(self, model, device, objectives, args, tokenizer, tokenizer_type='amino', + tracker=None): + self.model = model + self.device = device + self.objectives = objectives + self.args = args + self.num_objectives = len(objectives) + self.peptide_analyzer = PeptideAnalyzer() + self.pdc_analyzer = PDCAnalyzer(device) + self.tracker = tracker + self.tokenizer = tokenizer + self.tokenizer_type = tokenizer_type + self.guidance_method = GuidanceMethod(args.guidance_method) + self.profiler = Profiler(enabled=getattr(args, 'profile', False)) + + cascade_workers = max(1, getattr(args, 'cascade_workers', 1)) + self._cascade_pool = ( + ThreadPoolExecutor(max_workers=cascade_workers) + if cascade_workers > 1 else None + ) + + if tokenizer_type == 'amino': + # PepReDi natural-AA vocab is 24 tokens; 0-3 = cls/pad/eos/unk, + # 4-23 = canonical AAs. Block specials only. (UAA used id 38 + # for D-AA 'g'; that's out of PepReDi vocab range.) + self.invalid_tokens = [0, 1, 2, 3] + self.smiles_tokenizer = SMILES_SPE_Tokenizer( + args.smiles_vocab_path, args.smiles_splits_path + ) + else: + self.invalid_tokens = [0, 1, 2, 3, 4, 585, 586] + self.smiles_tokenizer = None + + self.conjugation_obj = None + for obj in objectives: + if isinstance(obj, Conjugation): + self.conjugation_obj = obj + break + + # ------------------------------------------------------------------ + # x0 generation (Algorithm 2, Line 6) + # ------------------------------------------------------------------ + def _generate_single_x0(self, n_steps=16, temperature=1.0): + self.model.eval() + + if self.tokenizer_type == 'amino': + vocab_size = self.model.vocab_size + valid_tokens = [i for i in range(vocab_size) if i not in self.invalid_tokens] + valid_tokens = torch.tensor(valid_tokens, device=self.device) + rand_indices = torch.randint( + 0, len(valid_tokens), + (self.args.num_samples, self.args.gen_len), + device=self.device + ) + body = valid_tokens[rand_indices] + cls_col = torch.full((self.args.num_samples, 1), self.tokenizer.cls_id, device=self.device) + eos_col = torch.full((self.args.num_samples, 1), self.tokenizer.eos_id, device=self.device) + x = torch.cat([cls_col, body, eos_col], dim=1) + else: + vocab_size = self.model.tokenizer.vocab_size + x = torch.randint( + 0, vocab_size, + (self.args.num_samples, self.args.gen_len), + device=self.device + ) + + time_steps = torch.linspace(0.0, 1.0, n_steps + 1, device=self.device) + + with torch.no_grad(): + for i in tqdm(range(n_steps), desc="Initial Flow Matching", leave=False): + t_curr = time_steps[i] + t_next = time_steps[i + 1] + t_tensor = torch.full((self.args.num_samples,), t_curr, device=self.device) + + logits = self.model(x, t_tensor) / temperature + for inv_tok in self.invalid_tokens: + if inv_tok < logits.shape[-1]: + logits[:, :, inv_tok] = -1000 + + pred_x1 = torch.argmax(logits, dim=-1) + if i == n_steps - 1: + x = pred_x1 + break + + noise_prob = 1.0 - t_next + mask = torch.rand(x.shape, device=self.device) < noise_prob + noise = torch.randint(0, vocab_size, x.shape, device=self.device) + x = torch.where(mask, noise, pred_x1) + + return x + + def _aa_to_smiles_batch(self, x_batch): + """Decode AA tokens, convert to SMILES via three-letter codes.""" + with self.profiler.section("aa2smiles.decode"): + seqs = self.tokenizer.batch_decode(x_batch) + with self.profiler.section("aa2smiles.one_to_three"): + three_letter_seqs = [one_letter_to_three_letter(s) for s in seqs] + with self.profiler.section("aa2smiles.aa_to_smiles"): + with ThreadPoolExecutor() as pool: + smiles_seqs = list(pool.map( + lambda s: aa_to_smiles(s, cyclic=self.args.cyclic), + three_letter_seqs + )) + return seqs, smiles_seqs + + def _check_conjugation_validity(self, x): + if self.tokenizer_type == 'amino': + _, smiles_seqs = self._aa_to_smiles_batch(x) + validities = [ + self.peptide_analyzer.is_peptide(s) if isinstance(s, str) else False + for s in smiles_seqs + ] + return torch.tensor(validities, device=self.device) + + if self.conjugation_obj is None: + sequences = self.tokenizer.batch_decode(x) + return torch.tensor( + [self.peptide_analyzer.is_peptide(seq) for seq in sequences], + device=self.device + ) + return self.conjugation_obj(x) > 0 + + def generate_x0(self, n_steps=16, temperature=1.0, + require_valid=False, max_retries=10): + print("Generating initial sequences (x0 from prior)...") + x = self._generate_single_x0(n_steps, temperature) + + if require_valid: + print("Enforcing initial validity constraint...") + for retry in range(max_retries): + validity = self._check_conjugation_validity(x) + 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 are valid!") + break + print(f" Retry {retry + 1}/{max_retries}: {num_valid}/{self.args.num_samples} valid") + invalid_indices = torch.where(~validity)[0] + original_num = self.args.num_samples + self.args.num_samples = num_invalid + new_x = self._generate_single_x0(n_steps, temperature) + self.args.num_samples = original_num + x[invalid_indices] = new_x + + final_validity = self._check_conjugation_validity(x) + if final_validity.sum().item() < self.args.num_samples: + print(f"⚠ After {max_retries} retries, " + f"{final_validity.sum().item()}/{self.args.num_samples} valid") + + generated_sequences = self.tokenizer.batch_decode(x) + for seq in generated_sequences: + print(f" {seq}") + return x + + # ------------------------------------------------------------------ + # Scoring + # ------------------------------------------------------------------ + def _get_scores(self, x_batch): + with self.profiler.section("get_scores.total"): + scores = [] + + if self.tokenizer_type == 'amino': + _, smiles_seqs = self._aa_to_smiles_batch(x_batch) + with self.profiler.section("get_scores.valid_mask"): + valid_mask = torch.tensor( + [1 if isinstance(s, str) else 0 for s in smiles_seqs], + device=self.device + ) + for i in torch.where((1 - valid_mask) == 1)[0].tolist(): + smiles_seqs[i] = 'C' + + with self.profiler.section("get_scores.smiles_tokenize"): + smiles_batch = self.smiles_tokenizer( + smiles_seqs, return_tensors='pt', padding=True + ).to(self.device) + attention_mask = smiles_batch.get("attention_mask", None) + + for obj_func in self.objectives: + obj_name = type(obj_func).__name__ + with self.profiler.section(f"get_scores.obj.{obj_name}"): + if getattr(obj_func, "input_type", None) == "aa": + # AA-input objective: score raw AA tokens (no + # SMILES conversion, no valid_mask multiply). + score = obj_func(x_batch.to(self.device)).to(self.device) + scores.append(score) + else: + try: + score = obj_func( + smiles_batch["input_ids"], attention_mask=attention_mask + ).to(self.device) + except TypeError: + score = obj_func(smiles_batch["input_ids"]).to(self.device) + scores.append(score * valid_mask) + else: + for obj_func in self.objectives: + obj_name = type(obj_func).__name__ + with self.profiler.section(f"get_scores.obj.{obj_name}"): + scores.append(obj_func(x_batch.to(self.device))) + + return torch.stack(scores, dim=0).to(self.device) + + def _compute_aggregated_score(self, scores, weights): + if self.args.score_aggregation == "weighted_min": + return torch.min(weights * scores, dim=0).values + return (weights * scores).sum(dim=0) + + # ------------------------------------------------------------------ + # Cascade scoring (cheap filter + affinity on all → full PDC on top-K) + # ------------------------------------------------------------------ + def _get_scores_cascaded(self, x_batch): + """Run cheap conjugation pre-filter + BindingAffinity on all candidates, + then run the expensive PDC validation only on the top `cascade_k` by + affinity (among those that pass the cheap filter). + + Returns [num_objectives, batch] in the same layout as `_get_scores`. + Falls back to full scoring if Conjugation isn't an objective or + tokenizer_type != 'amino'. + """ + if self.tokenizer_type != 'amino' or self.conjugation_obj is None: + return self._get_scores(x_batch) + + with self.profiler.section("cascade.total"): + n = x_batch.shape[0] + cascade_k = self.args.cascade_k + conj_obj = self.conjugation_obj + + _, smiles_seqs = self._aa_to_smiles_batch(x_batch) + valid_mask = torch.tensor( + [1.0 if isinstance(s, str) else 0.0 for s in smiles_seqs], + device=self.device + ) + safe_smiles = [s if isinstance(s, str) else 'C' for s in smiles_seqs] + + with self.profiler.section("cascade.cheap_filter"): + cheap_pass = [] + for s, vm in zip(safe_smiles, valid_mask.tolist()): + if vm == 0.0: + cheap_pass.append(False) + continue + cheap_pass.append( + conj_obj.is_valid_peptide(s) + and conj_obj.has_conjugation_site(s) + ) + cheap_pass_t = torch.tensor( + cheap_pass, device=self.device, dtype=torch.float + ) + + with self.profiler.section("cascade.smiles_tokenize"): + smiles_batch = self.smiles_tokenizer( + safe_smiles, return_tensors='pt', padding=True + ).to(self.device) + attention_mask = smiles_batch.get("attention_mask", None) + + aff_obj = next( + (o for o in self.objectives if not isinstance(o, Conjugation)), + None + ) + if aff_obj is None: + aff_scores = torch.zeros(n, device=self.device) + else: + with self.profiler.section(f"cascade.obj.{type(aff_obj).__name__}"): + if getattr(aff_obj, "input_type", None) == "aa": + # AA-input affinity: score raw AA tokens directly. + # Don't multiply by valid_mask (the AA->SMILES outcome + # is irrelevant for the AA-input scorer). + aff_scores = aff_obj(x_batch.to(self.device)).to(self.device) + else: + try: + aff_scores = aff_obj( + smiles_batch["input_ids"], attention_mask=attention_mask + ).to(self.device) + except TypeError: + aff_scores = aff_obj(smiles_batch["input_ids"]).to(self.device) + aff_scores = aff_scores * valid_mask + + eligible_idx = torch.where(cheap_pass_t > 0)[0] + if cascade_k > 0 and len(eligible_idx) > cascade_k: + topk_local = torch.topk(aff_scores[eligible_idx], k=cascade_k).indices + validate_idx = eligible_idx[topk_local] + else: + validate_idx = eligible_idx + + conj_scores = torch.zeros(n, device=self.device) + with self.profiler.section("cascade.full_validate"): + for j in validate_idx.tolist(): + ok, conjs, _, _ = conj_obj.conjugate_peptide( + safe_smiles[j], exclude_n_terminus=True + ) + if not ok or not conjs: + continue + has_valid, _ = conj_obj.validate_conjugates(conjs) + if has_valid: + conj_scores[j] = 1.0 + + out = [] + for obj in self.objectives: + if isinstance(obj, Conjugation): + out.append(conj_scores) + else: + out.append(aff_scores) + return torch.stack(out, dim=0).to(self.device) + + # ------------------------------------------------------------------ + # Guidance methods + # ------------------------------------------------------------------ + def _exponential_tilt_guidance(self, x, pos_probs, candidates, mut_idx, sample_idx, + beta, weights, current_score=None): + num_candidates = len(candidates) + current_token = x[sample_idx, mut_idx].item() + + if num_candidates == 0: + return current_token, {'method': 'exp_tilt', 'fallback': True, 'accepted': False} + + if num_candidates > self.args.top_k: + candidates = candidates[:self.args.top_k] + num_candidates = self.args.top_k + + x_proposals = x[sample_idx].unsqueeze(0).repeat(num_candidates, 1) + x_proposals[:, mut_idx] = candidates + + with self.profiler.section("exp_tilt.proposal_scores"): + if self.args.cascade_k > 0: + proposal_scores = self._get_scores_cascaded(x_proposals) + else: + proposal_scores = self._get_scores(x_proposals) + aggregated_scores = self._compute_aggregated_score(proposal_scores, weights) + validity_mask = (proposal_scores[0] > 0).float() + base_probs = pos_probs[sample_idx, candidates] + + log_base_probs = torch.log(base_probs + 1e-10) + log_exp_scores = beta * aggregated_scores + log_validity = torch.log(validity_mask + 1e-10) + log_tilted = log_base_probs + log_exp_scores + log_validity + + log_tilted_max = log_tilted.max() + fallback = False + if torch.isinf(log_tilted_max) or torch.isnan(log_tilted_max): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + else: + log_tilted_stable = log_tilted - log_tilted_max + tilted_probs = torch.exp(log_tilted_stable) + prob_sum = tilted_probs.sum() + if prob_sum < 1e-10 or torch.isnan(prob_sum) or torch.isinf(prob_sum): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + else: + final_probs = tilted_probs / prob_sum + + if torch.any(torch.isnan(final_probs)) or torch.any(torch.isinf(final_probs)): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + + final_probs = torch.clamp(final_probs, min=1e-10) + final_probs = final_probs / final_probs.sum() + + selected_idx = torch.multinomial(final_probs, 1).item() + selected_token = candidates[selected_idx].item() + selected_score = aggregated_scores[selected_idx].item() + + accepted = True + if self.args.use_acceptance and current_score is not None: + if selected_score < current_score: + selected_token = current_token + accepted = False + + return selected_token, { + 'method': 'exp_tilt', 'beta': beta, 'num_candidates': num_candidates, + 'proposal_score': aggregated_scores[selected_idx].item(), + 'selected_score': selected_score if accepted else current_score, + 'fallback': fallback, 'validity_rate': validity_mask.mean().item(), + 'accepted': accepted + } + + def _classifier_guidance(self, x, logits, mut_idx, sample_idx, + guidance_weight, weights): + base_logits = logits[sample_idx, mut_idx, :] + sorted_logits, sorted_indices = torch.sort(base_logits, descending=True) + candidates = sorted_indices[:self.args.top_k] + + valid_mask = ~torch.isin( + candidates, torch.tensor(self.invalid_tokens, device=self.device) + ) + candidates = candidates[valid_mask] + + if len(candidates) == 0: + return x[sample_idx, mut_idx].item(), {'method': 'cg', 'fallback': True} + + x_proposals = x[sample_idx].unsqueeze(0).repeat(len(candidates), 1) + x_proposals[:, mut_idx] = candidates + + with self.profiler.section("cg.proposal_scores"): + if self.args.cascade_k > 0: + proposal_scores = self._get_scores_cascaded(x_proposals) + else: + proposal_scores = self._get_scores(x_proposals) + aggregated_scores = self._compute_aggregated_score(proposal_scores, weights) + + score_logits = aggregated_scores * self.args.score_temperature + base_candidate_logits = base_logits[candidates] + guided_logits = base_candidate_logits + guidance_weight * score_logits + guided_probs = F.softmax(guided_logits, dim=-1) + + selected_idx = torch.multinomial(guided_probs, 1).item() + return candidates[selected_idx].item(), { + 'method': 'cg', 'guidance_weight': guidance_weight, + 'num_candidates': len(candidates), + 'selected_score': aggregated_scores[selected_idx].item(), 'fallback': False + } + + def _best_of_k_guidance(self, x, pos_probs, candidates, mut_idx, sample_idx, + k, weights): + num_candidates = len(candidates) + if num_candidates == 0: + return x[sample_idx, mut_idx].item(), {'method': 'bok', 'fallback': True} + + k = min(k, num_candidates) + candidate_probs = pos_probs[sample_idx, candidates] + candidate_probs = candidate_probs / (candidate_probs.sum() + 1e-9) + + if k >= num_candidates: + sampled_indices = torch.arange(num_candidates, device=self.device) + else: + sampled_indices = torch.multinomial(candidate_probs, k, replacement=False) + + sampled_tokens = candidates[sampled_indices] + x_proposals = x[sample_idx].unsqueeze(0).repeat(len(sampled_tokens), 1) + x_proposals[:, mut_idx] = sampled_tokens + + with self.profiler.section("bok.proposal_scores"): + if self.args.cascade_k > 0: + proposal_scores = self._get_scores_cascaded(x_proposals) + else: + proposal_scores = self._get_scores(x_proposals) + aggregated_scores = self._compute_aggregated_score(proposal_scores, weights) + + best_idx = torch.argmax(aggregated_scores).item() + return sampled_tokens[best_idx].item(), { + 'method': 'bok', 'k': k, 'num_candidates': num_candidates, + 'selected_score': aggregated_scores[best_idx].item(), + 'mean_score': aggregated_scores.mean().item(), + 'max_score': aggregated_scores.max().item(), 'fallback': False + } + + def _hybrid_guidance(self, x, pos_probs, logits, candidates, mut_idx, sample_idx, + step, beta, weights): + progress = step / max(self.args.optimization_steps - 1, 1) + if progress < 0.3: + return self._best_of_k_guidance( + x, pos_probs, candidates, mut_idx, sample_idx, + k=self.args.bok_k, weights=weights + ) + elif progress < 0.7: + return self._exponential_tilt_guidance( + x, pos_probs, candidates, mut_idx, sample_idx, + beta=beta, weights=weights + ) + else: + return self._classifier_guidance( + x, logits, mut_idx, sample_idx, + guidance_weight=self.args.cg_weight, weights=weights + ) + + # ------------------------------------------------------------------ + # Main loop + # ------------------------------------------------------------------ + def generate(self): + self.profiler.reset() + x = self.generate_x0( + require_valid=self.args.require_valid_x0, + max_retries=self.args.max_x0_retries + ) + + if self.args.weights is None: + weights = torch.tensor( + [1.0] + [1.0 / (self.num_objectives - 1)] * (self.num_objectives - 1), + device=self.device + ).view(-1, 1) + else: + weights = torch.tensor(self.args.weights, device=self.device).view(-1, 1) + if len(weights) != self.num_objectives: + raise ValueError("Number of weights must match number of objectives.") + + print(f"\nGuidance Method: {self.guidance_method.value}") + print(f"Weights: {weights.squeeze().tolist()}") + print(f"β range: [{self.args.beta_min}, {self.args.beta_max}]") + print(f"Acceptance criterion: {'ENABLED' if self.args.use_acceptance else 'DISABLED'}") + + with torch.no_grad(): + for step in tqdm(range(self.args.optimization_steps), desc="Guided Generation"): + improved_indices = [] + + progress = step / max(self.args.optimization_steps - 1, 1) + beta = self.args.beta_min + (self.args.beta_max - self.args.beta_min) * progress + + if self.args.position_strategy == "random": + mut_idx = random.randint(1, self.args.gen_len - 2) + else: + mut_idx = (step % (self.args.gen_len - 2)) + 1 + + time_t = torch.full( + (self.args.num_samples,), + step / self.args.optimization_steps, + device=self.device + ) + + with self.profiler.section("loop.model_forward"): + logits = self.model(x, time_t) + probs = F.softmax(logits, dim=-1) + pos_probs = probs[:, mut_idx, :] + + pos_probs[torch.arange(self.args.num_samples), x[:, mut_idx]] = 0 + for inv_tok in self.invalid_tokens: + pos_probs[:, inv_tok] = 0 + + with self.profiler.section("loop.top_p_sort"): + sorted_probs, sorted_indices = torch.sort(pos_probs, descending=True) + + with self.profiler.section("loop.current_scores"): + current_scores = self._get_scores(x) + current_aggregated = self._compute_aggregated_score(current_scores, weights) + + if step == 0: + print(f"\nInitial Scores: {current_scores.cpu().numpy()}") + if self.tracker and self.conjugation_obj: + detailed = self.conjugation_obj.get_last_detailed_results() + self.tracker.log_step(step, current_scores[0], detailed) + self.tracker.print_step_summary( + step, current_scores[0], detailed, self.guidance_method.value + ) + + final_tokens = [] + for i in range(self.args.num_samples): + sample_probs = sorted_probs[i] + sample_indices = sorted_indices[i] + + cumsum = torch.cumsum(sample_probs, dim=0) + mask = cumsum <= self.args.top_p + mask[0] = True + candidates = sample_indices[mask] + + valid_candidate_mask = ~torch.isin( + candidates, torch.tensor(self.invalid_tokens, device=self.device) + ) + candidates = candidates[valid_candidate_mask] + + current_sample_score = current_aggregated[i].item() + + if beta > 0 or self.guidance_method != GuidanceMethod.EXPONENTIAL_TILT: + with self.profiler.section(f"loop.guidance.{self.guidance_method.value}"): + if self.guidance_method == GuidanceMethod.EXPONENTIAL_TILT: + token, _ = self._exponential_tilt_guidance( + x, pos_probs, candidates, mut_idx, i, beta, weights, + current_score=current_sample_score + ) + elif self.guidance_method == GuidanceMethod.CLASSIFIER_GUIDANCE: + token, _ = self._classifier_guidance( + x, logits, mut_idx, i, self.args.cg_weight, weights + ) + elif self.guidance_method == GuidanceMethod.BEST_OF_K: + token, _ = self._best_of_k_guidance( + x, pos_probs, candidates, mut_idx, i, self.args.bok_k, weights + ) + elif self.guidance_method == GuidanceMethod.HYBRID: + token, _ = self._hybrid_guidance( + x, pos_probs, logits, candidates, mut_idx, i, step, beta, weights + ) + else: + raise ValueError(f"Unknown guidance: {self.guidance_method}") + else: + if len(candidates) == 0: + token = x[i, mut_idx].item() + else: + cprobs = pos_probs[i, candidates] + cprobs = cprobs / (cprobs.sum() + 1e-9) + idx = torch.multinomial(cprobs, 1).item() + token = candidates[idx].item() + + final_tokens.append(token) + + x[:, mut_idx] = torch.tensor(final_tokens, device=self.device) + + with self.profiler.section("loop.new_scores"): + new_scores = self._get_scores(x) + new_aggregated = self._compute_aggregated_score(new_scores, weights) + for i in range(self.args.num_samples): + if new_aggregated[i] > current_aggregated[i]: + improved_indices.append(i) + + if improved_indices: + for i in improved_indices: + prev_weighted = (weights.squeeze(1) * current_scores[:, i]).sum() + new_weighted = (weights.squeeze(1) * new_scores[:, i]).sum() + print(f"Previous Weighted Sum: {prev_weighted}") + print(f"Previous Scores: {current_scores[:, i]}") + print(f"New Weighted Sum: {new_weighted}") + print(f"New Scores: {new_scores[:, i]}") + print(self.tokenizer.batch_decode(x[improved_indices])) + + if self.tracker and self.conjugation_obj and \ + (step % 4 == 0 or step == self.args.optimization_steps - 1): + detailed = self.conjugation_obj.get_last_detailed_results() + self.tracker.log_step( + step, new_scores[0], detailed, improved_indices, + guidance_info={'method': self.guidance_method.value, 'beta': beta} + ) + if step == self.args.optimization_steps - 1: + self.tracker.print_step_summary( + step, new_scores[0], detailed, self.guidance_method.value + ) + + if (self.profiler.enabled + and self.args.profile_every > 0 + and (step + 1) % self.args.profile_every == 0): + self.profiler.print_summary(header=f"(through step {step + 1})") + + self.profiler.print_summary(header="(end of generate)") + return x + + +# ============================================================================= +# Main +# ============================================================================= + +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + print(f"Tokenizer type: {args.tokenizer_type}") + print(f"Guidance method: {args.guidance_method}") + + if args.tokenizer_type == 'amino': + print("Using Amino Acid tokenizer") + tokenizer = AminoTokenizer() + smiles_tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + else: + print("Using SMILES tokenizer") + tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + smiles_tokenizer = tokenizer + + conjugation_model = Conjugation(smiles_tokenizer, device=device, builder_type=args.builder_type) + # Binding affinity: AA-input or SMILES-input depending on --affinity_type. + affinity_model = make_affinity( + args.affinity_type, + target_protein_seq=args.target, + device=device, + aa_ckpt_path=args.aa_affinity_ckpt, + ) + print(f"Affinity scorer: {type(affinity_model).__name__} (input_type={getattr(affinity_model, 'input_type', '?')})") + OBJECTIVE_FUNCTIONS = [conjugation_model, affinity_model] + + tracker = GenerationTracker(output_dir=args.output_dir) + + 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) + print(f"Random seed: {args.seed}") + + print(f"Loading model from: {args.checkpoint}") + checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False) + + if args.tokenizer_type == 'amino': + raw_model_args = checkpoint['args'] + if isinstance(raw_model_args, argparse.Namespace): + raw_model_args = vars(raw_model_args) + model_args = edict(raw_model_args) + model = MDLM( + vocab_size=model_args.vocab_size, + seq_len=model_args.seq_len, + model_dim=model_args.model_dim, + n_heads=model_args.n_heads, + n_layers=model_args.n_layers, + ).to(device) + model.load_state_dict(checkpoint['model_state_dict']) + print(f"Amino model loaded. Vocab size: {model_args.vocab_size}") + else: + model_hparams = checkpoint["hyper_parameters"]["args"] + model = MDLMLightningModule.load_from_checkpoint( + args.checkpoint, args=model_hparams, tokenizer=tokenizer, + map_location=device, strict=False + ) + model.to(device) + model.eval() + print("Model loaded successfully.") + + generator = CupOfMoCAGeneratorUAA( + model, device, OBJECTIVE_FUNCTIONS, args, + tokenizer=tokenizer, tokenizer_type=args.tokenizer_type, tracker=tracker + ) + + all_sequences = [] + all_smiles = [] + all_final_results = [] + + for batch_idx in range(args.num_batches): + print(f"\n{'=' * 60}") + print(f"BATCH {batch_idx + 1}/{args.num_batches}") + print(f"{'=' * 60}") + + generated_tokens = generator.generate() + final_scores = generator._get_scores(generated_tokens.detach()).detach().cpu().numpy() + sequence_str = tokenizer.batch_decode(generated_tokens) + + print("\n--- Batch Results ---") + print(f"Sequences: {sequence_str}") + print(f"Scores: {final_scores}") + + all_sequences.extend(sequence_str) + + if args.tokenizer_type == 'amino': + smiles_list = [] + for seq in sequence_str: + three_letter = one_letter_to_three_letter(seq) + smiles = aa_to_smiles(three_letter, cyclic=args.cyclic) + smiles_list.append(smiles if isinstance(smiles, str) else 'C') + smiles_tokens = smiles_tokenizer( + smiles_list, return_tensors='pt', padding=True + ).to(device) + detailed_with_props = conjugation_model.get_detailed_scores_with_properties( + smiles_tokens["input_ids"] + ) + all_smiles.extend(smiles_list) + tracker.log_final(sequence_str, detailed_with_props, smiles_list=smiles_list) + else: + detailed_with_props = conjugation_model.get_detailed_scores_with_properties(generated_tokens) + tracker.log_final(sequence_str, detailed_with_props) + all_final_results.extend(detailed_with_props) + + tracker.print_final_summary() + tracker.save(filename_prefix=f'cupofmoca_uaa_{args.guidance_method}') + + with open(args.output_file, 'w') as f: + for i, seq in enumerate(all_sequences): + if args.tokenizer_type == 'amino': + smiles = all_smiles[i] if i < len(all_smiles) else '' + f.write(f"{seq}\t{smiles}\n") + else: + f.write(seq + '\n') + print(f"\nSequences saved to: {args.output_file}") + print("\nGeneration complete.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="CupOfMoCA-style Guided Generation with UAA Foundation Model" + ) + + parser.add_argument("--checkpoint", type=str, required=True) + parser.add_argument("--target", type=str, required=True) + + parser.add_argument("--num_samples", type=int, default=10) + parser.add_argument("--num_batches", type=int, default=10) + parser.add_argument("--gen_len", type=int, default=50) + parser.add_argument("--optimization_steps", type=int, default=128) + + parser.add_argument("--guidance_method", type=str, default="exp_tilt", + choices=["exp_tilt", "cg", "bok", "hybrid"]) + + parser.add_argument("--beta_min", type=float, default=1.0) + parser.add_argument("--beta_max", type=float, default=20.0) + parser.add_argument("--cg_weight", type=float, default=2.0) + parser.add_argument("--use_acceptance", action="store_true") + parser.add_argument("--bok_k", type=int, default=8) + parser.add_argument("--top_k", type=int, default=200) + parser.add_argument("--top_p", type=float, default=0.9) + parser.add_argument("--score_temperature", type=float, default=1.0) + parser.add_argument("--score_aggregation", type=str, default="weighted_sum", + choices=["weighted_sum", "weighted_min"]) + + parser.add_argument("--weights", type=float, nargs='+', default=None) + + parser.add_argument("--position_strategy", type=str, default="random", + choices=["random", "sequential"]) + + parser.add_argument("--require_valid_x0", action="store_true") + parser.add_argument("--max_x0_retries", type=int, default=10) + + parser.add_argument("--builder_type", type=str, default="AoaDau", + choices=["AoaDau", "ValAlaDau", "ValCitDau", "GlutarylDau", "Dau", + "SSCpt", "VcMMAE", "DSPACpt", "MalHexCpt", "DSECCpt", "Cpt", + "DSPASN38", "DSEBSN38", "DSEPSN38", "SN38"]) + + parser.add_argument("--tokenizer_type", type=str, default="amino", + choices=["smiles", "amino"]) + parser.add_argument("--cyclic", action="store_true", + help="Generate cyclic peptides (only for amino tokenizer).") + parser.add_argument("--smiles_vocab_path", type=str, + default="./smiles/smiles_tokenizer/new_vocab.txt") + parser.add_argument("--smiles_splits_path", type=str, + default="./smiles/smiles_tokenizer/new_splits.txt") + + # AA-mode binding-affinity selection (this is the WT-AA / PepReDi script). + parser.add_argument("--affinity_type", type=str, default="smiles", + choices=["aa", "smiles"], + help="Which binding-affinity oracle to guide on: " + "'aa' = AA-input UnpooledBindingPredictor " + "(matches moPPIt's setup); 'smiles' = " + "SMILES-input BindingAffinity (matches the " + "SMILES CupOFMoCA tables).") + parser.add_argument("--aa_affinity_ckpt", type=str, + default=DEFAULT_AA_AFFINITY_CKPT, + help="Path to AA-input affinity predictor checkpoint.") + + parser.add_argument("--output_file", type=str, default="./smiles.txt") + parser.add_argument("--output_dir", type=str, default="./generation_logs") + + parser.add_argument("--seed", type=int, default=None) + + parser.add_argument("--profile", action="store_true", + help="Enable per-section wall-clock profiling.") + parser.add_argument("--profile_every", type=int, default=0, + help="Print profiler summary every N optimization steps " + "(0 = only at end of each generate()).") + + parser.add_argument("--cascade_k", type=int, default=0, + help="Cascade scoring: score all proposals with cheap " + "Conjugation pre-filter + BindingAffinity, then run " + "the expensive PDC validation only on the top-K by " + "affinity (among cheap-filter survivors). 0 disables " + "(full scoring on all proposals, original behavior).") + parser.add_argument("--cascade_workers", type=int, default=1, + help="Number of worker threads for parallelizing " + "cascade.full_validate (CPU-bound RDKit work). " + "1 = sequential (original behavior). Try 4-8 when " + "--cascade_k is high. Ignored if --cascade_k=0.") + + args = parser.parse_args() + main(args) diff --git a/smiles/generation/cupofmoca_guided_uaa.py b/smiles/generation/cupofmoca_guided_uaa.py new file mode 100644 index 0000000000000000000000000000000000000000..6a7eb46bbd8b69ac0a0438fdaee659740dae2db5 --- /dev/null +++ b/smiles/generation/cupofmoca_guided_uaa.py @@ -0,0 +1,1196 @@ +""" +CupOfMoCA-style Guided Generation with UAA Foundation Model + +Combines CupOfMoCA Algorithm 2 guidance strategies (exponential tilting, +classifier guidance, best-of-K, hybrid) with the amino-acid (UAA) foundation +model. Sequences are generated in AA token space, converted to SMILES for +scoring against Conjugation (peptide+PDC validity) and BindingAffinity. +""" + +import argparse +import csv +import json +import os +import random +import sys +import time +from collections import defaultdict +from concurrent.futures import ThreadPoolExecutor +from contextlib import contextmanager +from datetime import datetime +from enum import Enum +from typing import Dict, Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from easydict import EasyDict as edict +from tqdm import tqdm + +from smiles.models.train import MDLMLightningModule +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +from smiles.evaluation.peptide_analyzer import PeptideAnalyzer +from smiles.evaluation.pdc_analyzer import PDCAnalyzer +from smiles.scoring.classifiers import Conjugation, BindingAffinity + +from third_party.SMILESToPeptide.peptide_to_smiles import aa_to_smiles +from third_party.SMILESToPeptide.aminoacid_selective import one_letter_to_three_letter + + +# ============================================================================= +# Amino Acid Foundation Model (DiT-based MDLM) +# ============================================================================= + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class TimestepEmbedder(nn.Module): + def __init__(self, hidden_size): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(1, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + + def forward(self, t): + return self.mlp(t.unsqueeze(-1)) + + +class DiTBlock(nn.Module): + def __init__(self, hidden_size, n_heads): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = nn.MultiheadAttention(hidden_size, n_heads, batch_first=True) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.mlp = nn.Sequential( + nn.Linear(hidden_size, 4 * hidden_size), + nn.GELU(), + nn.Linear(4 * hidden_size, hidden_size), + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True), + ) + + def forward(self, x, c): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = \ + self.adaLN_modulation(c).chunk(6, dim=1) + x_norm1 = modulate(self.norm1(x), shift_msa, scale_msa) + attn_output, _ = self.attn(x_norm1, x_norm1, x_norm1) + x = x + gate_msa.unsqueeze(1) * attn_output + x_norm2 = modulate(self.norm2(x), shift_mlp, scale_mlp) + mlp_output = self.mlp(x_norm2) + x = x + gate_mlp.unsqueeze(1) * mlp_output + return x + + +class MDLM(nn.Module): + def __init__(self, vocab_size, seq_len, model_dim, n_heads, n_layers): + super().__init__() + self.vocab_size = vocab_size + self.seq_len = seq_len + self.model_dim = model_dim + + self.token_embedder = nn.Embedding(vocab_size, model_dim) + self.pos_embedder = nn.Parameter(torch.randn(1, seq_len, model_dim)) + self.time_embedder = TimestepEmbedder(model_dim) + + self.transformer_blocks = nn.ModuleList([ + DiTBlock(model_dim, n_heads) for _ in range(n_layers) + ]) + self.final_norm = nn.LayerNorm(model_dim) + self.lm_head = nn.Linear(model_dim, vocab_size) + + def forward(self, x, t): + seq_len = x.shape[1] + x_embed = self.token_embedder(x) + self.pos_embedder[:, :seq_len, :] + t_embed = self.time_embedder(t) + for block in self.transformer_blocks: + x_embed = block(x_embed, t_embed) + x_embed = self.final_norm(x_embed) + return self.lm_head(x_embed) + + +class AminoTokenizer: + def __init__(self): + self.vocab = { + '': 0, '': 1, '': 2, '': 3, + 'L': 4, 'A': 5, 'G': 6, 'V': 7, 'S': 8, 'E': 9, + 'R': 10, 'T': 11, 'I': 12, 'D': 13, 'P': 14, 'K': 15, + 'Q': 16, 'N': 17, 'F': 18, 'Y': 19, 'M': 20, 'H': 21, + 'W': 22, 'C': 23, + 'Ŷ': 24, 'Ĉ': 25, 'Ĝ': 26, 'Ė': 27, 'Ƙ': 28, + 'Ṃ': 29, 'Ł': 30, 'Ċ': 31, 'Ṯ': 32, + 'a': 33, 'c': 34, 'd': 35, 'e': 36, 'f': 37, + 'g': 38, 'h': 39, 'i': 40, 'k': 41, 'l': 42, + 'm': 43, 'n': 44, 'p': 45, 'q': 46, 'r': 47, + 's': 48, 't': 49, 'v': 50, 'w': 51, 'y': 52, + } + self.id_to_token = {i: t for t, i in self.vocab.items()} + self.cls_token, self.pad_token = '', '' + self.eos_token, self.unk_token = '', '' + self.cls_id = self.vocab[self.cls_token] + self.pad_id = self.vocab[self.pad_token] + self.eos_id = self.vocab[self.eos_token] + self.unk_id = self.vocab[self.unk_token] + self.vocab_size = len(self.vocab) + + def decode(self, ids, skip_special_tokens=True): + if isinstance(ids, torch.Tensor): + ids = ids.tolist() + tokens = [] + for i in ids: + tok = self.id_to_token.get(int(i), self.unk_token) + if skip_special_tokens and tok in {self.cls_token, self.eos_token, self.pad_token}: + continue + tokens.append(tok) + return ''.join(tokens) + + def batch_decode(self, batch_ids, skip_special_tokens=True): + return [self.decode(ids, skip_special_tokens=skip_special_tokens) for ids in batch_ids] + + +# ============================================================================= +# Profiler +# ============================================================================= + +class Profiler: + """Aggregates wall-clock time per named section. Optionally syncs CUDA. + + `reset()` clears all state (use between batches for self-contained summaries). + `print_summary()` shows cumulative totals since the last reset, plus a + delta column showing time since the previous `print_summary()` call. + """ + + def __init__(self, enabled=False, cuda_sync=True): + self.enabled = enabled + self.cuda_sync = cuda_sync and torch.cuda.is_available() + self.totals = defaultdict(float) + self.counts = defaultdict(int) + self._last_totals = defaultdict(float) + self._last_counts = defaultdict(int) + + @contextmanager + def section(self, name): + if not self.enabled: + yield + return + if self.cuda_sync: + torch.cuda.synchronize() + t0 = time.perf_counter() + try: + yield + finally: + if self.cuda_sync: + torch.cuda.synchronize() + self.totals[name] += time.perf_counter() - t0 + self.counts[name] += 1 + + def reset(self): + self.totals.clear() + self.counts.clear() + self._last_totals.clear() + self._last_counts.clear() + + def print_summary(self, header=""): + if not self.enabled or not self.totals: + return + cumulative_total = sum(self.totals.values()) + delta_totals = {k: self.totals[k] - self._last_totals.get(k, 0.0) + for k in self.totals} + delta_counts = {k: self.counts[k] - self._last_counts.get(k, 0) + for k in self.counts} + interval_total = sum(delta_totals.values()) + + print(f"\n=== Profiler summary {header} ===") + print(f" (cumulative since reset | interval since previous summary)") + print(f" {'section':<34} " + f"{'calls':>6} {'cum(s)':>8} {'cum_avg':>8} " + f"{'Δcalls':>6} {'Δ(s)':>7} {'Δavg':>7} {'%Δ':>5}") + for name, tot in sorted(self.totals.items(), key=lambda kv: -kv[1]): + n = self.counts[name] + cum_avg = (tot / n) * 1000.0 if n else 0.0 + dt = delta_totals[name] + dn = delta_counts[name] + d_avg = (dt / dn) * 1000.0 if dn else 0.0 + d_pct = (dt / interval_total) * 100.0 if interval_total > 0 else 0.0 + print(f" {name:<34} " + f"{n:>6d} {tot:>8.2f} {cum_avg:>7.1f}ms " + f"{dn:>6d} {dt:>7.2f} {d_avg:>6.1f}ms {d_pct:>4.1f}%") + print(f" {'-' * 34} {'-' * 6} {'-' * 8} {'-' * 8} " + f"{'-' * 6} {'-' * 7} {'-' * 7} {'-' * 5}") + print(f" {'TOTAL':<34} {'':>6} {cumulative_total:>8.2f} {'':>8} " + f"{'':>6} {interval_total:>7.2f}") + + self._last_totals = dict(self.totals) + self._last_counts = dict(self.counts) + + +# ============================================================================= +# Guidance Method Enum +# ============================================================================= + +class GuidanceMethod(Enum): + EXPONENTIAL_TILT = "exp_tilt" + CLASSIFIER_GUIDANCE = "cg" + BEST_OF_K = "bok" + HYBRID = "hybrid" + + +# ============================================================================= +# Generation Tracker +# ============================================================================= + +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, 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(sum(1 for s in scores if s > 0)) if hasattr(scores, '__iter__') else 0, + 'improved_indices': improved_indices or [], + 'guidance_info': guidance_info or {} + } + if detailed_results: + step_info['detailed'] = [] + for i, result in enumerate(detailed_results): + step_info['detailed'].append({ + 'sample_idx': i, + 'is_valid_peptide': result.get('is_valid_peptide', False), + 'has_conjugation_site': result.get('has_conjugation_site', False), + 'conjugation_success': result.get('conjugation_success', False), + 'pdc_valid': result.get('pdc_valid', False), + 'num_conjugates': len(result.get('conjugates', [])), + 'error': result.get('error') + }) + self.step_logs.append(step_info) + + def log_final(self, sequences, detailed_results_with_props, smiles_list=None): + for i, (seq, result) in enumerate(zip(sequences, detailed_results_with_props)): + final_info = { + 'sample_idx': i, + 'aa_sequence': seq, + 'peptide_smiles': smiles_list[i] if smiles_list else seq, + 'is_valid_peptide': result.get('is_valid_peptide', False), + 'has_conjugation_site': result.get('has_conjugation_site', False), + 'num_conjugates': len(result.get('conjugates', [])), + 'num_valid_pdcs': len(result.get('valid_pdcs', [])), + 'best_pdc': None, + 'linker_used': result.get('linker_used', ''), + 'all_conjugates': result.get('conjugates', []), + 'properties': result.get('properties', {}) + } + if result.get('best_pdc'): + final_info['best_pdc'] = { + 'smiles': result['best_pdc'].get('smiles', ''), + 'checks': result['best_pdc'].get('checks', {}) + } + self.final_results.append(final_info) + + def save(self, filename_prefix='generation'): + step_log_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_steps.json") + with open(step_log_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', 'aa_sequence', 'peptide_smiles', 'is_valid_peptide', + 'has_conjugation_site', 'linker_used', 'num_conjugates', 'num_valid_pdcs', + 'best_pdc_smiles', 'mf', 'polararea', 'xlogp', 'heavycnt', 'rotbonds' + ]) + for result in self.final_results: + props = result.get('properties', {}) + writer.writerow([ + result['sample_idx'], + result.get('aa_sequence', ''), + result['peptide_smiles'], + result['is_valid_peptide'], + result['has_conjugation_site'], + result.get('linker_used', ''), + result['num_conjugates'], + result['num_valid_pdcs'], + result['best_pdc']['smiles'] if result.get('best_pdc') else '', + props.get('mf', ''), + props.get('polararea', ''), + props.get('xlogp', ''), + props.get('heavycnt', ''), + props.get('rotbonds', '') + ]) + + pdc_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_pdcs.csv") + with open(pdc_path, 'w', newline='') as f: + writer = csv.writer(f) + writer.writerow(['sample_idx', 'aa_sequence', 'peptide_smiles', 'linker_used', + 'conjugate_idx', 'pdc_smiles']) + for result in self.final_results: + for j, conj in enumerate(result.get('all_conjugates', [])): + writer.writerow([ + result['sample_idx'], + result.get('aa_sequence', ''), + result['peptide_smiles'], + result.get('linker_used', ''), + j, + conj + ]) + + print(f"\n=== Generation logs saved ===") + print(f" Step logs: {step_log_path}") + print(f" Final results: {final_path}") + print(f" Summary CSV: {csv_path}") + print(f" PDC conjugates: {pdc_path}") + return step_log_path, final_path, csv_path, pdc_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) + num_valid = sum(1 for s in scores_list if s > 0) + print(f"\n--- Step {step} Summary ({guidance_method or 'unknown'}) ---") + print(f" Valid: {num_valid}/{len(scores_list)}") + if detailed_results: + print(f" Breakdown:") + print(f" Valid peptides: {sum(1 for r in detailed_results if r.get('is_valid_peptide'))}") + print(f" Has conjugation site: {sum(1 for r in detailed_results if r.get('has_conjugation_site'))}") + print(f" Conjugation success: {sum(1 for r in detailed_results if r.get('conjugation_success'))}") + print(f" PDC valid: {sum(1 for r in detailed_results if r.get('pdc_valid'))}") + + def print_final_summary(self): + print("\n" + "=" * 60) + print("FINAL GENERATION SUMMARY") + print("=" * 60) + total = len(self.final_results) + valid_pdcs = sum(1 for r in self.final_results if r.get('num_valid_pdcs', 0) > 0) + print(f"\nTotal sequences: {total}") + print(f"With valid PDCs: {valid_pdcs}") + for result in self.final_results: + status = "" if result.get('num_valid_pdcs', 0) > 0 else "" + print(f"\n [{status}] Sample {result['sample_idx']}:") + print(f" AA seq: {result.get('aa_sequence', '')}") + print(f" Peptide SMILES: {result['peptide_smiles'][:60]}...") + print(f" Valid peptide: {result['is_valid_peptide']}") + print(f" Has conjugation site: {result['has_conjugation_site']}") + print(f" Conjugates: {result['num_conjugates']}") + print(f" Valid PDCs: {result['num_valid_pdcs']}") + + +# ============================================================================= +# CupOfMoCA Generator (UAA-aware) +# ============================================================================= + +class CupOfMoCAGeneratorUAA: + """CupOfMoCA Algorithm 2 generator that operates in AA token space. + + AA tokens are converted to SMILES on the fly for scoring with the + Conjugation and BindingAffinity objectives. + """ + + def __init__(self, model, device, objectives, args, tokenizer, tokenizer_type='amino', + tracker=None): + self.model = model + self.device = device + self.objectives = objectives + self.args = args + self.num_objectives = len(objectives) + self.peptide_analyzer = PeptideAnalyzer() + self.pdc_analyzer = PDCAnalyzer(device) + self.tracker = tracker + self.tokenizer = tokenizer + self.tokenizer_type = tokenizer_type + self.guidance_method = GuidanceMethod(args.guidance_method) + self.profiler = Profiler(enabled=getattr(args, 'profile', False)) + + cascade_workers = max(1, getattr(args, 'cascade_workers', 1)) + self._cascade_pool = ( + ThreadPoolExecutor(max_workers=cascade_workers) + if cascade_workers > 1 else None + ) + + if tokenizer_type == 'amino': + self.invalid_tokens = [0, 1, 2, 3, 38] # specials + 'g' + self.smiles_tokenizer = SMILES_SPE_Tokenizer( + args.smiles_vocab_path, args.smiles_splits_path + ) + else: + self.invalid_tokens = [0, 1, 2, 3, 4, 585, 586] + self.smiles_tokenizer = None + + self.conjugation_obj = None + for obj in objectives: + if isinstance(obj, Conjugation): + self.conjugation_obj = obj + break + + # ------------------------------------------------------------------ + # x0 generation (Algorithm 2, Line 6) + # ------------------------------------------------------------------ + def _generate_single_x0(self, n_steps=16, temperature=1.0): + self.model.eval() + + if self.tokenizer_type == 'amino': + vocab_size = self.model.vocab_size + valid_tokens = [i for i in range(vocab_size) if i not in self.invalid_tokens] + valid_tokens = torch.tensor(valid_tokens, device=self.device) + rand_indices = torch.randint( + 0, len(valid_tokens), + (self.args.num_samples, self.args.gen_len), + device=self.device + ) + body = valid_tokens[rand_indices] + cls_col = torch.full((self.args.num_samples, 1), self.tokenizer.cls_id, device=self.device) + eos_col = torch.full((self.args.num_samples, 1), self.tokenizer.eos_id, device=self.device) + x = torch.cat([cls_col, body, eos_col], dim=1) + else: + vocab_size = self.model.tokenizer.vocab_size + x = torch.randint( + 0, vocab_size, + (self.args.num_samples, self.args.gen_len), + device=self.device + ) + + time_steps = torch.linspace(0.0, 1.0, n_steps + 1, device=self.device) + + with torch.no_grad(): + for i in tqdm(range(n_steps), desc="Initial Flow Matching", leave=False): + t_curr = time_steps[i] + t_next = time_steps[i + 1] + t_tensor = torch.full((self.args.num_samples,), t_curr, device=self.device) + + logits = self.model(x, t_tensor) / temperature + for inv_tok in self.invalid_tokens: + if inv_tok < logits.shape[-1]: + logits[:, :, inv_tok] = -1000 + + pred_x1 = torch.argmax(logits, dim=-1) + if i == n_steps - 1: + x = pred_x1 + break + + noise_prob = 1.0 - t_next + mask = torch.rand(x.shape, device=self.device) < noise_prob + noise = torch.randint(0, vocab_size, x.shape, device=self.device) + x = torch.where(mask, noise, pred_x1) + + return x + + def _aa_to_smiles_batch(self, x_batch): + """Decode AA tokens, convert to SMILES via three-letter codes.""" + with self.profiler.section("aa2smiles.decode"): + seqs = self.tokenizer.batch_decode(x_batch) + with self.profiler.section("aa2smiles.one_to_three"): + three_letter_seqs = [one_letter_to_three_letter(s) for s in seqs] + with self.profiler.section("aa2smiles.aa_to_smiles"): + with ThreadPoolExecutor() as pool: + smiles_seqs = list(pool.map( + lambda s: aa_to_smiles(s, cyclic=self.args.cyclic), + three_letter_seqs + )) + return seqs, smiles_seqs + + def _check_conjugation_validity(self, x): + if self.tokenizer_type == 'amino': + _, smiles_seqs = self._aa_to_smiles_batch(x) + validities = [ + self.peptide_analyzer.is_peptide(s) if isinstance(s, str) else False + for s in smiles_seqs + ] + return torch.tensor(validities, device=self.device) + + if self.conjugation_obj is None: + sequences = self.tokenizer.batch_decode(x) + return torch.tensor( + [self.peptide_analyzer.is_peptide(seq) for seq in sequences], + device=self.device + ) + return self.conjugation_obj(x) > 0 + + def generate_x0(self, n_steps=16, temperature=1.0, + require_valid=False, max_retries=10): + print("Generating initial sequences (x0 from prior)...") + x = self._generate_single_x0(n_steps, temperature) + + if require_valid: + print("Enforcing initial validity constraint...") + for retry in range(max_retries): + validity = self._check_conjugation_validity(x) + 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 are valid!") + break + print(f" Retry {retry + 1}/{max_retries}: {num_valid}/{self.args.num_samples} valid") + invalid_indices = torch.where(~validity)[0] + original_num = self.args.num_samples + self.args.num_samples = num_invalid + new_x = self._generate_single_x0(n_steps, temperature) + self.args.num_samples = original_num + x[invalid_indices] = new_x + + final_validity = self._check_conjugation_validity(x) + if final_validity.sum().item() < self.args.num_samples: + print(f"⚠ After {max_retries} retries, " + f"{final_validity.sum().item()}/{self.args.num_samples} valid") + + generated_sequences = self.tokenizer.batch_decode(x) + for seq in generated_sequences: + print(f" {seq}") + return x + + # ------------------------------------------------------------------ + # Scoring + # ------------------------------------------------------------------ + def _get_scores(self, x_batch): + with self.profiler.section("get_scores.total"): + scores = [] + + if self.tokenizer_type == 'amino': + _, smiles_seqs = self._aa_to_smiles_batch(x_batch) + with self.profiler.section("get_scores.valid_mask"): + valid_mask = torch.tensor( + [1 if isinstance(s, str) else 0 for s in smiles_seqs], + device=self.device + ) + for i in torch.where((1 - valid_mask) == 1)[0].tolist(): + smiles_seqs[i] = 'C' + + with self.profiler.section("get_scores.smiles_tokenize"): + smiles_batch = self.smiles_tokenizer( + smiles_seqs, return_tensors='pt', padding=True + ).to(self.device) + attention_mask = smiles_batch.get("attention_mask", None) + + for obj_func in self.objectives: + obj_name = type(obj_func).__name__ + with self.profiler.section(f"get_scores.obj.{obj_name}"): + try: + score = obj_func( + smiles_batch["input_ids"], attention_mask=attention_mask + ).to(self.device) + except TypeError: + score = obj_func(smiles_batch["input_ids"]).to(self.device) + scores.append(score * valid_mask) + else: + for obj_func in self.objectives: + obj_name = type(obj_func).__name__ + with self.profiler.section(f"get_scores.obj.{obj_name}"): + scores.append(obj_func(x_batch.to(self.device))) + + return torch.stack(scores, dim=0).to(self.device) + + def _compute_aggregated_score(self, scores, weights): + if self.args.score_aggregation == "weighted_min": + return torch.min(weights * scores, dim=0).values + return (weights * scores).sum(dim=0) + + # ------------------------------------------------------------------ + # Cascade scoring (cheap filter + affinity on all → full PDC on top-K) + # ------------------------------------------------------------------ + def _get_scores_cascaded(self, x_batch): + """Run cheap conjugation pre-filter + BindingAffinity on all candidates, + then run the expensive PDC validation only on the top `cascade_k` by + affinity (among those that pass the cheap filter). + + Returns [num_objectives, batch] in the same layout as `_get_scores`. + Falls back to full scoring if Conjugation isn't an objective or + tokenizer_type != 'amino'. + """ + if self.tokenizer_type != 'amino' or self.conjugation_obj is None: + return self._get_scores(x_batch) + + with self.profiler.section("cascade.total"): + n = x_batch.shape[0] + cascade_k = self.args.cascade_k + conj_obj = self.conjugation_obj + + _, smiles_seqs = self._aa_to_smiles_batch(x_batch) + valid_mask = torch.tensor( + [1.0 if isinstance(s, str) else 0.0 for s in smiles_seqs], + device=self.device + ) + safe_smiles = [s if isinstance(s, str) else 'C' for s in smiles_seqs] + + with self.profiler.section("cascade.cheap_filter"): + cheap_pass = [] + for s, vm in zip(safe_smiles, valid_mask.tolist()): + if vm == 0.0: + cheap_pass.append(False) + continue + cheap_pass.append( + conj_obj.is_valid_peptide(s) + and conj_obj.has_conjugation_site(s) + ) + cheap_pass_t = torch.tensor( + cheap_pass, device=self.device, dtype=torch.float + ) + + with self.profiler.section("cascade.smiles_tokenize"): + smiles_batch = self.smiles_tokenizer( + safe_smiles, return_tensors='pt', padding=True + ).to(self.device) + attention_mask = smiles_batch.get("attention_mask", None) + + aff_obj = next( + (o for o in self.objectives if not isinstance(o, Conjugation)), + None + ) + if aff_obj is None: + aff_scores = torch.zeros(n, device=self.device) + else: + with self.profiler.section(f"cascade.obj.{type(aff_obj).__name__}"): + try: + aff_scores = aff_obj( + smiles_batch["input_ids"], attention_mask=attention_mask + ).to(self.device) + except TypeError: + aff_scores = aff_obj(smiles_batch["input_ids"]).to(self.device) + aff_scores = aff_scores * valid_mask + + eligible_idx = torch.where(cheap_pass_t > 0)[0] + if cascade_k > 0 and len(eligible_idx) > cascade_k: + topk_local = torch.topk(aff_scores[eligible_idx], k=cascade_k).indices + validate_idx = eligible_idx[topk_local] + else: + validate_idx = eligible_idx + + conj_scores = torch.zeros(n, device=self.device) + with self.profiler.section("cascade.full_validate"): + for j in validate_idx.tolist(): + ok, conjs, _, _ = conj_obj.conjugate_peptide( + safe_smiles[j], exclude_n_terminus=True + ) + if not ok or not conjs: + continue + has_valid, _ = conj_obj.validate_conjugates(conjs) + if has_valid: + conj_scores[j] = 1.0 + + out = [] + for obj in self.objectives: + if isinstance(obj, Conjugation): + out.append(conj_scores) + else: + out.append(aff_scores) + return torch.stack(out, dim=0).to(self.device) + + # ------------------------------------------------------------------ + # Guidance methods + # ------------------------------------------------------------------ + def _exponential_tilt_guidance(self, x, pos_probs, candidates, mut_idx, sample_idx, + beta, weights, current_score=None): + num_candidates = len(candidates) + current_token = x[sample_idx, mut_idx].item() + + if num_candidates == 0: + return current_token, {'method': 'exp_tilt', 'fallback': True, 'accepted': False} + + if num_candidates > self.args.top_k: + candidates = candidates[:self.args.top_k] + num_candidates = self.args.top_k + + x_proposals = x[sample_idx].unsqueeze(0).repeat(num_candidates, 1) + x_proposals[:, mut_idx] = candidates + + with self.profiler.section("exp_tilt.proposal_scores"): + if self.args.cascade_k > 0: + proposal_scores = self._get_scores_cascaded(x_proposals) + else: + proposal_scores = self._get_scores(x_proposals) + aggregated_scores = self._compute_aggregated_score(proposal_scores, weights) + validity_mask = (proposal_scores[0] > 0).float() + base_probs = pos_probs[sample_idx, candidates] + + log_base_probs = torch.log(base_probs + 1e-10) + log_exp_scores = beta * aggregated_scores + log_validity = torch.log(validity_mask + 1e-10) + log_tilted = log_base_probs + log_exp_scores + log_validity + + log_tilted_max = log_tilted.max() + fallback = False + if torch.isinf(log_tilted_max) or torch.isnan(log_tilted_max): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + else: + log_tilted_stable = log_tilted - log_tilted_max + tilted_probs = torch.exp(log_tilted_stable) + prob_sum = tilted_probs.sum() + if prob_sum < 1e-10 or torch.isnan(prob_sum) or torch.isinf(prob_sum): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + else: + final_probs = tilted_probs / prob_sum + + if torch.any(torch.isnan(final_probs)) or torch.any(torch.isinf(final_probs)): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + + final_probs = torch.clamp(final_probs, min=1e-10) + final_probs = final_probs / final_probs.sum() + + selected_idx = torch.multinomial(final_probs, 1).item() + selected_token = candidates[selected_idx].item() + selected_score = aggregated_scores[selected_idx].item() + + accepted = True + if self.args.use_acceptance and current_score is not None: + if selected_score < current_score: + selected_token = current_token + accepted = False + + return selected_token, { + 'method': 'exp_tilt', 'beta': beta, 'num_candidates': num_candidates, + 'proposal_score': aggregated_scores[selected_idx].item(), + 'selected_score': selected_score if accepted else current_score, + 'fallback': fallback, 'validity_rate': validity_mask.mean().item(), + 'accepted': accepted + } + + def _classifier_guidance(self, x, logits, mut_idx, sample_idx, + guidance_weight, weights): + base_logits = logits[sample_idx, mut_idx, :] + sorted_logits, sorted_indices = torch.sort(base_logits, descending=True) + candidates = sorted_indices[:self.args.top_k] + + valid_mask = ~torch.isin( + candidates, torch.tensor(self.invalid_tokens, device=self.device) + ) + candidates = candidates[valid_mask] + + if len(candidates) == 0: + return x[sample_idx, mut_idx].item(), {'method': 'cg', 'fallback': True} + + x_proposals = x[sample_idx].unsqueeze(0).repeat(len(candidates), 1) + x_proposals[:, mut_idx] = candidates + + with self.profiler.section("cg.proposal_scores"): + if self.args.cascade_k > 0: + proposal_scores = self._get_scores_cascaded(x_proposals) + else: + proposal_scores = self._get_scores(x_proposals) + aggregated_scores = self._compute_aggregated_score(proposal_scores, weights) + + score_logits = aggregated_scores * self.args.score_temperature + base_candidate_logits = base_logits[candidates] + guided_logits = base_candidate_logits + guidance_weight * score_logits + guided_probs = F.softmax(guided_logits, dim=-1) + + selected_idx = torch.multinomial(guided_probs, 1).item() + return candidates[selected_idx].item(), { + 'method': 'cg', 'guidance_weight': guidance_weight, + 'num_candidates': len(candidates), + 'selected_score': aggregated_scores[selected_idx].item(), 'fallback': False + } + + def _best_of_k_guidance(self, x, pos_probs, candidates, mut_idx, sample_idx, + k, weights): + num_candidates = len(candidates) + if num_candidates == 0: + return x[sample_idx, mut_idx].item(), {'method': 'bok', 'fallback': True} + + k = min(k, num_candidates) + candidate_probs = pos_probs[sample_idx, candidates] + candidate_probs = candidate_probs / (candidate_probs.sum() + 1e-9) + + if k >= num_candidates: + sampled_indices = torch.arange(num_candidates, device=self.device) + else: + sampled_indices = torch.multinomial(candidate_probs, k, replacement=False) + + sampled_tokens = candidates[sampled_indices] + x_proposals = x[sample_idx].unsqueeze(0).repeat(len(sampled_tokens), 1) + x_proposals[:, mut_idx] = sampled_tokens + + with self.profiler.section("bok.proposal_scores"): + if self.args.cascade_k > 0: + proposal_scores = self._get_scores_cascaded(x_proposals) + else: + proposal_scores = self._get_scores(x_proposals) + aggregated_scores = self._compute_aggregated_score(proposal_scores, weights) + + best_idx = torch.argmax(aggregated_scores).item() + return sampled_tokens[best_idx].item(), { + 'method': 'bok', 'k': k, 'num_candidates': num_candidates, + 'selected_score': aggregated_scores[best_idx].item(), + 'mean_score': aggregated_scores.mean().item(), + 'max_score': aggregated_scores.max().item(), 'fallback': False + } + + def _hybrid_guidance(self, x, pos_probs, logits, candidates, mut_idx, sample_idx, + step, beta, weights): + progress = step / max(self.args.optimization_steps - 1, 1) + if progress < 0.3: + return self._best_of_k_guidance( + x, pos_probs, candidates, mut_idx, sample_idx, + k=self.args.bok_k, weights=weights + ) + elif progress < 0.7: + return self._exponential_tilt_guidance( + x, pos_probs, candidates, mut_idx, sample_idx, + beta=beta, weights=weights + ) + else: + return self._classifier_guidance( + x, logits, mut_idx, sample_idx, + guidance_weight=self.args.cg_weight, weights=weights + ) + + # ------------------------------------------------------------------ + # Main loop + # ------------------------------------------------------------------ + def generate(self): + self.profiler.reset() + x = self.generate_x0( + require_valid=self.args.require_valid_x0, + max_retries=self.args.max_x0_retries + ) + + if self.args.weights is None: + weights = torch.tensor( + [1.0] + [1.0 / (self.num_objectives - 1)] * (self.num_objectives - 1), + device=self.device + ).view(-1, 1) + else: + weights = torch.tensor(self.args.weights, device=self.device).view(-1, 1) + if len(weights) != self.num_objectives: + raise ValueError("Number of weights must match number of objectives.") + + print(f"\nGuidance Method: {self.guidance_method.value}") + print(f"Weights: {weights.squeeze().tolist()}") + print(f"β range: [{self.args.beta_min}, {self.args.beta_max}]") + print(f"Acceptance criterion: {'ENABLED' if self.args.use_acceptance else 'DISABLED'}") + + with torch.no_grad(): + for step in tqdm(range(self.args.optimization_steps), desc="Guided Generation"): + improved_indices = [] + + progress = step / max(self.args.optimization_steps - 1, 1) + beta = self.args.beta_min + (self.args.beta_max - self.args.beta_min) * progress + + if self.args.position_strategy == "random": + mut_idx = random.randint(1, self.args.gen_len - 2) + else: + mut_idx = (step % (self.args.gen_len - 2)) + 1 + + time_t = torch.full( + (self.args.num_samples,), + step / self.args.optimization_steps, + device=self.device + ) + + with self.profiler.section("loop.model_forward"): + logits = self.model(x, time_t) + probs = F.softmax(logits, dim=-1) + pos_probs = probs[:, mut_idx, :] + + pos_probs[torch.arange(self.args.num_samples), x[:, mut_idx]] = 0 + for inv_tok in self.invalid_tokens: + pos_probs[:, inv_tok] = 0 + + with self.profiler.section("loop.top_p_sort"): + sorted_probs, sorted_indices = torch.sort(pos_probs, descending=True) + + with self.profiler.section("loop.current_scores"): + current_scores = self._get_scores(x) + current_aggregated = self._compute_aggregated_score(current_scores, weights) + + if step == 0: + print(f"\nInitial Scores: {current_scores.cpu().numpy()}") + if self.tracker and self.conjugation_obj: + detailed = self.conjugation_obj.get_last_detailed_results() + self.tracker.log_step(step, current_scores[0], detailed) + self.tracker.print_step_summary( + step, current_scores[0], detailed, self.guidance_method.value + ) + + final_tokens = [] + for i in range(self.args.num_samples): + sample_probs = sorted_probs[i] + sample_indices = sorted_indices[i] + + cumsum = torch.cumsum(sample_probs, dim=0) + mask = cumsum <= self.args.top_p + mask[0] = True + candidates = sample_indices[mask] + + valid_candidate_mask = ~torch.isin( + candidates, torch.tensor(self.invalid_tokens, device=self.device) + ) + candidates = candidates[valid_candidate_mask] + + current_sample_score = current_aggregated[i].item() + + if beta > 0 or self.guidance_method != GuidanceMethod.EXPONENTIAL_TILT: + with self.profiler.section(f"loop.guidance.{self.guidance_method.value}"): + if self.guidance_method == GuidanceMethod.EXPONENTIAL_TILT: + token, _ = self._exponential_tilt_guidance( + x, pos_probs, candidates, mut_idx, i, beta, weights, + current_score=current_sample_score + ) + elif self.guidance_method == GuidanceMethod.CLASSIFIER_GUIDANCE: + token, _ = self._classifier_guidance( + x, logits, mut_idx, i, self.args.cg_weight, weights + ) + elif self.guidance_method == GuidanceMethod.BEST_OF_K: + token, _ = self._best_of_k_guidance( + x, pos_probs, candidates, mut_idx, i, self.args.bok_k, weights + ) + elif self.guidance_method == GuidanceMethod.HYBRID: + token, _ = self._hybrid_guidance( + x, pos_probs, logits, candidates, mut_idx, i, step, beta, weights + ) + else: + raise ValueError(f"Unknown guidance: {self.guidance_method}") + else: + if len(candidates) == 0: + token = x[i, mut_idx].item() + else: + cprobs = pos_probs[i, candidates] + cprobs = cprobs / (cprobs.sum() + 1e-9) + idx = torch.multinomial(cprobs, 1).item() + token = candidates[idx].item() + + final_tokens.append(token) + + x[:, mut_idx] = torch.tensor(final_tokens, device=self.device) + + with self.profiler.section("loop.new_scores"): + new_scores = self._get_scores(x) + new_aggregated = self._compute_aggregated_score(new_scores, weights) + for i in range(self.args.num_samples): + if new_aggregated[i] > current_aggregated[i]: + improved_indices.append(i) + + if improved_indices: + for i in improved_indices: + prev_weighted = (weights.squeeze(1) * current_scores[:, i]).sum() + new_weighted = (weights.squeeze(1) * new_scores[:, i]).sum() + print(f"Previous Weighted Sum: {prev_weighted}") + print(f"Previous Scores: {current_scores[:, i]}") + print(f"New Weighted Sum: {new_weighted}") + print(f"New Scores: {new_scores[:, i]}") + print(self.tokenizer.batch_decode(x[improved_indices])) + + if self.tracker and self.conjugation_obj and \ + (step % 4 == 0 or step == self.args.optimization_steps - 1): + detailed = self.conjugation_obj.get_last_detailed_results() + self.tracker.log_step( + step, new_scores[0], detailed, improved_indices, + guidance_info={'method': self.guidance_method.value, 'beta': beta} + ) + if step == self.args.optimization_steps - 1: + self.tracker.print_step_summary( + step, new_scores[0], detailed, self.guidance_method.value + ) + + if (self.profiler.enabled + and self.args.profile_every > 0 + and (step + 1) % self.args.profile_every == 0): + self.profiler.print_summary(header=f"(through step {step + 1})") + + self.profiler.print_summary(header="(end of generate)") + return x + + +# ============================================================================= +# Main +# ============================================================================= + +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + print(f"Tokenizer type: {args.tokenizer_type}") + print(f"Guidance method: {args.guidance_method}") + + if args.tokenizer_type == 'amino': + print("Using Amino Acid tokenizer") + tokenizer = AminoTokenizer() + smiles_tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + else: + print("Using SMILES tokenizer") + tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + smiles_tokenizer = tokenizer + + conjugation_model = Conjugation(smiles_tokenizer, device=device, builder_type=args.builder_type) + affinity_model = BindingAffinity(args.target, device) + OBJECTIVE_FUNCTIONS = [conjugation_model, affinity_model] + + tracker = GenerationTracker(output_dir=args.output_dir) + + 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) + print(f"Random seed: {args.seed}") + + print(f"Loading model from: {args.checkpoint}") + checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False) + + if args.tokenizer_type == 'amino': + raw_model_args = checkpoint['args'] + if isinstance(raw_model_args, argparse.Namespace): + raw_model_args = vars(raw_model_args) + model_args = edict(raw_model_args) + model = MDLM( + vocab_size=model_args.vocab_size, + seq_len=model_args.seq_len, + model_dim=model_args.model_dim, + n_heads=model_args.n_heads, + n_layers=model_args.n_layers, + ).to(device) + model.load_state_dict(checkpoint['model_state_dict']) + print(f"Amino model loaded. Vocab size: {model_args.vocab_size}") + else: + model_hparams = checkpoint["hyper_parameters"]["args"] + model = MDLMLightningModule.load_from_checkpoint( + args.checkpoint, args=model_hparams, tokenizer=tokenizer, + map_location=device, strict=False + ) + model.to(device) + model.eval() + print("Model loaded successfully.") + + generator = CupOfMoCAGeneratorUAA( + model, device, OBJECTIVE_FUNCTIONS, args, + tokenizer=tokenizer, tokenizer_type=args.tokenizer_type, tracker=tracker + ) + + all_sequences = [] + all_smiles = [] + all_final_results = [] + + for batch_idx in range(args.num_batches): + print(f"\n{'=' * 60}") + print(f"BATCH {batch_idx + 1}/{args.num_batches}") + print(f"{'=' * 60}") + + generated_tokens = generator.generate() + final_scores = generator._get_scores(generated_tokens.detach()).detach().cpu().numpy() + sequence_str = tokenizer.batch_decode(generated_tokens) + + print("\n--- Batch Results ---") + print(f"Sequences: {sequence_str}") + print(f"Scores: {final_scores}") + + all_sequences.extend(sequence_str) + + if args.tokenizer_type == 'amino': + smiles_list = [] + for seq in sequence_str: + three_letter = one_letter_to_three_letter(seq) + smiles = aa_to_smiles(three_letter, cyclic=args.cyclic) + smiles_list.append(smiles if isinstance(smiles, str) else 'C') + smiles_tokens = smiles_tokenizer( + smiles_list, return_tensors='pt', padding=True + ).to(device) + detailed_with_props = conjugation_model.get_detailed_scores_with_properties( + smiles_tokens["input_ids"] + ) + all_smiles.extend(smiles_list) + tracker.log_final(sequence_str, detailed_with_props, smiles_list=smiles_list) + else: + detailed_with_props = conjugation_model.get_detailed_scores_with_properties(generated_tokens) + tracker.log_final(sequence_str, detailed_with_props) + all_final_results.extend(detailed_with_props) + + tracker.print_final_summary() + tracker.save(filename_prefix=f'cupofmoca_uaa_{args.guidance_method}') + + with open(args.output_file, 'w') as f: + for i, seq in enumerate(all_sequences): + if args.tokenizer_type == 'amino': + smiles = all_smiles[i] if i < len(all_smiles) else '' + f.write(f"{seq}\t{smiles}\n") + else: + f.write(seq + '\n') + print(f"\nSequences saved to: {args.output_file}") + print("\nGeneration complete.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="CupOfMoCA-style Guided Generation with UAA Foundation Model" + ) + + parser.add_argument("--checkpoint", type=str, required=True) + parser.add_argument("--target", type=str, required=True) + + parser.add_argument("--num_samples", type=int, default=10) + parser.add_argument("--num_batches", type=int, default=10) + parser.add_argument("--gen_len", type=int, default=50) + parser.add_argument("--optimization_steps", type=int, default=128) + + parser.add_argument("--guidance_method", type=str, default="exp_tilt", + choices=["exp_tilt", "cg", "bok", "hybrid"]) + + parser.add_argument("--beta_min", type=float, default=1.0) + parser.add_argument("--beta_max", type=float, default=20.0) + parser.add_argument("--cg_weight", type=float, default=2.0) + parser.add_argument("--use_acceptance", action="store_true") + parser.add_argument("--bok_k", type=int, default=8) + parser.add_argument("--top_k", type=int, default=200) + parser.add_argument("--top_p", type=float, default=0.9) + parser.add_argument("--score_temperature", type=float, default=1.0) + parser.add_argument("--score_aggregation", type=str, default="weighted_sum", + choices=["weighted_sum", "weighted_min"]) + + parser.add_argument("--weights", type=float, nargs='+', default=None) + + parser.add_argument("--position_strategy", type=str, default="random", + choices=["random", "sequential"]) + + parser.add_argument("--require_valid_x0", action="store_true") + parser.add_argument("--max_x0_retries", type=int, default=10) + + parser.add_argument("--builder_type", type=str, default="AoaDau", + choices=["AoaDau", "ValAlaDau", "ValCitDau", "GlutarylDau", "Dau", + "SSCpt", "VcMMAE", "DSPACpt", "MalHexCpt", "DSECCpt", "Cpt", + "DSPASN38", "DSEBSN38", "DSEPSN38", "SN38"]) + + parser.add_argument("--tokenizer_type", type=str, default="amino", + choices=["smiles", "amino"]) + parser.add_argument("--cyclic", action="store_true", + help="Generate cyclic peptides (only for amino tokenizer).") + parser.add_argument("--smiles_vocab_path", type=str, + default="./smiles/smiles_tokenizer/new_vocab.txt") + parser.add_argument("--smiles_splits_path", type=str, + default="./smiles/smiles_tokenizer/new_splits.txt") + + parser.add_argument("--output_file", type=str, default="./smiles.txt") + parser.add_argument("--output_dir", type=str, default="./generation_logs") + + parser.add_argument("--seed", type=int, default=None) + + parser.add_argument("--profile", action="store_true", + help="Enable per-section wall-clock profiling.") + parser.add_argument("--profile_every", type=int, default=0, + help="Print profiler summary every N optimization steps " + "(0 = only at end of each generate()).") + + parser.add_argument("--cascade_k", type=int, default=0, + help="Cascade scoring: score all proposals with cheap " + "Conjugation pre-filter + BindingAffinity, then run " + "the expensive PDC validation only on the top-K by " + "affinity (among cheap-filter survivors). 0 disables " + "(full scoring on all proposals, original behavior).") + parser.add_argument("--cascade_workers", type=int, default=1, + help="Number of worker threads for parallelizing " + "cascade.full_validate (CPU-bound RDKit work). " + "1 = sequential (original behavior). Try 4-8 when " + "--cascade_k is high. Ignored if --cascade_k=0.") + + args = parser.parse_args() + main(args) diff --git a/smiles/generation/cupofmoca_staged.py b/smiles/generation/cupofmoca_staged.py new file mode 100644 index 0000000000000000000000000000000000000000..8eb62869f8969641fa8a4487385e74dd7c481183 --- /dev/null +++ b/smiles/generation/cupofmoca_staged.py @@ -0,0 +1,584 @@ +""" +CupOfMoCA-style Staged Generation with Exponential Tilting + +This implements Algorithm 2 (Sampling with CupOfMoCA) with exponential tilting for staged generation. +Unlike the guided version, this uses: +- Peptide analyzer (not conjugation) as validity checker +- No PDC building during generation +- Simpler objectives focused on peptide validity and binding affinity + +Exponential Tilting: p'(z') ∝ p(z') · exp(β · S(z')) · 1_valid(z') +""" + +import argparse +import os +import json +import csv +import random +from collections import Counter +from datetime import datetime +from typing import List, Optional, Tuple, Dict, Any + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from tqdm import tqdm + +# Import from the original codebase +from smiles.models.train import MDLMLightningModule, PeptideAnalyzer +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +from smiles.scoring.classifiers import Analyzer, BindingAffinity + + +class GenerationTracker: + """Tracks detailed validity information during generation""" + 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, improved_indices=None, guidance_info: Optional[Dict] = None): + """Log information for a generation step""" + step_info = { + 'step': step, + 'scores': scores.cpu().numpy().tolist() if isinstance(scores, torch.Tensor) else scores, + 'num_valid': int(sum(1 for s in scores if s > 0)) 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, sequences, scores): + """Log final results""" + for i, (seq, score_vec) in enumerate(zip(sequences, scores.T)): + final_info = { + 'sample_idx': i, + 'peptide_smiles': seq, + 'is_valid_peptide': bool(score_vec[0] > 0), + 'binding_score': float(score_vec[1]) if len(score_vec) > 1 else None, + } + self.final_results.append(final_info) + + def save(self, filename_prefix='generation'): + """Save all logs to files""" + step_log_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_steps.json") + with open(step_log_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', 'peptide_smiles', 'is_valid_peptide', 'binding_score']) + for result in self.final_results: + writer.writerow([ + result['sample_idx'], + result['peptide_smiles'], + result['is_valid_peptide'], + result.get('binding_score', '') + ]) + + print(f"\n=== Generation logs saved ===") + print(f" Step logs: {step_log_path}") + print(f" Final results: {final_path}") + print(f" Summary CSV: {csv_path}") + + return step_log_path, final_path, csv_path + + def print_step_summary(self, step, scores): + """Print a summary for the current step""" + if isinstance(scores, torch.Tensor): + scores_list = scores.cpu().numpy().tolist() + else: + scores_list = list(scores) + + num_valid = sum(1 for s in scores_list if s > 0) + + print(f"\n--- Step {step} Summary ---") + print(f" Valid peptides: {num_valid}/{len(scores_list)}") + + def print_final_summary(self): + """Print final summary""" + print("\n" + "=" * 60) + print("FINAL GENERATION SUMMARY") + print("=" * 60) + + total = len(self.final_results) + valid_peptides = sum(1 for r in self.final_results if r.get('is_valid_peptide', False)) + + print(f"\nTotal sequences: {total}") + print(f"Valid peptides: {valid_peptides}") + + if any(r.get('binding_score') is not None for r in self.final_results): + avg_binding = np.mean([r['binding_score'] for r in self.final_results if r.get('binding_score') is not None]) + print(f"Average binding score: {avg_binding:.3f}") + + +class CupOfMoCAStaged: + """ + CupOfMoCA-style staged generator with exponential tilting. + + Uses peptide analyzer instead of conjugation model. + Focuses on generating valid peptides with good binding affinity. + """ + + def __init__(self, model, device, objectives, args, tracker=None): + self.model = model + self.device = device + self.objectives = objectives + self.args = args + self.num_objectives = len(objectives) + self.peptide_analyzer = PeptideAnalyzer() + self.invalid_tokens = [0, 1, 2, 3, 4, 585, 586] # Special tokens to avoid + self.tracker = tracker + + # Find the Analyzer objective for validity checking + self.analyzer_obj = None + for obj in objectives: + if isinstance(obj, Analyzer): + self.analyzer_obj = obj + break + + # ========================================================================= + # Initial Generation (x0 from prior) + # ========================================================================= + + def generate_x0(self, n_steps: int = 16, temperature: float = 1.0) -> torch.Tensor: + """ + Generate initial sequences using flow matching. + This follows the flow from noise to data without guidance. + """ + print("Generating initial sequences (x0 from prior)...") + self.model.eval() + + # Start with random tokens (noise at t=0) + x = torch.randint( + 0, self.model.tokenizer.vocab_size, + (self.args.num_samples, self.args.gen_len), + device=self.device + ) + + # Time schedule + time_steps = torch.linspace(0.0, 1.0, n_steps + 1, device=self.device) + + with torch.no_grad(): + for i in tqdm(range(n_steps), desc="Initial Flow Matching", leave=False): + t_curr = time_steps[i] + t_next = time_steps[i+1] + + t_tensor = torch.full((self.args.num_samples,), t_curr, device=self.device) + + # Get model's prediction + logits = self.model(x, t_tensor) + logits = logits / temperature + logits[:, :, 586] = -1000 # Avoid padding token + + pred_x1 = torch.argmax(logits, dim=-1) + + if i == n_steps - 1: + x = pred_x1 + break + + # Construct next state with noise + noise_prob = 1.0 - t_next + mask = torch.rand(x.shape, device=self.device) < noise_prob + noise = torch.randint(0, self.model.tokenizer.vocab_size, x.shape, device=self.device) + x = torch.where(mask, noise, pred_x1) + + generated_sequences = self.model.tokenizer.batch_decode(x) + + # Analyze the validity of the generated sequences + validities = [] + for seq in generated_sequences: + validities.append(self.peptide_analyzer.is_peptide(seq)) + print(seq) + print(f"Initial Sequence Validity: {validities}") + + return x + + # ========================================================================= + # Score Functions + # ========================================================================= + + def _get_scores(self, x_batch: torch.Tensor) -> torch.Tensor: + """Calculate scores for all objectives""" + scores = [] + for obj_func in self.objectives: + scores.append(obj_func(x_batch.to(self.device))) + return torch.stack(scores, dim=0).to(self.device) + + def _compute_aggregated_score(self, scores: torch.Tensor, weights: torch.Tensor) -> torch.Tensor: + """Compute weighted aggregated score for guidance""" + if self.args.score_aggregation == "weighted_sum": + return (weights * scores).sum(dim=0) + elif self.args.score_aggregation == "weighted_min": + return torch.min(weights * scores, dim=0).values + else: + return (weights * scores).sum(dim=0) + + # ========================================================================= + # Exponential Tilting Guidance + # ========================================================================= + + def _exponential_tilt_guidance( + self, + x: torch.Tensor, + pos_probs: torch.Tensor, + candidates: torch.Tensor, + mut_idx: int, + sample_idx: int, + beta: float, + weights: torch.Tensor, + current_score: float = None + ) -> Tuple[int, Dict]: + """ + Exponential Tilting Guidance with acceptance criterion + + p'(z') ∝ p(z') · exp(β · S(z')) · 1_valid(z') + + Only accept if new_score >= current_score (prevents regression) + """ + num_candidates = len(candidates) + current_token = x[sample_idx, mut_idx].item() + + if num_candidates == 0: + return current_token, {'method': 'exp_tilt', 'fallback': True, 'accepted': False} + + # Limit candidates for efficiency + if num_candidates > self.args.top_k: + candidates = candidates[:self.args.top_k] + num_candidates = self.args.top_k + + # Create batch of proposals + x_proposals = x[sample_idx].unsqueeze(0).repeat(num_candidates, 1) + x_proposals[:, mut_idx] = candidates + + # Compute scores for all candidates + proposal_scores = self._get_scores(x_proposals) + aggregated_scores = self._compute_aggregated_score(proposal_scores, weights) + + # Validity mask (using first objective - peptide analyzer) + validity_mask = (proposal_scores[0] > 0).float() + + # Get base probabilities + base_probs = pos_probs[sample_idx, candidates] + + # Apply exponential tilting in log-space for numerical stability + log_base_probs = torch.log(base_probs + 1e-10) + log_exp_scores = beta * aggregated_scores + log_validity = torch.log(validity_mask + 1e-10) + + log_tilted = log_base_probs + log_exp_scores + log_validity + + # Numerical stability + log_tilted_max = log_tilted.max() + fallback = False + + if torch.isinf(log_tilted_max) or torch.isnan(log_tilted_max): + # Fallback to base distribution + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + else: + log_tilted_stable = log_tilted - log_tilted_max + tilted_probs = torch.exp(log_tilted_stable) + + prob_sum = tilted_probs.sum() + if prob_sum < 1e-10 or torch.isnan(prob_sum) or torch.isinf(prob_sum): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + else: + final_probs = tilted_probs / prob_sum + + # Safety check + if torch.any(torch.isnan(final_probs)) or torch.any(torch.isinf(final_probs)): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + + # Ensure valid probabilities + final_probs = torch.clamp(final_probs, min=1e-10) + final_probs = final_probs / final_probs.sum() + + # Sample from guided distribution + selected_idx = torch.multinomial(final_probs, 1).item() + selected_token = candidates[selected_idx].item() + selected_score = aggregated_scores[selected_idx].item() + + # ACCEPTANCE CRITERION: Only accept if score improves or stays same + accepted = True + if self.args.use_acceptance and current_score is not None: + if selected_score < current_score: + selected_token = current_token + selected_score = current_score + accepted = False + + info = { + 'method': 'exp_tilt', + 'beta': beta, + 'num_candidates': num_candidates, + 'proposal_score': aggregated_scores[selected_idx].item(), + 'selected_score': selected_score, + 'fallback': fallback, + 'validity_rate': validity_mask.mean().item(), + 'accepted': accepted + } + + return selected_token, info + + # ========================================================================= + # Main Generation Loop + # ========================================================================= + + def generate(self) -> torch.Tensor: + """Main generation loop with exponential tilting guidance""" + # Generate initial x0 + x = self.generate_x0() + + # Setup weights + if self.args.weights is None: + weights = torch.tensor( + [1.0] + [1.0 / (self.num_objectives - 1)] * (self.num_objectives - 1), + device=self.device + ).view(-1, 1) + else: + weights = torch.tensor(self.args.weights, device=self.device).view(-1, 1) + if len(weights) != self.num_objectives: + raise ValueError("Number of weights must match number of objectives.") + + print(f"\nGuidance Method: Exponential Tilting") + print(f"Weights: {weights.squeeze().tolist()}") + print(f"β range: [{self.args.beta_min}, {self.args.beta_max}]") + print(f"Acceptance criterion: {'ENABLED' if self.args.use_acceptance else 'DISABLED'}") + + # Main optimization loop + with torch.no_grad(): + for step in tqdm(range(self.args.optimization_steps), desc="Guided Generation"): + # Anneal guidance strength + beta = self.args.beta_min + (self.args.beta_max - self.args.beta_min) * ( + step / max(self.args.optimization_steps - 1, 1) + ) + + # Select mutation position + if self.args.position_strategy == "random": + mut_idx = random.randint(1, self.args.gen_len - 2) + else: # sequential + mut_idx = (step % (self.args.gen_len - 2)) + 1 + + # Determine generation timestep + generation_step = step % self.args.optimization_steps + time_t = torch.full( + (self.args.num_samples,), + (generation_step / self.args.optimization_steps), + device=self.device + ) + + # Get proposal distribution from model + logits = self.model(x, time_t) + probs = F.softmax(logits, dim=-1) + pos_probs = probs[:, mut_idx, :] + + # Zero out current token and invalid tokens + pos_probs[torch.arange(self.args.num_samples), x[:, mut_idx]] = 0 + for invalid_tok in self.invalid_tokens: + pos_probs[:, invalid_tok] = 0 + + # Top-p filtering + sorted_probs, sorted_indices = torch.sort(pos_probs, descending=True) + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) + remove_mask = cumulative_probs > self.args.top_p + remove_mask[..., 1:] = remove_mask[..., :-1].clone() + remove_mask[..., 0] = 0 + + # Get current scores + current_scores = self._get_scores(x) + current_aggregated = self._compute_aggregated_score(current_scores, weights) + + if step == 0: + print(f"\nInitial Scores: {current_scores.cpu().numpy()}") + + # Apply guidance for each sample + improved_count = 0 + for i in range(self.args.num_samples): + sample_mask = remove_mask[i] + candidates = sorted_indices[i, ~sample_mask] + + # Filter invalid tokens from candidates + valid_candidate_mask = ~torch.isin( + candidates, + torch.tensor(self.invalid_tokens, device=self.device) + ) + candidates = candidates[valid_candidate_mask] + + if len(candidates) == 0: + continue + + # Apply exponential tilting + selected_token, guidance_info = self._exponential_tilt_guidance( + x, pos_probs, candidates, mut_idx, i, beta, weights, + current_score=current_aggregated[i].item() + ) + + if guidance_info['accepted']: + improved_count += 1 + + x[i, mut_idx] = selected_token + + # Log progress + if step % 10 == 0 or improved_count > 0: + new_scores = self._get_scores(x) + if self.tracker: + self.tracker.log_step(step, new_scores[0]) + + if step % 20 == 0: + print(f"\nStep {step}: {improved_count}/{self.args.num_samples} improved") + print(f" β = {beta:.2f}") + print(f" Valid peptides: {(new_scores[0] > 0).sum().item()}/{self.args.num_samples}") + + return x + + +# ============================================================================= +# Main Execution +# ============================================================================= + +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + + tokenizer = SMILES_SPE_Tokenizer( + './smiles/smiles_tokenizer/new_vocab.txt', + './smiles/smiles_tokenizer/new_splits.txt' + ) + + # Initialize objectives + analyzer_model = Analyzer(tokenizer) + affinity_model = BindingAffinity(args.target, device) + OBJECTIVE_FUNCTIONS = [analyzer_model, affinity_model] + + # Initialize tracker + tracker = GenerationTracker(output_dir=args.output_dir) + + # Set random seeds + 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) + print(f"Random seed: {args.seed}") + + # Load model + print(f"Loading model from: {args.checkpoint}") + checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False) + model_hparams = checkpoint["hyper_parameters"]["args"] + + model = MDLMLightningModule.load_from_checkpoint( + args.checkpoint, + args=model_hparams, + tokenizer=tokenizer, + map_location=device, + strict=False + ) + model.to(device) + print("Model loaded successfully.") + + # Create generator + generator = CupOfMoCAStaged(model, device, OBJECTIVE_FUNCTIONS, args, tracker=tracker) + + all_sequences = [] + all_scores = [] + + for batch_idx in range(args.num_batches): + print(f"\n{'=' * 60}") + print(f"BATCH {batch_idx + 1}/{args.num_batches}") + print(f"{'=' * 60}") + + generated_tokens = generator.generate() + final_scores = generator._get_scores(generated_tokens.detach()).detach().cpu().numpy() + sequence_str = tokenizer.batch_decode(generated_tokens) + + print("\n--- Batch Results ---") + for i, seq in enumerate(sequence_str): + print(f" {i}: {seq}") + print(f"Scores: {final_scores}") + + all_sequences.extend(sequence_str) + all_scores.append(final_scores) + + tracker.log_final(sequence_str, final_scores) + + # Save results + tracker.print_final_summary() + tracker.save(filename_prefix='cupofmoca_staged') + + with open(args.output_file, 'w') as f: + for seq in all_sequences: + f.write(seq + '\n') + print(f"\nSequences saved to: {args.output_file}") + print("\nGeneration complete.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="CupOfMoCA-style Staged Generation with Exponential Tilting" + ) + + # Model and data + parser.add_argument("--checkpoint", type=str, required=True, + help="Path to trained model checkpoint") + parser.add_argument("--target", type=str, required=True, + help="Target protein sequence for binding affinity") + + # Generation settings + parser.add_argument("--num_samples", type=int, default=10, + help="Number of samples per batch") + parser.add_argument("--num_batches", type=int, default=10, + help="Number of batches to generate") + parser.add_argument("--gen_len", type=int, default=50, + help="Length of sequences to generate") + parser.add_argument("--optimization_steps", type=int, default=128, + help="Number of guided optimization steps") + + # Guidance parameters + parser.add_argument("--beta_min", type=float, default=1.0, + help="Minimum guidance strength β") + parser.add_argument("--beta_max", type=float, default=20.0, + help="Maximum guidance strength β") + parser.add_argument("--use_acceptance", action="store_true", + help="Use acceptance criterion: only accept if new_score >= current_score") + parser.add_argument("--top_k", type=int, default=200, + help="Top-K candidates to consider for guidance") + parser.add_argument("--top_p", type=float, default=0.9, + help="Top-p threshold for candidate filtering") + parser.add_argument("--score_aggregation", type=str, default="weighted_sum", + choices=["weighted_sum", "weighted_min"], + help="How to aggregate multi-objective scores") + + # Weights + parser.add_argument("--weights", type=float, nargs='+', default=None, + help="Weights for objectives (e.g., 1.0 1.0)") + + # Position selection + parser.add_argument("--position_strategy", type=str, default="random", + choices=["random", "sequential"], + help="Strategy for selecting mutation positions") + + # Output + parser.add_argument("--output_file", type=str, default="./smiles.txt", + help="File to save generated sequences") + parser.add_argument("--output_dir", type=str, default="./generation_logs", + help="Directory for detailed logs") + + # Reproducibility + parser.add_argument("--seed", type=int, default=None, + help="Random seed") + + args = parser.parse_args() + main(args) diff --git a/smiles/generation/cupofmoca_staged_aa.py b/smiles/generation/cupofmoca_staged_aa.py new file mode 100644 index 0000000000000000000000000000000000000000..44da08f3059e9c01af28c84a59f18d2eea0d015f --- /dev/null +++ b/smiles/generation/cupofmoca_staged_aa.py @@ -0,0 +1,724 @@ +"""CupOfMoCA-style staged generation with the natural-AA foundation model.""" + +import argparse +import csv +import json +import os +import random +import sys +from concurrent.futures import ThreadPoolExecutor +from datetime import datetime +from typing import Dict, Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from easydict import EasyDict as edict +from tqdm import tqdm + +from smiles.models.train import MDLMLightningModule, PeptideAnalyzer +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +from smiles.scoring.classifiers import Analyzer, BindingAffinity + +# AA-mode shared utilities +from smiles.models.aa_helpers import ( + ESM2AminoTokenizer as _AAEsmTokenizer, + MDLM_PepReDi as _AAMDLM, + make_affinity, + DEFAULT_AA_AFFINITY_CKPT, + INVALID_AA_TOKEN_IDS as _AA_INVALID_IDS, +) + +from third_party.SMILESToPeptide.peptide_to_smiles import aa_to_smiles +from third_party.SMILESToPeptide.aminoacid_selective import one_letter_to_three_letter + + +# ============================================================================= +# Amino Acid Foundation Model (DiT-based MDLM) +# ============================================================================= + +# --- AA-mode Model + Tokenizer (imported from aa_helpers) --- +MDLM = _AAMDLM +AminoTokenizer = _AAEsmTokenizer + + +# ============================================================================= +# Generation Tracker +# ============================================================================= + +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, scores, 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(sum(1 for s in scores if s > 0)) 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, sequences, scores, smiles_list=None): + for i, (seq, score_vec) in enumerate(zip(sequences, scores.T)): + self.final_results.append({ + 'sample_idx': i, + 'aa_sequence': seq, + 'peptide_smiles': smiles_list[i] if smiles_list else seq, + 'is_valid_peptide': bool(score_vec[0] > 0), + 'binding_score': float(score_vec[1]) if len(score_vec) > 1 else None, + }) + + def save(self, filename_prefix='generation'): + step_log_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_steps.json") + with open(step_log_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', 'aa_sequence', 'peptide_smiles', + 'is_valid_peptide', 'binding_score']) + for r in self.final_results: + writer.writerow([ + r['sample_idx'], r.get('aa_sequence', ''), r['peptide_smiles'], + r['is_valid_peptide'], r.get('binding_score', '') + ]) + + print(f"\n=== Generation logs saved ===") + print(f" Step logs: {step_log_path}") + print(f" Final results: {final_path}") + print(f" Summary CSV: {csv_path}") + return step_log_path, final_path, csv_path + + def print_step_summary(self, step, scores): + scores_list = scores.cpu().numpy().tolist() if isinstance(scores, torch.Tensor) else list(scores) + num_valid = sum(1 for s in scores_list if s > 0) + print(f"\n--- Step {step} Summary ---") + print(f" Valid peptides: {num_valid}/{len(scores_list)}") + + def print_final_summary(self): + print("\n" + "=" * 60) + print("FINAL GENERATION SUMMARY") + print("=" * 60) + total = len(self.final_results) + valid = sum(1 for r in self.final_results if r.get('is_valid_peptide', False)) + print(f"\nTotal sequences: {total}") + print(f"Valid peptides: {valid}") + if any(r.get('binding_score') is not None for r in self.final_results): + avg = np.mean([r['binding_score'] for r in self.final_results + if r.get('binding_score') is not None]) + print(f"Average binding score: {avg:.3f}") + + +# ============================================================================= +# CupOfMoCA Staged Generator (UAA-aware) +# ============================================================================= + +class CupOfMoCAStagedUAA: + """Exponential-tilting staged generator that operates in AA token space.""" + + def __init__(self, model, device, objectives, args, tokenizer, tokenizer_type='amino', + tracker=None): + self.model = model + self.device = device + self.objectives = objectives + self.args = args + self.num_objectives = len(objectives) + self.peptide_analyzer = PeptideAnalyzer() + self.tracker = tracker + self.tokenizer = tokenizer + self.tokenizer_type = tokenizer_type + + if tokenizer_type == 'amino': + # PepReDi natural-AA vocab is 24 tokens; 0-3 = cls/pad/eos/unk, + # 4-23 = canonical AAs. Block specials only. (UAA used id 38 + # for D-AA 'g'; that's out of PepReDi vocab range.) + self.invalid_tokens = [0, 1, 2, 3] + self.smiles_tokenizer = SMILES_SPE_Tokenizer( + args.smiles_vocab_path, args.smiles_splits_path + ) + else: + self.invalid_tokens = [0, 1, 2, 3, 4, 585, 586] + self.smiles_tokenizer = None + + self.analyzer_obj = None + for obj in objectives: + if isinstance(obj, Analyzer): + self.analyzer_obj = obj + break + + # ------------------------------------------------------------------ + # x0 generation + # ------------------------------------------------------------------ + def generate_x0(self, n_steps=16, temperature=1.0): + print("Generating initial sequences (x0 from prior)...") + self.model.eval() + + if self.tokenizer_type == 'amino': + vocab_size = self.model.vocab_size + valid_tokens = [i for i in range(vocab_size) if i not in self.invalid_tokens] + valid_tokens = torch.tensor(valid_tokens, device=self.device) + rand_indices = torch.randint( + 0, len(valid_tokens), + (self.args.num_samples, self.args.gen_len), + device=self.device + ) + body = valid_tokens[rand_indices] + cls_col = torch.full((self.args.num_samples, 1), self.tokenizer.cls_id, device=self.device) + eos_col = torch.full((self.args.num_samples, 1), self.tokenizer.eos_id, device=self.device) + x = torch.cat([cls_col, body, eos_col], dim=1) + else: + vocab_size = self.model.tokenizer.vocab_size + x = torch.randint(0, vocab_size, + (self.args.num_samples, self.args.gen_len), + device=self.device) + + time_steps = torch.linspace(0.0, 1.0, n_steps + 1, device=self.device) + + with torch.no_grad(): + for i in tqdm(range(n_steps), desc="Initial Flow Matching", leave=False): + t_curr = time_steps[i] + t_next = time_steps[i + 1] + t_tensor = torch.full((self.args.num_samples,), t_curr, device=self.device) + + logits = self.model(x, t_tensor) / temperature + for inv_tok in self.invalid_tokens: + if inv_tok < logits.shape[-1]: + logits[:, :, inv_tok] = -1000 + + pred_x1 = torch.argmax(logits, dim=-1) + if i == n_steps - 1: + x = pred_x1 + break + + noise_prob = 1.0 - t_next + mask = torch.rand(x.shape, device=self.device) < noise_prob + noise = torch.randint(0, vocab_size, x.shape, device=self.device) + x = torch.where(mask, noise, pred_x1) + + generated_sequences = self.tokenizer.batch_decode(x) + for seq in generated_sequences: + print(f" {seq}") + return x + + # ------------------------------------------------------------------ + # Scoring + # ------------------------------------------------------------------ + def _aa_to_smiles_batch(self, x_batch): + seqs = self.tokenizer.batch_decode(x_batch) + three_letter_seqs = [one_letter_to_three_letter(s) for s in seqs] + with ThreadPoolExecutor() as pool: + smiles_seqs = list(pool.map( + lambda s: aa_to_smiles(s, cyclic=self.args.cyclic), + three_letter_seqs + )) + return seqs, smiles_seqs + + def _get_scores(self, x_batch): + scores = [] + + if self.tokenizer_type == 'amino': + _, smiles_seqs = self._aa_to_smiles_batch(x_batch) + valid_mask = torch.tensor( + [1 if isinstance(s, str) else 0 for s in smiles_seqs], + device=self.device + ) + for i in torch.where((1 - valid_mask) == 1)[0].tolist(): + smiles_seqs[i] = 'C' + + smiles_batch = self.smiles_tokenizer( + smiles_seqs, return_tensors='pt', padding=True + ).to(self.device) + attention_mask = smiles_batch.get("attention_mask", None) + + for obj_func in self.objectives: + if getattr(obj_func, "input_type", None) == "aa": + # AA-input objective: score raw AA tokens directly. + score = obj_func(x_batch.to(self.device)).to(self.device) + scores.append(score) + else: + try: + score = obj_func( + smiles_batch["input_ids"], attention_mask=attention_mask + ).to(self.device) + except TypeError: + score = obj_func(smiles_batch["input_ids"]).to(self.device) + scores.append(score * valid_mask) + else: + for obj_func in self.objectives: + scores.append(obj_func(x_batch.to(self.device))) + + return torch.stack(scores, dim=0).to(self.device) + + def _compute_aggregated_score(self, scores, weights): + if self.args.score_aggregation == "weighted_min": + return torch.min(weights * scores, dim=0).values + return (weights * scores).sum(dim=0) + + # ------------------------------------------------------------------ + # Exponential tilting + # ------------------------------------------------------------------ + def _exponential_tilt_guidance(self, x, pos_probs, candidates, mut_idx, sample_idx, + beta, weights, current_score=None, print_tilted=False, + step=None): + num_candidates = len(candidates) + current_token = x[sample_idx, mut_idx].item() + + if num_candidates == 0: + return current_token, {'method': 'exp_tilt', 'fallback': True, 'accepted': False} + + if num_candidates > self.args.top_k: + candidates = candidates[:self.args.top_k] + num_candidates = self.args.top_k + + x_proposals = x[sample_idx].unsqueeze(0).repeat(num_candidates, 1) + x_proposals[:, mut_idx] = candidates + + proposal_scores = self._get_scores(x_proposals) + aggregated_scores = self._compute_aggregated_score(proposal_scores, weights) + validity_mask = (proposal_scores[0] > 0).float() + base_probs = pos_probs[sample_idx, candidates] + + log_base_probs = torch.log(base_probs + 1e-10) + log_exp_scores = beta * aggregated_scores + log_validity = torch.log(validity_mask + 1e-10) + log_tilted = log_base_probs + log_exp_scores + log_validity + + log_tilted_max = log_tilted.max() + fallback = False + if torch.isinf(log_tilted_max) or torch.isnan(log_tilted_max): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + else: + log_tilted_stable = log_tilted - log_tilted_max + tilted_probs = torch.exp(log_tilted_stable) + prob_sum = tilted_probs.sum() + if prob_sum < 1e-10 or torch.isnan(prob_sum) or torch.isinf(prob_sum): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + else: + final_probs = tilted_probs / prob_sum + + if torch.any(torch.isnan(final_probs)) or torch.any(torch.isinf(final_probs)): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + + final_probs = torch.clamp(final_probs, min=1e-10) + final_probs = final_probs / final_probs.sum() + + if print_tilted: + id_to_token = self.tokenizer.id_to_token + topk = min(self.args.print_logits_topk, num_candidates) + top_vals, top_local = torch.topk(final_probs, k=topk) + print(f"\n[tilted] step={step} sample={sample_idx} mut_idx={mut_idx} " + f"beta={beta:.2f} fallback={fallback}") + print(f" Top-{topk} candidates by TILTED prob " + f"(shows: base p, agg score S, β·S, valid, tilted p):") + for tv, li in zip(top_vals.tolist(), top_local.tolist()): + tid = candidates[li].item() + tok = id_to_token.get(int(tid), '?') + bp = base_probs[li].item() + sc = aggregated_scores[li].item() + bs = (beta * aggregated_scores[li]).item() + vd = validity_mask[li].item() + print(f" id={tid:>3} tok={tok!r:>6} base_p={bp:.3e} " + f"S={sc:+.3f} β·S={bs:+.3f} valid={vd:.0f} tilted_p={tv:.3e}") + d_local = [li for li in range(num_candidates) + if 33 <= candidates[li].item() <= 52 + and candidates[li].item() not in self.invalid_tokens] + if d_local: + print(" D-AA lowercase candidates (if any survived top-p):") + for li in d_local: + tid = candidates[li].item() + tok = id_to_token.get(int(tid), '?') + print(f" id={tid:>3} tok={tok!r:>4} base_p={base_probs[li]:.3e} " + f"S={aggregated_scores[li]:+.3f} " + f"valid={validity_mask[li]:.0f} tilted_p={final_probs[li]:.3e}") + else: + print(" D-AA lowercase candidates: NONE made it into the top-p pool") + + selected_idx = torch.multinomial(final_probs, 1).item() + selected_token = candidates[selected_idx].item() + selected_score = aggregated_scores[selected_idx].item() + + accepted = True + if self.args.use_acceptance and current_score is not None: + if selected_score < current_score: + selected_token = current_token + selected_score = current_score + accepted = False + + return selected_token, { + 'method': 'exp_tilt', 'beta': beta, 'num_candidates': num_candidates, + 'proposal_score': aggregated_scores[selected_idx].item(), + 'selected_score': selected_score, 'fallback': fallback, + 'validity_rate': validity_mask.mean().item(), 'accepted': accepted + } + + # ------------------------------------------------------------------ + # Logit inspection + # ------------------------------------------------------------------ + def _print_logit_distribution(self, raw_probs, masked_probs, mut_idx, step, sample_idx): + """Print the model's per-position probability distribution for inspection. + + raw_probs: [vocab_size] probabilities BEFORE masking current/invalid tokens + masked_probs: [vocab_size] probabilities AFTER masking + """ + topk = self.args.print_logits_topk + id_to_token = self.tokenizer.id_to_token + + # Token-category index ranges. Clip to model.vocab_size so we don't + # index out of bounds when the model uses a smaller vocab than the + # UAA tokenizer (e.g. PepReDi natural-AA vocab = 24). + V = int(raw_probs.shape[0]) + cat_ranges = { + 'special' : [i for i in range(0, 4) if i < V], + 'L-AA upper': [i for i in range(4, 24) if i < V], + 'non-canon' : [i for i in range(24, 33) if i < V], + 'D-AA lower': [i for i in range(33, 53) if i < V], + } + + print(f"\n[logits] step={step} sample={sample_idx} mut_idx={mut_idx}") + + # Top-k from raw probs + top_vals, top_ids = torch.topk(raw_probs, k=min(topk, raw_probs.shape[0])) + print(f" Top-{topk} (raw model probs, pre-mask):") + for v, tid in zip(top_vals.tolist(), top_ids.tolist()): + tok = id_to_token.get(int(tid), '?') + print(f" id={tid:>3} tok={tok!r:>6} p={v:.4e}") + + # Per-category mass + print(" Probability mass by category (raw / masked):") + for name, ids in cat_ranges.items(): + ids_t = torch.tensor(ids, device=raw_probs.device) + raw_mass = raw_probs[ids_t].sum().item() + msk_mass = masked_probs[ids_t].sum().item() + print(f" {name:>10}: raw={raw_mass:.4e} masked={msk_mass:.4e}") + + # Specifically: per-token probability for D-AA lowercase (excluding 'g'=38) + d_ids = [i for i in cat_ranges['D-AA lower'] if i not in self.invalid_tokens] + d_ids_t = torch.tensor(d_ids, device=raw_probs.device) + d_vals_raw = raw_probs[d_ids_t] + d_vals_msk = masked_probs[d_ids_t] + print(" D-AA lowercase per-token probs (raw / masked):") + for tid, vr, vm in zip(d_ids, d_vals_raw.tolist(), d_vals_msk.tolist()): + tok = id_to_token.get(int(tid), '?') + print(f" id={tid:>3} tok={tok!r:>4} raw={vr:.4e} masked={vm:.4e}") + + # ------------------------------------------------------------------ + # Main loop + # ------------------------------------------------------------------ + def generate(self): + x = self.generate_x0() + + if self.args.weights is None: + weights = torch.tensor( + [1.0] + [1.0 / (self.num_objectives - 1)] * (self.num_objectives - 1), + device=self.device + ).view(-1, 1) + else: + weights = torch.tensor(self.args.weights, device=self.device).view(-1, 1) + if len(weights) != self.num_objectives: + raise ValueError("Number of weights must match number of objectives.") + + print(f"\nGuidance Method: Exponential Tilting") + print(f"Weights: {weights.squeeze().tolist()}") + print(f"β range: [{self.args.beta_min}, {self.args.beta_max}]") + print(f"Acceptance criterion: {'ENABLED' if self.args.use_acceptance else 'DISABLED'}") + + with torch.no_grad(): + for step in tqdm(range(self.args.optimization_steps), desc="Guided Generation"): + beta = self.args.beta_min + (self.args.beta_max - self.args.beta_min) * ( + step / max(self.args.optimization_steps - 1, 1) + ) + + if self.args.position_strategy == "random": + mut_idx = random.randint(1, self.args.gen_len - 2) + else: + mut_idx = (step % (self.args.gen_len - 2)) + 1 + + generation_step = step % self.args.optimization_steps + time_t = torch.full( + (self.args.num_samples,), + generation_step / self.args.optimization_steps, + device=self.device + ) + + logits = self.model(x, time_t) + probs = F.softmax(logits, dim=-1) + pos_probs = probs[:, mut_idx, :] + raw_pos_probs = pos_probs.clone() + + pos_probs[torch.arange(self.args.num_samples), x[:, mut_idx]] = 0 + for inv_tok in self.invalid_tokens: + pos_probs[:, inv_tok] = 0 + + should_print_logits = ( + self.args.print_logits + and (step % max(self.args.print_logits_every, 1) == 0) + ) + print_sample_idx = min(self.args.print_logits_sample, + self.args.num_samples - 1) + if should_print_logits: + self._print_logit_distribution( + raw_pos_probs[print_sample_idx], pos_probs[print_sample_idx], + mut_idx, step, print_sample_idx + ) + + sorted_probs, sorted_indices = torch.sort(pos_probs, descending=True) + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) + remove_mask = cumulative_probs > self.args.top_p + remove_mask[..., 1:] = remove_mask[..., :-1].clone() + remove_mask[..., 0] = 0 + + current_scores = self._get_scores(x) + current_aggregated = self._compute_aggregated_score(current_scores, weights) + + if step == 0: + print(f"\nInitial Scores: {current_scores.cpu().numpy()}") + + improved = False + for i in range(self.args.num_samples): + sample_mask = remove_mask[i] + candidates = sorted_indices[i, ~sample_mask] + + valid_candidate_mask = ~torch.isin( + candidates, torch.tensor(self.invalid_tokens, device=self.device) + ) + candidates = candidates[valid_candidate_mask] + + if len(candidates) == 0: + continue + + prev_token = x[i, mut_idx].item() + prev_sample_scores = current_scores[:, i].clone() + prev_weighted_sum = (weights.squeeze(1) * prev_sample_scores).sum() + + selected_token, info = self._exponential_tilt_guidance( + x, pos_probs, candidates, mut_idx, i, beta, weights, + current_score=current_aggregated[i].item(), + print_tilted=(should_print_logits and i == print_sample_idx), + step=step, + ) + + if selected_token == prev_token: + continue + + x[i, mut_idx] = selected_token + + new_sample_scores = self._get_scores(x[i].unsqueeze(0))[:, 0] + new_weighted_sum = (weights.squeeze(1) * new_sample_scores).sum() + + if new_weighted_sum >= prev_weighted_sum: + improved = True + print(f"Previous Weighted Sum: {prev_weighted_sum}") + print(f"Previous Scores: {prev_sample_scores}") + print(f"New Weighted Sum: {new_weighted_sum}") + print(f"New Scores: {new_sample_scores}") + current_scores[:, i] = new_sample_scores + current_aggregated[i] = self._compute_aggregated_score( + new_sample_scores.unsqueeze(1), weights + )[0] + else: + x[i, mut_idx] = prev_token + + if improved: + print(self.tokenizer.batch_decode(x)) + if self.tracker: + self.tracker.log_step( + step, current_scores[0], + guidance_info={'method': 'exp_tilt', 'beta': beta} + ) + + return x + + +# ============================================================================= +# Main +# ============================================================================= + +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + print(f"Tokenizer type: {args.tokenizer_type}") + + 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) + print(f"Random seed: {args.seed}") + + if args.tokenizer_type == 'amino': + print("Using Amino Acid tokenizer") + tokenizer = AminoTokenizer() + smiles_tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + else: + print("Using SMILES tokenizer") + tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + smiles_tokenizer = tokenizer + + analyzer_model = Analyzer(smiles_tokenizer) + # Binding affinity: AA-input or SMILES-input depending on --affinity_type. + affinity_model = make_affinity( + args.affinity_type, + target_protein_seq=args.target, + device=device, + aa_ckpt_path=args.aa_affinity_ckpt, + ) + print(f"Affinity scorer: {type(affinity_model).__name__} (input_type={getattr(affinity_model, 'input_type', '?')})") + OBJECTIVE_FUNCTIONS = [analyzer_model, affinity_model] + + tracker = GenerationTracker(output_dir=args.output_dir) + + print(f"Loading model from: {args.checkpoint}") + checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False) + + if args.tokenizer_type == 'amino': + raw_model_args = checkpoint['args'] + if isinstance(raw_model_args, argparse.Namespace): + raw_model_args = vars(raw_model_args) + model_args = edict(raw_model_args) + model = MDLM( + vocab_size=model_args.vocab_size, + seq_len=model_args.seq_len, + model_dim=model_args.model_dim, + n_heads=model_args.n_heads, + n_layers=model_args.n_layers, + ).to(device) + model.load_state_dict(checkpoint['model_state_dict']) + print(f"Amino model loaded. Vocab size: {model_args.vocab_size}") + else: + model_hparams = checkpoint["hyper_parameters"]["args"] + model = MDLMLightningModule.load_from_checkpoint( + args.checkpoint, args=model_hparams, tokenizer=tokenizer, + map_location=device, strict=False + ) + model.to(device) + model.eval() + print("Model loaded successfully.") + + generator = CupOfMoCAStagedUAA( + model, device, OBJECTIVE_FUNCTIONS, args, + tokenizer=tokenizer, tokenizer_type=args.tokenizer_type, tracker=tracker + ) + + all_sequences = [] + all_smiles = [] + all_scores = [] + + for batch_idx in range(args.num_batches): + print(f"\n{'=' * 60}") + print(f"BATCH {batch_idx + 1}/{args.num_batches}") + print(f"{'=' * 60}") + + generated_tokens = generator.generate() + final_scores = generator._get_scores(generated_tokens.detach()).detach().cpu().numpy() + sequence_str = tokenizer.batch_decode(generated_tokens) + + print("\n--- Batch Results ---") + for i, seq in enumerate(sequence_str): + print(f" {i}: {seq}") + print(f"Scores: {final_scores}") + + all_sequences.extend(sequence_str) + all_scores.append(final_scores) + + if args.tokenizer_type == 'amino': + smiles_list = [] + for seq in sequence_str: + three_letter = one_letter_to_three_letter(seq) + smiles = aa_to_smiles(three_letter, cyclic=args.cyclic) + smiles_list.append(smiles if isinstance(smiles, str) else 'INVALID_SMILES') + all_smiles.extend(smiles_list) + tracker.log_final(sequence_str, final_scores, smiles_list=smiles_list) + else: + tracker.log_final(sequence_str, final_scores) + + tracker.print_final_summary() + tracker.save(filename_prefix='cupofmoca_staged_uaa') + + with open(args.output_file, 'w') as f: + for i, seq in enumerate(all_sequences): + if args.tokenizer_type == 'amino': + smiles = all_smiles[i] if i < len(all_smiles) else '' + f.write(f"{seq}\t{smiles}\n") + else: + f.write(seq + '\n') + print(f"\nSequences saved to: {args.output_file}") + print("\nGeneration complete.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="CupOfMoCA-style Staged Generation with UAA Foundation Model" + ) + + parser.add_argument("--checkpoint", type=str, required=True) + parser.add_argument("--target", type=str, required=True) + + parser.add_argument("--num_samples", type=int, default=10) + parser.add_argument("--num_batches", type=int, default=10) + parser.add_argument("--gen_len", type=int, default=50) + parser.add_argument("--optimization_steps", type=int, default=128) + + parser.add_argument("--beta_min", type=float, default=1.0) + parser.add_argument("--beta_max", type=float, default=20.0) + parser.add_argument("--use_acceptance", action="store_true") + parser.add_argument("--top_k", type=int, default=200) + parser.add_argument("--top_p", type=float, default=0.9) + parser.add_argument("--score_aggregation", type=str, default="weighted_sum", + choices=["weighted_sum", "weighted_min"]) + + parser.add_argument("--weights", type=float, nargs='+', default=None) + + parser.add_argument("--position_strategy", type=str, default="random", + choices=["random", "sequential"]) + + parser.add_argument("--tokenizer_type", type=str, default="amino", + choices=["smiles", "amino"]) + parser.add_argument("--cyclic", action="store_true", + help="Generate cyclic peptides (only for amino tokenizer).") + parser.add_argument("--smiles_vocab_path", type=str, + default="./smiles/smiles_tokenizer/new_vocab.txt") + parser.add_argument("--smiles_splits_path", type=str, + default="./smiles/smiles_tokenizer/new_splits.txt") + + # AA-mode binding-affinity selection (this is the WT-AA / PepReDi script). + parser.add_argument("--affinity_type", type=str, default="smiles", + choices=["aa", "smiles"], + help="Which binding-affinity oracle to use: " + "'aa' = AA-input UnpooledBindingPredictor, " + "'smiles' = SMILES-input BindingAffinity.") + parser.add_argument("--aa_affinity_ckpt", type=str, + default=DEFAULT_AA_AFFINITY_CKPT, + help="Path to AA-input affinity predictor checkpoint.") + + parser.add_argument("--output_file", type=str, default="./smiles.txt") + parser.add_argument("--output_dir", type=str, default="./generation_logs") + + parser.add_argument("--seed", type=int, default=None) + + parser.add_argument("--print_logits", action="store_true", + help="Print per-step probability distribution at the mutation position.") + parser.add_argument("--print_logits_every", type=int, default=1, + help="Print logits every N optimization steps (requires --print_logits).") + parser.add_argument("--print_logits_topk", type=int, default=20, + help="Number of top tokens to show per logit printout.") + parser.add_argument("--print_logits_sample", type=int, default=0, + help="Which sample index to print logits for (default: 0).") + + args = parser.parse_args() + main(args) diff --git a/smiles/generation/cupofmoca_staged_uaa.py b/smiles/generation/cupofmoca_staged_uaa.py new file mode 100644 index 0000000000000000000000000000000000000000..c1ddd359dbbc293a5448c64f34b30bf0e5eb9fdc --- /dev/null +++ b/smiles/generation/cupofmoca_staged_uaa.py @@ -0,0 +1,797 @@ +"""CupOfMoCA-style staged generation with the UAA foundation model.""" + +import argparse +import csv +import json +import os +import random +import sys +from concurrent.futures import ThreadPoolExecutor +from datetime import datetime +from typing import Dict, Optional, Tuple + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from easydict import EasyDict as edict +from tqdm import tqdm + +from smiles.models.train import MDLMLightningModule, PeptideAnalyzer +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +from smiles.scoring.classifiers import Analyzer, BindingAffinity + +from third_party.SMILESToPeptide.peptide_to_smiles import aa_to_smiles +from third_party.SMILESToPeptide.aminoacid_selective import one_letter_to_three_letter + + +# ============================================================================= +# Amino Acid Foundation Model (DiT-based MDLM) +# ============================================================================= + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class TimestepEmbedder(nn.Module): + def __init__(self, hidden_size): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(1, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + + def forward(self, t): + return self.mlp(t.unsqueeze(-1)) + + +class DiTBlock(nn.Module): + def __init__(self, hidden_size, n_heads): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = nn.MultiheadAttention(hidden_size, n_heads, batch_first=True) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.mlp = nn.Sequential( + nn.Linear(hidden_size, 4 * hidden_size), + nn.GELU(), + nn.Linear(4 * hidden_size, hidden_size), + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True), + ) + + def forward(self, x, c): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = \ + self.adaLN_modulation(c).chunk(6, dim=1) + x_norm1 = modulate(self.norm1(x), shift_msa, scale_msa) + attn_output, _ = self.attn(x_norm1, x_norm1, x_norm1) + x = x + gate_msa.unsqueeze(1) * attn_output + x_norm2 = modulate(self.norm2(x), shift_mlp, scale_mlp) + mlp_output = self.mlp(x_norm2) + x = x + gate_mlp.unsqueeze(1) * mlp_output + return x + + +class MDLM(nn.Module): + def __init__(self, vocab_size, seq_len, model_dim, n_heads, n_layers): + super().__init__() + self.vocab_size = vocab_size + self.seq_len = seq_len + self.model_dim = model_dim + + self.token_embedder = nn.Embedding(vocab_size, model_dim) + self.pos_embedder = nn.Parameter(torch.randn(1, seq_len, model_dim)) + self.time_embedder = TimestepEmbedder(model_dim) + + self.transformer_blocks = nn.ModuleList([ + DiTBlock(model_dim, n_heads) for _ in range(n_layers) + ]) + self.final_norm = nn.LayerNorm(model_dim) + self.lm_head = nn.Linear(model_dim, vocab_size) + + def forward(self, x, t): + seq_len = x.shape[1] + x_embed = self.token_embedder(x) + self.pos_embedder[:, :seq_len, :] + t_embed = self.time_embedder(t) + for block in self.transformer_blocks: + x_embed = block(x_embed, t_embed) + x_embed = self.final_norm(x_embed) + return self.lm_head(x_embed) + + +class AminoTokenizer: + def __init__(self): + self.vocab = { + '': 0, '': 1, '': 2, '': 3, + 'L': 4, 'A': 5, 'G': 6, 'V': 7, 'S': 8, 'E': 9, + 'R': 10, 'T': 11, 'I': 12, 'D': 13, 'P': 14, 'K': 15, + 'Q': 16, 'N': 17, 'F': 18, 'Y': 19, 'M': 20, 'H': 21, + 'W': 22, 'C': 23, + 'Ŷ': 24, 'Ĉ': 25, 'Ĝ': 26, 'Ė': 27, 'Ƙ': 28, + 'Ṃ': 29, 'Ł': 30, 'Ċ': 31, 'Ṯ': 32, + 'a': 33, 'c': 34, 'd': 35, 'e': 36, 'f': 37, + 'g': 38, 'h': 39, 'i': 40, 'k': 41, 'l': 42, + 'm': 43, 'n': 44, 'p': 45, 'q': 46, 'r': 47, + 's': 48, 't': 49, 'v': 50, 'w': 51, 'y': 52, + } + self.id_to_token = {i: t for t, i in self.vocab.items()} + self.cls_token, self.pad_token = '', '' + self.eos_token, self.unk_token = '', '' + self.cls_id = self.vocab[self.cls_token] + self.pad_id = self.vocab[self.pad_token] + self.eos_id = self.vocab[self.eos_token] + self.unk_id = self.vocab[self.unk_token] + self.vocab_size = len(self.vocab) + + def decode(self, ids, skip_special_tokens=True): + if isinstance(ids, torch.Tensor): + ids = ids.tolist() + tokens = [] + for i in ids: + tok = self.id_to_token.get(int(i), self.unk_token) + if skip_special_tokens and tok in {self.cls_token, self.eos_token, self.pad_token}: + continue + tokens.append(tok) + return ''.join(tokens) + + def batch_decode(self, batch_ids, skip_special_tokens=True): + return [self.decode(ids, skip_special_tokens=skip_special_tokens) for ids in batch_ids] + + +# ============================================================================= +# Generation Tracker +# ============================================================================= + +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, scores, 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(sum(1 for s in scores if s > 0)) 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, sequences, scores, smiles_list=None): + for i, (seq, score_vec) in enumerate(zip(sequences, scores.T)): + self.final_results.append({ + 'sample_idx': i, + 'aa_sequence': seq, + 'peptide_smiles': smiles_list[i] if smiles_list else seq, + 'is_valid_peptide': bool(score_vec[0] > 0), + 'binding_score': float(score_vec[1]) if len(score_vec) > 1 else None, + }) + + def save(self, filename_prefix='generation'): + step_log_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_steps.json") + with open(step_log_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', 'aa_sequence', 'peptide_smiles', + 'is_valid_peptide', 'binding_score']) + for r in self.final_results: + writer.writerow([ + r['sample_idx'], r.get('aa_sequence', ''), r['peptide_smiles'], + r['is_valid_peptide'], r.get('binding_score', '') + ]) + + print(f"\n=== Generation logs saved ===") + print(f" Step logs: {step_log_path}") + print(f" Final results: {final_path}") + print(f" Summary CSV: {csv_path}") + return step_log_path, final_path, csv_path + + def print_step_summary(self, step, scores): + scores_list = scores.cpu().numpy().tolist() if isinstance(scores, torch.Tensor) else list(scores) + num_valid = sum(1 for s in scores_list if s > 0) + print(f"\n--- Step {step} Summary ---") + print(f" Valid peptides: {num_valid}/{len(scores_list)}") + + def print_final_summary(self): + print("\n" + "=" * 60) + print("FINAL GENERATION SUMMARY") + print("=" * 60) + total = len(self.final_results) + valid = sum(1 for r in self.final_results if r.get('is_valid_peptide', False)) + print(f"\nTotal sequences: {total}") + print(f"Valid peptides: {valid}") + if any(r.get('binding_score') is not None for r in self.final_results): + avg = np.mean([r['binding_score'] for r in self.final_results + if r.get('binding_score') is not None]) + print(f"Average binding score: {avg:.3f}") + + +# ============================================================================= +# CupOfMoCA Staged Generator (UAA-aware) +# ============================================================================= + +class CupOfMoCAStagedUAA: + """Exponential-tilting staged generator that operates in AA token space.""" + + def __init__(self, model, device, objectives, args, tokenizer, tokenizer_type='amino', + tracker=None): + self.model = model + self.device = device + self.objectives = objectives + self.args = args + self.num_objectives = len(objectives) + self.peptide_analyzer = PeptideAnalyzer() + self.tracker = tracker + self.tokenizer = tokenizer + self.tokenizer_type = tokenizer_type + + if tokenizer_type == 'amino': + self.invalid_tokens = [0, 1, 2, 3, 38] # specials + 'g' + self.smiles_tokenizer = SMILES_SPE_Tokenizer( + args.smiles_vocab_path, args.smiles_splits_path + ) + else: + self.invalid_tokens = [0, 1, 2, 3, 4, 585, 586] + self.smiles_tokenizer = None + + self.analyzer_obj = None + for obj in objectives: + if isinstance(obj, Analyzer): + self.analyzer_obj = obj + break + + # ------------------------------------------------------------------ + # x0 generation + # ------------------------------------------------------------------ + def generate_x0(self, n_steps=16, temperature=1.0): + print("Generating initial sequences (x0 from prior)...") + self.model.eval() + + if self.tokenizer_type == 'amino': + vocab_size = self.model.vocab_size + valid_tokens = [i for i in range(vocab_size) if i not in self.invalid_tokens] + valid_tokens = torch.tensor(valid_tokens, device=self.device) + rand_indices = torch.randint( + 0, len(valid_tokens), + (self.args.num_samples, self.args.gen_len), + device=self.device + ) + body = valid_tokens[rand_indices] + cls_col = torch.full((self.args.num_samples, 1), self.tokenizer.cls_id, device=self.device) + eos_col = torch.full((self.args.num_samples, 1), self.tokenizer.eos_id, device=self.device) + x = torch.cat([cls_col, body, eos_col], dim=1) + else: + vocab_size = self.model.tokenizer.vocab_size + x = torch.randint(0, vocab_size, + (self.args.num_samples, self.args.gen_len), + device=self.device) + + time_steps = torch.linspace(0.0, 1.0, n_steps + 1, device=self.device) + + with torch.no_grad(): + for i in tqdm(range(n_steps), desc="Initial Flow Matching", leave=False): + t_curr = time_steps[i] + t_next = time_steps[i + 1] + t_tensor = torch.full((self.args.num_samples,), t_curr, device=self.device) + + logits = self.model(x, t_tensor) / temperature + for inv_tok in self.invalid_tokens: + if inv_tok < logits.shape[-1]: + logits[:, :, inv_tok] = -1000 + + pred_x1 = torch.argmax(logits, dim=-1) + if i == n_steps - 1: + x = pred_x1 + break + + noise_prob = 1.0 - t_next + mask = torch.rand(x.shape, device=self.device) < noise_prob + noise = torch.randint(0, vocab_size, x.shape, device=self.device) + x = torch.where(mask, noise, pred_x1) + + generated_sequences = self.tokenizer.batch_decode(x) + for seq in generated_sequences: + print(f" {seq}") + return x + + # ------------------------------------------------------------------ + # Scoring + # ------------------------------------------------------------------ + def _aa_to_smiles_batch(self, x_batch): + seqs = self.tokenizer.batch_decode(x_batch) + three_letter_seqs = [one_letter_to_three_letter(s) for s in seqs] + with ThreadPoolExecutor() as pool: + smiles_seqs = list(pool.map( + lambda s: aa_to_smiles(s, cyclic=self.args.cyclic), + three_letter_seqs + )) + return seqs, smiles_seqs + + def _get_scores(self, x_batch): + scores = [] + + if self.tokenizer_type == 'amino': + _, smiles_seqs = self._aa_to_smiles_batch(x_batch) + valid_mask = torch.tensor( + [1 if isinstance(s, str) else 0 for s in smiles_seqs], + device=self.device + ) + for i in torch.where((1 - valid_mask) == 1)[0].tolist(): + smiles_seqs[i] = 'C' + + smiles_batch = self.smiles_tokenizer( + smiles_seqs, return_tensors='pt', padding=True + ).to(self.device) + attention_mask = smiles_batch.get("attention_mask", None) + + for obj_func in self.objectives: + try: + score = obj_func( + smiles_batch["input_ids"], attention_mask=attention_mask + ).to(self.device) + except TypeError: + score = obj_func(smiles_batch["input_ids"]).to(self.device) + scores.append(score * valid_mask) + else: + for obj_func in self.objectives: + scores.append(obj_func(x_batch.to(self.device))) + + return torch.stack(scores, dim=0).to(self.device) + + def _compute_aggregated_score(self, scores, weights): + if self.args.score_aggregation == "weighted_min": + return torch.min(weights * scores, dim=0).values + return (weights * scores).sum(dim=0) + + # ------------------------------------------------------------------ + # Exponential tilting + # ------------------------------------------------------------------ + def _exponential_tilt_guidance(self, x, pos_probs, candidates, mut_idx, sample_idx, + beta, weights, current_score=None, print_tilted=False, + step=None): + num_candidates = len(candidates) + current_token = x[sample_idx, mut_idx].item() + + if num_candidates == 0: + return current_token, {'method': 'exp_tilt', 'fallback': True, 'accepted': False} + + if num_candidates > self.args.top_k: + candidates = candidates[:self.args.top_k] + num_candidates = self.args.top_k + + x_proposals = x[sample_idx].unsqueeze(0).repeat(num_candidates, 1) + x_proposals[:, mut_idx] = candidates + + proposal_scores = self._get_scores(x_proposals) + aggregated_scores = self._compute_aggregated_score(proposal_scores, weights) + validity_mask = (proposal_scores[0] > 0).float() + base_probs = pos_probs[sample_idx, candidates] + + log_base_probs = torch.log(base_probs + 1e-10) + log_exp_scores = beta * aggregated_scores + log_validity = torch.log(validity_mask + 1e-10) + log_tilted = log_base_probs + log_exp_scores + log_validity + + log_tilted_max = log_tilted.max() + fallback = False + if torch.isinf(log_tilted_max) or torch.isnan(log_tilted_max): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + else: + log_tilted_stable = log_tilted - log_tilted_max + tilted_probs = torch.exp(log_tilted_stable) + prob_sum = tilted_probs.sum() + if prob_sum < 1e-10 or torch.isnan(prob_sum) or torch.isinf(prob_sum): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + else: + final_probs = tilted_probs / prob_sum + + if torch.any(torch.isnan(final_probs)) or torch.any(torch.isinf(final_probs)): + fallback = True + final_probs = base_probs / (base_probs.sum() + 1e-9) + + final_probs = torch.clamp(final_probs, min=1e-10) + final_probs = final_probs / final_probs.sum() + + if print_tilted: + id_to_token = self.tokenizer.id_to_token + topk = min(self.args.print_logits_topk, num_candidates) + top_vals, top_local = torch.topk(final_probs, k=topk) + print(f"\n[tilted] step={step} sample={sample_idx} mut_idx={mut_idx} " + f"beta={beta:.2f} fallback={fallback}") + print(f" Top-{topk} candidates by TILTED prob " + f"(shows: base p, agg score S, β·S, valid, tilted p):") + for tv, li in zip(top_vals.tolist(), top_local.tolist()): + tid = candidates[li].item() + tok = id_to_token.get(int(tid), '?') + bp = base_probs[li].item() + sc = aggregated_scores[li].item() + bs = (beta * aggregated_scores[li]).item() + vd = validity_mask[li].item() + print(f" id={tid:>3} tok={tok!r:>6} base_p={bp:.3e} " + f"S={sc:+.3f} β·S={bs:+.3f} valid={vd:.0f} tilted_p={tv:.3e}") + d_local = [li for li in range(num_candidates) + if 33 <= candidates[li].item() <= 52 + and candidates[li].item() not in self.invalid_tokens] + if d_local: + print(" D-AA lowercase candidates (if any survived top-p):") + for li in d_local: + tid = candidates[li].item() + tok = id_to_token.get(int(tid), '?') + print(f" id={tid:>3} tok={tok!r:>4} base_p={base_probs[li]:.3e} " + f"S={aggregated_scores[li]:+.3f} " + f"valid={validity_mask[li]:.0f} tilted_p={final_probs[li]:.3e}") + else: + print(" D-AA lowercase candidates: NONE made it into the top-p pool") + + selected_idx = torch.multinomial(final_probs, 1).item() + selected_token = candidates[selected_idx].item() + selected_score = aggregated_scores[selected_idx].item() + + accepted = True + if self.args.use_acceptance and current_score is not None: + if selected_score < current_score: + selected_token = current_token + selected_score = current_score + accepted = False + + return selected_token, { + 'method': 'exp_tilt', 'beta': beta, 'num_candidates': num_candidates, + 'proposal_score': aggregated_scores[selected_idx].item(), + 'selected_score': selected_score, 'fallback': fallback, + 'validity_rate': validity_mask.mean().item(), 'accepted': accepted + } + + # ------------------------------------------------------------------ + # Logit inspection + # ------------------------------------------------------------------ + def _print_logit_distribution(self, raw_probs, masked_probs, mut_idx, step, sample_idx): + """Print the model's per-position probability distribution for inspection. + + raw_probs: [vocab_size] probabilities BEFORE masking current/invalid tokens + masked_probs: [vocab_size] probabilities AFTER masking + """ + topk = self.args.print_logits_topk + id_to_token = self.tokenizer.id_to_token + + # Token-category index ranges (AminoTokenizer): + # specials : 0-3 + # L-AA upper: 4-23 + # non-canon : 24-32 + # D-AA lower: 33-52 (38='g' is invalid) + cat_ranges = { + 'special' : list(range(0, 4)), + 'L-AA upper': list(range(4, 24)), + 'non-canon' : list(range(24, 33)), + 'D-AA lower': list(range(33, 53)), + } + + print(f"\n[logits] step={step} sample={sample_idx} mut_idx={mut_idx}") + + # Top-k from raw probs + top_vals, top_ids = torch.topk(raw_probs, k=min(topk, raw_probs.shape[0])) + print(f" Top-{topk} (raw model probs, pre-mask):") + for v, tid in zip(top_vals.tolist(), top_ids.tolist()): + tok = id_to_token.get(int(tid), '?') + print(f" id={tid:>3} tok={tok!r:>6} p={v:.4e}") + + # Per-category mass + print(" Probability mass by category (raw / masked):") + for name, ids in cat_ranges.items(): + ids_t = torch.tensor(ids, device=raw_probs.device) + raw_mass = raw_probs[ids_t].sum().item() + msk_mass = masked_probs[ids_t].sum().item() + print(f" {name:>10}: raw={raw_mass:.4e} masked={msk_mass:.4e}") + + # Specifically: per-token probability for D-AA lowercase (excluding 'g'=38) + d_ids = [i for i in cat_ranges['D-AA lower'] if i not in self.invalid_tokens] + d_ids_t = torch.tensor(d_ids, device=raw_probs.device) + d_vals_raw = raw_probs[d_ids_t] + d_vals_msk = masked_probs[d_ids_t] + print(" D-AA lowercase per-token probs (raw / masked):") + for tid, vr, vm in zip(d_ids, d_vals_raw.tolist(), d_vals_msk.tolist()): + tok = id_to_token.get(int(tid), '?') + print(f" id={tid:>3} tok={tok!r:>4} raw={vr:.4e} masked={vm:.4e}") + + # ------------------------------------------------------------------ + # Main loop + # ------------------------------------------------------------------ + def generate(self): + x = self.generate_x0() + + if self.args.weights is None: + weights = torch.tensor( + [1.0] + [1.0 / (self.num_objectives - 1)] * (self.num_objectives - 1), + device=self.device + ).view(-1, 1) + else: + weights = torch.tensor(self.args.weights, device=self.device).view(-1, 1) + if len(weights) != self.num_objectives: + raise ValueError("Number of weights must match number of objectives.") + + print(f"\nGuidance Method: Exponential Tilting") + print(f"Weights: {weights.squeeze().tolist()}") + print(f"β range: [{self.args.beta_min}, {self.args.beta_max}]") + print(f"Acceptance criterion: {'ENABLED' if self.args.use_acceptance else 'DISABLED'}") + + with torch.no_grad(): + for step in tqdm(range(self.args.optimization_steps), desc="Guided Generation"): + beta = self.args.beta_min + (self.args.beta_max - self.args.beta_min) * ( + step / max(self.args.optimization_steps - 1, 1) + ) + + if self.args.position_strategy == "random": + mut_idx = random.randint(1, self.args.gen_len - 2) + else: + mut_idx = (step % (self.args.gen_len - 2)) + 1 + + generation_step = step % self.args.optimization_steps + time_t = torch.full( + (self.args.num_samples,), + generation_step / self.args.optimization_steps, + device=self.device + ) + + logits = self.model(x, time_t) + probs = F.softmax(logits, dim=-1) + pos_probs = probs[:, mut_idx, :] + raw_pos_probs = pos_probs.clone() + + pos_probs[torch.arange(self.args.num_samples), x[:, mut_idx]] = 0 + for inv_tok in self.invalid_tokens: + pos_probs[:, inv_tok] = 0 + + should_print_logits = ( + self.args.print_logits + and (step % max(self.args.print_logits_every, 1) == 0) + ) + print_sample_idx = min(self.args.print_logits_sample, + self.args.num_samples - 1) + if should_print_logits: + self._print_logit_distribution( + raw_pos_probs[print_sample_idx], pos_probs[print_sample_idx], + mut_idx, step, print_sample_idx + ) + + sorted_probs, sorted_indices = torch.sort(pos_probs, descending=True) + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) + remove_mask = cumulative_probs > self.args.top_p + remove_mask[..., 1:] = remove_mask[..., :-1].clone() + remove_mask[..., 0] = 0 + + current_scores = self._get_scores(x) + current_aggregated = self._compute_aggregated_score(current_scores, weights) + + if step == 0: + print(f"\nInitial Scores: {current_scores.cpu().numpy()}") + + improved = False + for i in range(self.args.num_samples): + sample_mask = remove_mask[i] + candidates = sorted_indices[i, ~sample_mask] + + valid_candidate_mask = ~torch.isin( + candidates, torch.tensor(self.invalid_tokens, device=self.device) + ) + candidates = candidates[valid_candidate_mask] + + if len(candidates) == 0: + continue + + prev_token = x[i, mut_idx].item() + prev_sample_scores = current_scores[:, i].clone() + prev_weighted_sum = (weights.squeeze(1) * prev_sample_scores).sum() + + selected_token, info = self._exponential_tilt_guidance( + x, pos_probs, candidates, mut_idx, i, beta, weights, + current_score=current_aggregated[i].item(), + print_tilted=(should_print_logits and i == print_sample_idx), + step=step, + ) + + if selected_token == prev_token: + continue + + x[i, mut_idx] = selected_token + + new_sample_scores = self._get_scores(x[i].unsqueeze(0))[:, 0] + new_weighted_sum = (weights.squeeze(1) * new_sample_scores).sum() + + if new_weighted_sum >= prev_weighted_sum: + improved = True + print(f"Previous Weighted Sum: {prev_weighted_sum}") + print(f"Previous Scores: {prev_sample_scores}") + print(f"New Weighted Sum: {new_weighted_sum}") + print(f"New Scores: {new_sample_scores}") + current_scores[:, i] = new_sample_scores + current_aggregated[i] = self._compute_aggregated_score( + new_sample_scores.unsqueeze(1), weights + )[0] + else: + x[i, mut_idx] = prev_token + + if improved: + print(self.tokenizer.batch_decode(x)) + if self.tracker: + self.tracker.log_step( + step, current_scores[0], + guidance_info={'method': 'exp_tilt', 'beta': beta} + ) + + return x + + +# ============================================================================= +# Main +# ============================================================================= + +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + print(f"Tokenizer type: {args.tokenizer_type}") + + 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) + print(f"Random seed: {args.seed}") + + if args.tokenizer_type == 'amino': + print("Using Amino Acid tokenizer") + tokenizer = AminoTokenizer() + smiles_tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + else: + print("Using SMILES tokenizer") + tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + smiles_tokenizer = tokenizer + + analyzer_model = Analyzer(smiles_tokenizer) + affinity_model = BindingAffinity(args.target, device) + OBJECTIVE_FUNCTIONS = [analyzer_model, affinity_model] + + tracker = GenerationTracker(output_dir=args.output_dir) + + print(f"Loading model from: {args.checkpoint}") + checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False) + + if args.tokenizer_type == 'amino': + raw_model_args = checkpoint['args'] + if isinstance(raw_model_args, argparse.Namespace): + raw_model_args = vars(raw_model_args) + model_args = edict(raw_model_args) + model = MDLM( + vocab_size=model_args.vocab_size, + seq_len=model_args.seq_len, + model_dim=model_args.model_dim, + n_heads=model_args.n_heads, + n_layers=model_args.n_layers, + ).to(device) + model.load_state_dict(checkpoint['model_state_dict']) + print(f"Amino model loaded. Vocab size: {model_args.vocab_size}") + else: + model_hparams = checkpoint["hyper_parameters"]["args"] + model = MDLMLightningModule.load_from_checkpoint( + args.checkpoint, args=model_hparams, tokenizer=tokenizer, + map_location=device, strict=False + ) + model.to(device) + model.eval() + print("Model loaded successfully.") + + generator = CupOfMoCAStagedUAA( + model, device, OBJECTIVE_FUNCTIONS, args, + tokenizer=tokenizer, tokenizer_type=args.tokenizer_type, tracker=tracker + ) + + all_sequences = [] + all_smiles = [] + all_scores = [] + + for batch_idx in range(args.num_batches): + print(f"\n{'=' * 60}") + print(f"BATCH {batch_idx + 1}/{args.num_batches}") + print(f"{'=' * 60}") + + generated_tokens = generator.generate() + final_scores = generator._get_scores(generated_tokens.detach()).detach().cpu().numpy() + sequence_str = tokenizer.batch_decode(generated_tokens) + + print("\n--- Batch Results ---") + for i, seq in enumerate(sequence_str): + print(f" {i}: {seq}") + print(f"Scores: {final_scores}") + + all_sequences.extend(sequence_str) + all_scores.append(final_scores) + + if args.tokenizer_type == 'amino': + smiles_list = [] + for seq in sequence_str: + three_letter = one_letter_to_three_letter(seq) + smiles = aa_to_smiles(three_letter, cyclic=args.cyclic) + smiles_list.append(smiles if isinstance(smiles, str) else 'INVALID_SMILES') + all_smiles.extend(smiles_list) + tracker.log_final(sequence_str, final_scores, smiles_list=smiles_list) + else: + tracker.log_final(sequence_str, final_scores) + + tracker.print_final_summary() + tracker.save(filename_prefix='cupofmoca_staged_uaa') + + with open(args.output_file, 'w') as f: + for i, seq in enumerate(all_sequences): + if args.tokenizer_type == 'amino': + smiles = all_smiles[i] if i < len(all_smiles) else '' + f.write(f"{seq}\t{smiles}\n") + else: + f.write(seq + '\n') + print(f"\nSequences saved to: {args.output_file}") + print("\nGeneration complete.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="CupOfMoCA-style Staged Generation with UAA Foundation Model" + ) + + parser.add_argument("--checkpoint", type=str, required=True) + parser.add_argument("--target", type=str, required=True) + + parser.add_argument("--num_samples", type=int, default=10) + parser.add_argument("--num_batches", type=int, default=10) + parser.add_argument("--gen_len", type=int, default=50) + parser.add_argument("--optimization_steps", type=int, default=128) + + parser.add_argument("--beta_min", type=float, default=1.0) + parser.add_argument("--beta_max", type=float, default=20.0) + parser.add_argument("--use_acceptance", action="store_true") + parser.add_argument("--top_k", type=int, default=200) + parser.add_argument("--top_p", type=float, default=0.9) + parser.add_argument("--score_aggregation", type=str, default="weighted_sum", + choices=["weighted_sum", "weighted_min"]) + + parser.add_argument("--weights", type=float, nargs='+', default=None) + + parser.add_argument("--position_strategy", type=str, default="random", + choices=["random", "sequential"]) + + parser.add_argument("--tokenizer_type", type=str, default="amino", + choices=["smiles", "amino"]) + parser.add_argument("--cyclic", action="store_true", + help="Generate cyclic peptides (only for amino tokenizer).") + parser.add_argument("--smiles_vocab_path", type=str, + default="./smiles/smiles_tokenizer/new_vocab.txt") + parser.add_argument("--smiles_splits_path", type=str, + default="./smiles/smiles_tokenizer/new_splits.txt") + + parser.add_argument("--output_file", type=str, default="./smiles.txt") + parser.add_argument("--output_dir", type=str, default="./generation_logs") + + parser.add_argument("--seed", type=int, default=None) + + parser.add_argument("--print_logits", action="store_true", + help="Print per-step probability distribution at the mutation position.") + parser.add_argument("--print_logits_every", type=int, default=1, + help="Print logits every N optimization steps (requires --print_logits).") + parser.add_argument("--print_logits_topk", type=int, default=20, + help="Number of top tokens to show per logit printout.") + parser.add_argument("--print_logits_sample", type=int, default=0, + help="Which sample index to print logits for (default: 0).") + + args = parser.parse_args() + main(args) diff --git a/smiles/generation/guided.py b/smiles/generation/guided.py new file mode 100644 index 0000000000000000000000000000000000000000..a663a96e33b7920f4a14b47593e5e5fdaf7a0699 --- /dev/null +++ b/smiles/generation/guided.py @@ -0,0 +1,651 @@ +import argparse +import re +import random +from collections import Counter +import csv +import json +import os +from datetime import datetime +import torch +import torch.nn as nn +import torch.nn.functional as F +from tqdm import tqdm +from smiles.models.train import MDLMLightningModule +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +import numpy as np + +from smiles.evaluation.peptide_analyzer import PeptideAnalyzer +from smiles.evaluation.pdc_analyzer import PDCAnalyzer +from smiles.scoring.classifiers import Conjugation, BindingAffinity + + +class GenerationTracker: + """ + Tracks detailed validity information during generation + """ + 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, scores, detailed_results=None, improved_indices=None): + """Log information for a generation step""" + step_info = { + 'step': step, + 'scores': scores.cpu().numpy().tolist() if isinstance(scores, torch.Tensor) else scores, + 'num_valid': int(sum(1 for s in scores if s > 0)) if hasattr(scores, '__iter__') else 0, + 'improved_indices': improved_indices or [] + } + + if detailed_results: + step_info['detailed'] = [] + for i, result in enumerate(detailed_results): + step_info['detailed'].append({ + 'sample_idx': i, + 'is_valid_peptide': result.get('is_valid_peptide', False), + 'has_conjugation_site': result.get('has_conjugation_site', False), + 'conjugation_success': result.get('conjugation_success', False), + 'pdc_valid': result.get('pdc_valid', False), + 'num_conjugates': len(result.get('conjugates', [])), + 'error': result.get('error') + }) + + self.step_logs.append(step_info) + + def log_final(self, sequences, detailed_results_with_props): + """Log final results with properties""" + for i, (seq, result) in enumerate(zip(sequences, detailed_results_with_props)): + final_info = { + 'sample_idx': i, + 'peptide_smiles': seq, + 'is_valid_peptide': result.get('is_valid_peptide', False), + 'has_conjugation_site': result.get('has_conjugation_site', False), + 'num_conjugates': len(result.get('conjugates', [])), + 'num_valid_pdcs': len(result.get('valid_pdcs', [])), + 'best_pdc': None, + 'properties': result.get('properties', {}) + } + + if result.get('best_pdc'): + final_info['best_pdc'] = { + 'smiles': result['best_pdc'].get('smiles', ''), + 'checks': result['best_pdc'].get('checks', {}) + } + + self.final_results.append(final_info) + + def save(self, filename_prefix='generation'): + """Save all logs to files""" + # Save step logs + step_log_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_steps.json") + with open(step_log_path, 'w') as f: + json.dump(self.step_logs, f, indent=2, default=str) + + # Save final results + 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) + + # Save summary CSV + 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', 'peptide_smiles', 'is_valid_peptide', 'has_conjugation_site', + 'num_conjugates', 'num_valid_pdcs', 'best_pdc_smiles', + 'mf', 'polararea', 'xlogp', 'heavycnt', 'rotbonds' + ]) + for result in self.final_results: + props = result.get('properties', {}) + writer.writerow([ + result['sample_idx'], + result['peptide_smiles'], + result['is_valid_peptide'], + result['has_conjugation_site'], + result['num_conjugates'], + result['num_valid_pdcs'], + result['best_pdc']['smiles'] if result.get('best_pdc') else '', + props.get('mf', ''), + props.get('polararea', ''), + props.get('xlogp', ''), + props.get('heavycnt', ''), + props.get('rotbonds', '') + ]) + + print(f"\n=== Generation logs saved ===") + print(f" Step logs: {step_log_path}") + print(f" Final results: {final_path}") + print(f" Summary CSV: {csv_path}") + + return step_log_path, final_path, csv_path + + def print_step_summary(self, step, scores, detailed_results=None): + """Print a summary for the current step""" + if isinstance(scores, torch.Tensor): + scores_list = scores.cpu().numpy().tolist() + else: + scores_list = list(scores) + + num_valid = sum(1 for s in scores_list if s > 0) + + print(f"\n--- Step {step} Summary ---") + print(f" Valid: {num_valid}/{len(scores_list)}") + + if detailed_results: + valid_peptide = sum(1 for r in detailed_results if r.get('is_valid_peptide')) + has_site = sum(1 for r in detailed_results if r.get('has_conjugation_site')) + conj_success = sum(1 for r in detailed_results if r.get('conjugation_success')) + pdc_valid = sum(1 for r in detailed_results if r.get('pdc_valid')) + + print(f" Breakdown:") + print(f" Valid peptides: {valid_peptide}") + print(f" Has conjugation site: {has_site}") + print(f" Conjugation success: {conj_success}") + print(f" PDC valid: {pdc_valid}") + + def print_final_summary(self): + """Print final summary""" + print("\n" + "=" * 60) + print("FINAL GENERATION SUMMARY") + print("=" * 60) + + total = len(self.final_results) + valid_pdcs = sum(1 for r in self.final_results if r.get('num_valid_pdcs', 0) > 0) + + print(f"\nTotal sequences: {total}") + print(f"With valid PDCs: {valid_pdcs}") + + print("\nDetailed results:") + for result in self.final_results: + status = "" if result.get('num_valid_pdcs', 0) > 0 else "" + print(f"\n [{status}] Sample {result['sample_idx']}:") + print(f" Peptide: {result['peptide_smiles'][:60]}...") + print(f" Valid peptide: {result['is_valid_peptide']}") + print(f" Has conjugation site: {result['has_conjugation_site']}") + print(f" Conjugates: {result['num_conjugates']}") + print(f" Valid PDCs: {result['num_valid_pdcs']}") + + if result.get('properties'): + props = result['properties'] + print(f" Properties:") + for k, v in props.items(): + print(f" {k}: {v}") + + +def peptide_bond_mask(smiles_list): + # Initialize the batch mask + batch_size = len(smiles_list) + max_seq_length = 1035 #max(len(smiles) for smiles in smiles_list) # Find the longest SMILES + mask = torch.zeros((batch_size, max_seq_length), dtype=torch.int) # Mask filled with zeros + + bond_patterns = [ + (r'OC\(=O\)', 'ester'), + (r'N\(C\)C\(=O\)', 'n_methyl'), + (r'N[12]C\(=O\)', 'peptide'), # Pro peptide bonds + (r'NC\(=O\)', 'peptide'), # Regular peptide bonds + (r'C\(=O\)N\(C\)', 'n_methyl'), + (r'C\(=O\)N[12]?', 'peptide') + ] + + for batch_idx, smiles in enumerate(smiles_list): + positions = [] + used = set() + + # Identify bonds + for pattern, bond_type in bond_patterns: + for match in re.finditer(pattern, smiles): + if not any(p in range(match.start(), match.end()) for p in used): + positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': bond_type, + 'pattern': match.group() + }) + used.update(range(match.start(), match.end())) + + # Update the mask for the current SMILES + for pos in positions: + mask[batch_idx, pos['start']:pos['end']] = 1 + + return mask + +def peptide_token_mask(smiles_list, token_lists): + # Initialize the batch mask + batch_size = len(smiles_list) + token_seq_length = max(len(tokens) for tokens in token_lists) # Find the longest tokenized sequence + tokenized_masks = torch.zeros((batch_size, token_seq_length), dtype=torch.int) # Mask filled with zeros + atomwise_masks = peptide_bond_mask(smiles_list) + + + for batch_idx, atomwise_mask in enumerate(atomwise_masks): + token_seq = token_lists[batch_idx] + atom_idx = 0 + + for token_idx, token in enumerate(token_seq): + if token_idx != 0 and token_idx != len(token_seq) - 1: + if torch.sum(atomwise_mask[atom_idx:atom_idx+len(token)]) >= 1: + tokenized_masks[batch_idx][token_idx] = 1 + atom_idx += len(token) + + return tokenized_masks + +class MOGGenerator: + def __init__(self, model, device, objectives, args, tracker=None): + self.model = model + self.device = device + self.objectives = objectives + self.args = args + self.num_objectives = len(objectives) + self.peptide_analyzer = PeptideAnalyzer() + self.pdc_analyzer = PDCAnalyzer(device) + self.invalid = [0, 1, 2, 3, 4, 585, 586] + self.tracker = tracker + + # Find the Conjugation objective for detailed tracking + self.conjugation_obj = None + for obj in objectives: + if isinstance(obj, Conjugation): + self.conjugation_obj = obj + break + + def _generate_single_x0(self, n_steps=16, temperature=1.0): + """Generate a single batch of x0 sequences.""" + self.model.eval() + + # 1. Start with a tensor of random tokens (pure noise at t=0) + x = torch.randint( + 0, + self.model.tokenizer.vocab_size, + (self.args.num_samples, self.args.gen_len), + device=self.device + ) + + # 2. Define the time schedule for the forward process (0.0 to 1.0) + time_steps = torch.linspace(0.0, 1.0, n_steps + 1, device=self.device) + + # 3. Iteratively follow the flow from noise to data + with torch.no_grad(): + for i in tqdm(range(n_steps), desc="Flow Matching Steps", leave=False): + t_curr = time_steps[i] + t_next = time_steps[i+1] + + # Prepare the current timestep tensor for the model + t_tensor = torch.full((self.args.num_samples,), t_curr, device=self.device) + + # Get the model's prediction for the final clean sequence (at t=1) + logits = self.model(x, t_tensor) + logits = logits / temperature + logits[:, :, 586] = -1000 + + pred_x1 = torch.argmax(logits, dim=-1) + + # On the last step, the result is the final prediction + if i == n_steps - 1: + x = pred_x1 + break + + # --- Construct the next state x_{t_next} --- + # The probability of a token being noise at time t_next is (1 - t_next). + noise_prob = 1.0 - t_next + mask = torch.rand(x.shape, device=self.device) < noise_prob + + # Generate new random tokens for the noise positions + noise = torch.randint( + 0, + self.model.tokenizer.vocab_size, + x.shape, + device=self.device + ) + + # Combine the final prediction with noise to form the next intermediate state + x = torch.where(mask, noise, pred_x1) + + return x + + def _check_conjugation_validity(self, x): + """ + Check if sequences can be conjugated. + Returns a boolean tensor indicating validity for each sample. + """ + if self.conjugation_obj is None: + # Fall back to peptide validity check + sequences = self.model.tokenizer.batch_decode(x) + return torch.tensor([self.peptide_analyzer.is_peptide(seq) for seq in sequences], device=self.device) + + # Use conjugation objective - score > 0 means valid + scores = self.conjugation_obj(x) + return scores > 0 + + def generate_x0(self, n_steps=16, temperature=1.0, require_valid=False, max_retries=10): + """ + Generate initial x0 sequences. + + Args: + n_steps: Number of flow matching steps + temperature: Sampling temperature + require_valid: If True, regenerate invalid samples until all are valid conjugatable peptides + max_retries: Maximum number of regeneration attempts per sample + """ + print("Starting initial SMILES generation...") + + x = self._generate_single_x0(n_steps, temperature) + + if require_valid: + print("Checking conjugation validity and regenerating invalid samples...") + + for retry in range(max_retries): + validity = self._check_conjugation_validity(x) + 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 are valid conjugatable peptides!") + break + + print(f"Retry {retry + 1}/{max_retries}: {num_valid}/{self.args.num_samples} valid, regenerating {num_invalid} invalid samples...") + + # Get indices of invalid samples + invalid_indices = torch.where(~validity)[0] + + # Generate new candidates for invalid samples + # Temporarily adjust num_samples to generate replacements + original_num_samples = self.args.num_samples + self.args.num_samples = num_invalid + + new_x = self._generate_single_x0(n_steps, temperature) + + self.args.num_samples = original_num_samples + + # Replace invalid samples with new ones + x[invalid_indices] = new_x + + # Final validity check + final_validity = self._check_conjugation_validity(x) + num_final_valid = final_validity.sum().item() + if num_final_valid < self.args.num_samples: + print(f"Warning: After {max_retries} retries, only {num_final_valid}/{self.args.num_samples} samples are valid.") + else: + print(f"Success: All {self.args.num_samples} samples are valid conjugatable peptides.") + + # 4. Decode the final token IDs into SMILES strings + generated_sequences = self.model.tokenizer.batch_decode(x) + + # 5. Analyze the validity of the generated sequences + validities = [] + for seq in generated_sequences: + validities.append(self.peptide_analyzer.is_peptide(seq)) + print(seq) + print(f"Initial Sequence Validity: {validities}") + return x + + def _get_scores(self, x_batch): + """Calculates the normalized scores for a batch of sequences.""" + scores = [] + for obj_func in self.objectives: + scores.append(obj_func(x_batch.to(self.device))) + return torch.stack(scores, dim=0).to(self.device) + + def _barker_g(self, u): + """Barker balancing function.""" + return u / (1 + u) + + def validity(self, x): + sampled_sequences = self.model.tokenizer.batch_decode(x) + + return [1.0 if self.peptide_analyzer.is_peptide(seq) else 0.0 for seq in sampled_sequences] + + def generate(self): + """Main generation loop with tracking.""" + shape = (self.args.num_samples, self.args.gen_len) + x = self.generate_x0( + require_valid=self.args.require_valid_x0, + max_retries=self.args.max_x0_retries + ) + print(x) + + if args.weights is None: + # The first weight is for conjugation (peptide + PDC validity) + # We need to ensure the SMILES sequences are valid peptides with valid PDCs + weights = torch.tensor([1] + [1/(self.num_objectives-1)] * (self.num_objectives-1), device=self.device).view(-1,1) + else: + weights = torch.tensor(self.args.weights, device=self.device).view(-1, 1) + if len(weights) != self.num_objectives: + raise ValueError("Number of weights must match number of objectives.") + print(f"Weights: {weights}") + + with torch.no_grad(): + for t in tqdm(range(self.args.optimization_steps), desc="MOG Generation"): + improved = False + improved_indices = [] + + # Anneal guidance strength + eta_t = self.args.eta_min + (self.args.eta_max - self.args.eta_min) * (t / (self.args.optimization_steps - 1)) + # Choose a random position to mutate + mut_idx = random.randint(1, self.args.gen_len-2) + + # Determine the generation timestep + # We cycle through the timesteps to ensure all are visited + generation_step = t % self.args.optimization_steps + time_t = torch.full((self.args.num_samples,), (generation_step / self.args.optimization_steps), device=self.device) + + # Get proposal distribution from ReDi model for the chosen position + logits = self.model(x, time_t) + probs = F.softmax(logits, dim=-1) + pos_probs = probs[:, mut_idx, :] + pos_probs[:, x[:, mut_idx]] = 0 # We don't evalute the same token + pos_probs[:, self.invalid] = 0 + + # Prune candidate vocabulary using top-p sampling + sorted_probs, sorted_indices = torch.sort(pos_probs, descending=True) + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) + remove_mask = cumulative_probs > self.args.top_p + remove_mask[..., 1:] = remove_mask[..., :-1].clone() + remove_mask[..., 0] = 0 + + # Get the set of candidate tokens for each sample in the batch + candidate_tokens_list = [] + for i in range(self.args.num_samples): + sample_mask = remove_mask[i] + candidates = sorted_indices[i, ~sample_mask] + candidate_tokens_list.append(candidates) + + # Get current scores + current_scores = self._get_scores(x) + w_current = torch.exp(eta_t * torch.min(weights * current_scores, dim=0).values) + + if t == 0: + print(f"Initial Scores: {current_scores}") + # Log initial step with detailed results + if self.tracker and self.conjugation_obj: + detailed = self.conjugation_obj.get_last_detailed_results() + self.tracker.log_step(t, current_scores[0], detailed) + self.tracker.print_step_summary(t, current_scores[0], detailed) + + # Evaluate all candidate tokens for each sample + final_proposal_tokens = [] + for i in range(self.args.num_samples): + candidates = candidate_tokens_list[i] + candidates = torch.tensor([token for token in candidates if token not in self.invalid], device=candidates.device) + if len(candidates) >= 200: + candidates = candidates[:200] + num_candidates = len(candidates) + + # Create a batch of proposed sequences for the current sample + x_prop_batch = x[i].repeat(num_candidates, 1) + x_prop_batch[:, mut_idx] = candidates + + # Evaluate all proposals + proposal_scores = self._get_scores(x_prop_batch) + proposal_s_omega = torch.min(weights * proposal_scores, dim=0).values + w_proposal = torch.exp(eta_t * proposal_s_omega) + + # Get ReDi probabilities for the candidates + redi_probs = pos_probs[i, candidates] + + # Calculate unnormalized guided probabilities + tilde_q = redi_probs * self._barker_g(w_proposal / w_current[i]) + + # Normalize and sample the final token + final_probs = tilde_q / (torch.sum(tilde_q) + 1e-9) + + index = torch.multinomial(final_probs, 1).item() + + if (random.uniform(0,1) > 1 and proposal_scores[:,index][0] == 1) or torch.sum(weights.squeeze(1) * proposal_scores[:, index]) >= torch.sum(weights.squeeze(1) * current_scores[:,i]): + final_token = candidates[index] + print(f"Previous Weighted Sum: {torch.sum(weights.squeeze(1) * current_scores[:,i])}") + print(f"Previous Scores: {current_scores[:,i]}") + + print(f"New Weighted Sum: {torch.sum(weights.squeeze(1) * proposal_scores[:, index])}") + print(f"New Scores: {proposal_scores[:,index]}") + improved = True + improved_indices.append(i) + + else: + final_token = x[i][mut_idx] + + final_proposal_tokens.append(final_token) + + # Update the sequences with the chosen tokens + x[torch.arange(self.args.num_samples), mut_idx] = torch.stack(final_proposal_tokens) + if improved: + print(self.model.tokenizer.batch_decode(x)) + + # Log step with tracking (every 4 steps to reduce overhead) + if self.tracker and self.conjugation_obj and (t % 4 == 0 or t == self.args.optimization_steps - 1): + # Re-evaluate for detailed logging + step_scores = self._get_scores(x) + detailed = self.conjugation_obj.get_last_detailed_results() + self.tracker.log_step(t, step_scores[0], detailed, improved_indices) + + # Print summary for final step + if t == self.args.optimization_steps - 1: + self.tracker.print_step_summary(t, step_scores[0], detailed) + + return x + + +# --- Main Execution --- +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + + target = args.target + tokenizer = SMILES_SPE_Tokenizer('./smiles/smiles_tokenizer/new_vocab.txt', './smiles/smiles_tokenizer/new_splits.txt') + + # Initialize Conjugation model with specified builder type + conjugation_model = Conjugation(tokenizer, device=device, builder_type=args.builder_type) + # hemolysis_model = Hemolysis(device) + # nonfouling_model = Nonfouling(device) + # solubility_model = Solubility(device) + # permeability_model = Permeability(device) + affinity_model = BindingAffinity(target, device) + + # List of all objective functions + OBJECTIVE_FUNCTIONS = [conjugation_model, affinity_model] + + # Initialize tracker for logging + tracker = GenerationTracker(output_dir=args.output_dir) + + # --- Set random seeds for reproducibility --- + if args.seed is not None: + import random + import numpy as np + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + print(f"Random seed set to: {args.seed}") + + # --- Load Model --- + print(f"Loading model from checkpoint: {args.checkpoint}") + checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False) + model_hparams = checkpoint["hyper_parameters"]["args"] + + model = MDLMLightningModule.load_from_checkpoint( + args.checkpoint, + args=model_hparams, + tokenizer=tokenizer, + map_location=device, + strict=False + ) + model.to(device) + print("Model loaded successfully.") + + mog_generator = MOGGenerator(model, device, OBJECTIVE_FUNCTIONS, args, tracker=tracker) + + all_sequences = [] + all_final_results = [] + + for batch_idx in range(args.num_batches): + print(f"\n{'='*60}") + print(f"BATCH {batch_idx + 1}/{args.num_batches}") + print(f"{'='*60}") + + generated_tokens = mog_generator.generate() + final_scores = mog_generator._get_scores(generated_tokens.detach()).detach().cpu().numpy() + sequence_str = tokenizer.batch_decode(generated_tokens) + + print("\n--- Batch Results ---") + print(f"Sequences: {sequence_str}") + print(f"Scores: {final_scores}") + + all_sequences.extend(sequence_str) + + # Get detailed results with properties for final sequences + detailed_with_props = conjugation_model.get_detailed_scores_with_properties(generated_tokens) + tracker.log_final(sequence_str, detailed_with_props) + all_final_results.extend(detailed_with_props) + + # Print and save final summary + tracker.print_final_summary() + tracker.save(filename_prefix='pdc_generation') + + # Also save sequences to simple text file + with open(args.output_file, 'w') as f: + for seq in all_sequences: + f.write(seq + '\n') + print(f"\nSequences saved to: {args.output_file}") + + print("\nGeneration complete.") + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Multi-Objective Generation with LBP-MOG-ReDi (Single Mutation).") + + parser.add_argument("--checkpoint", type=str, required=True, help="Path to the trained ReDi model checkpoint.") + parser.add_argument("--num_samples", type=int, default=10, help="Number of samples to generate.") + parser.add_argument("--num_batches", type=int, default=10, help="Number of batches to generate.") + parser.add_argument("--output_file", type=str, default="./smiles.txt", help="File to save the generated sequences.") + parser.add_argument("--output_dir", type=str, default="./generation_logs", help="Directory to save detailed generation logs.") + parser.add_argument("--gen_len", type=int, default=50, help="Length of the sequences to generate.") + parser.add_argument("--optimization_steps", type=int, default=16, help="Number of optimization steps per generation.") + parser.add_argument("--weights", type=float, nargs='+', required=False, help="Weights for the objectives (e.g., 0.5 0.5).") + parser.add_argument("--eta_min", type=float, default=1.0, help="Minimum guidance strength for annealing.") + parser.add_argument("--eta_max", type=float, default=20.0, help="Maximum guidance strength for annealing.") + parser.add_argument("--top_p", type=float, default=0.9, help="Top-p for pruning candidate tokens.") + + # PDC builder + parser.add_argument("--builder_type", type=str, default="AoaDau", + choices=["AoaDau", "ValAlaDau", "ValCitDau", "GlutarylDau", "Dau", + "SSCpt", "VcMMAE", "DSPACpt", "MalHexCpt", "DSECCpt", "Cpt", + "DSPASN38", "DSEBSN38", "DSEPSN38", "SN38"], + help="PDC builder type for conjugation. " + "'Dau' samples linker by GNRHR+Dau frequency. " + "'Cpt' samples linker by ERBB2+Cpt frequency. " + "'SN38' samples linker by NRP1+SN38 frequency.") + + parser.add_argument("--seed", type=int, default=None, help="Random seed for reproducibility.") + parser.add_argument("--require_valid_x0", action="store_true", + help="If set, regenerate x0 samples until they are valid conjugatable peptides.") + parser.add_argument("--max_x0_retries", type=int, default=10, + help="Maximum retries when require_valid_x0 is enabled.") + + parser.add_argument("--target", type=str, required=True, help="Target protein sequence for binding affinity.") + args = parser.parse_args() + main(args) diff --git a/smiles/generation/guided_aa.py b/smiles/generation/guided_aa.py new file mode 100644 index 0000000000000000000000000000000000000000..ed0406430565db3327e3b675c126a7e1ba0a1476 --- /dev/null +++ b/smiles/generation/guided_aa.py @@ -0,0 +1,872 @@ +"""Natural-AA variant of guided_uaa.py.""" +import argparse +import re +import random +import sys +from collections import Counter +from concurrent.futures import ThreadPoolExecutor +import csv +import json +import os +from datetime import datetime +import torch +import torch.nn as nn +import torch.nn.functional as F +from tqdm import tqdm +from smiles.models.train import MDLMLightningModule +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +import numpy as np +from easydict import EasyDict as edict + +from smiles.evaluation.peptide_analyzer import PeptideAnalyzer +from smiles.evaluation.pdc_analyzer import PDCAnalyzer +from smiles.scoring.classifiers import Conjugation, BindingAffinity + +from smiles.models.aa_helpers import ( + ESM2AminoTokenizer as _AAEsmTokenizer, + MDLM_PepReDi as _AAMDLM, + make_affinity, + DEFAULT_AA_AFFINITY_CKPT, + INVALID_AA_TOKEN_IDS as _AA_INVALID_IDS, +) + +from third_party.SMILESToPeptide.peptide_to_smiles import aa_to_smiles +from third_party.SMILESToPeptide.aminoacid_selective import one_letter_to_three_letter + + +MDLM = _AAMDLM +AminoTokenizer = _AAEsmTokenizer + + +class GenerationTracker: + """ + Tracks detailed validity information during generation + """ + 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, scores, detailed_results=None, improved_indices=None): + """Log information for a generation step""" + step_info = { + 'step': step, + 'scores': scores.cpu().numpy().tolist() if isinstance(scores, torch.Tensor) else scores, + 'num_valid': int(sum(1 for s in scores if s > 0)) if hasattr(scores, '__iter__') else 0, + 'improved_indices': improved_indices or [] + } + + if detailed_results: + step_info['detailed'] = [] + for i, result in enumerate(detailed_results): + step_info['detailed'].append({ + 'sample_idx': i, + 'is_valid_peptide': result.get('is_valid_peptide', False), + 'has_conjugation_site': result.get('has_conjugation_site', False), + 'conjugation_success': result.get('conjugation_success', False), + 'pdc_valid': result.get('pdc_valid', False), + 'num_conjugates': len(result.get('conjugates', [])), + 'error': result.get('error') + }) + + self.step_logs.append(step_info) + + def log_final(self, sequences, detailed_results_with_props): + """Log final results with properties""" + for i, (seq, result) in enumerate(zip(sequences, detailed_results_with_props)): + final_info = { + 'sample_idx': i, + 'peptide_smiles': seq, + 'is_valid_peptide': result.get('is_valid_peptide', False), + 'has_conjugation_site': result.get('has_conjugation_site', False), + 'num_conjugates': len(result.get('conjugates', [])), + 'num_valid_pdcs': len(result.get('valid_pdcs', [])), + 'best_pdc': None, + 'properties': result.get('properties', {}) + } + + if result.get('best_pdc'): + final_info['best_pdc'] = { + 'smiles': result['best_pdc'].get('smiles', ''), + 'checks': result['best_pdc'].get('checks', {}) + } + + self.final_results.append(final_info) + + def save(self, filename_prefix='generation'): + """Save all logs to files""" + # Save step logs + step_log_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_steps.json") + with open(step_log_path, 'w') as f: + json.dump(self.step_logs, f, indent=2, default=str) + + # Save final results + 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) + + # Save summary CSV + 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', 'peptide_smiles', 'is_valid_peptide', 'has_conjugation_site', + 'num_conjugates', 'num_valid_pdcs', 'best_pdc_smiles', + 'mf', 'polararea', 'xlogp', 'heavycnt', 'rotbonds' + ]) + for result in self.final_results: + props = result.get('properties', {}) + writer.writerow([ + result['sample_idx'], + result['peptide_smiles'], + result['is_valid_peptide'], + result['has_conjugation_site'], + result['num_conjugates'], + result['num_valid_pdcs'], + result['best_pdc']['smiles'] if result.get('best_pdc') else '', + props.get('mf', ''), + props.get('polararea', ''), + props.get('xlogp', ''), + props.get('heavycnt', ''), + props.get('rotbonds', '') + ]) + + print(f"\n=== Generation logs saved ===") + print(f" Step logs: {step_log_path}") + print(f" Final results: {final_path}") + print(f" Summary CSV: {csv_path}") + + return step_log_path, final_path, csv_path + + def print_step_summary(self, step, scores, detailed_results=None): + """Print a summary for the current step""" + if isinstance(scores, torch.Tensor): + scores_list = scores.cpu().numpy().tolist() + else: + scores_list = list(scores) + + num_valid = sum(1 for s in scores_list if s > 0) + + print(f"\n--- Step {step} Summary ---") + print(f" Valid: {num_valid}/{len(scores_list)}") + + if detailed_results: + valid_peptide = sum(1 for r in detailed_results if r.get('is_valid_peptide')) + has_site = sum(1 for r in detailed_results if r.get('has_conjugation_site')) + conj_success = sum(1 for r in detailed_results if r.get('conjugation_success')) + pdc_valid = sum(1 for r in detailed_results if r.get('pdc_valid')) + + print(f" Breakdown:") + print(f" Valid peptides: {valid_peptide}") + print(f" Has conjugation site: {has_site}") + print(f" Conjugation success: {conj_success}") + print(f" PDC valid: {pdc_valid}") + + def print_final_summary(self): + """Print final summary""" + print("\n" + "=" * 60) + print("FINAL GENERATION SUMMARY") + print("=" * 60) + + total = len(self.final_results) + valid_pdcs = sum(1 for r in self.final_results if r.get('num_valid_pdcs', 0) > 0) + + print(f"\nTotal sequences: {total}") + print(f"With valid PDCs: {valid_pdcs}") + + print("\nDetailed results:") + for result in self.final_results: + status = "" if result.get('num_valid_pdcs', 0) > 0 else "" + print(f"\n [{status}] Sample {result['sample_idx']}:") + print(f" Peptide: {result['peptide_smiles'][:60]}...") + print(f" Valid peptide: {result['is_valid_peptide']}") + print(f" Has conjugation site: {result['has_conjugation_site']}") + print(f" Conjugates: {result['num_conjugates']}") + print(f" Valid PDCs: {result['num_valid_pdcs']}") + + if result.get('properties'): + props = result['properties'] + print(f" Properties:") + for k, v in props.items(): + print(f" {k}: {v}") + + +def peptide_bond_mask(smiles_list): + # Initialize the batch mask + batch_size = len(smiles_list) + max_seq_length = 1035 #max(len(smiles) for smiles in smiles_list) # Find the longest SMILES + mask = torch.zeros((batch_size, max_seq_length), dtype=torch.int) # Mask filled with zeros + + bond_patterns = [ + (r'OC\(=O\)', 'ester'), + (r'N\(C\)C\(=O\)', 'n_methyl'), + (r'N[12]C\(=O\)', 'peptide'), # Pro peptide bonds + (r'NC\(=O\)', 'peptide'), # Regular peptide bonds + (r'C\(=O\)N\(C\)', 'n_methyl'), + (r'C\(=O\)N[12]?', 'peptide') + ] + + for batch_idx, smiles in enumerate(smiles_list): + positions = [] + used = set() + + # Identify bonds + for pattern, bond_type in bond_patterns: + for match in re.finditer(pattern, smiles): + if not any(p in range(match.start(), match.end()) for p in used): + positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': bond_type, + 'pattern': match.group() + }) + used.update(range(match.start(), match.end())) + + # Update the mask for the current SMILES + for pos in positions: + mask[batch_idx, pos['start']:pos['end']] = 1 + + return mask + +def peptide_token_mask(smiles_list, token_lists): + # Initialize the batch mask + batch_size = len(smiles_list) + token_seq_length = max(len(tokens) for tokens in token_lists) # Find the longest tokenized sequence + tokenized_masks = torch.zeros((batch_size, token_seq_length), dtype=torch.int) # Mask filled with zeros + atomwise_masks = peptide_bond_mask(smiles_list) + + + for batch_idx, atomwise_mask in enumerate(atomwise_masks): + token_seq = token_lists[batch_idx] + atom_idx = 0 + + for token_idx, token in enumerate(token_seq): + if token_idx != 0 and token_idx != len(token_seq) - 1: + if torch.sum(atomwise_mask[atom_idx:atom_idx+len(token)]) >= 1: + tokenized_masks[batch_idx][token_idx] = 1 + atom_idx += len(token) + + return tokenized_masks + +class MOGGenerator: + def __init__(self, model, device, objectives, args, tracker=None, tokenizer=None, tokenizer_type='smiles'): + self.model = model + self.device = device + self.objectives = objectives + self.args = args + self.num_objectives = len(objectives) + self.peptide_analyzer = PeptideAnalyzer() + self.pdc_analyzer = PDCAnalyzer(device) + self.tracker = tracker + self.tokenizer = tokenizer + self.tokenizer_type = tokenizer_type + + # Set invalid tokens based on tokenizer type + if tokenizer_type == 'amino': + # PepReDi (natural-AA) vocab is 24 tokens: 0-3 are specials + # (cls/pad/eos/unk) and 4-23 are the 20 canonical AAs. The UAA + # script also blocked id 38 ('g' D-AA), but that's out of range + # for PepReDi and would crash the `pos_probs[:, self.invalid] = 0` + # masking. Block special tokens only. + self.invalid = [0, 1, 2, 3] + # Initialize SMILES tokenizer for AA-to-SMILES conversion + self.smiles_tokenizer = SMILES_SPE_Tokenizer( + args.smiles_vocab_path, + args.smiles_splits_path + ) + else: + self.invalid = [0, 1, 2, 3, 4, 585, 586] # SMILES special tokens + self.smiles_tokenizer = None + + # Find the Conjugation objective for detailed tracking + self.conjugation_obj = None + for obj in objectives: + if isinstance(obj, Conjugation): + self.conjugation_obj = obj + break + + def _generate_single_x0(self, n_steps=16, temperature=1.0): + """Generate a single batch of x0 sequences.""" + self.model.eval() + + # Get vocab size based on tokenizer type + if self.tokenizer_type == 'amino': + vocab_size = self.model.vocab_size + else: + vocab_size = self.model.tokenizer.vocab_size + + # 1. Start with a tensor of random tokens (pure noise at t=0) + # For amino tokenizer, exclude invalid tokens from initialization + if self.tokenizer_type == 'amino': + valid_tokens = [i for i in range(vocab_size) if i not in self.invalid] + valid_tokens = torch.tensor(valid_tokens, device=self.device) + rand_indices = torch.randint(0, len(valid_tokens), (self.args.num_samples, self.args.gen_len), device=self.device) + body = valid_tokens[rand_indices] + # Add CLS and EOS tokens + cls_col = torch.full((self.args.num_samples, 1), self.tokenizer.cls_id, device=self.device) + eos_col = torch.full((self.args.num_samples, 1), self.tokenizer.eos_id, device=self.device) + x = torch.cat([cls_col, body, eos_col], dim=1) + else: + x = torch.randint( + 0, + vocab_size, + (self.args.num_samples, self.args.gen_len), + device=self.device + ) + + # 2. Define the time schedule for the forward process (0.0 to 1.0) + time_steps = torch.linspace(0.0, 1.0, n_steps + 1, device=self.device) + + # 3. Iteratively follow the flow from noise to data + with torch.no_grad(): + for i in tqdm(range(n_steps), desc="Flow Matching Steps", leave=False): + t_curr = time_steps[i] + t_next = time_steps[i+1] + + # Prepare the current timestep tensor for the model + t_tensor = torch.full((self.args.num_samples,), t_curr, device=self.device) + + # Get the model's prediction for the final clean sequence (at t=1) + logits = self.model(x, t_tensor) + logits = logits / temperature + + # Mask out invalid tokens + for inv_tok in self.invalid: + if inv_tok < logits.shape[-1]: + logits[:, :, inv_tok] = -1000 + + pred_x1 = torch.argmax(logits, dim=-1) + + # On the last step, the result is the final prediction + if i == n_steps - 1: + x = pred_x1 + break + + # --- Construct the next state x_{t_next} --- + # The probability of a token being noise at time t_next is (1 - t_next). + noise_prob = 1.0 - t_next + mask = torch.rand(x.shape, device=self.device) < noise_prob + + # Generate new random tokens for the noise positions + noise = torch.randint( + 0, + vocab_size, + x.shape, + device=self.device + ) + + # Combine the final prediction with noise to form the next intermediate state + x = torch.where(mask, noise, pred_x1) + + return x + + def _check_conjugation_validity(self, x): + """ + Check if sequences can be conjugated. + Returns a boolean tensor indicating validity for each sample. + """ + if self.tokenizer_type == 'amino': + # For amino tokenizer, convert to SMILES first + seqs = self.tokenizer.batch_decode(x) + three_letter_seqs = [one_letter_to_three_letter(seq) for seq in seqs] + smiles_seqs = [aa_to_smiles(three_letter_seq, cyclic=self.args.cyclic) + for three_letter_seq in three_letter_seqs] + + # Check if SMILES conversion succeeded and result is valid peptide + validities = [] + for smiles in smiles_seqs: + if isinstance(smiles, str): + validities.append(self.peptide_analyzer.is_peptide(smiles)) + else: + validities.append(False) + return torch.tensor(validities, device=self.device) + + if self.conjugation_obj is None: + # Fall back to peptide validity check + sequences = self.tokenizer.batch_decode(x) + return torch.tensor([self.peptide_analyzer.is_peptide(seq) for seq in sequences], device=self.device) + + # Use conjugation objective - score > 0 means valid + scores = self.conjugation_obj(x) + return scores > 0 + + def generate_x0(self, n_steps=16, temperature=1.0, require_valid=False, max_retries=10): + """ + Generate initial x0 sequences. + + Args: + n_steps: Number of flow matching steps + temperature: Sampling temperature + require_valid: If True, regenerate invalid samples until all are valid conjugatable peptides + max_retries: Maximum number of regeneration attempts per sample + """ + print("Starting initial SMILES generation...") + + x = self._generate_single_x0(n_steps, temperature) + + if require_valid: + print("Checking conjugation validity and regenerating invalid samples...") + + for retry in range(max_retries): + validity = self._check_conjugation_validity(x) + 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 are valid conjugatable peptides!") + break + + print(f"Retry {retry + 1}/{max_retries}: {num_valid}/{self.args.num_samples} valid, regenerating {num_invalid} invalid samples...") + + # Get indices of invalid samples + invalid_indices = torch.where(~validity)[0] + + # Generate new candidates for invalid samples + # Temporarily adjust num_samples to generate replacements + original_num_samples = self.args.num_samples + self.args.num_samples = num_invalid + + new_x = self._generate_single_x0(n_steps, temperature) + + self.args.num_samples = original_num_samples + + # Replace invalid samples with new ones + x[invalid_indices] = new_x + + # Final validity check + final_validity = self._check_conjugation_validity(x) + num_final_valid = final_validity.sum().item() + if num_final_valid < self.args.num_samples: + print(f"Warning: After {max_retries} retries, only {num_final_valid}/{self.args.num_samples} samples are valid.") + else: + print(f"Success: All {self.args.num_samples} samples are valid conjugatable peptides.") + + # 4. Decode the final token IDs into strings + generated_sequences = self.tokenizer.batch_decode(x) + + # 5. Analyze the validity of the generated sequences + validities = [] + if self.tokenizer_type == 'amino': + # For amino mode, report conjugation-relevant validity after AA->SMILES conversion. + validity_tensor = self._check_conjugation_validity(x) + validities = [bool(v.item()) for v in validity_tensor] + for seq in generated_sequences: + print(seq) + else: + for seq in generated_sequences: + validities.append(self.peptide_analyzer.is_peptide(seq)) + print(seq) + if self.tokenizer_type == 'amino': + print(f"Initial Peptide Validity (AA->SMILES): {validities}") + else: + print(f"Initial Sequence Validity: {validities}") + return x + + def _get_scores(self, x_batch): + """Calculates the normalized scores for a batch of sequences.""" + scores = [] + + if self.tokenizer_type == 'amino': + # Convert AA tokens to SMILES for scoring + seqs = self.tokenizer.batch_decode(x_batch) + + # Convert one-letter AA codes to three-letter, then to SMILES + three_letter_seqs = [one_letter_to_three_letter(seq) for seq in seqs] + with ThreadPoolExecutor() as pool: + smiles_seqs = list(pool.map( + lambda s: aa_to_smiles(s, cyclic=self.args.cyclic), + three_letter_seqs + )) + + # Track valid conversions + valid_mask = torch.tensor( + [1 if isinstance(seq, str) else 0 for seq in smiles_seqs], + device=self.device + ) + + # Replace invalid SMILES with dummy 'C' for tokenization + for i in torch.where((1 - valid_mask) == 1)[0].tolist(): + smiles_seqs[i] = 'C' + + # Tokenize SMILES for objective functions + smiles_batch = self.smiles_tokenizer( + smiles_seqs, return_tensors='pt', padding=True + ).to(self.device) + attention_mask = smiles_batch.get("attention_mask", None) + + # Evaluate each objective. Objectives may declare `.input_type`: + # 'aa' -> feed raw AA tokens (no SMILES conversion) + # 'smiles' -> feed SMILES tokens (default for the existing + # Conjugation / SMILES BindingAffinity) + for obj_func in self.objectives: + if getattr(obj_func, "input_type", None) == "aa": + # AA-input objective (e.g. UnpooledBindingPredictor): + # score directly on the original AA tokens; do not multiply + # by valid_mask (the AA->SMILES conversion outcome doesn't + # affect this scorer). + score = obj_func(x_batch.to(self.device)).to(self.device) + scores.append(score) + else: + try: + score = obj_func( + smiles_batch["input_ids"], + attention_mask=attention_mask + ).to(self.device) + except TypeError: + score = obj_func(smiles_batch["input_ids"]).to(self.device) + scores.append(score * valid_mask) # Zero out invalid conversions + else: + # Original SMILES-based scoring + for obj_func in self.objectives: + scores.append(obj_func(x_batch.to(self.device))) + + return torch.stack(scores, dim=0).to(self.device) + + def _barker_g(self, u): + """Barker balancing function.""" + return u / (1 + u) + + def validity(self, x): + sampled_sequences = self.tokenizer.batch_decode(x) + + if self.tokenizer_type == 'amino': + # For amino tokenizer, convert to SMILES first and check validity + three_letter_seqs = [one_letter_to_three_letter(seq) for seq in sampled_sequences] + smiles_seqs = [aa_to_smiles(three_letter_seq, cyclic=self.args.cyclic) + for three_letter_seq in three_letter_seqs] + return [1.0 if isinstance(seq, str) and self.peptide_analyzer.is_peptide(seq) else 0.0 + for seq in smiles_seqs] + else: + return [1.0 if self.peptide_analyzer.is_peptide(seq) else 0.0 for seq in sampled_sequences] + + def generate(self): + """Main generation loop with tracking.""" + shape = (self.args.num_samples, self.args.gen_len) + x = self.generate_x0( + require_valid=self.args.require_valid_x0, + max_retries=self.args.max_x0_retries + ) + print(x) + + if self.args.weights is None: + # The first weight is for conjugation (peptide + PDC validity) + # We need to ensure the SMILES sequences are valid peptides with valid PDCs + weights = torch.tensor([1] + [1/(self.num_objectives-1)] * (self.num_objectives-1), device=self.device).view(-1,1) + else: + weights = torch.tensor(self.args.weights, device=self.device).view(-1, 1) + if len(weights) != self.num_objectives: + raise ValueError("Number of weights must match number of objectives.") + print(f"Weights: {weights}") + + with torch.no_grad(): + current_scores = self._get_scores(x) + for t in tqdm(range(self.args.optimization_steps), desc="MOG Generation"): + improved = False + improved_indices = [] + + # Anneal guidance strength + eta_t = self.args.eta_min + (self.args.eta_max - self.args.eta_min) * (t / (self.args.optimization_steps - 1)) + # Choose a random position to mutate + mut_idx = random.randint(1, self.args.gen_len-2) + + # Determine the generation timestep + # We cycle through the timesteps to ensure all are visited + generation_step = t % self.args.optimization_steps + time_t = torch.full((self.args.num_samples,), (generation_step / self.args.optimization_steps), device=self.device) + + # Get proposal distribution from ReDi model for the chosen position + logits = self.model(x, time_t) + probs = F.softmax(logits, dim=-1) + pos_probs = probs[:, mut_idx, :] + pos_probs[:, x[:, mut_idx]] = 0 # We don't evalute the same token + pos_probs[:, self.invalid] = 0 + + # Prune candidate vocabulary using top-p sampling + sorted_probs, sorted_indices = torch.sort(pos_probs, descending=True) + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) + remove_mask = cumulative_probs > self.args.top_p + remove_mask[..., 1:] = remove_mask[..., :-1].clone() + remove_mask[..., 0] = 0 + + # Get the set of candidate tokens for each sample in the batch + candidate_tokens_list = [] + for i in range(self.args.num_samples): + sample_mask = remove_mask[i] + candidates = sorted_indices[i, ~sample_mask] + candidate_tokens_list.append(candidates) + + w_current = torch.exp(eta_t * torch.min(weights * current_scores, dim=0).values) + + if t == 0: + print(f"Initial Scores: {current_scores}") + # Log initial step with detailed results + if self.tracker and self.conjugation_obj: + detailed = self.conjugation_obj.get_last_detailed_results() + self.tracker.log_step(t, current_scores[0], detailed) + self.tracker.print_step_summary(t, current_scores[0], detailed) + + # Evaluate all candidate tokens for each sample + final_proposal_tokens = [] + for i in range(self.args.num_samples): + candidates = candidate_tokens_list[i] + candidates = torch.tensor([token for token in candidates if token not in self.invalid], device=candidates.device) + if len(candidates) >= 200: + candidates = candidates[:200] + num_candidates = len(candidates) + + # Create a batch of proposed sequences for the current sample + x_prop_batch = x[i].repeat(num_candidates, 1) + x_prop_batch[:, mut_idx] = candidates + + # Evaluate all proposals + proposal_scores = self._get_scores(x_prop_batch) + proposal_s_omega = torch.min(weights * proposal_scores, dim=0).values + w_proposal = torch.exp(eta_t * proposal_s_omega) + + # Get ReDi probabilities for the candidates + redi_probs = pos_probs[i, candidates] + + # Calculate unnormalized guided probabilities + tilde_q = redi_probs * self._barker_g(w_proposal / w_current[i]) + + # Normalize and sample the final token + final_probs = tilde_q / (torch.sum(tilde_q) + 1e-9) + + index = torch.multinomial(final_probs, 1).item() + + if (random.uniform(0,1) > 1 and proposal_scores[:,index][0] == 1) or torch.sum(weights.squeeze(1) * proposal_scores[:, index]) >= torch.sum(weights.squeeze(1) * current_scores[:,i]): + final_token = candidates[index] + print(f"Previous Weighted Sum: {torch.sum(weights.squeeze(1) * current_scores[:,i])}") + print(f"Previous Scores: {current_scores[:,i]}") + + print(f"New Weighted Sum: {torch.sum(weights.squeeze(1) * proposal_scores[:, index])}") + print(f"New Scores: {proposal_scores[:,index]}") + improved = True + improved_indices.append(i) + + else: + final_token = x[i][mut_idx] + + final_proposal_tokens.append(final_token) + + # Update the sequences with the chosen tokens + x[torch.arange(self.args.num_samples), mut_idx] = torch.stack(final_proposal_tokens) + if improved: + current_scores = self._get_scores(x) + print(self.tokenizer.batch_decode(x)) + + # Log step with tracking (every 4 steps to reduce overhead) + if self.tracker and self.conjugation_obj and (t % 4 == 0 or t == self.args.optimization_steps - 1): + detailed = self.conjugation_obj.get_last_detailed_results() + self.tracker.log_step(t, current_scores[0], detailed, improved_indices) + + # Print summary for final step + if t == self.args.optimization_steps - 1: + self.tracker.print_step_summary(t, current_scores[0], detailed) + + return x + + +# --- Main Execution --- +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + + target = args.target + + # --- Initialize tokenizer based on type --- + if args.tokenizer_type == 'amino': + print("Using Amino Acid tokenizer") + tokenizer = AminoTokenizer() + smiles_tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + else: + print("Using SMILES tokenizer") + tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + smiles_tokenizer = tokenizer + + # --- Initialize objective functions --- + # Conjugation always uses SMILES tokens (AA->SMILES->builder). + conjugation_model = Conjugation(smiles_tokenizer, device=device, builder_type=args.builder_type) + # Binding affinity: AA-input or SMILES-input, depending on --affinity_type. + # The returned object carries `.input_type` ('aa' or 'smiles') so the + # generator routes inputs correctly in _get_scores. + affinity_model = make_affinity( + args.affinity_type, + target_protein_seq=target, + device=device, + aa_ckpt_path=args.aa_affinity_ckpt, + ) + print(f"Affinity scorer: {type(affinity_model).__name__} (input_type={getattr(affinity_model, 'input_type', '?')})") + + # List of all objective functions + OBJECTIVE_FUNCTIONS = [conjugation_model, affinity_model] + + # Initialize tracker for logging + tracker = GenerationTracker(output_dir=args.output_dir) + + # --- Set random seeds for reproducibility --- + if args.seed is not None: + import random + import numpy as np + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + print(f"Random seed set to: {args.seed}") + + # --- Load Model --- + print(f"Loading model from checkpoint: {args.checkpoint}") + checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False) + + if args.tokenizer_type == 'amino': + # Vanilla PyTorch checkpoint for amino model + raw_model_args = checkpoint['args'] + if isinstance(raw_model_args, argparse.Namespace): + raw_model_args = vars(raw_model_args) + model_args = edict(raw_model_args) + model = MDLM( + vocab_size=model_args.vocab_size, + seq_len=model_args.seq_len, + model_dim=model_args.model_dim, + n_heads=model_args.n_heads, + n_layers=model_args.n_layers + ).to(device) + model.load_state_dict(checkpoint['model_state_dict']) + print(f"Amino model loaded. Vocab size: {model_args.vocab_size}") + else: + # PyTorch Lightning checkpoint for SMILES model + model_hparams = checkpoint["hyper_parameters"]["args"] + model = MDLMLightningModule.load_from_checkpoint( + args.checkpoint, + args=model_hparams, + tokenizer=tokenizer, + map_location=device, + strict=False + ) + model.to(device) + model.eval() + print("Model loaded successfully.") + + mog_generator = MOGGenerator( + model, device, OBJECTIVE_FUNCTIONS, args, tracker=tracker, + tokenizer=tokenizer, tokenizer_type=args.tokenizer_type + ) + + all_sequences = [] + all_final_results = [] + + for batch_idx in range(args.num_batches): + print(f"\n{'='*60}") + print(f"BATCH {batch_idx + 1}/{args.num_batches}") + print(f"{'='*60}") + + generated_tokens = mog_generator.generate() + final_scores = mog_generator._get_scores(generated_tokens.detach()).detach().cpu().numpy() + sequence_str = tokenizer.batch_decode(generated_tokens) + + print("\n--- Batch Results ---") + print(f"Sequences: {sequence_str}") + print(f"Scores: {final_scores}") + + all_sequences.extend(sequence_str) + + # Get detailed results with properties for final sequences + # For amino tokenizer, we need to convert to SMILES first for detailed scoring + if args.tokenizer_type == 'amino': + # Convert AA to SMILES for detailed conjugation analysis + smiles_list = [] + for seq in sequence_str: + three_letter = one_letter_to_three_letter(seq) + smiles = aa_to_smiles(three_letter, cyclic=args.cyclic) + smiles_list.append(smiles if isinstance(smiles, str) else 'C') + # Tokenize SMILES for conjugation model + smiles_tokens = smiles_tokenizer(smiles_list, return_tensors='pt', padding=True).to(device) + detailed_with_props = conjugation_model.get_detailed_scores_with_properties(smiles_tokens["input_ids"]) + else: + detailed_with_props = conjugation_model.get_detailed_scores_with_properties(generated_tokens) + + tracker.log_final(sequence_str, detailed_with_props) + all_final_results.extend(detailed_with_props) + + # Print and save final summary + tracker.print_final_summary() + tracker.save(filename_prefix='pdc_generation') + + # Save sequences to text file + with open(args.output_file, 'w') as f: + for i, seq in enumerate(all_sequences): + if args.tokenizer_type == 'amino': + # Also save converted SMILES for amino sequences + three_letter = one_letter_to_three_letter(seq) + smiles = aa_to_smiles(three_letter, cyclic=args.cyclic) + if isinstance(smiles, str): + f.write(f"{seq}\t{smiles}\n") + else: + f.write(f"{seq}\tINVALID_SMILES\n") + else: + f.write(seq + '\n') + print(f"\nSequences saved to: {args.output_file}") + + print("\nGeneration complete.") + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Multi-Objective Generation with LBP-MOG-ReDi (Single Mutation).") + + parser.add_argument("--checkpoint", type=str, required=True, help="Path to the trained ReDi model checkpoint.") + parser.add_argument("--num_samples", type=int, default=10, help="Number of samples to generate.") + parser.add_argument("--num_batches", type=int, default=10, help="Number of batches to generate.") + parser.add_argument("--output_file", type=str, default="./smiles.txt", help="File to save the generated sequences.") + parser.add_argument("--output_dir", type=str, default="./generation_logs", help="Directory to save detailed generation logs.") + parser.add_argument("--gen_len", type=int, default=50, help="Length of the sequences to generate.") + parser.add_argument("--optimization_steps", type=int, default=16, help="Number of optimization steps per generation.") + parser.add_argument("--weights", type=float, nargs='+', required=False, help="Weights for the objectives.") + parser.add_argument("--eta_min", type=float, default=1.0, help="Minimum guidance strength.") + parser.add_argument("--eta_max", type=float, default=20.0, help="Maximum guidance strength.") + parser.add_argument("--top_p", type=float, default=0.9, help="Top-p for pruning candidate tokens.") + parser.add_argument("--builder_type", type=str, default="AoaDau", + choices=["AoaDau", "SSCpt", "VcMMAE", "DSPACpt", "MalHexCpt", + "DSECCpt", "Cpt", "DSPASN38", "DSEBSN38", "DSEPSN38", "SN38"], + help="PDC builder type for conjugation.") + parser.add_argument("--seed", type=int, default=None, help="Random seed for reproducibility.") + parser.add_argument("--require_valid_x0", action="store_true", + help="If set, regenerate x0 samples until they are valid conjugatable peptides.") + parser.add_argument("--max_x0_retries", type=int, default=10, + help="Maximum retries when require_valid_x0 is enabled.") + + parser.add_argument("--target", type=str, required=True, help="Target protein sequence for binding affinity.") + + # Tokenizer and model type arguments + parser.add_argument("--tokenizer_type", type=str, default="smiles", choices=["smiles", "amino"], + help="Type of tokenizer: 'smiles' for SMILES SPE tokenizer, 'amino' for amino acid tokenizer.") + parser.add_argument("--cyclic", action="store_true", + help="Generate cyclic peptides (only for amino tokenizer).") + parser.add_argument("--smiles_vocab_path", type=str, + default="./smiles/smiles_tokenizer/new_vocab.txt", + help="Path to SMILES tokenizer vocabulary file.") + parser.add_argument("--smiles_splits_path", type=str, + default="./smiles/smiles_tokenizer/new_splits.txt", + help="Path to SMILES tokenizer splits file.") + + # AA-mode binding-affinity selection (this is the WT-AA / PepReDi script). + parser.add_argument("--affinity_type", type=str, default="smiles", + choices=["aa", "smiles"], + help="Which binding-affinity oracle to use during " + "guidance: 'aa' = AA-input UnpooledBindingPredictor " + "(matches moPPIt's setup), 'smiles' = SMILES-input " + "BindingAffinity (matches the SMILES CupOFMoCA " + "tables). Defaults to 'aa' since this script is " + "the WT-AA pipeline.") + parser.add_argument("--aa_affinity_ckpt", type=str, + default=DEFAULT_AA_AFFINITY_CKPT, + help="Path to the AA-input affinity predictor " + "checkpoint (binding_affinity_unpooled.pt).") + + args = parser.parse_args() + main(args) diff --git a/smiles/generation/guided_uaa.py b/smiles/generation/guided_uaa.py new file mode 100644 index 0000000000000000000000000000000000000000..af4ca60685917816d6f07c87180a9bc939d43c0c --- /dev/null +++ b/smiles/generation/guided_uaa.py @@ -0,0 +1,981 @@ +import argparse +import re +import random +import sys +from collections import Counter +from concurrent.futures import ThreadPoolExecutor +import csv +import json +import os +from datetime import datetime +import torch +import torch.nn as nn +import torch.nn.functional as F +from tqdm import tqdm +from smiles.models.train import MDLMLightningModule +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +import numpy as np +from easydict import EasyDict as edict + +from smiles.evaluation.peptide_analyzer import PeptideAnalyzer +from smiles.evaluation.pdc_analyzer import PDCAnalyzer +from smiles.scoring.classifiers import Conjugation, BindingAffinity + +# Import AA-to-SMILES conversion utilities +from third_party.SMILESToPeptide.peptide_to_smiles import aa_to_smiles +from third_party.SMILESToPeptide.aminoacid_selective import one_letter_to_three_letter + + +# --- Model Architecture for Amino Acid Model --- +def modulate(x, shift, scale): + """Modulates the input tensor x with a shift and scale.""" + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class TimestepEmbedder(nn.Module): + """Embeds a continuous scalar timestep t in [0, 1] into a vector representation.""" + def __init__(self, hidden_size): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(1, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + + def forward(self, t): + return self.mlp(t.unsqueeze(-1)) + + +class DiTBlock(nn.Module): + """A single block of the Diffusion Transformer.""" + def __init__(self, hidden_size, n_heads): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = nn.MultiheadAttention(hidden_size, n_heads, batch_first=True) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.mlp = nn.Sequential( + nn.Linear(hidden_size, 4 * hidden_size), + nn.GELU(), + nn.Linear(4 * hidden_size, hidden_size) + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + def forward(self, x, c): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1) + x_norm1 = modulate(self.norm1(x), shift_msa, scale_msa) + attn_output, _ = self.attn(x_norm1, x_norm1, x_norm1) + x = x + gate_msa.unsqueeze(1) * attn_output + x_norm2 = modulate(self.norm2(x), shift_mlp, scale_mlp) + mlp_output = self.mlp(x_norm2) + x = x + gate_mlp.unsqueeze(1) * mlp_output + return x + + +class MDLM(nn.Module): + """Masked Diffusion Language Model (MDLM) using a DiT backbone.""" + def __init__(self, vocab_size, seq_len, model_dim, n_heads, n_layers): + super().__init__() + self.vocab_size = vocab_size + self.seq_len = seq_len + self.model_dim = model_dim + + self.token_embedder = nn.Embedding(vocab_size, model_dim) + self.pos_embedder = nn.Parameter(torch.randn(1, seq_len, model_dim)) + self.time_embedder = TimestepEmbedder(model_dim) + + self.transformer_blocks = nn.ModuleList([ + DiTBlock(model_dim, n_heads) for _ in range(n_layers) + ]) + + self.final_norm = nn.LayerNorm(model_dim) + self.lm_head = nn.Linear(model_dim, vocab_size) + + def forward(self, x, t): + seq_len = x.shape[1] + x_embed = self.token_embedder(x) + self.pos_embedder[:, :seq_len, :] + t_embed = self.time_embedder(t) + for block in self.transformer_blocks: + x_embed = block(x_embed, t_embed) + x_embed = self.final_norm(x_embed) + logits = self.lm_head(x_embed) + return logits + + +class AminoTokenizer: + """Tokenizer for amino acid sequences (natural and unnatural AAs).""" + def __init__(self): + self.vocab = { + '': 0, '': 1, '': 2, '': 3, + 'L': 4, 'A': 5, 'G': 6, 'V': 7, 'S': 8, 'E': 9, + 'R': 10, 'T': 11, 'I': 12, 'D': 13, 'P': 14, 'K': 15, + 'Q': 16, 'N': 17, 'F': 18, 'Y': 19, 'M': 20, 'H': 21, + 'W': 22, 'C': 23, + # Non-canonical amino acids + 'Ŷ': 24, 'Ĉ': 25, 'Ĝ': 26, 'Ė': 27, 'Ƙ': 28, + 'Ṃ': 29, 'Ł': 30, 'Ċ': 31, 'Ṯ': 32, + # Lowercase tokens (D-amino acids) + 'a': 33, 'c': 34, 'd': 35, 'e': 36, 'f': 37, + 'g': 38, 'h': 39, 'i': 40, 'k': 41, 'l': 42, + 'm': 43, 'n': 44, 'p': 45, 'q': 46, 'r': 47, + 's': 48, 't': 49, 'v': 50, 'w': 51, 'y': 52, + } + self.id_to_token = {i: t for t, i in self.vocab.items()} + + self.cls_token = '' + self.pad_token = '' + self.eos_token = '' + self.unk_token = '' + + self.cls_id = self.vocab[self.cls_token] + self.pad_id = self.vocab[self.pad_token] + self.eos_id = self.vocab[self.eos_token] + self.unk_id = self.vocab[self.unk_token] + + self.vocab_size = len(self.vocab.keys()) + + self._ncaa_to_nat_token = { + 'Ŷ': 'Y', 'Ĉ': 'C', 'Ċ': 'C', 'Ĝ': 'G', 'Ė': 'E', + 'Ƙ': 'K', 'Ṃ': 'M', 'Ł': 'L', 'Ṯ': 'T', + } + + id_map = torch.arange(self.vocab_size, dtype=torch.long) + for ncaa_tok, nat_tok in self._ncaa_to_nat_token.items(): + if ncaa_tok in self.vocab and nat_tok in self.vocab: + id_map[self.vocab[ncaa_tok]] = self.vocab[nat_tok] + self._ncaa_to_nat_id_map = id_map + + def encode(self, seq: str, add_special_tokens: bool = True): + token_ids = [] + if add_special_tokens: + token_ids.append(self.cls_id) + for ch in seq: + token_ids.append(self.vocab.get(ch, self.unk_id)) + if add_special_tokens: + token_ids.append(self.eos_id) + return token_ids + + def batch_encode(self, seqs, add_special_tokens: bool = True): + return torch.tensor([self.encode(s, add_special_tokens=add_special_tokens) for s in seqs]) + + def decode(self, ids, skip_special_tokens: bool = True): + if isinstance(ids, torch.Tensor): + ids = ids.tolist() + tokens = [] + for i in ids: + tok = self.id_to_token.get(int(i), self.unk_token) + if skip_special_tokens and tok in {self.cls_token, self.eos_token, self.pad_token}: + continue + tokens.append(tok) + return ''.join(tokens) + + def batch_decode(self, batch_ids, skip_special_tokens: bool = True): + return [self.decode(ids, skip_special_tokens=skip_special_tokens) for ids in batch_ids] + + def to_natural_ids(self, batch_ids: torch.Tensor) -> torch.Tensor: + """Map non-canonical AA token IDs to natural AA token IDs.""" + if not torch.is_tensor(batch_ids): + batch_ids = torch.tensor(batch_ids, dtype=torch.long) + id_map = self._ncaa_to_nat_id_map.to(device=batch_ids.device) + out = batch_ids.clone() + valid = (out >= 0) & (out < id_map.numel()) + out[valid] = id_map[out[valid]] + out[~valid] = self.unk_id + return out + + +class GenerationTracker: + """ + Tracks detailed validity information during generation + """ + 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, scores, detailed_results=None, improved_indices=None): + """Log information for a generation step""" + step_info = { + 'step': step, + 'scores': scores.cpu().numpy().tolist() if isinstance(scores, torch.Tensor) else scores, + 'num_valid': int(sum(1 for s in scores if s > 0)) if hasattr(scores, '__iter__') else 0, + 'improved_indices': improved_indices or [] + } + + if detailed_results: + step_info['detailed'] = [] + for i, result in enumerate(detailed_results): + step_info['detailed'].append({ + 'sample_idx': i, + 'is_valid_peptide': result.get('is_valid_peptide', False), + 'has_conjugation_site': result.get('has_conjugation_site', False), + 'conjugation_success': result.get('conjugation_success', False), + 'pdc_valid': result.get('pdc_valid', False), + 'num_conjugates': len(result.get('conjugates', [])), + 'error': result.get('error') + }) + + self.step_logs.append(step_info) + + def log_final(self, sequences, detailed_results_with_props): + """Log final results with properties""" + for i, (seq, result) in enumerate(zip(sequences, detailed_results_with_props)): + final_info = { + 'sample_idx': i, + 'peptide_smiles': seq, + 'is_valid_peptide': result.get('is_valid_peptide', False), + 'has_conjugation_site': result.get('has_conjugation_site', False), + 'num_conjugates': len(result.get('conjugates', [])), + 'num_valid_pdcs': len(result.get('valid_pdcs', [])), + 'best_pdc': None, + 'properties': result.get('properties', {}) + } + + if result.get('best_pdc'): + final_info['best_pdc'] = { + 'smiles': result['best_pdc'].get('smiles', ''), + 'checks': result['best_pdc'].get('checks', {}) + } + + self.final_results.append(final_info) + + def save(self, filename_prefix='generation'): + """Save all logs to files""" + # Save step logs + step_log_path = os.path.join(self.output_dir, f"{filename_prefix}_{self.run_id}_steps.json") + with open(step_log_path, 'w') as f: + json.dump(self.step_logs, f, indent=2, default=str) + + # Save final results + 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) + + # Save summary CSV + 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', 'peptide_smiles', 'is_valid_peptide', 'has_conjugation_site', + 'num_conjugates', 'num_valid_pdcs', 'best_pdc_smiles', + 'mf', 'polararea', 'xlogp', 'heavycnt', 'rotbonds' + ]) + for result in self.final_results: + props = result.get('properties', {}) + writer.writerow([ + result['sample_idx'], + result['peptide_smiles'], + result['is_valid_peptide'], + result['has_conjugation_site'], + result['num_conjugates'], + result['num_valid_pdcs'], + result['best_pdc']['smiles'] if result.get('best_pdc') else '', + props.get('mf', ''), + props.get('polararea', ''), + props.get('xlogp', ''), + props.get('heavycnt', ''), + props.get('rotbonds', '') + ]) + + print(f"\n=== Generation logs saved ===") + print(f" Step logs: {step_log_path}") + print(f" Final results: {final_path}") + print(f" Summary CSV: {csv_path}") + + return step_log_path, final_path, csv_path + + def print_step_summary(self, step, scores, detailed_results=None): + """Print a summary for the current step""" + if isinstance(scores, torch.Tensor): + scores_list = scores.cpu().numpy().tolist() + else: + scores_list = list(scores) + + num_valid = sum(1 for s in scores_list if s > 0) + + print(f"\n--- Step {step} Summary ---") + print(f" Valid: {num_valid}/{len(scores_list)}") + + if detailed_results: + valid_peptide = sum(1 for r in detailed_results if r.get('is_valid_peptide')) + has_site = sum(1 for r in detailed_results if r.get('has_conjugation_site')) + conj_success = sum(1 for r in detailed_results if r.get('conjugation_success')) + pdc_valid = sum(1 for r in detailed_results if r.get('pdc_valid')) + + print(f" Breakdown:") + print(f" Valid peptides: {valid_peptide}") + print(f" Has conjugation site: {has_site}") + print(f" Conjugation success: {conj_success}") + print(f" PDC valid: {pdc_valid}") + + def print_final_summary(self): + """Print final summary""" + print("\n" + "=" * 60) + print("FINAL GENERATION SUMMARY") + print("=" * 60) + + total = len(self.final_results) + valid_pdcs = sum(1 for r in self.final_results if r.get('num_valid_pdcs', 0) > 0) + + print(f"\nTotal sequences: {total}") + print(f"With valid PDCs: {valid_pdcs}") + + print("\nDetailed results:") + for result in self.final_results: + status = "" if result.get('num_valid_pdcs', 0) > 0 else "" + print(f"\n [{status}] Sample {result['sample_idx']}:") + print(f" Peptide: {result['peptide_smiles'][:60]}...") + print(f" Valid peptide: {result['is_valid_peptide']}") + print(f" Has conjugation site: {result['has_conjugation_site']}") + print(f" Conjugates: {result['num_conjugates']}") + print(f" Valid PDCs: {result['num_valid_pdcs']}") + + if result.get('properties'): + props = result['properties'] + print(f" Properties:") + for k, v in props.items(): + print(f" {k}: {v}") + + +def peptide_bond_mask(smiles_list): + # Initialize the batch mask + batch_size = len(smiles_list) + max_seq_length = 1035 #max(len(smiles) for smiles in smiles_list) # Find the longest SMILES + mask = torch.zeros((batch_size, max_seq_length), dtype=torch.int) # Mask filled with zeros + + bond_patterns = [ + (r'OC\(=O\)', 'ester'), + (r'N\(C\)C\(=O\)', 'n_methyl'), + (r'N[12]C\(=O\)', 'peptide'), # Pro peptide bonds + (r'NC\(=O\)', 'peptide'), # Regular peptide bonds + (r'C\(=O\)N\(C\)', 'n_methyl'), + (r'C\(=O\)N[12]?', 'peptide') + ] + + for batch_idx, smiles in enumerate(smiles_list): + positions = [] + used = set() + + # Identify bonds + for pattern, bond_type in bond_patterns: + for match in re.finditer(pattern, smiles): + if not any(p in range(match.start(), match.end()) for p in used): + positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': bond_type, + 'pattern': match.group() + }) + used.update(range(match.start(), match.end())) + + # Update the mask for the current SMILES + for pos in positions: + mask[batch_idx, pos['start']:pos['end']] = 1 + + return mask + +def peptide_token_mask(smiles_list, token_lists): + # Initialize the batch mask + batch_size = len(smiles_list) + token_seq_length = max(len(tokens) for tokens in token_lists) # Find the longest tokenized sequence + tokenized_masks = torch.zeros((batch_size, token_seq_length), dtype=torch.int) # Mask filled with zeros + atomwise_masks = peptide_bond_mask(smiles_list) + + + for batch_idx, atomwise_mask in enumerate(atomwise_masks): + token_seq = token_lists[batch_idx] + atom_idx = 0 + + for token_idx, token in enumerate(token_seq): + if token_idx != 0 and token_idx != len(token_seq) - 1: + if torch.sum(atomwise_mask[atom_idx:atom_idx+len(token)]) >= 1: + tokenized_masks[batch_idx][token_idx] = 1 + atom_idx += len(token) + + return tokenized_masks + +class MOGGenerator: + def __init__(self, model, device, objectives, args, tracker=None, tokenizer=None, tokenizer_type='smiles'): + self.model = model + self.device = device + self.objectives = objectives + self.args = args + self.num_objectives = len(objectives) + self.peptide_analyzer = PeptideAnalyzer() + self.pdc_analyzer = PDCAnalyzer(device) + self.tracker = tracker + self.tokenizer = tokenizer + self.tokenizer_type = tokenizer_type + + # Set invalid tokens based on tokenizer type + if tokenizer_type == 'amino': + self.invalid = [0, 1, 2, 3, 38] # special tokens + 'g' for amino + # Initialize SMILES tokenizer for AA-to-SMILES conversion + self.smiles_tokenizer = SMILES_SPE_Tokenizer( + args.smiles_vocab_path, + args.smiles_splits_path + ) + else: + self.invalid = [0, 1, 2, 3, 4, 585, 586] # SMILES special tokens + self.smiles_tokenizer = None + + # Find the Conjugation objective for detailed tracking + self.conjugation_obj = None + for obj in objectives: + if isinstance(obj, Conjugation): + self.conjugation_obj = obj + break + + def _generate_single_x0(self, n_steps=16, temperature=1.0): + """Generate a single batch of x0 sequences.""" + self.model.eval() + + # Get vocab size based on tokenizer type + if self.tokenizer_type == 'amino': + vocab_size = self.model.vocab_size + else: + vocab_size = self.model.tokenizer.vocab_size + + # 1. Start with a tensor of random tokens (pure noise at t=0) + # For amino tokenizer, exclude invalid tokens from initialization + if self.tokenizer_type == 'amino': + valid_tokens = [i for i in range(vocab_size) if i not in self.invalid] + valid_tokens = torch.tensor(valid_tokens, device=self.device) + rand_indices = torch.randint(0, len(valid_tokens), (self.args.num_samples, self.args.gen_len), device=self.device) + body = valid_tokens[rand_indices] + # Add CLS and EOS tokens + cls_col = torch.full((self.args.num_samples, 1), self.tokenizer.cls_id, device=self.device) + eos_col = torch.full((self.args.num_samples, 1), self.tokenizer.eos_id, device=self.device) + x = torch.cat([cls_col, body, eos_col], dim=1) + else: + x = torch.randint( + 0, + vocab_size, + (self.args.num_samples, self.args.gen_len), + device=self.device + ) + + # 2. Define the time schedule for the forward process (0.0 to 1.0) + time_steps = torch.linspace(0.0, 1.0, n_steps + 1, device=self.device) + + # 3. Iteratively follow the flow from noise to data + with torch.no_grad(): + for i in tqdm(range(n_steps), desc="Flow Matching Steps", leave=False): + t_curr = time_steps[i] + t_next = time_steps[i+1] + + # Prepare the current timestep tensor for the model + t_tensor = torch.full((self.args.num_samples,), t_curr, device=self.device) + + # Get the model's prediction for the final clean sequence (at t=1) + logits = self.model(x, t_tensor) + logits = logits / temperature + + # Mask out invalid tokens + for inv_tok in self.invalid: + if inv_tok < logits.shape[-1]: + logits[:, :, inv_tok] = -1000 + + pred_x1 = torch.argmax(logits, dim=-1) + + # On the last step, the result is the final prediction + if i == n_steps - 1: + x = pred_x1 + break + + # --- Construct the next state x_{t_next} --- + # The probability of a token being noise at time t_next is (1 - t_next). + noise_prob = 1.0 - t_next + mask = torch.rand(x.shape, device=self.device) < noise_prob + + # Generate new random tokens for the noise positions + noise = torch.randint( + 0, + vocab_size, + x.shape, + device=self.device + ) + + # Combine the final prediction with noise to form the next intermediate state + x = torch.where(mask, noise, pred_x1) + + return x + + def _check_conjugation_validity(self, x): + """ + Check if sequences can be conjugated. + Returns a boolean tensor indicating validity for each sample. + """ + if self.tokenizer_type == 'amino': + # For amino tokenizer, convert to SMILES first + seqs = self.tokenizer.batch_decode(x) + three_letter_seqs = [one_letter_to_three_letter(seq) for seq in seqs] + smiles_seqs = [aa_to_smiles(three_letter_seq, cyclic=self.args.cyclic) + for three_letter_seq in three_letter_seqs] + + # Check if SMILES conversion succeeded and result is valid peptide + validities = [] + for smiles in smiles_seqs: + if isinstance(smiles, str): + validities.append(self.peptide_analyzer.is_peptide(smiles)) + else: + validities.append(False) + return torch.tensor(validities, device=self.device) + + if self.conjugation_obj is None: + # Fall back to peptide validity check + sequences = self.tokenizer.batch_decode(x) + return torch.tensor([self.peptide_analyzer.is_peptide(seq) for seq in sequences], device=self.device) + + # Use conjugation objective - score > 0 means valid + scores = self.conjugation_obj(x) + return scores > 0 + + def generate_x0(self, n_steps=16, temperature=1.0, require_valid=False, max_retries=10): + """ + Generate initial x0 sequences. + + Args: + n_steps: Number of flow matching steps + temperature: Sampling temperature + require_valid: If True, regenerate invalid samples until all are valid conjugatable peptides + max_retries: Maximum number of regeneration attempts per sample + """ + print("Starting initial SMILES generation...") + + x = self._generate_single_x0(n_steps, temperature) + + if require_valid: + print("Checking conjugation validity and regenerating invalid samples...") + + for retry in range(max_retries): + validity = self._check_conjugation_validity(x) + 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 are valid conjugatable peptides!") + break + + print(f"Retry {retry + 1}/{max_retries}: {num_valid}/{self.args.num_samples} valid, regenerating {num_invalid} invalid samples...") + + # Get indices of invalid samples + invalid_indices = torch.where(~validity)[0] + + # Generate new candidates for invalid samples + # Temporarily adjust num_samples to generate replacements + original_num_samples = self.args.num_samples + self.args.num_samples = num_invalid + + new_x = self._generate_single_x0(n_steps, temperature) + + self.args.num_samples = original_num_samples + + # Replace invalid samples with new ones + x[invalid_indices] = new_x + + # Final validity check + final_validity = self._check_conjugation_validity(x) + num_final_valid = final_validity.sum().item() + if num_final_valid < self.args.num_samples: + print(f"Warning: After {max_retries} retries, only {num_final_valid}/{self.args.num_samples} samples are valid.") + else: + print(f"Success: All {self.args.num_samples} samples are valid conjugatable peptides.") + + # 4. Decode the final token IDs into strings + generated_sequences = self.tokenizer.batch_decode(x) + + # 5. Analyze the validity of the generated sequences + validities = [] + if self.tokenizer_type == 'amino': + # For amino mode, report conjugation-relevant validity after AA->SMILES conversion. + validity_tensor = self._check_conjugation_validity(x) + validities = [bool(v.item()) for v in validity_tensor] + for seq in generated_sequences: + print(seq) + else: + for seq in generated_sequences: + validities.append(self.peptide_analyzer.is_peptide(seq)) + print(seq) + if self.tokenizer_type == 'amino': + print(f"Initial Peptide Validity (AA->SMILES): {validities}") + else: + print(f"Initial Sequence Validity: {validities}") + return x + + def _get_scores(self, x_batch): + """Calculates the normalized scores for a batch of sequences.""" + scores = [] + + if self.tokenizer_type == 'amino': + # Convert AA tokens to SMILES for scoring + seqs = self.tokenizer.batch_decode(x_batch) + + # Convert one-letter AA codes to three-letter, then to SMILES + three_letter_seqs = [one_letter_to_three_letter(seq) for seq in seqs] + with ThreadPoolExecutor() as pool: + smiles_seqs = list(pool.map( + lambda s: aa_to_smiles(s, cyclic=self.args.cyclic), + three_letter_seqs + )) + + # Track valid conversions + valid_mask = torch.tensor( + [1 if isinstance(seq, str) else 0 for seq in smiles_seqs], + device=self.device + ) + + # Replace invalid SMILES with dummy 'C' for tokenization + for i in torch.where((1 - valid_mask) == 1)[0].tolist(): + smiles_seqs[i] = 'C' + + # Tokenize SMILES for objective functions + smiles_batch = self.smiles_tokenizer( + smiles_seqs, return_tensors='pt', padding=True + ).to(self.device) + attention_mask = smiles_batch.get("attention_mask", None) + + # Evaluate each objective function on SMILES tokens + for obj_func in self.objectives: + try: + score = obj_func( + smiles_batch["input_ids"], + attention_mask=attention_mask + ).to(self.device) + except TypeError: + score = obj_func(smiles_batch["input_ids"]).to(self.device) + scores.append(score * valid_mask) # Zero out invalid conversions + else: + # Original SMILES-based scoring + for obj_func in self.objectives: + scores.append(obj_func(x_batch.to(self.device))) + + return torch.stack(scores, dim=0).to(self.device) + + def _barker_g(self, u): + """Barker balancing function.""" + return u / (1 + u) + + def validity(self, x): + sampled_sequences = self.tokenizer.batch_decode(x) + + if self.tokenizer_type == 'amino': + # For amino tokenizer, convert to SMILES first and check validity + three_letter_seqs = [one_letter_to_three_letter(seq) for seq in sampled_sequences] + smiles_seqs = [aa_to_smiles(three_letter_seq, cyclic=self.args.cyclic) + for three_letter_seq in three_letter_seqs] + return [1.0 if isinstance(seq, str) and self.peptide_analyzer.is_peptide(seq) else 0.0 + for seq in smiles_seqs] + else: + return [1.0 if self.peptide_analyzer.is_peptide(seq) else 0.0 for seq in sampled_sequences] + + def generate(self): + """Main generation loop with tracking.""" + shape = (self.args.num_samples, self.args.gen_len) + x = self.generate_x0( + require_valid=self.args.require_valid_x0, + max_retries=self.args.max_x0_retries + ) + print(x) + + if self.args.weights is None: + # The first weight is for conjugation (peptide + PDC validity) + # We need to ensure the SMILES sequences are valid peptides with valid PDCs + weights = torch.tensor([1] + [1/(self.num_objectives-1)] * (self.num_objectives-1), device=self.device).view(-1,1) + else: + weights = torch.tensor(self.args.weights, device=self.device).view(-1, 1) + if len(weights) != self.num_objectives: + raise ValueError("Number of weights must match number of objectives.") + print(f"Weights: {weights}") + + with torch.no_grad(): + current_scores = self._get_scores(x) + for t in tqdm(range(self.args.optimization_steps), desc="MOG Generation"): + improved = False + improved_indices = [] + + # Anneal guidance strength + eta_t = self.args.eta_min + (self.args.eta_max - self.args.eta_min) * (t / (self.args.optimization_steps - 1)) + # Choose a random position to mutate + mut_idx = random.randint(1, self.args.gen_len-2) + + # Determine the generation timestep + # We cycle through the timesteps to ensure all are visited + generation_step = t % self.args.optimization_steps + time_t = torch.full((self.args.num_samples,), (generation_step / self.args.optimization_steps), device=self.device) + + # Get proposal distribution from ReDi model for the chosen position + logits = self.model(x, time_t) + probs = F.softmax(logits, dim=-1) + pos_probs = probs[:, mut_idx, :] + pos_probs[:, x[:, mut_idx]] = 0 # We don't evalute the same token + pos_probs[:, self.invalid] = 0 + + # Prune candidate vocabulary using top-p sampling + sorted_probs, sorted_indices = torch.sort(pos_probs, descending=True) + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) + remove_mask = cumulative_probs > self.args.top_p + remove_mask[..., 1:] = remove_mask[..., :-1].clone() + remove_mask[..., 0] = 0 + + # Get the set of candidate tokens for each sample in the batch + candidate_tokens_list = [] + for i in range(self.args.num_samples): + sample_mask = remove_mask[i] + candidates = sorted_indices[i, ~sample_mask] + candidate_tokens_list.append(candidates) + + w_current = torch.exp(eta_t * torch.min(weights * current_scores, dim=0).values) + + if t == 0: + print(f"Initial Scores: {current_scores}") + # Log initial step with detailed results + if self.tracker and self.conjugation_obj: + detailed = self.conjugation_obj.get_last_detailed_results() + self.tracker.log_step(t, current_scores[0], detailed) + self.tracker.print_step_summary(t, current_scores[0], detailed) + + # Evaluate all candidate tokens for each sample + final_proposal_tokens = [] + for i in range(self.args.num_samples): + candidates = candidate_tokens_list[i] + candidates = torch.tensor([token for token in candidates if token not in self.invalid], device=candidates.device) + if len(candidates) >= 200: + candidates = candidates[:200] + num_candidates = len(candidates) + + # Create a batch of proposed sequences for the current sample + x_prop_batch = x[i].repeat(num_candidates, 1) + x_prop_batch[:, mut_idx] = candidates + + # Evaluate all proposals + proposal_scores = self._get_scores(x_prop_batch) + proposal_s_omega = torch.min(weights * proposal_scores, dim=0).values + w_proposal = torch.exp(eta_t * proposal_s_omega) + + # Get ReDi probabilities for the candidates + redi_probs = pos_probs[i, candidates] + + # Calculate unnormalized guided probabilities + tilde_q = redi_probs * self._barker_g(w_proposal / w_current[i]) + + # Normalize and sample the final token + final_probs = tilde_q / (torch.sum(tilde_q) + 1e-9) + + index = torch.multinomial(final_probs, 1).item() + + if (random.uniform(0,1) > 1 and proposal_scores[:,index][0] == 1) or torch.sum(weights.squeeze(1) * proposal_scores[:, index]) >= torch.sum(weights.squeeze(1) * current_scores[:,i]): + final_token = candidates[index] + print(f"Previous Weighted Sum: {torch.sum(weights.squeeze(1) * current_scores[:,i])}") + print(f"Previous Scores: {current_scores[:,i]}") + + print(f"New Weighted Sum: {torch.sum(weights.squeeze(1) * proposal_scores[:, index])}") + print(f"New Scores: {proposal_scores[:,index]}") + improved = True + improved_indices.append(i) + + else: + final_token = x[i][mut_idx] + + final_proposal_tokens.append(final_token) + + # Update the sequences with the chosen tokens + x[torch.arange(self.args.num_samples), mut_idx] = torch.stack(final_proposal_tokens) + if improved: + current_scores = self._get_scores(x) + print(self.tokenizer.batch_decode(x)) + + # Log step with tracking (every 4 steps to reduce overhead) + if self.tracker and self.conjugation_obj and (t % 4 == 0 or t == self.args.optimization_steps - 1): + detailed = self.conjugation_obj.get_last_detailed_results() + self.tracker.log_step(t, current_scores[0], detailed, improved_indices) + + # Print summary for final step + if t == self.args.optimization_steps - 1: + self.tracker.print_step_summary(t, current_scores[0], detailed) + + return x + + +# --- Main Execution --- +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + + target = args.target + + # --- Initialize tokenizer based on type --- + if args.tokenizer_type == 'amino': + print("Using Amino Acid tokenizer") + tokenizer = AminoTokenizer() + smiles_tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + else: + print("Using SMILES tokenizer") + tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + smiles_tokenizer = tokenizer + + # --- Initialize objective functions --- + # Use SMILES tokenizer for objectives (they expect SMILES tokens) + conjugation_model = Conjugation(smiles_tokenizer, device=device, builder_type=args.builder_type) + affinity_model = BindingAffinity(target, device) + + # List of all objective functions + OBJECTIVE_FUNCTIONS = [conjugation_model, affinity_model] + + # Initialize tracker for logging + tracker = GenerationTracker(output_dir=args.output_dir) + + # --- Set random seeds for reproducibility --- + if args.seed is not None: + import random + import numpy as np + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + print(f"Random seed set to: {args.seed}") + + # --- Load Model --- + print(f"Loading model from checkpoint: {args.checkpoint}") + checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False) + + if args.tokenizer_type == 'amino': + # Vanilla PyTorch checkpoint for amino model + raw_model_args = checkpoint['args'] + if isinstance(raw_model_args, argparse.Namespace): + raw_model_args = vars(raw_model_args) + model_args = edict(raw_model_args) + model = MDLM( + vocab_size=model_args.vocab_size, + seq_len=model_args.seq_len, + model_dim=model_args.model_dim, + n_heads=model_args.n_heads, + n_layers=model_args.n_layers + ).to(device) + model.load_state_dict(checkpoint['model_state_dict']) + print(f"Amino model loaded. Vocab size: {model_args.vocab_size}") + else: + # PyTorch Lightning checkpoint for SMILES model + model_hparams = checkpoint["hyper_parameters"]["args"] + model = MDLMLightningModule.load_from_checkpoint( + args.checkpoint, + args=model_hparams, + tokenizer=tokenizer, + map_location=device, + strict=False + ) + model.to(device) + model.eval() + print("Model loaded successfully.") + + mog_generator = MOGGenerator( + model, device, OBJECTIVE_FUNCTIONS, args, tracker=tracker, + tokenizer=tokenizer, tokenizer_type=args.tokenizer_type + ) + + all_sequences = [] + all_final_results = [] + + for batch_idx in range(args.num_batches): + print(f"\n{'='*60}") + print(f"BATCH {batch_idx + 1}/{args.num_batches}") + print(f"{'='*60}") + + generated_tokens = mog_generator.generate() + final_scores = mog_generator._get_scores(generated_tokens.detach()).detach().cpu().numpy() + sequence_str = tokenizer.batch_decode(generated_tokens) + + print("\n--- Batch Results ---") + print(f"Sequences: {sequence_str}") + print(f"Scores: {final_scores}") + + all_sequences.extend(sequence_str) + + # Get detailed results with properties for final sequences + # For amino tokenizer, we need to convert to SMILES first for detailed scoring + if args.tokenizer_type == 'amino': + # Convert AA to SMILES for detailed conjugation analysis + smiles_list = [] + for seq in sequence_str: + three_letter = one_letter_to_three_letter(seq) + smiles = aa_to_smiles(three_letter, cyclic=args.cyclic) + smiles_list.append(smiles if isinstance(smiles, str) else 'C') + # Tokenize SMILES for conjugation model + smiles_tokens = smiles_tokenizer(smiles_list, return_tensors='pt', padding=True).to(device) + detailed_with_props = conjugation_model.get_detailed_scores_with_properties(smiles_tokens["input_ids"]) + else: + detailed_with_props = conjugation_model.get_detailed_scores_with_properties(generated_tokens) + + tracker.log_final(sequence_str, detailed_with_props) + all_final_results.extend(detailed_with_props) + + # Print and save final summary + tracker.print_final_summary() + tracker.save(filename_prefix='pdc_generation') + + # Save sequences to text file + with open(args.output_file, 'w') as f: + for i, seq in enumerate(all_sequences): + if args.tokenizer_type == 'amino': + # Also save converted SMILES for amino sequences + three_letter = one_letter_to_three_letter(seq) + smiles = aa_to_smiles(three_letter, cyclic=args.cyclic) + if isinstance(smiles, str): + f.write(f"{seq}\t{smiles}\n") + else: + f.write(f"{seq}\tINVALID_SMILES\n") + else: + f.write(seq + '\n') + print(f"\nSequences saved to: {args.output_file}") + + print("\nGeneration complete.") + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Multi-Objective Generation with LBP-MOG-ReDi (Single Mutation).") + + parser.add_argument("--checkpoint", type=str, required=True, help="Path to the trained ReDi model checkpoint.") + parser.add_argument("--num_samples", type=int, default=10, help="Number of samples to generate.") + parser.add_argument("--num_batches", type=int, default=10, help="Number of batches to generate.") + parser.add_argument("--output_file", type=str, default="./smiles.txt", help="File to save the generated sequences.") + parser.add_argument("--output_dir", type=str, default="./generation_logs", help="Directory to save detailed generation logs.") + parser.add_argument("--gen_len", type=int, default=50, help="Length of the sequences to generate.") + parser.add_argument("--optimization_steps", type=int, default=16, help="Number of optimization steps per generation.") + parser.add_argument("--weights", type=float, nargs='+', required=False, help="Weights for the objectives.") + parser.add_argument("--eta_min", type=float, default=1.0, help="Minimum guidance strength.") + parser.add_argument("--eta_max", type=float, default=20.0, help="Maximum guidance strength.") + parser.add_argument("--top_p", type=float, default=0.9, help="Top-p for pruning candidate tokens.") + parser.add_argument("--builder_type", type=str, default="AoaDau", + choices=["AoaDau", "SSCpt", "VcMMAE", "DSPACpt", "MalHexCpt", + "DSECCpt", "Cpt", "DSPASN38", "DSEBSN38", "DSEPSN38", "SN38"], + help="PDC builder type for conjugation.") + parser.add_argument("--seed", type=int, default=None, help="Random seed for reproducibility.") + parser.add_argument("--require_valid_x0", action="store_true", + help="If set, regenerate x0 samples until they are valid conjugatable peptides.") + parser.add_argument("--max_x0_retries", type=int, default=10, + help="Maximum retries when require_valid_x0 is enabled.") + + parser.add_argument("--target", type=str, required=True, help="Target protein sequence for binding affinity.") + + # Tokenizer and model type arguments + parser.add_argument("--tokenizer_type", type=str, default="smiles", choices=["smiles", "amino"], + help="Type of tokenizer: 'smiles' for SMILES SPE tokenizer, 'amino' for amino acid tokenizer.") + parser.add_argument("--cyclic", action="store_true", + help="Generate cyclic peptides (only for amino tokenizer).") + parser.add_argument("--smiles_vocab_path", type=str, + default="./smiles/smiles_tokenizer/new_vocab.txt", + help="Path to SMILES tokenizer vocabulary file.") + parser.add_argument("--smiles_splits_path", type=str, + default="./smiles/smiles_tokenizer/new_splits.txt", + help="Path to SMILES tokenizer splits file.") + + args = parser.parse_args() + main(args) diff --git a/smiles/generation/staged.py b/smiles/generation/staged.py new file mode 100644 index 0000000000000000000000000000000000000000..309ea0b56c3d24003ea3bd823c2e1392bfbbeae0 --- /dev/null +++ b/smiles/generation/staged.py @@ -0,0 +1,348 @@ +import argparse +import re +import random +import torch +import torch.nn.functional as F +from tqdm import tqdm +from smiles.models.train import MDLMLightningModule, PeptideAnalyzer +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +import numpy as np + +from smiles.scoring.classifiers import Analyzer, BindingAffinity + +def peptide_bond_mask(smiles_list): + # Initialize the batch mask + batch_size = len(smiles_list) + max_seq_length = 1035 #max(len(smiles) for smiles in smiles_list) # Find the longest SMILES + mask = torch.zeros((batch_size, max_seq_length), dtype=torch.int) # Mask filled with zeros + + bond_patterns = [ + (r'OC\(=O\)', 'ester'), + (r'N\(C\)C\(=O\)', 'n_methyl'), + (r'N[12]C\(=O\)', 'peptide'), # Pro peptide bonds + (r'NC\(=O\)', 'peptide'), # Regular peptide bonds + (r'C\(=O\)N\(C\)', 'n_methyl'), + (r'C\(=O\)N[12]?', 'peptide') + ] + + for batch_idx, smiles in enumerate(smiles_list): + positions = [] + used = set() + + # Identify bonds + for pattern, bond_type in bond_patterns: + for match in re.finditer(pattern, smiles): + if not any(p in range(match.start(), match.end()) for p in used): + positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': bond_type, + 'pattern': match.group() + }) + used.update(range(match.start(), match.end())) + + # Update the mask for the current SMILES + for pos in positions: + mask[batch_idx, pos['start']:pos['end']] = 1 + + return mask + +def peptide_token_mask(smiles_list, token_lists): + # Initialize the batch mask + batch_size = len(smiles_list) + token_seq_length = max(len(tokens) for tokens in token_lists) # Find the longest tokenized sequence + tokenized_masks = torch.zeros((batch_size, token_seq_length), dtype=torch.int) # Mask filled with zeros + atomwise_masks = peptide_bond_mask(smiles_list) + + + for batch_idx, atomwise_mask in enumerate(atomwise_masks): + token_seq = token_lists[batch_idx] + atom_idx = 0 + + for token_idx, token in enumerate(token_seq): + if token_idx != 0 and token_idx != len(token_seq) - 1: + if torch.sum(atomwise_mask[atom_idx:atom_idx+len(token)]) >= 1: + tokenized_masks[batch_idx][token_idx] = 1 + atom_idx += len(token) + + return tokenized_masks + +class MOGGenerator: + def __init__(self, model, device, objectives, args): + self.model = model + self.device = device + self.objectives = objectives + self.args = args + self.num_objectives = len(objectives) + self.peptide_analyzer = PeptideAnalyzer() + self.invalid = [0, 1, 2, 3, 4, 585, 586] + + def generate_x0(self, n_steps=16, temperature=1.0): + print("Starting initial SMILES generation...") + self.model.eval() + + # 1. Start with a tensor of random tokens (pure noise at t=0) + x = torch.randint( + 0, + self.model.tokenizer.vocab_size, + (args.num_samples, args.gen_len), + device=self.device + ) + + # 2. Define the time schedule for the forward process (0.0 to 1.0) + time_steps = torch.linspace(0.0, 1.0, n_steps + 1, device=self.device) + + # 3. Iteratively follow the flow from noise to data + with torch.no_grad(): + for i in tqdm(range(n_steps), desc="Flow Matching Steps"): + t_curr = time_steps[i] + t_next = time_steps[i+1] + + # Prepare the current timestep tensor for the model + t_tensor = torch.full((args.num_samples,), t_curr, device=self.device) + + # Get the model's prediction for the final clean sequence (at t=1) + logits = self.model(x, t_tensor) + logits = logits / temperature + logits[:, :, 586] = -1000 + + pred_x1 = torch.argmax(logits, dim=-1) + + # On the last step, the result is the final prediction + if i == n_steps - 1: + x = pred_x1 + break + + # --- Construct the next state x_{t_next} --- + # The probability of a token being noise at time t_next is (1 - t_next). + noise_prob = 1.0 - t_next + mask = torch.rand(x.shape, device=self.device) < noise_prob + + # Generate new random tokens for the noise positions + noise = torch.randint( + 0, + self.model.tokenizer.vocab_size, + x.shape, + device=self.device + ) + + # Combine the final prediction with noise to form the next intermediate state + x = torch.where(mask, noise, pred_x1) + + # 4. Decode the final token IDs into SMILES strings + generated_sequences = self.model.tokenizer.batch_decode(x) + + # 5. Analyze the validity of the generated sequences + validities = [] + for seq in generated_sequences: + validities.append(self.peptide_analyzer.is_peptide(seq)) + print(seq) + print(f"Initial Sequence Validity: {validities}") + return x + + def _get_scores(self, x_batch): + """Calculates the normalized scores for a batch of sequences.""" + scores = [] + for obj_func in self.objectives: + scores.append(obj_func(x_batch.to(self.device))) + return torch.stack(scores, dim=0).to(self.device) + + def _barker_g(self, u): + """Barker balancing function.""" + return u / (1 + u) + + def validity(self, x): + sampled_sequences = self.model.tokenizer.batch_decode(x) + + return [1.0 if self.peptide_analyzer.is_peptide(seq) else 0.0 for seq in sampled_sequences] + + def generate(self): + """Main generation loop.""" + shape = (self.args.num_samples, self.args.gen_len) + x = self.generate_x0() + print(x) + + if args.weights is None: + # The first weight is for peptide analyzer + # We need to ensure the SMILES sequences are valid peptides + weights = torch.tensor([1] + [1/(self.num_objectives-1)] * (self.num_objectives-1), device=self.device).view(-1,1) + else: + weights = torch.tensor(self.args.weights, device=self.device).view(-1, 1) + if len(weights) != self.num_objectives: + raise ValueError("Number of weights must match number of objectives.") + print(f"Weights: {weights}") + + with torch.no_grad(): + for t in tqdm(range(self.args.optimization_steps), desc="MOG Generation"): + improved = False + # Anneal guidance strength + eta_t = self.args.eta_min + (self.args.eta_max - self.args.eta_min) * (t / (self.args.optimization_steps - 1)) + # Choose a random position to mutate + mut_idx = random.randint(1, self.args.gen_len-2) + + # Determine the generation timestep + # We cycle through the timesteps to ensure all are visited + generation_step = t % self.args.optimization_steps + time_t = torch.full((self.args.num_samples,), (generation_step / self.args.optimization_steps), device=self.device) + + # Get proposal distribution from ReDi model for the chosen position + logits = self.model(x, time_t) + probs = F.softmax(logits, dim=-1) + pos_probs = probs[:, mut_idx, :] + pos_probs[:, x[:, mut_idx]] = 0 # We don't evalute the same token + pos_probs[:, self.invalid] = 0 + + # Prune candidate vocabulary using top-p sampling + sorted_probs, sorted_indices = torch.sort(pos_probs, descending=True) + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) + remove_mask = cumulative_probs > self.args.top_p + remove_mask[..., 1:] = remove_mask[..., :-1].clone() + remove_mask[..., 0] = 0 + + # Get the set of candidate tokens for each sample in the batch + candidate_tokens_list = [] + for i in range(self.args.num_samples): + sample_mask = remove_mask[i] + candidates = sorted_indices[i, ~sample_mask] + candidate_tokens_list.append(candidates) + + # Get current scores + current_scores = self._get_scores(x) + w_current = torch.exp(eta_t * torch.min(weights * current_scores, dim=0).values) + + if t == 0: + print(f"Initial Scores: {current_scores}") + + # Evaluate all candidate tokens for each sample + final_proposal_tokens = [] + for i in range(self.args.num_samples): + candidates = candidate_tokens_list[i] + candidates = torch.tensor([token for token in candidates if token not in self.invalid], device=candidates.device) + if len(candidates) >= 200: + candidates = candidates[:200] + num_candidates = len(candidates) + + # Create a batch of proposed sequences for the current sample + x_prop_batch = x[i].repeat(num_candidates, 1) + x_prop_batch[:, mut_idx] = candidates + + # Evaluate all proposals + proposal_scores = self._get_scores(x_prop_batch) + proposal_s_omega = torch.min(weights * proposal_scores, dim=0).values + w_proposal = torch.exp(eta_t * proposal_s_omega) + + # Get ReDi probabilities for the candidates + redi_probs = pos_probs[i, candidates] + + # Calculate unnormalized guided probabilities + tilde_q = redi_probs * self._barker_g(w_proposal / w_current[i]) + + # Normalize and sample the final token + final_probs = tilde_q / (torch.sum(tilde_q) + 1e-9) + + index = torch.multinomial(final_probs, 1).item() + + if (random.uniform(0,1) > 1 and proposal_scores[:,index][0] == 1) or torch.sum(weights.squeeze(1) * proposal_scores[:, index]) >= torch.sum(weights.squeeze(1) * current_scores[:,i]): + final_token = candidates[index] + print(f"Previous Weighted Sum: {torch.sum(weights.squeeze(1) * current_scores[:,i])}") + print(f"Previous Scores: {current_scores[:,i]}") + + print(f"New Weighted Sum: {torch.sum(weights.squeeze(1) * proposal_scores[:, index])}") + print(f"New Scores: {proposal_scores[:,index]}") + improved = True + + else: + final_token = x[i][mut_idx] + + final_proposal_tokens.append(final_token) + + # Update the sequences with the chosen tokens + x[torch.arange(self.args.num_samples), mut_idx] = torch.stack(final_proposal_tokens) + if improved: + print(self.model.tokenizer.batch_decode(x)) + + return x + + +# --- Main Execution --- +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + + # --- Set random seeds for reproducibility --- + if args.seed is not None: + import random + import numpy as np + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + print(f"Random seed set to: {args.seed}") + + target = args.target + tokenizer = SMILES_SPE_Tokenizer('./smiles/smiles_tokenizer/new_vocab.txt', './smiles/smiles_tokenizer/new_splits.txt') + + analyzer_model = Analyzer(tokenizer) + # hemolysis_model = Hemolysis(device) + # nonfouling_model = Nonfouling(device) + # solubility_model = Solubility(device) + # permeability_model = Permeability(device) + affinity_model = BindingAffinity(target, device) + + # List of all objective functions + OBJECTIVE_FUNCTIONS = [analyzer_model, affinity_model] + + # --- Load Model --- + print(f"Loading model from checkpoint: {args.checkpoint}") + checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False) + model_hparams = checkpoint["hyper_parameters"]["args"] + + model = MDLMLightningModule.load_from_checkpoint( + args.checkpoint, + args=model_hparams, + tokenizer=tokenizer, + map_location=device, + strict=False + ) + model.to(device) + print("Model loaded successfully.") + + mog_generator = MOGGenerator(model, device, OBJECTIVE_FUNCTIONS, args) + + for _ in range(args.num_batches): + generated_tokens = mog_generator.generate() + final_scores = mog_generator._get_scores(generated_tokens.detach()).detach().cpu().numpy() + sequence_str = tokenizer.batch_decode(generated_tokens) + + print(sequence_str) + print(final_scores) + + if args.output_file: + with open(args.output_file, "a") as f: + for seq in sequence_str: + f.write(seq + "\n") + + print("Generation complete.") + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Multi-Objective Generation with LBP-MOG-ReDi (Single Mutation).") + + parser.add_argument("--checkpoint", type=str, required=True, help="Path to the trained ReDi model checkpoint.") + parser.add_argument("--num_samples", type=int, default=10, help="Number of samples to generate.") + parser.add_argument("--num_batches", type=int, default=10, help="Number of samples to generate.") + parser.add_argument("--output_file", type=str, default="./smiles.txt", help="File to save the generated sequences.") + parser.add_argument("--gen_len", type=int, default=50, help="Length of the sequences to generate.") + parser.add_argument("--optimization_steps", type=int, default=16, help="Number of passes over the sequence.") + parser.add_argument("--weights", type=float, nargs='+', required=False, help="Weights for the objectives (e.g., 0.5 0.5).") + parser.add_argument("--eta_min", type=float, default=1.0, help="Minimum guidance strength for annealing.") + parser.add_argument("--eta_max", type=float, default=20.0, help="Maximum guidance strength for annealing.") + parser.add_argument("--top_p", type=float, default=0.9, help="Top-p for pruning candidate tokens.") + parser.add_argument("--seed", type=int, default=None, help="Random seed for reproducibility.") + + parser.add_argument("--target", type=str, required=True) + args = parser.parse_args() + main(args) diff --git a/smiles/generation/staged_aa.py b/smiles/generation/staged_aa.py new file mode 100644 index 0000000000000000000000000000000000000000..a24e89541fe24edcbaaf7636d73b4ea1c24878d7 --- /dev/null +++ b/smiles/generation/staged_aa.py @@ -0,0 +1,535 @@ +"""Natural-AA variant of staged_uaa.py.""" +import argparse +import os +import re +import random +import sys +from concurrent.futures import ThreadPoolExecutor +import torch +import torch.nn as nn +import torch.nn.functional as F +from tqdm import tqdm +from smiles.models.train import MDLMLightningModule, PeptideAnalyzer +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +from easydict import EasyDict as edict + +from smiles.scoring.classifiers import Analyzer, BindingAffinity + +# AA-mode shared utilities +from smiles.models.aa_helpers import ( + ESM2AminoTokenizer as _AAEsmTokenizer, + MDLM_PepReDi as _AAMDLM, + make_affinity, + DEFAULT_AA_AFFINITY_CKPT, + INVALID_AA_TOKEN_IDS as _AA_INVALID_IDS, +) + +# Import AA-to-SMILES conversion utilities +from third_party.SMILESToPeptide.peptide_to_smiles import aa_to_smiles +from third_party.SMILESToPeptide.aminoacid_selective import one_letter_to_three_letter + + +MDLM = _AAMDLM +AminoTokenizer = _AAEsmTokenizer + +def peptide_bond_mask(smiles_list): + # Initialize the batch mask + batch_size = len(smiles_list) + max_seq_length = 1035 #max(len(smiles) for smiles in smiles_list) # Find the longest SMILES + mask = torch.zeros((batch_size, max_seq_length), dtype=torch.int) # Mask filled with zeros + + bond_patterns = [ + (r'OC\(=O\)', 'ester'), + (r'N\(C\)C\(=O\)', 'n_methyl'), + (r'N[12]C\(=O\)', 'peptide'), # Pro peptide bonds + (r'NC\(=O\)', 'peptide'), # Regular peptide bonds + (r'C\(=O\)N\(C\)', 'n_methyl'), + (r'C\(=O\)N[12]?', 'peptide') + ] + + for batch_idx, smiles in enumerate(smiles_list): + positions = [] + used = set() + + # Identify bonds + for pattern, bond_type in bond_patterns: + for match in re.finditer(pattern, smiles): + if not any(p in range(match.start(), match.end()) for p in used): + positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': bond_type, + 'pattern': match.group() + }) + used.update(range(match.start(), match.end())) + + # Update the mask for the current SMILES + for pos in positions: + mask[batch_idx, pos['start']:pos['end']] = 1 + + return mask + +def peptide_token_mask(smiles_list, token_lists): + # Initialize the batch mask + batch_size = len(smiles_list) + token_seq_length = max(len(tokens) for tokens in token_lists) # Find the longest tokenized sequence + tokenized_masks = torch.zeros((batch_size, token_seq_length), dtype=torch.int) # Mask filled with zeros + atomwise_masks = peptide_bond_mask(smiles_list) + + + for batch_idx, atomwise_mask in enumerate(atomwise_masks): + token_seq = token_lists[batch_idx] + atom_idx = 0 + + for token_idx, token in enumerate(token_seq): + if token_idx != 0 and token_idx != len(token_seq) - 1: + if torch.sum(atomwise_mask[atom_idx:atom_idx+len(token)]) >= 1: + tokenized_masks[batch_idx][token_idx] = 1 + atom_idx += len(token) + + return tokenized_masks + +class MOGGenerator: + def __init__(self, model, device, objectives, args, tokenizer, tokenizer_type='smiles'): + self.model = model + self.device = device + self.objectives = objectives + self.args = args + self.num_objectives = len(objectives) + self.peptide_analyzer = PeptideAnalyzer() + self.tokenizer = tokenizer + self.tokenizer_type = tokenizer_type + + # Set invalid tokens based on tokenizer type + if tokenizer_type == 'amino': + # PepReDi natural-AA vocab is 24 tokens; 0-3 = cls/pad/eos/unk, + # 4-23 = the 20 canonical AAs. Block specials only. (UAA also + # blocked id 38 ('g' D-AA), which is out-of-range for PepReDi.) + self.invalid = [0, 1, 2, 3] + # Initialize SMILES tokenizer for AA-to-SMILES conversion + self.smiles_tokenizer = SMILES_SPE_Tokenizer( + args.smiles_vocab_path, + args.smiles_splits_path + ) + else: + self.invalid = [0, 1, 2, 3, 4, 585, 586] # SMILES special tokens + self.smiles_tokenizer = None + + def generate_x0(self, n_steps=16, temperature=1.0): + print("Starting initial sequence generation...") + self.model.eval() + + # Get vocab size based on tokenizer type + if self.tokenizer_type == 'amino': + vocab_size = self.model.vocab_size + else: + vocab_size = self.model.tokenizer.vocab_size + + # 1. Start with a tensor of random tokens (pure noise at t=0) + # For amino tokenizer, exclude invalid tokens from initialization + if self.tokenizer_type == 'amino': + valid_tokens = [i for i in range(vocab_size) if i not in self.invalid] + valid_tokens = torch.tensor(valid_tokens, device=self.device) + rand_indices = torch.randint(0, len(valid_tokens), (self.args.num_samples, self.args.gen_len), device=self.device) + body = valid_tokens[rand_indices] + # Add CLS and EOS tokens + cls_col = torch.full((self.args.num_samples, 1), self.tokenizer.cls_id, device=self.device) + eos_col = torch.full((self.args.num_samples, 1), self.tokenizer.eos_id, device=self.device) + x = torch.cat([cls_col, body, eos_col], dim=1) + else: + x = torch.randint( + 0, + vocab_size, + (self.args.num_samples, self.args.gen_len), + device=self.device + ) + + # 2. Define the time schedule for the forward process (0.0 to 1.0) + time_steps = torch.linspace(0.0, 1.0, n_steps + 1, device=self.device) + + # 3. Iteratively follow the flow from noise to data + with torch.no_grad(): + for i in tqdm(range(n_steps), desc="Flow Matching Steps"): + t_curr = time_steps[i] + t_next = time_steps[i+1] + + # Prepare the current timestep tensor for the model + t_tensor = torch.full((self.args.num_samples,), t_curr, device=self.device) + + # Get the model's prediction for the final clean sequence (at t=1) + logits = self.model(x, t_tensor) + logits = logits / temperature + + # Mask out invalid tokens + for inv_tok in self.invalid: + if inv_tok < logits.shape[-1]: + logits[:, :, inv_tok] = -1000 + + pred_x1 = torch.argmax(logits, dim=-1) + + # On the last step, the result is the final prediction + if i == n_steps - 1: + x = pred_x1 + break + + # --- Construct the next state x_{t_next} --- + # The probability of a token being noise at time t_next is (1 - t_next). + noise_prob = 1.0 - t_next + mask = torch.rand(x.shape, device=self.device) < noise_prob + + # Generate new random tokens for the noise positions + noise = torch.randint( + 0, + vocab_size, + x.shape, + device=self.device + ) + + # Combine the final prediction with noise to form the next intermediate state + x = torch.where(mask, noise, pred_x1) + + # 4. Decode the final token IDs into strings + generated_sequences = self.tokenizer.batch_decode(x) + + # 5. Analyze the validity of the generated sequences + validities = [] + for seq in generated_sequences: + if self.tokenizer_type == 'amino': + # For amino, just print the AA sequence + print(seq) + validities.append(True) # AA sequences are always "valid" structurally + else: + validities.append(self.peptide_analyzer.is_peptide(seq)) + print(seq) + print(f"Initial Sequence Validity: {validities}") + return x + + def _get_scores(self, x_batch): + """Calculates the normalized scores for a batch of sequences.""" + scores = [] + + if self.tokenizer_type == 'amino': + # Convert AA tokens to SMILES for scoring + seqs = self.tokenizer.batch_decode(x_batch) + + # Convert one-letter AA codes to three-letter, then to SMILES + three_letter_seqs = [one_letter_to_three_letter(seq) for seq in seqs] + with ThreadPoolExecutor() as pool: + smiles_seqs = list(pool.map( + lambda s: aa_to_smiles(s, cyclic=self.args.cyclic), + three_letter_seqs + )) + + # Track valid conversions + valid_mask = torch.tensor( + [1 if isinstance(seq, str) else 0 for seq in smiles_seqs], + device=self.device + ) + + # Replace invalid SMILES with dummy 'C' for tokenization + for i in torch.where((1 - valid_mask) == 1)[0].tolist(): + smiles_seqs[i] = 'C' + + # Tokenize SMILES for objective functions + smiles_batch = self.smiles_tokenizer( + smiles_seqs, return_tensors='pt', padding=True + ).to(self.device) + attention_mask = smiles_batch.get("attention_mask", None) + + # Evaluate each objective. AA-input objectives (input_type=='aa') + # get the raw AA tokens; SMILES-input objectives get the converted + # SMILES tokens (and are zeroed for failed AA->SMILES conversions). + for obj_func in self.objectives: + if getattr(obj_func, "input_type", None) == "aa": + score = obj_func(x_batch.to(self.device)).to(self.device) + scores.append(score) + else: + try: + score = obj_func( + smiles_batch["input_ids"], + attention_mask=attention_mask, + ).to(self.device) + except TypeError: + score = obj_func(smiles_batch["input_ids"]).to(self.device) + scores.append(score * valid_mask) # Zero out invalid conversions + else: + # Original SMILES-based scoring + for obj_func in self.objectives: + scores.append(obj_func(x_batch.to(self.device))) + + return torch.stack(scores, dim=0).to(self.device) + + def _barker_g(self, u): + """Barker balancing function.""" + return u / (1 + u) + + def validity(self, x): + sampled_sequences = self.tokenizer.batch_decode(x) + + if self.tokenizer_type == 'amino': + # For amino tokenizer, convert to SMILES first and check validity + three_letter_seqs = [one_letter_to_three_letter(seq) for seq in sampled_sequences] + smiles_seqs = [aa_to_smiles(three_letter_seq, cyclic=self.args.cyclic) + for three_letter_seq in three_letter_seqs] + return [1.0 if isinstance(seq, str) and self.peptide_analyzer.is_peptide(seq) else 0.0 + for seq in smiles_seqs] + else: + return [1.0 if self.peptide_analyzer.is_peptide(seq) else 0.0 for seq in sampled_sequences] + + def generate(self): + """Main generation loop.""" + shape = (self.args.num_samples, self.args.gen_len) + x = self.generate_x0() + print(x) + + if args.weights is None: + # The first weight is for peptide analyzer + # We need to ensure the SMILES sequences are valid peptides + weights = torch.tensor([1] + [1/(self.num_objectives-1)] * (self.num_objectives-1), device=self.device).view(-1,1) + else: + weights = torch.tensor(self.args.weights, device=self.device).view(-1, 1) + if len(weights) != self.num_objectives: + raise ValueError("Number of weights must match number of objectives.") + print(f"Weights: {weights}") + + with torch.no_grad(): + current_scores = self._get_scores(x) + for t in tqdm(range(self.args.optimization_steps), desc="MOG Generation"): + improved = False + # Anneal guidance strength + eta_t = self.args.eta_min + (self.args.eta_max - self.args.eta_min) * (t / (self.args.optimization_steps - 1)) + # Choose a random position to mutate + mut_idx = random.randint(1, self.args.gen_len-2) + + # Determine the generation timestep + # We cycle through the timesteps to ensure all are visited + generation_step = t % self.args.optimization_steps + time_t = torch.full((self.args.num_samples,), (generation_step / self.args.optimization_steps), device=self.device) + + # Get proposal distribution from ReDi model for the chosen position + logits = self.model(x, time_t) + probs = F.softmax(logits, dim=-1) + pos_probs = probs[:, mut_idx, :] + pos_probs[:, x[:, mut_idx]] = 0 # We don't evalute the same token + pos_probs[:, self.invalid] = 0 + + # Prune candidate vocabulary using top-p sampling + sorted_probs, sorted_indices = torch.sort(pos_probs, descending=True) + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) + remove_mask = cumulative_probs > self.args.top_p + remove_mask[..., 1:] = remove_mask[..., :-1].clone() + remove_mask[..., 0] = 0 + + # Get the set of candidate tokens for each sample in the batch + candidate_tokens_list = [] + for i in range(self.args.num_samples): + sample_mask = remove_mask[i] + candidates = sorted_indices[i, ~sample_mask] + candidate_tokens_list.append(candidates) + + w_current = torch.exp(eta_t * torch.min(weights * current_scores, dim=0).values) + + if t == 0: + print(f"Initial Scores: {current_scores}") + + # Evaluate all candidate tokens for each sample + final_proposal_tokens = [] + for i in range(self.args.num_samples): + candidates = candidate_tokens_list[i] + candidates = torch.tensor([token for token in candidates if token not in self.invalid], device=candidates.device) + if len(candidates) >= 200: + candidates = candidates[:200] + num_candidates = len(candidates) + + # Create a batch of proposed sequences for the current sample + x_prop_batch = x[i].repeat(num_candidates, 1) + x_prop_batch[:, mut_idx] = candidates + + # Evaluate all proposals + proposal_scores = self._get_scores(x_prop_batch) + proposal_s_omega = torch.min(weights * proposal_scores, dim=0).values + w_proposal = torch.exp(eta_t * proposal_s_omega) + + # Get ReDi probabilities for the candidates + redi_probs = pos_probs[i, candidates] + + # Calculate unnormalized guided probabilities + tilde_q = redi_probs * self._barker_g(w_proposal / w_current[i]) + + # Normalize and sample the final token + final_probs = tilde_q / (torch.sum(tilde_q) + 1e-9) + + index = torch.multinomial(final_probs, 1).item() + + if (random.uniform(0,1) > 1 and proposal_scores[:,index][0] == 1) or torch.sum(weights.squeeze(1) * proposal_scores[:, index]) >= torch.sum(weights.squeeze(1) * current_scores[:,i]): + final_token = candidates[index] + print(f"Previous Weighted Sum: {torch.sum(weights.squeeze(1) * current_scores[:,i])}") + print(f"Previous Scores: {current_scores[:,i]}") + + print(f"New Weighted Sum: {torch.sum(weights.squeeze(1) * proposal_scores[:, index])}") + print(f"New Scores: {proposal_scores[:,index]}") + improved = True + + else: + final_token = x[i][mut_idx] + + final_proposal_tokens.append(final_token) + + # Update the sequences with the chosen tokens + x[torch.arange(self.args.num_samples), mut_idx] = torch.stack(final_proposal_tokens) + if improved: + current_scores = self._get_scores(x) + print(self.tokenizer.batch_decode(x)) + + return x + + +# --- Main Execution --- +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + + # --- Set random seeds for reproducibility --- + if args.seed is not None: + import random + import numpy as np + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + print(f"Random seed set to: {args.seed}") + + target = args.target + + # --- Initialize tokenizer based on type --- + if args.tokenizer_type == 'amino': + print("Using Amino Acid tokenizer") + tokenizer = AminoTokenizer() + smiles_tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + else: + print("Using SMILES tokenizer") + tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + smiles_tokenizer = tokenizer + + # --- Initialize objective functions --- + # Conjugation-relevant analyzer always uses SMILES tokens. + analyzer_model = Analyzer(smiles_tokenizer) + # Binding affinity: AA-input or SMILES-input depending on --affinity_type. + affinity_model = make_affinity( + args.affinity_type, + target_protein_seq=target, + device=device, + aa_ckpt_path=args.aa_affinity_ckpt, + ) + print(f"Affinity scorer: {type(affinity_model).__name__} (input_type={getattr(affinity_model, 'input_type', '?')})") + + # List of all objective functions + OBJECTIVE_FUNCTIONS = [analyzer_model, affinity_model] + + # --- Load Model --- + print(f"Loading model from checkpoint: {args.checkpoint}") + checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False) + + if args.tokenizer_type == 'amino': + # Vanilla PyTorch checkpoint for amino model + raw_model_args = checkpoint['args'] + if isinstance(raw_model_args, argparse.Namespace): + raw_model_args = vars(raw_model_args) + model_args = edict(raw_model_args) + model = MDLM( + vocab_size=model_args.vocab_size, + seq_len=model_args.seq_len, + model_dim=model_args.model_dim, + n_heads=model_args.n_heads, + n_layers=model_args.n_layers + ).to(device) + model.load_state_dict(checkpoint['model_state_dict']) + print(f"Amino model loaded. Vocab size: {model_args.vocab_size}") + else: + # PyTorch Lightning checkpoint for SMILES model + model_hparams = checkpoint["hyper_parameters"]["args"] + model = MDLMLightningModule.load_from_checkpoint( + args.checkpoint, + args=model_hparams, + tokenizer=tokenizer, + map_location=device, + strict=False + ) + model.to(device) + model.eval() + print("Model loaded successfully.") + + mog_generator = MOGGenerator( + model, device, OBJECTIVE_FUNCTIONS, args, + tokenizer=tokenizer, tokenizer_type=args.tokenizer_type + ) + + for _ in range(args.num_batches): + generated_tokens = mog_generator.generate() + final_scores = mog_generator._get_scores(generated_tokens.detach()).detach().cpu().numpy() + sequence_str = tokenizer.batch_decode(generated_tokens) + + print(sequence_str) + print(final_scores) + + if args.output_file: + with open(args.output_file, "a") as f: + for i, seq in enumerate(sequence_str): + if args.tokenizer_type == 'amino': + # Also save converted SMILES for amino sequences + three_letter = one_letter_to_three_letter(seq) + smiles = aa_to_smiles(three_letter, cyclic=args.cyclic) + if isinstance(smiles, str): + f.write(f"{seq}\t{smiles}\n") + else: + f.write(f"{seq}\tINVALID_SMILES\n") + else: + f.write(seq + "\n") + + print("Generation complete.") + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Multi-Objective Generation with LBP-MOG-ReDi (Single Mutation).") + + parser.add_argument("--checkpoint", type=str, required=True, help="Path to the trained ReDi model checkpoint.") + parser.add_argument("--num_samples", type=int, default=10, help="Number of samples to generate.") + parser.add_argument("--num_batches", type=int, default=10, help="Number of batches to generate.") + parser.add_argument("--output_file", type=str, default="./smiles.txt", help="File to save the generated sequences.") + parser.add_argument("--gen_len", type=int, default=50, help="Length of the sequences to generate.") + parser.add_argument("--optimization_steps", type=int, default=16, help="Number of optimization passes.") + parser.add_argument("--weights", type=float, nargs='+', required=False, help="Weights for the objectives.") + parser.add_argument("--eta_min", type=float, default=1.0, help="Minimum guidance strength.") + parser.add_argument("--eta_max", type=float, default=20.0, help="Maximum guidance strength.") + parser.add_argument("--top_p", type=float, default=0.9, help="Top-p for pruning candidate tokens.") + parser.add_argument("--seed", type=int, default=None, help="Random seed for reproducibility.") + + parser.add_argument("--target", type=str, required=True, help="Target protein sequence for binding affinity.") + + # Tokenizer and model type arguments + parser.add_argument("--tokenizer_type", type=str, default="smiles", choices=["smiles", "amino"], + help="Type of tokenizer: 'smiles' for SMILES SPE tokenizer, 'amino' for amino acid tokenizer.") + parser.add_argument("--cyclic", action="store_true", + help="Generate cyclic peptides (only for amino tokenizer).") + parser.add_argument("--smiles_vocab_path", type=str, + default="./smiles/smiles_tokenizer/new_vocab.txt", + help="Path to SMILES tokenizer vocabulary file.") + parser.add_argument("--smiles_splits_path", type=str, + default="./smiles/smiles_tokenizer/new_splits.txt", + help="Path to SMILES tokenizer splits file.") + + # AA-mode binding-affinity selection (this is the WT-AA / PepReDi script). + parser.add_argument("--affinity_type", type=str, default="smiles", + choices=["aa", "smiles"], + help="Which binding-affinity oracle to use during " + "scoring: 'aa' = AA-input UnpooledBindingPredictor, " + "'smiles' = SMILES-input BindingAffinity. " + "Default 'aa'.") + parser.add_argument("--aa_affinity_ckpt", type=str, + default=DEFAULT_AA_AFFINITY_CKPT, + help="Path to AA-input affinity predictor checkpoint.") + args = parser.parse_args() + main(args) diff --git a/smiles/generation/staged_uaa.py b/smiles/generation/staged_uaa.py new file mode 100644 index 0000000000000000000000000000000000000000..47556593218e0fd3d238bddaaf511fdb737d8137 --- /dev/null +++ b/smiles/generation/staged_uaa.py @@ -0,0 +1,654 @@ +import argparse +import os +import re +import random +import sys +from concurrent.futures import ThreadPoolExecutor +import torch +import torch.nn as nn +import torch.nn.functional as F +from tqdm import tqdm +from smiles.models.train import MDLMLightningModule, PeptideAnalyzer +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +from easydict import EasyDict as edict + +from smiles.scoring.classifiers import Analyzer, BindingAffinity + +# Import AA-to-SMILES conversion utilities +from third_party.SMILESToPeptide.peptide_to_smiles import aa_to_smiles +from third_party.SMILESToPeptide.aminoacid_selective import one_letter_to_three_letter + + +# --- Model Architecture for Amino Acid Model --- +def modulate(x, shift, scale): + """Modulates the input tensor x with a shift and scale.""" + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class TimestepEmbedder(nn.Module): + """Embeds a continuous scalar timestep t in [0, 1] into a vector representation.""" + def __init__(self, hidden_size): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(1, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + + def forward(self, t): + return self.mlp(t.unsqueeze(-1)) + + +class DiTBlock(nn.Module): + """A single block of the Diffusion Transformer.""" + def __init__(self, hidden_size, n_heads): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = nn.MultiheadAttention(hidden_size, n_heads, batch_first=True) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.mlp = nn.Sequential( + nn.Linear(hidden_size, 4 * hidden_size), + nn.GELU(), + nn.Linear(4 * hidden_size, hidden_size) + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + def forward(self, x, c): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1) + x_norm1 = modulate(self.norm1(x), shift_msa, scale_msa) + attn_output, _ = self.attn(x_norm1, x_norm1, x_norm1) + x = x + gate_msa.unsqueeze(1) * attn_output + x_norm2 = modulate(self.norm2(x), shift_mlp, scale_mlp) + mlp_output = self.mlp(x_norm2) + x = x + gate_mlp.unsqueeze(1) * mlp_output + return x + + +class MDLM(nn.Module): + """Masked Diffusion Language Model (MDLM) using a DiT backbone.""" + def __init__(self, vocab_size, seq_len, model_dim, n_heads, n_layers): + super().__init__() + self.vocab_size = vocab_size + self.seq_len = seq_len + self.model_dim = model_dim + + self.token_embedder = nn.Embedding(vocab_size, model_dim) + self.pos_embedder = nn.Parameter(torch.randn(1, seq_len, model_dim)) + self.time_embedder = TimestepEmbedder(model_dim) + + self.transformer_blocks = nn.ModuleList([ + DiTBlock(model_dim, n_heads) for _ in range(n_layers) + ]) + + self.final_norm = nn.LayerNorm(model_dim) + self.lm_head = nn.Linear(model_dim, vocab_size) + + def forward(self, x, t): + seq_len = x.shape[1] + x_embed = self.token_embedder(x) + self.pos_embedder[:, :seq_len, :] + t_embed = self.time_embedder(t) + for block in self.transformer_blocks: + x_embed = block(x_embed, t_embed) + x_embed = self.final_norm(x_embed) + logits = self.lm_head(x_embed) + return logits + + +class AminoTokenizer: + """Tokenizer for amino acid sequences (natural and unnatural AAs).""" + def __init__(self): + self.vocab = { + '': 0, '': 1, '': 2, '': 3, + 'L': 4, 'A': 5, 'G': 6, 'V': 7, 'S': 8, 'E': 9, + 'R': 10, 'T': 11, 'I': 12, 'D': 13, 'P': 14, 'K': 15, + 'Q': 16, 'N': 17, 'F': 18, 'Y': 19, 'M': 20, 'H': 21, + 'W': 22, 'C': 23, + # Non-canonical amino acids + 'Ŷ': 24, 'Ĉ': 25, 'Ĝ': 26, 'Ė': 27, 'Ƙ': 28, + 'Ṃ': 29, 'Ł': 30, 'Ċ': 31, 'Ṯ': 32, + # Lowercase tokens (D-amino acids) + 'a': 33, 'c': 34, 'd': 35, 'e': 36, 'f': 37, + 'g': 38, 'h': 39, 'i': 40, 'k': 41, 'l': 42, + 'm': 43, 'n': 44, 'p': 45, 'q': 46, 'r': 47, + 's': 48, 't': 49, 'v': 50, 'w': 51, 'y': 52, + } + self.id_to_token = {i: t for t, i in self.vocab.items()} + + self.cls_token = '' + self.pad_token = '' + self.eos_token = '' + self.unk_token = '' + + self.cls_id = self.vocab[self.cls_token] + self.pad_id = self.vocab[self.pad_token] + self.eos_id = self.vocab[self.eos_token] + self.unk_id = self.vocab[self.unk_token] + + self.vocab_size = len(self.vocab.keys()) + + self._ncaa_to_nat_token = { + 'Ŷ': 'Y', 'Ĉ': 'C', 'Ċ': 'C', 'Ĝ': 'G', 'Ė': 'E', + 'Ƙ': 'K', 'Ṃ': 'M', 'Ł': 'L', 'Ṯ': 'T', + } + + id_map = torch.arange(self.vocab_size, dtype=torch.long) + for ncaa_tok, nat_tok in self._ncaa_to_nat_token.items(): + if ncaa_tok in self.vocab and nat_tok in self.vocab: + id_map[self.vocab[ncaa_tok]] = self.vocab[nat_tok] + self._ncaa_to_nat_id_map = id_map + + def encode(self, seq: str, add_special_tokens: bool = True): + token_ids = [] + if add_special_tokens: + token_ids.append(self.cls_id) + for ch in seq: + token_ids.append(self.vocab.get(ch, self.unk_id)) + if add_special_tokens: + token_ids.append(self.eos_id) + return token_ids + + def batch_encode(self, seqs, add_special_tokens: bool = True): + return torch.tensor([self.encode(s, add_special_tokens=add_special_tokens) for s in seqs]) + + def decode(self, ids, skip_special_tokens: bool = True): + if isinstance(ids, torch.Tensor): + ids = ids.tolist() + tokens = [] + for i in ids: + tok = self.id_to_token.get(int(i), self.unk_token) + if skip_special_tokens and tok in {self.cls_token, self.eos_token, self.pad_token}: + continue + tokens.append(tok) + return ''.join(tokens) + + def batch_decode(self, batch_ids, skip_special_tokens: bool = True): + return [self.decode(ids, skip_special_tokens=skip_special_tokens) for ids in batch_ids] + + def to_natural_ids(self, batch_ids: torch.Tensor) -> torch.Tensor: + """Map non-canonical AA token IDs to natural AA token IDs.""" + if not torch.is_tensor(batch_ids): + batch_ids = torch.tensor(batch_ids, dtype=torch.long) + id_map = self._ncaa_to_nat_id_map.to(device=batch_ids.device) + out = batch_ids.clone() + valid = (out >= 0) & (out < id_map.numel()) + out[valid] = id_map[out[valid]] + out[~valid] = self.unk_id + return out + +def peptide_bond_mask(smiles_list): + # Initialize the batch mask + batch_size = len(smiles_list) + max_seq_length = 1035 #max(len(smiles) for smiles in smiles_list) # Find the longest SMILES + mask = torch.zeros((batch_size, max_seq_length), dtype=torch.int) # Mask filled with zeros + + bond_patterns = [ + (r'OC\(=O\)', 'ester'), + (r'N\(C\)C\(=O\)', 'n_methyl'), + (r'N[12]C\(=O\)', 'peptide'), # Pro peptide bonds + (r'NC\(=O\)', 'peptide'), # Regular peptide bonds + (r'C\(=O\)N\(C\)', 'n_methyl'), + (r'C\(=O\)N[12]?', 'peptide') + ] + + for batch_idx, smiles in enumerate(smiles_list): + positions = [] + used = set() + + # Identify bonds + for pattern, bond_type in bond_patterns: + for match in re.finditer(pattern, smiles): + if not any(p in range(match.start(), match.end()) for p in used): + positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': bond_type, + 'pattern': match.group() + }) + used.update(range(match.start(), match.end())) + + # Update the mask for the current SMILES + for pos in positions: + mask[batch_idx, pos['start']:pos['end']] = 1 + + return mask + +def peptide_token_mask(smiles_list, token_lists): + # Initialize the batch mask + batch_size = len(smiles_list) + token_seq_length = max(len(tokens) for tokens in token_lists) # Find the longest tokenized sequence + tokenized_masks = torch.zeros((batch_size, token_seq_length), dtype=torch.int) # Mask filled with zeros + atomwise_masks = peptide_bond_mask(smiles_list) + + + for batch_idx, atomwise_mask in enumerate(atomwise_masks): + token_seq = token_lists[batch_idx] + atom_idx = 0 + + for token_idx, token in enumerate(token_seq): + if token_idx != 0 and token_idx != len(token_seq) - 1: + if torch.sum(atomwise_mask[atom_idx:atom_idx+len(token)]) >= 1: + tokenized_masks[batch_idx][token_idx] = 1 + atom_idx += len(token) + + return tokenized_masks + +class MOGGenerator: + def __init__(self, model, device, objectives, args, tokenizer, tokenizer_type='smiles'): + self.model = model + self.device = device + self.objectives = objectives + self.args = args + self.num_objectives = len(objectives) + self.peptide_analyzer = PeptideAnalyzer() + self.tokenizer = tokenizer + self.tokenizer_type = tokenizer_type + + # Set invalid tokens based on tokenizer type + if tokenizer_type == 'amino': + self.invalid = [0, 1, 2, 3, 38] # special tokens + 'g' for amino + # Initialize SMILES tokenizer for AA-to-SMILES conversion + self.smiles_tokenizer = SMILES_SPE_Tokenizer( + args.smiles_vocab_path, + args.smiles_splits_path + ) + else: + self.invalid = [0, 1, 2, 3, 4, 585, 586] # SMILES special tokens + self.smiles_tokenizer = None + + def generate_x0(self, n_steps=16, temperature=1.0): + print("Starting initial sequence generation...") + self.model.eval() + + # Get vocab size based on tokenizer type + if self.tokenizer_type == 'amino': + vocab_size = self.model.vocab_size + else: + vocab_size = self.model.tokenizer.vocab_size + + # 1. Start with a tensor of random tokens (pure noise at t=0) + # For amino tokenizer, exclude invalid tokens from initialization + if self.tokenizer_type == 'amino': + valid_tokens = [i for i in range(vocab_size) if i not in self.invalid] + valid_tokens = torch.tensor(valid_tokens, device=self.device) + rand_indices = torch.randint(0, len(valid_tokens), (self.args.num_samples, self.args.gen_len), device=self.device) + body = valid_tokens[rand_indices] + # Add CLS and EOS tokens + cls_col = torch.full((self.args.num_samples, 1), self.tokenizer.cls_id, device=self.device) + eos_col = torch.full((self.args.num_samples, 1), self.tokenizer.eos_id, device=self.device) + x = torch.cat([cls_col, body, eos_col], dim=1) + else: + x = torch.randint( + 0, + vocab_size, + (self.args.num_samples, self.args.gen_len), + device=self.device + ) + + # 2. Define the time schedule for the forward process (0.0 to 1.0) + time_steps = torch.linspace(0.0, 1.0, n_steps + 1, device=self.device) + + # 3. Iteratively follow the flow from noise to data + with torch.no_grad(): + for i in tqdm(range(n_steps), desc="Flow Matching Steps"): + t_curr = time_steps[i] + t_next = time_steps[i+1] + + # Prepare the current timestep tensor for the model + t_tensor = torch.full((self.args.num_samples,), t_curr, device=self.device) + + # Get the model's prediction for the final clean sequence (at t=1) + logits = self.model(x, t_tensor) + logits = logits / temperature + + # Mask out invalid tokens + for inv_tok in self.invalid: + if inv_tok < logits.shape[-1]: + logits[:, :, inv_tok] = -1000 + + pred_x1 = torch.argmax(logits, dim=-1) + + # On the last step, the result is the final prediction + if i == n_steps - 1: + x = pred_x1 + break + + # --- Construct the next state x_{t_next} --- + # The probability of a token being noise at time t_next is (1 - t_next). + noise_prob = 1.0 - t_next + mask = torch.rand(x.shape, device=self.device) < noise_prob + + # Generate new random tokens for the noise positions + noise = torch.randint( + 0, + vocab_size, + x.shape, + device=self.device + ) + + # Combine the final prediction with noise to form the next intermediate state + x = torch.where(mask, noise, pred_x1) + + # 4. Decode the final token IDs into strings + generated_sequences = self.tokenizer.batch_decode(x) + + # 5. Analyze the validity of the generated sequences + validities = [] + for seq in generated_sequences: + if self.tokenizer_type == 'amino': + # For amino, just print the AA sequence + print(seq) + validities.append(True) # AA sequences are always "valid" structurally + else: + validities.append(self.peptide_analyzer.is_peptide(seq)) + print(seq) + print(f"Initial Sequence Validity: {validities}") + return x + + def _get_scores(self, x_batch): + """Calculates the normalized scores for a batch of sequences.""" + scores = [] + + if self.tokenizer_type == 'amino': + # Convert AA tokens to SMILES for scoring + seqs = self.tokenizer.batch_decode(x_batch) + + # Convert one-letter AA codes to three-letter, then to SMILES + three_letter_seqs = [one_letter_to_three_letter(seq) for seq in seqs] + with ThreadPoolExecutor() as pool: + smiles_seqs = list(pool.map( + lambda s: aa_to_smiles(s, cyclic=self.args.cyclic), + three_letter_seqs + )) + + # Track valid conversions + valid_mask = torch.tensor( + [1 if isinstance(seq, str) else 0 for seq in smiles_seqs], + device=self.device + ) + + # Replace invalid SMILES with dummy 'C' for tokenization + for i in torch.where((1 - valid_mask) == 1)[0].tolist(): + smiles_seqs[i] = 'C' + + # Tokenize SMILES for objective functions + smiles_batch = self.smiles_tokenizer( + smiles_seqs, return_tensors='pt', padding=True + ).to(self.device) + attention_mask = smiles_batch.get("attention_mask", None) + + # Evaluate each objective function on SMILES tokens + for obj_func in self.objectives: + try: + score = obj_func( + smiles_batch["input_ids"], + attention_mask=attention_mask, + ).to(self.device) + except TypeError: + score = obj_func(smiles_batch["input_ids"]).to(self.device) + scores.append(score * valid_mask) # Zero out invalid conversions + else: + # Original SMILES-based scoring + for obj_func in self.objectives: + scores.append(obj_func(x_batch.to(self.device))) + + return torch.stack(scores, dim=0).to(self.device) + + def _barker_g(self, u): + """Barker balancing function.""" + return u / (1 + u) + + def validity(self, x): + sampled_sequences = self.tokenizer.batch_decode(x) + + if self.tokenizer_type == 'amino': + # For amino tokenizer, convert to SMILES first and check validity + three_letter_seqs = [one_letter_to_three_letter(seq) for seq in sampled_sequences] + smiles_seqs = [aa_to_smiles(three_letter_seq, cyclic=self.args.cyclic) + for three_letter_seq in three_letter_seqs] + return [1.0 if isinstance(seq, str) and self.peptide_analyzer.is_peptide(seq) else 0.0 + for seq in smiles_seqs] + else: + return [1.0 if self.peptide_analyzer.is_peptide(seq) else 0.0 for seq in sampled_sequences] + + def generate(self): + """Main generation loop.""" + shape = (self.args.num_samples, self.args.gen_len) + x = self.generate_x0() + print(x) + + if args.weights is None: + # The first weight is for peptide analyzer + # We need to ensure the SMILES sequences are valid peptides + weights = torch.tensor([1] + [1/(self.num_objectives-1)] * (self.num_objectives-1), device=self.device).view(-1,1) + else: + weights = torch.tensor(self.args.weights, device=self.device).view(-1, 1) + if len(weights) != self.num_objectives: + raise ValueError("Number of weights must match number of objectives.") + print(f"Weights: {weights}") + + with torch.no_grad(): + current_scores = self._get_scores(x) + for t in tqdm(range(self.args.optimization_steps), desc="MOG Generation"): + improved = False + # Anneal guidance strength + eta_t = self.args.eta_min + (self.args.eta_max - self.args.eta_min) * (t / (self.args.optimization_steps - 1)) + # Choose a random position to mutate + mut_idx = random.randint(1, self.args.gen_len-2) + + # Determine the generation timestep + # We cycle through the timesteps to ensure all are visited + generation_step = t % self.args.optimization_steps + time_t = torch.full((self.args.num_samples,), (generation_step / self.args.optimization_steps), device=self.device) + + # Get proposal distribution from ReDi model for the chosen position + logits = self.model(x, time_t) + probs = F.softmax(logits, dim=-1) + pos_probs = probs[:, mut_idx, :] + pos_probs[:, x[:, mut_idx]] = 0 # We don't evalute the same token + pos_probs[:, self.invalid] = 0 + + # Prune candidate vocabulary using top-p sampling + sorted_probs, sorted_indices = torch.sort(pos_probs, descending=True) + cumulative_probs = torch.cumsum(sorted_probs, dim=-1) + remove_mask = cumulative_probs > self.args.top_p + remove_mask[..., 1:] = remove_mask[..., :-1].clone() + remove_mask[..., 0] = 0 + + # Get the set of candidate tokens for each sample in the batch + candidate_tokens_list = [] + for i in range(self.args.num_samples): + sample_mask = remove_mask[i] + candidates = sorted_indices[i, ~sample_mask] + candidate_tokens_list.append(candidates) + + w_current = torch.exp(eta_t * torch.min(weights * current_scores, dim=0).values) + + if t == 0: + print(f"Initial Scores: {current_scores}") + + # Evaluate all candidate tokens for each sample + final_proposal_tokens = [] + for i in range(self.args.num_samples): + candidates = candidate_tokens_list[i] + candidates = torch.tensor([token for token in candidates if token not in self.invalid], device=candidates.device) + if len(candidates) >= 200: + candidates = candidates[:200] + num_candidates = len(candidates) + + # Create a batch of proposed sequences for the current sample + x_prop_batch = x[i].repeat(num_candidates, 1) + x_prop_batch[:, mut_idx] = candidates + + # Evaluate all proposals + proposal_scores = self._get_scores(x_prop_batch) + proposal_s_omega = torch.min(weights * proposal_scores, dim=0).values + w_proposal = torch.exp(eta_t * proposal_s_omega) + + # Get ReDi probabilities for the candidates + redi_probs = pos_probs[i, candidates] + + # Calculate unnormalized guided probabilities + tilde_q = redi_probs * self._barker_g(w_proposal / w_current[i]) + + # Normalize and sample the final token + final_probs = tilde_q / (torch.sum(tilde_q) + 1e-9) + + index = torch.multinomial(final_probs, 1).item() + + if (random.uniform(0,1) > 1 and proposal_scores[:,index][0] == 1) or torch.sum(weights.squeeze(1) * proposal_scores[:, index]) >= torch.sum(weights.squeeze(1) * current_scores[:,i]): + final_token = candidates[index] + print(f"Previous Weighted Sum: {torch.sum(weights.squeeze(1) * current_scores[:,i])}") + print(f"Previous Scores: {current_scores[:,i]}") + + print(f"New Weighted Sum: {torch.sum(weights.squeeze(1) * proposal_scores[:, index])}") + print(f"New Scores: {proposal_scores[:,index]}") + improved = True + + else: + final_token = x[i][mut_idx] + + final_proposal_tokens.append(final_token) + + # Update the sequences with the chosen tokens + x[torch.arange(self.args.num_samples), mut_idx] = torch.stack(final_proposal_tokens) + if improved: + current_scores = self._get_scores(x) + print(self.tokenizer.batch_decode(x)) + + return x + + +# --- Main Execution --- +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + + # --- Set random seeds for reproducibility --- + if args.seed is not None: + import random + import numpy as np + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(args.seed) + torch.cuda.manual_seed_all(args.seed) + print(f"Random seed set to: {args.seed}") + + target = args.target + + # --- Initialize tokenizer based on type --- + if args.tokenizer_type == 'amino': + print("Using Amino Acid tokenizer") + tokenizer = AminoTokenizer() + smiles_tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + else: + print("Using SMILES tokenizer") + tokenizer = SMILES_SPE_Tokenizer(args.smiles_vocab_path, args.smiles_splits_path) + smiles_tokenizer = tokenizer + + # --- Initialize objective functions --- + # Use SMILES tokenizer for objectives (they expect SMILES tokens) + analyzer_model = Analyzer(smiles_tokenizer) + affinity_model = BindingAffinity(target, device) + + # List of all objective functions + OBJECTIVE_FUNCTIONS = [analyzer_model, affinity_model] + + # --- Load Model --- + print(f"Loading model from checkpoint: {args.checkpoint}") + checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False) + + if args.tokenizer_type == 'amino': + # Vanilla PyTorch checkpoint for amino model + raw_model_args = checkpoint['args'] + if isinstance(raw_model_args, argparse.Namespace): + raw_model_args = vars(raw_model_args) + model_args = edict(raw_model_args) + model = MDLM( + vocab_size=model_args.vocab_size, + seq_len=model_args.seq_len, + model_dim=model_args.model_dim, + n_heads=model_args.n_heads, + n_layers=model_args.n_layers + ).to(device) + model.load_state_dict(checkpoint['model_state_dict']) + print(f"Amino model loaded. Vocab size: {model_args.vocab_size}") + else: + # PyTorch Lightning checkpoint for SMILES model + model_hparams = checkpoint["hyper_parameters"]["args"] + model = MDLMLightningModule.load_from_checkpoint( + args.checkpoint, + args=model_hparams, + tokenizer=tokenizer, + map_location=device, + strict=False + ) + model.to(device) + model.eval() + print("Model loaded successfully.") + + mog_generator = MOGGenerator( + model, device, OBJECTIVE_FUNCTIONS, args, + tokenizer=tokenizer, tokenizer_type=args.tokenizer_type + ) + + for _ in range(args.num_batches): + generated_tokens = mog_generator.generate() + final_scores = mog_generator._get_scores(generated_tokens.detach()).detach().cpu().numpy() + sequence_str = tokenizer.batch_decode(generated_tokens) + + print(sequence_str) + print(final_scores) + + if args.output_file: + with open(args.output_file, "a") as f: + for i, seq in enumerate(sequence_str): + if args.tokenizer_type == 'amino': + # Also save converted SMILES for amino sequences + three_letter = one_letter_to_three_letter(seq) + smiles = aa_to_smiles(three_letter, cyclic=args.cyclic) + if isinstance(smiles, str): + f.write(f"{seq}\t{smiles}\n") + else: + f.write(f"{seq}\tINVALID_SMILES\n") + else: + f.write(seq + "\n") + + print("Generation complete.") + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Multi-Objective Generation with LBP-MOG-ReDi (Single Mutation).") + + parser.add_argument("--checkpoint", type=str, required=True, help="Path to the trained ReDi model checkpoint.") + parser.add_argument("--num_samples", type=int, default=10, help="Number of samples to generate.") + parser.add_argument("--num_batches", type=int, default=10, help="Number of batches to generate.") + parser.add_argument("--output_file", type=str, default="./smiles.txt", help="File to save the generated sequences.") + parser.add_argument("--gen_len", type=int, default=50, help="Length of the sequences to generate.") + parser.add_argument("--optimization_steps", type=int, default=16, help="Number of optimization passes.") + parser.add_argument("--weights", type=float, nargs='+', required=False, help="Weights for the objectives.") + parser.add_argument("--eta_min", type=float, default=1.0, help="Minimum guidance strength.") + parser.add_argument("--eta_max", type=float, default=20.0, help="Maximum guidance strength.") + parser.add_argument("--top_p", type=float, default=0.9, help="Top-p for pruning candidate tokens.") + parser.add_argument("--seed", type=int, default=None, help="Random seed for reproducibility.") + + parser.add_argument("--target", type=str, required=True, help="Target protein sequence for binding affinity.") + + # Tokenizer and model type arguments + parser.add_argument("--tokenizer_type", type=str, default="smiles", choices=["smiles", "amino"], + help="Type of tokenizer: 'smiles' for SMILES SPE tokenizer, 'amino' for amino acid tokenizer.") + parser.add_argument("--cyclic", action="store_true", + help="Generate cyclic peptides (only for amino tokenizer).") + parser.add_argument("--smiles_vocab_path", type=str, + default="./smiles/smiles_tokenizer/new_vocab.txt", + help="Path to SMILES tokenizer vocabulary file.") + parser.add_argument("--smiles_splits_path", type=str, + default="./smiles/smiles_tokenizer/new_splits.txt", + help="Path to SMILES tokenizer splits file.") + args = parser.parse_args() + main(args) diff --git a/smiles/generation/unconditional.py b/smiles/generation/unconditional.py new file mode 100644 index 0000000000000000000000000000000000000000..09732126ff7ab7d6940af56c94c6329295813417 --- /dev/null +++ b/smiles/generation/unconditional.py @@ -0,0 +1,158 @@ +import argparse +from pathlib import Path + +import torch +import torch.nn.functional as F +from tqdm import tqdm + +# Import necessary classes from your training script +from smiles.models.train import MDLMLightningModule, PeptideAnalyzer +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer + +import pdb + + +def generate_smiles(model, tokenizer, args): + """ + Generates peptide SMILES strings using the trained MDLM model + with a forward (t=0 to t=1) flow matching process. + + Args: + model (MDLMLightningModule): The trained PyTorch Lightning model. + tokenizer (SMILES_SPE_Tokenizer): The tokenizer used for training. + args (argparse.Namespace): Command-line arguments containing sampling parameters. + + Returns: + list[str]: A list of generated SMILES strings. + float: The validity rate of the generated SMILES. + """ + print("Starting SMILES generation with forward flow matching (t=0 to t=1)...") + model.eval() + device = args.device + + # 1. Start with a tensor of random tokens (pure noise at t=0) + x = torch.randint( + 0, + model.model.vocab_size, + (args.n_samples, args.seq_len), + device=device + ) + + # 2. Define the time schedule for the forward process (0.0 to 1.0) + time_steps = torch.linspace(0.0, 1.0, args.n_steps + 1, device=device) + + # 3. Iteratively follow the flow from noise to data + with torch.no_grad(): + for i in tqdm(range(args.n_steps), desc="Flow Matching Steps"): + t_curr = time_steps[i] + t_next = time_steps[i+1] + + # Prepare the current timestep tensor for the model + t_tensor = torch.full((args.n_samples,), t_curr, device=device) + + # Get the model's prediction for the final clean sequence (at t=1) + logits = model(x, t_tensor) + logits = logits / args.temperature + + pred_x1 = torch.argmax(logits, dim=-1) + + # On the last step, the result is the final prediction + if i == args.n_steps - 1: + x = pred_x1 + break + + # --- Construct the next state x_{t_next} --- + # The probability of a token being noise at time t_next is (1 - t_next). + noise_prob = 1.0 - t_next + mask = torch.rand(x.shape, device=device) < noise_prob + + # Generate new random tokens for the noise positions + noise = torch.randint( + 0, + model.model.vocab_size, + x.shape, + device=device + ) + + # Combine the final prediction with noise to form the next intermediate state + x = torch.where(mask, noise, pred_x1) + + # 4. Decode the final token IDs into SMILES strings + generated_sequences = tokenizer.batch_decode(x) + + # 5. Analyze the validity of the generated sequences + peptide_analyzer = PeptideAnalyzer() + valid_count = 0 + valid_smiles = [] + for seq in generated_sequences: + if peptide_analyzer.is_peptide(seq): + valid_count += 1 + valid_smiles.append(seq) + + validity_rate = valid_count / len(generated_sequences) + + print(f"\nGeneration complete. Validity rate: {validity_rate:.2%}") + return valid_smiles, validity_rate + + +def main(): + parser = argparse.ArgumentParser(description="Sample from a trained ReDi model.") + + # --- Required Arguments --- + parser.add_argument("--checkpoint_path", type=str, required=True, help="Path to the model checkpoint (.ckpt file).") + + # --- Sampling Arguments --- + parser.add_argument("--n_samples", type=int, default=16, help="Number of SMILES strings to generate.") + parser.add_argument("--seq_len", type=int, default=256, help="Maximum sequence length for generated SMILES.") + parser.add_argument("--n_steps", type=int, default=100, help="Number of denoising steps for sampling.") + parser.add_argument("--temperature", type=float, default=1.0, help="Sampling temperature. Higher values increase diversity.") + + # --- Environment Arguments --- + parser.add_argument("--vocab_path", type=str, default='./smiles/smiles_tokenizer/new_vocab.txt', help="Path to tokenizer vocabulary file.") + parser.add_argument("--splits_path", type=str, default='./smiles/smiles_tokenizer/new_splits.txt', help="Path to tokenizer splits file.") + parser.add_argument("--output_file", type=str, default="generated_smiles.txt", help="File to save the valid generated SMILES.") + + args = parser.parse_args() + + # Set up device + device = "cuda" if torch.cuda.is_available() else "cpu" + args.device = device + print(f"Using device: {device}") + + # --- Load Model and Tokenizer --- + print("Loading tokenizer...") + tokenizer = SMILES_SPE_Tokenizer(args.vocab_path, args.splits_path) + + print(f"Loading model from checkpoint: {args.checkpoint_path}") + # Load hyperparameters from the checkpoint to ensure model architecture matches + checkpoint = torch.load(args.checkpoint_path, map_location=device, weights_only=False) + model_hparams = checkpoint["hyper_parameters"]["args"] + + # Instantiate the model with the loaded hyperparameters + model = MDLMLightningModule.load_from_checkpoint( + args.checkpoint_path, + args=model_hparams, + tokenizer=tokenizer, + map_location=device, + strict=True # Recommended if you have updated the code since training + ) + model.to(device) + + # --- Generate SMILES --- + valid_smiles, validity_rate = generate_smiles(model, tokenizer, args) + + # pdb.set_trace() + + if "base" in args.checkpoint_path: + version = "v0" + elif "v1" in args.checkpoint_path: + version = "v1" + + with open(f'./samples/unconditional/{version}_samples_len{args.seq_len}_nsteps{args.n_steps}_val{round(validity_rate*100)}.csv', 'a') as f: + for smiles in valid_smiles: + # print(smiles) + f.write(smiles + '\n') + print(validity_rate) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/smiles/models/__init__.py b/smiles/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f813689d927097a3d738b1bf9fa284c7074d705b --- /dev/null +++ b/smiles/models/__init__.py @@ -0,0 +1 @@ +"""ReDi model definitions and data utilities.""" diff --git a/smiles/models/aa_helpers.py b/smiles/models/aa_helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..ffcbc939afdd9281aaced9a5d331db51e29950a0 --- /dev/null +++ b/smiles/models/aa_helpers.py @@ -0,0 +1,288 @@ +"""Shared utilities for the natural-AA generation pipeline.""" + +from __future__ import annotations + +import os +import sys +from concurrent.futures import ThreadPoolExecutor +from typing import List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def _import_aa2smiles(): + from third_party.SMILESToPeptide.peptide_to_smiles import aa_to_smiles # noqa: WPS433 + from third_party.SMILESToPeptide.aminoacid_selective import one_letter_to_three_letter # noqa: WPS433 + return aa_to_smiles, one_letter_to_three_letter + + +def aa_to_smiles_batch(seqs: List[str], cyclic: bool = False, max_workers: Optional[int] = None + ) -> Tuple[List[bool], List[str]]: + """Convert a batch of one-letter peptide strings to SMILES.""" + aa_to_smiles, one_letter_to_three_letter = _import_aa2smiles() + three_letter_seqs = [one_letter_to_three_letter(s) for s in seqs] + with ThreadPoolExecutor(max_workers=max_workers) as pool: + smiles_seqs = list(pool.map(lambda s: aa_to_smiles(s, cyclic=cyclic), three_letter_seqs)) + valid_mask = [isinstance(s, str) for s in smiles_seqs] + smiles_seqs = [s if isinstance(s, str) else "C" for s in smiles_seqs] + return valid_mask, smiles_seqs + + +# ESM2 vocab layout (facebook/esm2_t33_650M_UR50D): +# 0=, 1=, 2=, 3=, 32= +# AAs at 4..23: L A G V S E R T I D P K Q N F Y M H W C +# 24..31 are: X B U Z O . - (treat as invalid for natural AAs) +NATURAL_AA_TOKEN_IDS: List[int] = list(range(4, 24)) # 20 canonical AAs +INVALID_AA_TOKEN_IDS: List[int] = [0, 1, 2, 3] + list(range(24, 33)) + + +class ESM2AminoTokenizer: + """Wrapper around HuggingFace ESM2 AutoTokenizer with the AminoTokenizer interface.""" + + def __init__(self, model_name: str = "facebook/esm2_t33_650M_UR50D"): + from transformers import AutoTokenizer # noqa: WPS433 + self._tokenizer = AutoTokenizer.from_pretrained(model_name) + + self.cls_token = self._tokenizer.cls_token or "" + self.pad_token = self._tokenizer.pad_token or "" + self.eos_token = self._tokenizer.eos_token or "" + self.unk_token = self._tokenizer.unk_token or "" + + self.cls_id = self._tokenizer.cls_token_id + self.pad_id = self._tokenizer.pad_token_id + self.eos_id = self._tokenizer.eos_token_id + self.unk_id = self._tokenizer.unk_token_id + + self.vocab_size = self._tokenizer.vocab_size + + self.vocab = {} + for tok in "LAGVSERTIDPKQNFYMHWC": + ids = self._tokenizer(tok, add_special_tokens=False)["input_ids"] + if len(ids) == 1: + self.vocab[tok] = int(ids[0]) + + def encode(self, seq: str, add_special_tokens: bool = True) -> List[int]: + return self._tokenizer(seq, add_special_tokens=add_special_tokens)["input_ids"] + + def batch_encode(self, seqs: List[str], add_special_tokens: bool = True) -> torch.Tensor: + out = self._tokenizer(seqs, add_special_tokens=add_special_tokens, padding=True, + return_tensors="pt") + return out["input_ids"] + + def decode(self, ids, skip_special_tokens: bool = True) -> str: + if isinstance(ids, torch.Tensor): + ids = ids.tolist() + s = self._tokenizer.decode(ids, skip_special_tokens=skip_special_tokens) + return s.replace(" ", "") + + def batch_decode(self, batch_ids, skip_special_tokens: bool = True) -> List[str]: + if isinstance(batch_ids, torch.Tensor): + batch_ids = batch_ids.tolist() + out = self._tokenizer.batch_decode(batch_ids, skip_special_tokens=skip_special_tokens) + return [s.replace(" ", "") for s in out] + + def to_natural_ids(self, batch_ids: torch.Tensor) -> torch.Tensor: + return batch_ids + + def __call__(self, *args, **kwargs): + return self._tokenizer(*args, **kwargs) + + +def _modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class _TimestepEmbedder(nn.Module): + def __init__(self, hidden_size): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(1, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + + def forward(self, t): + return self.mlp(t.unsqueeze(-1)) + + +class _DiTBlock(nn.Module): + def __init__(self, hidden_size, n_heads): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = nn.MultiheadAttention(hidden_size, n_heads, batch_first=True) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.mlp = nn.Sequential( + nn.Linear(hidden_size, 4 * hidden_size), + nn.GELU(), + nn.Linear(4 * hidden_size, hidden_size), + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + def forward(self, x, c): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = \ + self.adaLN_modulation(c).chunk(6, dim=1) + x_norm1 = _modulate(self.norm1(x), shift_msa, scale_msa) + attn_output, _ = self.attn(x_norm1, x_norm1, x_norm1) + x = x + gate_msa.unsqueeze(1) * attn_output + x_norm2 = _modulate(self.norm2(x), shift_mlp, scale_mlp) + mlp_output = self.mlp(x_norm2) + x = x + gate_mlp.unsqueeze(1) * mlp_output + return x + + +class MDLM_PepReDi(nn.Module): + """Discrete-flow model with vocab_size+1 embedding for the mask token.""" + + def __init__(self, vocab_size, seq_len, model_dim, n_heads, n_layers): + super().__init__() + self.vocab_size = vocab_size + self.seq_len = seq_len + self.model_dim = model_dim + self.mask_token_id = vocab_size + + self.token_embedder = nn.Embedding(vocab_size + 1, model_dim) + self.pos_embedder = nn.Parameter(torch.randn(1, seq_len, model_dim)) + self.time_embedder = _TimestepEmbedder(model_dim) + + self.transformer_blocks = nn.ModuleList( + [_DiTBlock(model_dim, n_heads) for _ in range(n_layers)] + ) + + self.final_norm = nn.LayerNorm(model_dim) + self.lm_head = nn.Linear(model_dim, vocab_size) + + def forward(self, x, t): + seq_len = x.shape[1] + x_embed = self.token_embedder(x) + self.pos_embedder[:, :seq_len, :] + t_embed = self.time_embedder(t) + for block in self.transformer_blocks: + x_embed = block(x_embed, t_embed) + x_embed = self.final_norm(x_embed) + logits = self.lm_head(x_embed) + return logits + + +DEFAULT_AA_AFFINITY_CKPT = os.path.join( + os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), + "checkpoints", + "binding_affinity_unpooled.pt", +) + + +class BindingAffinityAA(nn.Module): + """AA-input binding-affinity scorer.""" + + input_type = "aa" + + def __init__(self, target_protein_seq: str, device: torch.device, + ckpt_path: str = DEFAULT_AA_AFFINITY_CKPT, + disable_cache: bool = False): + super().__init__() + self.device = device + + from transformers import AutoTokenizer # noqa: WPS433 + try: + sys.path.insert(0, os.environ.get('PEPTIDE_CLASSIFIERS_PATH', './peptide')) + from peptide_classifiers import ( # noqa: WPS433 + load_affinity_predictor, + AffinityModel, + ) + except Exception: + sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../../peptide")) + from peptide_classifiers import ( # noqa: WPS433 + load_affinity_predictor, + AffinityModel, + ) + + esm_tok = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D") + target_ids = esm_tok(target_protein_seq, return_tensors="pt")["input_ids"].to(device) + self._protein_seq_len = int(target_ids.shape[1]) + + predictor = load_affinity_predictor(ckpt_path, device) + for p in predictor.parameters(): + p.requires_grad = False + predictor.eval() + + # Cache the protein ESM-2 embedding: it doesn't change across calls and + # recomputing it dominates the per-call cost on long proteins. + if not disable_cache: + with torch.no_grad(): + cached_protein_emb = predictor.compute_embeddings(target_ids).detach() + self._cached_protein_emb = cached_protein_emb + self._original_compute_embeddings = predictor.compute_embeddings + + def _cached_compute_embeddings(input_ids, attention_mask=None): + if input_ids.shape[1] == self._protein_seq_len: + bsz = int(input_ids.shape[0]) + if bsz == 1: + return self._cached_protein_emb + return self._cached_protein_emb.expand(bsz, -1, -1) + return self._original_compute_embeddings(input_ids, attention_mask) + + predictor.compute_embeddings = _cached_compute_embeddings + self._cache_enabled = True + else: + self._cached_protein_emb = None + self._cache_enabled = False + + self._wrapped = AffinityModel(predictor, target_ids).to(device) + self._wrapped.eval() + self._predictor = predictor + + self.target_ids = target_ids + + @torch.no_grad() + def forward(self, binder_input_ids: torch.Tensor, **kwargs) -> torch.Tensor: + binder_input_ids = binder_input_ids.to(self.device).long() + return self._wrapped(binder_input_ids) + + def __call__(self, binder_input_ids: torch.Tensor, **kwargs) -> torch.Tensor: + return self.forward(binder_input_ids, **kwargs) + + +class SMILESAffinityWithType: + """Wrapper that adds .input_type='smiles' to BindingAffinity for the per-objective router.""" + + input_type = "smiles" + + def __init__(self, smiles_binding_affinity): + self._inner = smiles_binding_affinity + + def __call__(self, *args, **kwargs): + return self._inner(*args, **kwargs) + + def __getattr__(self, name): + return getattr(self._inner, name) + + +def make_affinity(affinity_type: str, target_protein_seq: str, + device: torch.device, + aa_ckpt_path: Optional[str] = None) -> object: + """Factory: returns an AA-input or SMILES-input binding-affinity scorer.""" + affinity_type = affinity_type.lower() + if affinity_type == "aa": + ckpt = aa_ckpt_path or DEFAULT_AA_AFFINITY_CKPT + return BindingAffinityAA(target_protein_seq, device, ckpt_path=ckpt) + elif affinity_type == "smiles": + from smiles.scoring.classifiers import BindingAffinity # noqa: WPS433 + inner = BindingAffinity(target_protein_seq, device) + return SMILESAffinityWithType(inner) + else: + raise ValueError(f"Unknown affinity_type {affinity_type!r}; expected 'aa' or 'smiles'.") + + +__all__ = [ + "ESM2AminoTokenizer", + "MDLM_PepReDi", + "BindingAffinityAA", + "SMILESAffinityWithType", + "make_affinity", + "aa_to_smiles_batch", + "NATURAL_AA_TOKEN_IDS", + "INVALID_AA_TOKEN_IDS", + "DEFAULT_AA_AFFINITY_CKPT", +] diff --git a/smiles/models/dataloading_for_dynamic_batching.py b/smiles/models/dataloading_for_dynamic_batching.py new file mode 100644 index 0000000000000000000000000000000000000000..10f4be6f7d2a780205b62cadcea27428864e795b --- /dev/null +++ b/smiles/models/dataloading_for_dynamic_batching.py @@ -0,0 +1,210 @@ +#!/usr/bin/env +import torch +from torch.utils.data import Dataset, DataLoader +from datasets import Dataset,load_from_disk +import sys +import pytorch_lightning as pl +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +from functools import partial +import re +from tqdm import tqdm +import os +import pdb + + +class DynamicBatchingDataset(Dataset): + def __init__(self, dataset_dict, tokenizer): + print('Initializing dataset...') + self.dataset_dict = { + 'attention_mask': [torch.tensor(item) for item in tqdm(dataset_dict['attention_mask'])], + 'input_ids': [torch.tensor(item) for item in dataset_dict['input_ids']], + 'labels': dataset_dict['labels'] + } + self.tokenizer = tokenizer + + def __len__(self): + return len(self.dataset_dict['attention_mask']) + + def __getitem__(self, idx): + if isinstance(idx, int): + return { + 'input_ids': self.dataset_dict['input_ids'][idx], + 'attention_mask': self.dataset_dict['attention_mask'][idx], + 'labels': self.dataset_dict['labels'][idx] + } + elif isinstance(idx, list): + return { + 'input_ids': [self.dataset_dict['input_ids'][i] for i in idx], + 'attention_mask': [self.dataset_dict['attention_mask'][i] for i in idx], + 'labels': [self.dataset_dict['labels'][i] for i in idx] + } + else: + raise ValueError(f"Expected idx to be int or list, but got {type(idx)}") + +class CustomDataModule(pl.LightningDataModule): + def __init__(self, dataset_path, tokenizer): + super().__init__() + self.dataset = load_from_disk(dataset_path) + self.tokenizer = tokenizer + self.dataset_path = dataset_path + + def peptide_bond_mask(self, smiles_list): + """ + Returns a mask with shape (batch_size, seq_length) that has 1 at the locations + of recognized bonds in the positions dictionary and 0 elsewhere. + + Args: + smiles_list: List of peptide SMILES strings (batch of SMILES strings). + + Returns: + np.ndarray: A mask of shape (batch_size, seq_length) with 1s at bond positions. + """ + # Initialize the batch mask + batch_size = len(smiles_list) + max_seq_length = 1035 #max(len(smiles) for smiles in smiles_list) # Find the longest SMILES + mask = torch.zeros((batch_size, max_seq_length), dtype=torch.int) # Mask filled with zeros + + bond_patterns = [ + (r'OC\(=O\)', 'ester'), + (r'N\(C\)C\(=O\)', 'n_methyl'), + (r'N[12]C\(=O\)', 'peptide'), # Pro peptide bonds + (r'NC\(=O\)', 'peptide'), # Regular peptide bonds + (r'C\(=O\)N\(C\)', 'n_methyl'), + (r'C\(=O\)N[12]?', 'peptide') + ] + + for batch_idx, smiles in enumerate(smiles_list): + positions = [] + used = set() + + # Identify bonds + for pattern, bond_type in bond_patterns: + for match in re.finditer(pattern, smiles): + if not any(p in range(match.start(), match.end()) for p in used): + positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': bond_type, + 'pattern': match.group() + }) + used.update(range(match.start(), match.end())) + + # Update the mask for the current SMILES + for pos in positions: + mask[batch_idx, pos['start']:pos['end']] = 1 + + return mask + + def peptide_token_mask(self, smiles_list, token_lists): + """ + Returns a mask with shape (batch_size, num_tokens) that has 1 for tokens + where any part of the token overlaps with a peptide bond, and 0 elsewhere. + + Args: + smiles_list: List of peptide SMILES strings (batch of SMILES strings). + token_lists: List of tokenized SMILES strings (split into tokens). + + Returns: + np.ndarray: A mask of shape (batch_size, num_tokens) with 1s for peptide bond tokens. + """ + # Initialize the batch mask + batch_size = len(smiles_list) + token_seq_length = max(len(tokens) for tokens in token_lists) # Find the longest tokenized sequence + tokenized_masks = torch.zeros((batch_size, token_seq_length), dtype=torch.int) # Mask filled with zeros + atomwise_masks = self.peptide_bond_mask(smiles_list) + + + for batch_idx, atomwise_mask in enumerate(atomwise_masks): + token_seq = token_lists[batch_idx] + atom_idx = 0 + + for token_idx, token in enumerate(token_seq): + if token_idx != 0 and token_idx != len(token_seq) - 1: + if torch.sum(atomwise_mask[atom_idx:atom_idx+len(token)]) >= 1: + tokenized_masks[batch_idx][token_idx] = 1 + atom_idx += len(token) + + return tokenized_masks + + def collate_fn(self, batch): + item = batch[0] + # print(item) + # pdb.set_trace() + + token_array = self.tokenizer.get_token_split(item['input_ids']) + bond_mask = self.peptide_token_mask(item['labels'], token_array) + + return { + 'input_ids': item['input_ids'], + 'attention_mask': item['attention_mask'], + 'bond_mask': bond_mask + } + + def _train_dataset(self): + train_dataset = DynamicBatchingDataset(self.dataset['train'], tokenizer=self.tokenizer) + return train_dataset + + def _val_dataset(self): + val_dataset = DynamicBatchingDataset(self.dataset['val'], tokenizer=self.tokenizer) + return val_dataset + + def train_dataloader(self): + train_dataset = self._train_dataset() + # if train_dataset is None: + # # train_dataset = self._train_dataset() + # train_dataset = self.dataset_path + 'train' + + return DataLoader( + train_dataset, + batch_size=1, + collate_fn=self.collate_fn, # Use the instance method + shuffle=True, + num_workers=12, + pin_memory=True + ) + + def val_dataloader(self): + val_dataset = self._val_dataset() + # if val_dataset is None: + # # val_dataset = self._val_dataset() + # val_dataset = self.dataset_path + 'val' + + return DataLoader( + val_dataset, + batch_size=1, + collate_fn=self.collate_fn, # Use the instance method + num_workers=8, + pin_memory=True + ) + +class RectifyDataModule(pl.LightningDataModule): + def __init__(self, dataset_path): + super().__init__() + self.dataset_path = dataset_path + + def collate_fn(self, batch): + return { + 'source_ids': torch.tensor(batch[0]['source_ids']), + 'target_ids': torch.tensor(batch[0]['target_ids']), + 'bond_mask': torch.tensor(batch[0]['bond_mask']), + } + + def train_dataloader(self): + train_dataset = load_from_disk(os.path.join(self.dataset_path, 'train')) + return DataLoader( + train_dataset, + batch_size=1, + collate_fn=self.collate_fn, + num_workers=12, + pin_memory=True + ) + + def val_dataloader(self): + val_dataset = load_from_disk(os.path.join(self.dataset_path, 'validation')) + return DataLoader( + val_dataset, + batch_size=1, + collate_fn=self.collate_fn, + num_workers=8, + pin_memory=True + ) \ No newline at end of file diff --git a/smiles/models/train.py b/smiles/models/train.py new file mode 100644 index 0000000000000000000000000000000000000000..24b5241fda893249700bbe6b27ec18485138546c --- /dev/null +++ b/smiles/models/train.py @@ -0,0 +1,427 @@ +import argparse +import math +import os +from functools import partial +from collections import Counter + +import torch +import torch.nn as nn +import torch.nn.functional as F +from datasets import load_from_disk +from torch.optim import AdamW +from torch.optim.lr_scheduler import LambdaLR +from torch.utils.data import DataLoader +import pytorch_lightning as pl +from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor +from pytorch_lightning.loggers import WandbLogger +from pytorch_lightning.strategies import DDPStrategy +from rdkit import Chem + +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +from smiles.evaluation.peptide_analyzer import PeptideAnalyzer +from smiles.models import dataloading_for_dynamic_batching as dynamic_dataloader + + +class RotaryPositionalEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000): + super().__init__() + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer("inv_freq", inv_freq) + + def forward(self, x, seq_len=None): + if seq_len is None: + seq_len = x.shape[1] + + t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype) + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + emb = torch.cat((freqs, freqs), dim=-1) + + cos_emb = emb.cos()[None, :, :] + sin_emb = emb.sin()[None, :, :] + + return cos_emb, sin_emb + +def rotate_half(x): + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + +def apply_rotary_pos_emb(q, k, cos, sin): + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + +# --- Model Architecture with RoPE --- +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + +class TimestepEmbedder(nn.Module): + def __init__(self, hidden_size): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(1, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + + def forward(self, t): + return self.mlp(t.unsqueeze(-1)) + +class MultiHeadAttentionWithRoPE(nn.Module): + def __init__(self, hidden_size, n_heads): + super().__init__() + self.hidden_size = hidden_size + self.n_heads = n_heads + self.head_dim = hidden_size // n_heads + + assert self.head_dim * n_heads == hidden_size, "hidden_size must be divisible by n_heads" + + self.q_proj = nn.Linear(hidden_size, hidden_size, bias=False) + self.k_proj = nn.Linear(hidden_size, hidden_size, bias=False) + self.v_proj = nn.Linear(hidden_size, hidden_size, bias=False) + self.out_proj = nn.Linear(hidden_size, hidden_size) + + self.rope = RotaryPositionalEmbedding(self.head_dim) + + def forward(self, x): + batch_size, seq_len, hidden_size = x.shape + + q = self.q_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2) + k = self.k_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2) + v = self.v_proj(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2) + + cos, sin = self.rope(q, seq_len) + q, k = apply_rotary_pos_emb(q, k, cos, sin) + + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim) + attn_weights = F.softmax(scores, dim=-1) + attn_output = torch.matmul(attn_weights, v) + + attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_len, hidden_size) + output = self.out_proj(attn_output) + + return output + +class DiTBlock(nn.Module): + def __init__(self, hidden_size, n_heads): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = MultiHeadAttentionWithRoPE(hidden_size, n_heads) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.mlp = nn.Sequential( + nn.Linear(hidden_size, 4 * hidden_size), + nn.GELU(), + nn.Linear(4 * hidden_size, hidden_size) + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + def forward(self, x, c): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1) + x_norm1 = modulate(self.norm1(x), shift_msa, scale_msa) + attn_output = self.attn(x_norm1) + x = x + gate_msa.unsqueeze(1) * attn_output + x_norm2 = modulate(self.norm2(x), shift_mlp, scale_mlp) + mlp_output = self.mlp(x_norm2) + x = x + gate_mlp.unsqueeze(1) * mlp_output + return x + +class MDLM(nn.Module): + def __init__(self, vocab_size, model_dim, n_heads, n_layers): + super().__init__() + self.vocab_size = vocab_size + self.model_dim = model_dim + self.mask_token_id = vocab_size + + self.token_embedder = nn.Embedding(vocab_size, model_dim) + self.time_embedder = TimestepEmbedder(model_dim) + + self.transformer_blocks = nn.ModuleList([ + DiTBlock(model_dim, n_heads) for _ in range(n_layers) + ]) + + self.final_norm = nn.LayerNorm(model_dim) + self.lm_head = nn.Linear(model_dim, vocab_size) + + self.apply(self._init_weights) + + def _init_weights(self, module): + if isinstance(module, (nn.Linear, nn.Embedding)): + module.weight.data.normal_(mean=0.0, std=0.02) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + if module.weight is not None: + module.weight.data.fill_(1.0) + + def forward(self, x, t): + x_embed = self.token_embedder(x) + t_embed = self.time_embedder(t) + for block in self.transformer_blocks: + x_embed = block(x_embed, t_embed) + x_embed = self.final_norm(x_embed) + logits = self.lm_head(x_embed) + return logits + +# --- PyTorch Lightning Module --- +class MDLMLightningModule(pl.LightningModule): + def __init__(self, args, tokenizer): + super().__init__() + self.save_hyperparameters(ignore=['tokenizer']) + self.args = args + self.tokenizer = tokenizer + self.peptide_analyzer = PeptideAnalyzer() + + # Initialize model + self.model = MDLM( + vocab_size=tokenizer.vocab_size, + model_dim=args.model_dim, + n_heads=args.n_heads, + n_layers=args.n_layers + ) + + # For tracking steps + self.automatic_optimization = True + + self.validation_step_outputs = [] + + def forward(self, x, t): + return self.model(x, t) + + def _compute_invalid_loss(self, logits): + batch_token_ids = torch.argmax(logits, dim=-1) # (batch_size, seq_length) + + sampled_sequences = self.tokenizer.batch_decode(batch_token_ids) + + penalties = torch.tensor( + [1 if not self.peptide_analyzer.is_peptide(seq) else 0 for seq in sampled_sequences], + dtype=torch.float32, + device=self.device + ) + sampled_probs = torch.softmax(logits, dim=-1).gather(dim=-1, index=batch_token_ids.unsqueeze(-1)).squeeze(-1).to(self.device) + + scaled_penalty = penalties[:, None] * sampled_probs # (batch_size, seq_length) + + return scaled_penalty + + def _loss(self, logits, x_1, attn_mask): + # Standard cross-entropy loss + ce_loss = F.cross_entropy( + logits.view(-1, self.model.vocab_size), + x_1.view(-1), + reduction='none' + ).view(x_1.shape[0], -1) + # ce_loss = (ce_loss * attn_mask).sum() / attn_mask.sum() + + # validity_weight = self.args.validity_weight * min(1.0, (self.current_epoch + 1) / self.trainer.max_epochs) + + invalid_loss = self._compute_invalid_loss(logits) # (batch_size, seq_length) + + loss = ce_loss + self.args.validity_weight * invalid_loss + nlls = loss * attn_mask + + num_tokens = attn_mask.sum() + batch_nll = nlls.sum() + token_nll = batch_nll / num_tokens + + return token_nll, (ce_loss*attn_mask).sum() / num_tokens, (invalid_loss*attn_mask).sum() / num_tokens + + def training_step(self, batch, batch_idx): + x_1 = batch['input_ids'].clone().detach().to(self.device) + attn_mask = batch['attention_mask'].clone().detach().to(self.device) + bond_mask = batch['bond_mask'].clone().detach().to(self.device).bool() + batch_size, _ = x_1.shape + + # ReDi approach: random start -> target + x_0 = torch.randint(0, self.model.vocab_size, x_1.shape, device=self.device) + t_continuous = torch.rand(batch_size, device=self.device) + # mask = torch.rand(x_1.shape, device=self.device) < t_continuous.view(-1, 1) + # x_t = torch.where(mask, x_1, x_0) + + peptide_bond_prob = t_continuous.view(-1, 1) ** self.args.gamma # slower increase + non_peptide_prob = t_continuous.view(-1, 1) # linear increase + + masking_prob = torch.where(bond_mask, peptide_bond_prob, non_peptide_prob) + mask = torch.rand(x_1.shape, device=self.device) < masking_prob + x_t = torch.where(mask, x_1, x_0) + + logits = self.model(x_t, t_continuous) + + token_nll, ce_loss, invalid_loss = self._loss(logits, x_1, attn_mask) + + # Logging + self.log('train/token_nll', token_nll.item(), on_step=True, on_epoch=True, prog_bar=True, batch_size=x_1.size(0), sync_dist=True) + self.log('train/ce_loss', ce_loss.item(), on_step=True, on_epoch=True, batch_size=x_1.size(0), sync_dist=True) + self.log('train/invalid_loss', invalid_loss.item(), on_step=True, on_epoch=True, batch_size=x_1.size(0), sync_dist=True) + # self.log('train/validity_weight', validity_weight, on_step=False, on_epoch=True, batch_size=x_1.size(0)) + + return token_nll + + def validation_step(self, batch, batch_idx): + x_1 = batch['input_ids'].clone().detach().to(self.device) + attn_mask = batch['attention_mask'].clone().detach().to(self.device) + bond_mask = batch['bond_mask'].clone().detach().to(self.device).bool() + batch_size, _ = x_1.shape + + # ReDi approach: random start -> target + x_0 = torch.randint(0, self.model.vocab_size, x_1.shape, device=self.device) + t_continuous = torch.rand(batch_size, device=self.device) + + peptide_bond_prob = t_continuous.view(-1, 1) ** self.args.gamma # slower increase + non_peptide_prob = t_continuous.view(-1, 1) # linear increase + + masking_prob = torch.where(bond_mask, peptide_bond_prob, non_peptide_prob) + mask = torch.rand(x_1.shape, device=self.device) < masking_prob + x_t = torch.where(mask, x_1, x_0) + + logits = self.model(x_t, t_continuous) + + token_nll, ce_loss, invalid_loss = self._loss(logits, x_1, attn_mask) + self.log('val/token_nll', token_nll.item(), on_step=True, on_epoch=True, prog_bar=True, batch_size=x_1.size(0), sync_dist=True) + self.log('val/ce_loss', ce_loss.item(), on_step=True, on_epoch=True, batch_size=x_1.size(0), sync_dist=True) + self.log('val/invalid_loss', invalid_loss.item(), on_step=True, on_epoch=True, batch_size=x_1.size(0), sync_dist=True) + + def configure_optimizers(self): + optimizer = AdamW( + self.parameters(), + lr=self.args.learning_rate, + weight_decay=self.args.weight_decay + ) + + # Calculate total steps + if hasattr(self.trainer, 'estimated_stepping_batches'): + num_training_steps = self.trainer.estimated_stepping_batches + else: + # Fallback calculation + num_training_steps = len(self.trainer.datamodule.train_dataloader()) * self.trainer.max_epochs + + warmup_steps = int(num_training_steps * 0.1) + + def lr_lambda(current_step): + if current_step < warmup_steps: + lr_range = self.args.learning_rate - (self.args.learning_rate * 0.1) + lr = (self.args.learning_rate * 0.1) + lr_range * (current_step / warmup_steps) + return lr / self.args.learning_rate + else: + progress = (current_step - warmup_steps) / (num_training_steps - warmup_steps) + cosine_decay = 0.5 * (1.0 + math.cos(math.pi * progress)) + lr_range = self.args.learning_rate - (self.args.learning_rate * 0.1) + lr = (self.args.learning_rate * 0.1) + lr_range * cosine_decay + return lr / self.args.learning_rate + + scheduler = LambdaLR(optimizer, lr_lambda) + + return { + "optimizer": optimizer, + "lr_scheduler": { + "scheduler": scheduler, + "interval": "step", + "frequency": 1, + }, + } + + +# --- Main Execution --- +def main(args): + # Set up checkpoint directory + checkpoint_dir = (args.checkpoint_dir + + f"correct_lr{args.learning_rate}_wd{args.weight_decay}_layer{args.n_layers}_" + f"head{args.n_heads}_valweight{args.validity_weight}") + print(f"Saving to {checkpoint_dir}") + os.makedirs(checkpoint_dir, exist_ok=True) + + print("Loading tokenizer...") + tokenizer = SMILES_SPE_Tokenizer('./smiles/smiles_tokenizer/old_vocab.txt', + './smiles/smiles_tokenizer/old_splits.txt') + print(f"Tokenizer loaded. Vocab size: {tokenizer.vocab_size}") + + # Initialize data module + data_module = dynamic_dataloader.CustomDataModule('./data/11M_smiles_old_tokenizer_no_limit/', tokenizer) + + # Initialize model + model = MDLMLightningModule(args, tokenizer) + + # Set up logger + logger = WandbLogger( + project="smiles-redi-training", # or your preferred project name + entity="programmablebio", + name=f"lr{args.learning_rate}_dim{args.model_dim}_head{args.n_heads}_layer{args.n_layers}", + save_dir=checkpoint_dir + ) + + # Set up callbacks + callbacks = [ + ModelCheckpoint( + dirpath=checkpoint_dir, + filename='best', + monitor='val/token_nll', + mode='min', + save_top_k=1, + save_last=True, + # every_n_train_steps=10000 # This will save every 1000 steps AND when val/nll improves + ), + LearningRateMonitor(logging_interval='step') + ] + + # Initialize trainer + trainer = pl.Trainer( + max_epochs=args.epochs, + devices=torch.cuda.device_count(), + accelerator='gpu', + strategy=DDPStrategy(find_unused_parameters=False), + num_nodes=int(os.environ.get("SLURM_NNODES", 1)), + precision="bf16", + gradient_clip_val=args.grad_clip if args.grad_clip > 0 else None, + callbacks=callbacks, + logger=logger, + log_every_n_steps=100, + check_val_every_n_epoch=True, + # val_check_interval=10000, + accumulate_grad_batches=8, + enable_progress_bar=True, + enable_model_summary=True + ) + + print(f"Model initialized with {sum(p.numel() for p in model.parameters()):,} parameters.") + print("Starting training...") + + # Train the model + trainer.fit(model, data_module) + + print("Training complete.") + print(f"Best checkpoint saved at: {trainer.checkpoint_callback.best_model_path}") + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Train ReDi model for SMILES generation with RoPE using PyTorch Lightning") + + # Model arguments + parser.add_argument("--model_dim", type=int, default=1024) + parser.add_argument("--n_heads", type=int, default=8) + parser.add_argument("--n_layers", type=int, default=6) + + # Training arguments + parser.add_argument("--epochs", type=int, default=50) + parser.add_argument("--learning_rate", type=float, default=1e-4) + parser.add_argument("--weight_decay", type=float, default=1e-5) + parser.add_argument("--label_smoothing", type=float, default=0) + parser.add_argument("--grad_clip", type=float, default=1.0) + parser.add_argument("--gamma", type=float, default=2.0) + + # Validity arguments + parser.add_argument("--validity_weight", type=float, default=0.1) + parser.add_argument("--validity_check_freq", type=int, default=10) + parser.add_argument("--validity_eval_batches", type=int, default=20) + + # Logging arguments + parser.add_argument("--checkpoint_dir", type=str, default="./checkpoints_smiles") + + args = parser.parse_args() + main(args) \ No newline at end of file diff --git a/smiles/scoring/__init__.py b/smiles/scoring/__init__.py new file mode 100644 index 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\ No newline at end of file diff --git a/smiles/scoring/classifiers.py b/smiles/scoring/classifiers.py new file mode 100644 index 0000000000000000000000000000000000000000..dcd1c898b81f2887ccebe550686b917b0c608661 --- /dev/null +++ b/smiles/scoring/classifiers.py @@ -0,0 +1,633 @@ +import sys +import os + +sys.path.append(os.path.join(os.path.dirname(__file__))) + +# import xgboost as xgb +import torch +import numpy as np +from transformers import AutoModelForMaskedLM +import warnings +import numpy as np +import esm +import torch.nn as nn +from rdkit import Chem, rdBase +import io +import contextlib + +rdBase.DisableLog('rdApp.error') +warnings.filterwarnings("ignore", category=DeprecationWarning) +warnings.filterwarnings("ignore", category=UserWarning) +warnings.filterwarnings("ignore", category=FutureWarning) + + +class Conjugation: + """ + Conjugation classifier for PDC generation + + Takes generated peptide SMILES, conjugates them using AoaDau builder, + and validates using PDCAnalyzer. + + A sequence is valid (score=1) if: + 1. It is a valid peptide (parseable, has peptide bonds) + 2. It has a Lys ε-amine for conjugation (when exclude_n_terminus=True) + 3. At least one of its PDC conjugates passes the analyzer checks + """ + + # Builder types that conjugate via Cys thiol (disulfide/thioether) + # rather than Lys ε-amine (amide bond) + CYS_THIOL_BUILDERS = {'VcMMAE', 'DSPASN38', 'DSEBSN38', 'DSEPSN38', 'SN38'} + + def __init__(self, tokenizer, device='cpu', builder_type='AoaDau'): + """ + Initialize conjugation classifier + + Args: + tokenizer: SMILES tokenizer for decoding token IDs + device: Device for PDCAnalyzer + builder_type: Type of PDC builder. Options: + - 'AoaDau': AOA-Daunorubicin + - 'ValAlaDau': Val-Ala-PABC-Daunorubicin + - 'ValCitDau': Val-Cit-PABC-Daunorubicin + - 'GlutarylDau': Glutaryl-Daunorubicin + - 'Dau': Drug-only Daunorubicin (linker sampled by + GNRHR+Dau PDCdb frequency: AoaDau 21/27, others 2/27) + - 'SSCpt': Disulfide-Camptothecin (ester + carbonate) + - 'VcMMAE': Val-Cit-MMAE + - 'DSPACpt': Disulfanylpropanoic acid + Camptothecin + - 'MalHexCpt': Maleimide-hexanoate + Camptothecin + - 'DSECCpt': Disulfanylethyl carbonate + Camptothecin + - 'Cpt': Drug-only Camptothecin (linker sampled by + ERBB2+Cpt PDCdb frequency: SSCpt 7/22, others 5/22) + - 'DSPASN38': Disulfanylpropanoic acid + SN-38 (NRP1) + - 'DSEBSN38': 4-(2-Disulfanylethoxy)-4-oxobutanoic acid + SN-38 + - 'DSEPSN38': 5-(2-Disulfanylethoxy)-5-oxopentanoic acid + SN-38 + - 'SN38': Drug-only SN-38 (linker sampled by + NRP1+SN38 PDCdb frequency: DSPA 9/17, DSEB 6/17, DSEP 2/17) + """ + self.tokenizer = tokenizer + self.device = device + self.builder_type = builder_type + + # Import and initialize builder and analyzer + from smiles.evaluation.pdc_analyzer import PDCAnalyzer + self.pdc_analyzer = PDCAnalyzer(device, calculate_properties=False) + + # Initialize the appropriate builder + self._init_builder(builder_type) + + # Tracking for detailed results + self.last_detailed_results = None + + def _init_builder(self, builder_type): + """Initialize the PDC builder based on type""" + if builder_type == 'AoaDau': + from smiles.conjugation.AoaDau import AoaDaunorubicin_PDC_Builder + with contextlib.redirect_stdout(io.StringIO()): + self.builder = AoaDaunorubicin_PDC_Builder() + elif builder_type == 'SSCpt': + from smiles.conjugation.SSCpt import DisulfideCamptothecin_PDC_Builder + with contextlib.redirect_stdout(io.StringIO()): + self.builder = DisulfideCamptothecin_PDC_Builder() + elif builder_type == 'VcMMAE': + from smiles.conjugation.VcMMAE import VcMMAE_PDC_Builder + with contextlib.redirect_stdout(io.StringIO()): + self.builder = VcMMAE_PDC_Builder() + elif builder_type == 'DSPACpt': + from smiles.conjugation.DSPACpt import DisulfanylpropanoicAcidCpt_PDC_Builder + with contextlib.redirect_stdout(io.StringIO()): + self.builder = DisulfanylpropanoicAcidCpt_PDC_Builder() + elif builder_type == 'MalHexCpt': + from smiles.conjugation.MalHexCpt import MaleimideHexanoateCpt_PDC_Builder + with contextlib.redirect_stdout(io.StringIO()): + self.builder = MaleimideHexanoateCpt_PDC_Builder() + elif builder_type == 'DSECCpt': + from smiles.conjugation.DSECCpt import DisulfanylethylCarbonateCpt_PDC_Builder + with contextlib.redirect_stdout(io.StringIO()): + self.builder = DisulfanylethylCarbonateCpt_PDC_Builder() + elif builder_type == 'Cpt': + from smiles.conjugation.CptFactory import CamptothecinPDC_Factory + # Drug-only mode: linker sampled per peptide by PDCdb frequency + self.builder = CamptothecinPDC_Factory(linker='sample') + elif builder_type == 'DSPASN38': + from smiles.conjugation.DSPASN38 import DisulfanylpropanoicAcidSN38_PDC_Builder + with contextlib.redirect_stdout(io.StringIO()): + self.builder = DisulfanylpropanoicAcidSN38_PDC_Builder() + elif builder_type == 'DSEBSN38': + from smiles.conjugation.DSEBSN38 import DisulfanylethOxyButanoicAcidSN38_PDC_Builder + with contextlib.redirect_stdout(io.StringIO()): + self.builder = DisulfanylethOxyButanoicAcidSN38_PDC_Builder() + elif builder_type == 'DSEPSN38': + from smiles.conjugation.DSEPSN38 import DisulfanylethOxyPentanoicAcidSN38_PDC_Builder + with contextlib.redirect_stdout(io.StringIO()): + self.builder = DisulfanylethOxyPentanoicAcidSN38_PDC_Builder() + elif builder_type == 'SN38': + from smiles.conjugation.SN38Factory import SN38PDC_Factory + self.builder = SN38PDC_Factory(linker='sample') + else: + raise ValueError(f"Unknown builder type: {builder_type}") + + def is_valid_peptide(self, smiles): + """Check if SMILES represents a valid peptide structure""" + mol = Chem.MolFromSmiles(smiles) + if mol is None: + return False + + # Look for peptide bonds: NC(=O) pattern + peptide_bond_pattern = Chem.MolFromSmarts('[NH][C](=O)') + n_methyl_pattern = Chem.MolFromSmarts('[N;H0;$(NC)](C)[C](=O)') + + return mol.HasSubstructMatch(peptide_bond_pattern) or mol.HasSubstructMatch(n_methyl_pattern) + + def has_lys_epsilon_amine(self, smiles): + """Check if peptide has Lys ε-amine for conjugation""" + mol = Chem.MolFromSmiles(smiles) + if mol is None: + return False + + # Lys ε-amine: 4 CH2 chain ending in NH2 + lys_pattern = Chem.MolFromSmarts('[CH2][CH2][CH2][CH2][NH2]') + return mol.HasSubstructMatch(lys_pattern) + + def has_cys_thiol(self, smiles): + """Check if peptide has free Cys thiol (-SH) for disulfide conjugation""" + mol = Chem.MolFromSmiles(smiles) + if mol is None: + return False + + # Iterate over atoms to find S with free H (thiol, not disulfide) + for atom in mol.GetAtoms(): + if atom.GetAtomicNum() == 16: # Sulfur + if atom.GetTotalNumHs() >= 1 and atom.GetDegree() == 1: + return True + return False + + def has_conjugation_site(self, smiles): + """Check if peptide has the required conjugation site for this builder type""" + if self.builder_type in self.CYS_THIOL_BUILDERS: + return self.has_cys_thiol(smiles) + return self.has_lys_epsilon_amine(smiles) + + def conjugate_peptide(self, peptide_smiles, exclude_n_terminus=True): + """ + Attempt to conjugate a peptide SMILES + + Args: + peptide_smiles: SMILES string of peptide + exclude_n_terminus: If True, only use Lys ε-amines (default) + + Returns: + Tuple of (success: bool, conjugates: list or None, error: str or None) + """ + try: + # Suppress builder output + with contextlib.redirect_stdout(io.StringIO()): + conjugates = self.builder.conjugate_to_peptide( + peptide_smiles, + exclude_n_terminus=exclude_n_terminus + ) + # Capture which linker was used (meaningful for Factory sampling) + linker_used = getattr(self.builder, 'last_sampled_linker', self.builder_type) + return True, conjugates, None, linker_used + except Exception as e: + return False, None, str(e), None + + def validate_conjugates(self, conjugates): + """ + Validate a list of conjugates using PDCAnalyzer + + Args: + conjugates: List of conjugate SMILES + + Returns: + Tuple of (has_valid: bool, detailed_results: list) + """ + if not conjugates: + return False, [] + + # PDCAnalyzer expects list of lists (candidates per peptide) + detailed = self.pdc_analyzer.get_detailed_scores([conjugates])[0] + + # Check if any conjugate is valid + has_valid = any(r.get('valid', 0) == 1 for r in detailed) + + return has_valid, detailed + + def get_scores(self, x): + """ + Get validity scores for a batch of token sequences + + A sequence scores 1 if: + 1. Valid peptide structure + 2. Has Lys ε-amine (for exclude_n_terminus=True) + 3. At least one PDC conjugate passes analyzer + + Args: + x: Tensor of token IDs (batch_size, seq_len) + + Returns: + Tensor of 0/1 scores + """ + smiles_list = self.tokenizer.batch_decode(x) + scores = [] + detailed_results = [] + + for smiles in smiles_list: + result = { + 'peptide_smiles': smiles, + 'is_valid_peptide': False, + 'has_conjugation_site': False, + 'conjugation_success': False, + 'conjugates': [], + 'pdc_valid': False, + 'pdc_details': [], + 'error': None, + 'final_score': 0 + } + + # Step 1: Check if valid peptide + if not self.is_valid_peptide(smiles): + result['error'] = 'Not a valid peptide structure' + scores.append(0) + detailed_results.append(result) + continue + result['is_valid_peptide'] = True + + # Step 2: Check for conjugation site (Cys thiol or Lys ε-amine) + if not self.has_conjugation_site(smiles): + if self.builder_type in self.CYS_THIOL_BUILDERS: + result['error'] = 'No Cys thiol found for disulfide conjugation' + else: + result['error'] = 'No Lys ε-amine found for conjugation' + scores.append(0) + detailed_results.append(result) + continue + result['has_conjugation_site'] = True # "has conjugation site" on peptide level, not builder-specific at this point + + # Step 3: Attempt conjugation + success, conjugates, error, linker_used = self.conjugate_peptide(smiles, exclude_n_terminus=True) + if not success: + result['error'] = f'Conjugation failed: {error}' + scores.append(0) + detailed_results.append(result) + continue + result['conjugation_success'] = True + result['conjugates'] = conjugates + result['linker_used'] = linker_used + + # Step 4: Validate conjugates with PDCAnalyzer + has_valid, pdc_details = self.validate_conjugates(conjugates) + result['pdc_details'] = pdc_details + result['pdc_valid'] = has_valid + + if has_valid: + result['final_score'] = 1 + scores.append(1) + else: + result['error'] = 'No valid PDC conjugates' + scores.append(0) + + detailed_results.append(result) + + # Store for later retrieval + self.last_detailed_results = detailed_results + + return torch.tensor(scores, dtype=torch.float) + + def get_last_detailed_results(self): + """Get detailed results from the last get_scores call""" + return self.last_detailed_results + + def get_detailed_scores_with_properties(self, x): + """ + Get detailed scores with molecular properties for final sequences + + This enables property calculation and returns full details + """ + # Enable property calculation + self.pdc_analyzer.calculate_properties = True + self.pdc_analyzer.enable_all_properties() + + smiles_list = self.tokenizer.batch_decode(x) + detailed_results = [] + + for smiles in smiles_list: + result = { + 'peptide_smiles': smiles, + 'is_valid_peptide': self.is_valid_peptide(smiles), + 'has_conjugation_site': self.has_conjugation_site(smiles), + 'conjugates': [], + 'linker_used': None, + 'valid_pdcs': [], + 'best_pdc': None, + 'properties': {} + } + + if not result['is_valid_peptide'] or not result['has_conjugation_site']: + detailed_results.append(result) + continue + + # Conjugate + success, conjugates, _, linker_used = self.conjugate_peptide(smiles, exclude_n_terminus=True) + if not success or not conjugates: + detailed_results.append(result) + continue + + result['conjugates'] = conjugates + + # Get detailed validation with properties + pdc_details = self.pdc_analyzer.get_detailed_scores([conjugates])[0] + + # Find valid PDCs + for detail in pdc_details: + if detail.get('valid', 0) == 1: + result['valid_pdcs'].append(detail) + + # Get best PDC (first valid one) + if result['valid_pdcs']: + result['best_pdc'] = result['valid_pdcs'][0] + result['properties'] = result['best_pdc'].get('properties', {}) + + detailed_results.append(result) + + # Reset property calculation to default + self.pdc_analyzer.calculate_properties = False + + return detailed_results + + def __call__(self, x): + """Callable interface for use as objective function""" + return self.get_scores(x) + + +class Analyzer: + + def __init__(self, tokenizer): + self.tokenizer = tokenizer + + def get_scores(self, x): + """Check if the SMILES represents a peptide structure""" + results = [] + + smiles_list = self.tokenizer.batch_decode(x) + for smiles in smiles_list: + mol = Chem.MolFromSmiles(smiles) + if mol is None: + results.append(0) + continue + + # Look for peptide bonds: NC(=O) pattern + peptide_bond_pattern = Chem.MolFromSmarts('[NH][C](=O)') + + # Look for N-methylated peptide bonds: N(C)C(=O) pattern + n_methyl_pattern = Chem.MolFromSmarts('[N;H0;$(NC)](C)[C](=O)') + + if mol.HasSubstructMatch(peptide_bond_pattern) or mol.HasSubstructMatch(n_methyl_pattern): + results.append(1) + else: + results.append(0) + + return torch.tensor(results) + + def __call__(self, x): + scores = self.get_scores(x) + return torch.tensor(scores) + + +# class Hemolysis: + +# def __init__(self, device): +# self.predictor = xgb.Booster(model_file='./scoring/checkpoints/hemolysis-xgboost.json') +# self.emb_model = AutoModelForMaskedLM.from_pretrained('aaronfeller/PeptideCLM-23M-all').roformer.to(device) + +# def get_scores(self, x): +# scores = np.ones(len(x)) +# features = np.array(self.emb_model(input_ids=x).last_hidden_state.mean(dim=1).detach().cpu()) + +# if len(features) == 0: +# return scores + +# features = np.nan_to_num(features, nan=0.) +# features = np.clip(features, np.finfo(np.float32).min, np.finfo(np.float32).max) + +# features = xgb.DMatrix(features) + +# probs = self.predictor.predict(features) +# # return the probability of it being not hemolytic +# return scores - probs + +# def __call__(self, x): +# scores = self.get_scores(x) +# return torch.tensor(scores) + + +# class Nonfouling: + +# def __init__(self, device): +# self.predictor = xgb.Booster(model_file='./scoring/checkpoints/nonfouling-xgboost.json') +# self.emb_model = AutoModelForMaskedLM.from_pretrained('aaronfeller/PeptideCLM-23M-all').roformer.to(device) + +# def get_scores(self, x): +# scores = np.zeros(len(x)) +# features = np.array(self.emb_model(input_ids=x).last_hidden_state.mean(dim=1).detach().cpu()) + +# if len(features) == 0: +# return scores + +# features = np.nan_to_num(features, nan=0.) +# features = np.clip(features, np.finfo(np.float32).min, np.finfo(np.float32).max) + +# features = xgb.DMatrix(features) + +# scores = self.predictor.predict(features) +# # return the probability of it being not hemolytic +# return scores + +# def __call__(self, x): +# scores = self.get_scores(x) +# return torch.tensor(scores) + + +# class Solubility: +# def __init__(self, device): +# self.predictor = xgb.Booster(model_file='./scoring/checkpoints/solubility-xgboost.json') +# self.emb_model = AutoModelForMaskedLM.from_pretrained('aaronfeller/PeptideCLM-23M-all').roformer.to(device) + +# def get_scores(self, x): +# scores = np.zeros(len(x)) +# features = np.array(self.emb_model(input_ids=x).last_hidden_state.mean(dim=1).detach().cpu()) + +# if len(features) == 0: +# return scores + +# features = np.nan_to_num(features, nan=0.) +# features = np.clip(features, np.finfo(np.float32).min, np.finfo(np.float32).max) + +# features = xgb.DMatrix(features) + +# scores = self.predictor.predict(features) +# return scores + +# def __call__(self, x): +# scores = self.get_scores(x) +# return torch.tensor(scores) + + +class ImprovedBindingPredictor(nn.Module): + def __init__(self, + esm_dim=1280, + smiles_dim=768, + hidden_dim=512, + n_heads=8, + n_layers=3, + dropout=0.1): + super().__init__() + + # Define binding thresholds + self.tight_threshold = 7.5 # Kd/Ki/IC50 ≤ ~30nM + self.weak_threshold = 6.0 # Kd/Ki/IC50 > 1μM + + # Project to same dimension + self.smiles_projection = nn.Linear(smiles_dim, hidden_dim) + self.protein_projection = nn.Linear(esm_dim, hidden_dim) + self.protein_norm = nn.LayerNorm(hidden_dim) + self.smiles_norm = nn.LayerNorm(hidden_dim) + + # Cross attention blocks with layer norm + self.cross_attention_layers = nn.ModuleList([ + nn.ModuleDict({ + 'attention': nn.MultiheadAttention(hidden_dim, n_heads, dropout=dropout), + 'norm1': nn.LayerNorm(hidden_dim), + 'ffn': nn.Sequential( + nn.Linear(hidden_dim, hidden_dim * 4), + nn.ReLU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim * 4, hidden_dim) + ), + 'norm2': nn.LayerNorm(hidden_dim) + }) for _ in range(n_layers) + ]) + + # Prediction heads + self.shared_head = nn.Sequential( + nn.Linear(hidden_dim * 2, hidden_dim), + nn.ReLU(), + nn.Dropout(dropout), + ) + + # Regression head + self.regression_head = nn.Linear(hidden_dim, 1) + + # Classification head (3 classes: tight, medium, loose binding) + self.classification_head = nn.Linear(hidden_dim, 3) + + + def get_binding_class(self, affinity): + """Convert affinity values to class indices + 0: tight binding (>= 7.5) + 1: medium binding (6.0-7.5) + 2: weak binding (< 6.0) + """ + if isinstance(affinity, torch.Tensor): + tight_mask = affinity >= self.tight_threshold + weak_mask = affinity < self.weak_threshold + medium_mask = ~(tight_mask | weak_mask) + + classes = torch.zeros_like(affinity, dtype=torch.long) + classes[medium_mask] = 1 + classes[weak_mask] = 2 + return classes + else: + if affinity >= self.tight_threshold: + return 0 # tight binding + elif affinity < self.weak_threshold: + return 2 # weak binding + else: + return 1 # medium binding + + def forward(self, protein_emb, smiles_emb): + protein = self.protein_norm(self.protein_projection(protein_emb)) + smiles = self.smiles_norm(self.smiles_projection(smiles_emb)) + + #protein = protein.transpose(0, 1) + #smiles = smiles.transpose(0, 1) + + # Cross attention layers + for layer in self.cross_attention_layers: + # Protein attending to SMILES + attended_protein = layer['attention']( + protein, smiles, smiles + )[0] + protein = layer['norm1'](protein + attended_protein) + protein = layer['norm2'](protein + layer['ffn'](protein)) + + # SMILES attending to protein + attended_smiles = layer['attention']( + smiles, protein, protein + )[0] + smiles = layer['norm1'](smiles + attended_smiles) + smiles = layer['norm2'](smiles + layer['ffn'](smiles)) + + # Get sequence-level representations + protein_pool = torch.mean(protein, dim=0) + smiles_pool = torch.mean(smiles, dim=0) + + # Concatenate both representations + combined = torch.cat([protein_pool, smiles_pool], dim=-1) + + # Shared features + shared_features = self.shared_head(combined) + + regression_output = self.regression_head(shared_features) + classification_logits = self.classification_head(shared_features) + + return regression_output, classification_logits + +class BindingAffinity: + def __init__(self, prot_seq, device, model_type='PeptideCLM'): + super().__init__() + + self.pep_model = AutoModelForMaskedLM.from_pretrained('aaronfeller/PeptideCLM-23M-all').roformer.to(device) + + self.model = ImprovedBindingPredictor(smiles_dim=768).to(device) + checkpoint = torch.load('./smiles/scoring/checkpoints/binding-affinity.pt', weights_only=False) + self.model.load_state_dict(checkpoint['model_state_dict']) + + self.model.eval() + + self.esm_model, alphabet = esm.pretrained.esm2_t33_650M_UR50D() # load ESM-2 model + self.esm_model.to(device) + self.prot_tokenizer = alphabet.get_batch_converter() # load esm tokenizer + + data = [("target", prot_seq)] + # get tokenized protein + _, _, prot_tokens = self.prot_tokenizer(data) + prot_tokens = prot_tokens.to(device) + with torch.no_grad(): + results = self.esm_model.forward(prot_tokens, repr_layers=[33]) # Example with ESM-2 + prot_emb = results["representations"][33] + + self.prot_emb = prot_emb[0] + self.prot_emb = torch.mean(self.prot_emb, dim=0, keepdim=True).to(device) + + def forward(self, x, attention_mask=None): + with torch.no_grad(): + hs = self.pep_model( + input_ids=x, + attention_mask=attention_mask, + output_hidden_states=True, + ).last_hidden_state + if attention_mask is not None: + m = attention_mask.unsqueeze(-1).to(hs.dtype) + pep_emb = (hs * m).sum(dim=1, keepdim=True) / m.sum(dim=1, keepdim=True).clamp(min=1) + else: + pep_emb = hs.mean(dim=1, keepdim=True) + scores = [] + for pep in pep_emb: + score, logits = self.model.forward(self.prot_emb, pep) + scores.append(score.item() / 10) + + return torch.tensor(scores) + + def __call__(self, x, attention_mask=None): + return self.forward(x, attention_mask=attention_mask) diff --git a/smiles/scoring/functions/__init__.py b/smiles/scoring/functions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a09a1ba952ccb219e7477166804a3f537073d90d --- /dev/null +++ b/smiles/scoring/functions/__init__.py @@ -0,0 +1 @@ +"""Individual molecular-property scoring functions.""" diff --git a/smiles/scoring/functions/binding.py b/smiles/scoring/functions/binding.py new file mode 100644 index 0000000000000000000000000000000000000000..5096f54ea3f5e93c48008daafb33d5f0255e9327 --- /dev/null +++ b/smiles/scoring/functions/binding.py @@ -0,0 +1,205 @@ +import sys + +import numpy as np +from torch.utils.data import Dataset, DataLoader +from sklearn.model_selection import train_test_split +from collections import defaultdict +import torch +import pandas as pd +import torch.nn as nn +import esm +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +from transformers import AutoModelForMaskedLM, AutoModelForCausalLM, AutoTokenizer, AutoModel + + +class ImprovedBindingPredictor(nn.Module): + def __init__(self, + esm_dim=1280, + smiles_dim=768, + hidden_dim=512, + n_heads=8, + n_layers=3, + dropout=0.1): + super().__init__() + + # Define binding thresholds + self.tight_threshold = 7.5 # Kd/Ki/IC50 ≤ ~30nM + self.weak_threshold = 6.0 # Kd/Ki/IC50 > 1μM + + # Project to same dimension + self.smiles_projection = nn.Linear(smiles_dim, hidden_dim) + self.protein_projection = nn.Linear(esm_dim, hidden_dim) + self.protein_norm = nn.LayerNorm(hidden_dim) + self.smiles_norm = nn.LayerNorm(hidden_dim) + + # Cross attention blocks with layer norm + self.cross_attention_layers = nn.ModuleList([ + nn.ModuleDict({ + 'attention': nn.MultiheadAttention(hidden_dim, n_heads, dropout=dropout), + 'norm1': nn.LayerNorm(hidden_dim), + 'ffn': nn.Sequential( + nn.Linear(hidden_dim, hidden_dim * 4), + nn.ReLU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim * 4, hidden_dim) + ), + 'norm2': nn.LayerNorm(hidden_dim) + }) for _ in range(n_layers) + ]) + + # Prediction heads + self.shared_head = nn.Sequential( + nn.Linear(hidden_dim * 2, hidden_dim), + nn.ReLU(), + nn.Dropout(dropout), + ) + + # Regression head + self.regression_head = nn.Linear(hidden_dim, 1) + + # Classification head (3 classes: tight, medium, loose binding) + self.classification_head = nn.Linear(hidden_dim, 3) + + def get_binding_class(self, affinity): + """Convert affinity values to class indices + 0: tight binding (>= 7.5) + 1: medium binding (6.0-7.5) + 2: weak binding (< 6.0) + """ + if isinstance(affinity, torch.Tensor): + tight_mask = affinity >= self.tight_threshold + weak_mask = affinity < self.weak_threshold + medium_mask = ~(tight_mask | weak_mask) + + classes = torch.zeros_like(affinity, dtype=torch.long) + classes[medium_mask] = 1 + classes[weak_mask] = 2 + return classes + else: + if affinity >= self.tight_threshold: + return 0 # tight binding + elif affinity < self.weak_threshold: + return 2 # weak binding + else: + return 1 # medium binding + + def forward(self, protein_emb, smiles_emb): + protein = self.protein_norm(self.protein_projection(protein_emb)) + smiles = self.smiles_norm(self.smiles_projection(smiles_emb)) + + #protein = protein.transpose(0, 1) + #smiles = smiles.transpose(0, 1) + + # Cross attention layers + for layer in self.cross_attention_layers: + # Protein attending to SMILES + attended_protein = layer['attention']( + protein, smiles, smiles + )[0] + protein = layer['norm1'](protein + attended_protein) + protein = layer['norm2'](protein + layer['ffn'](protein)) + + # SMILES attending to protein + attended_smiles = layer['attention']( + smiles, protein, protein + )[0] + smiles = layer['norm1'](smiles + attended_smiles) + smiles = layer['norm2'](smiles + layer['ffn'](smiles)) + + # Get sequence-level representations + protein_pool = torch.mean(protein, dim=0) + smiles_pool = torch.mean(smiles, dim=0) + + # Concatenate both representations + combined = torch.cat([protein_pool, smiles_pool], dim=-1) + + # Shared features + shared_features = self.shared_head(combined) + + regression_output = self.regression_head(shared_features) + classification_logits = self.classification_head(shared_features) + + return regression_output, classification_logits + +class BindingAffinity: + def __init__(self, prot_seq, device, model_type='PeptideCLM'): + super().__init__() + + if model_type == 'PepDoRA': + # peptide embeddings + model_name = "ChatterjeeLab/PepDoRA" + self.pep_tokenizer = AutoTokenizer.from_pretrained(model_name) + self.pep_model = AutoModel.from_pretrained(model_name) + + self.model = ImprovedBindingPredictor(smiles_dim=384) + checkpoint = torch.load('./smiles/scoring/functions/binding/best_model_optuna1.pt') + self.model.load_state_dict(checkpoint['model_state_dict']) + else: + # peptide embeddings + self.pep_model = AutoModelForMaskedLM.from_pretrained('aaronfeller/PeptideCLM-23M-all').roformer + self.pep_tokenizer = SMILES_SPE_Tokenizer('./smiles/smiles_tokenizer/new_vocab.txt', + './smiles/smiles_tokenizer/new_splits.txt') + + + self.model = ImprovedBindingPredictor(smiles_dim=768).to(device) + checkpoint = torch.load('./smiles/scoring/checkpoints/binding-affinity.pt', weights_only=False) + self.model.load_state_dict(checkpoint['model_state_dict']) + + self.model.eval() + + self.esm_model, alphabet = esm.pretrained.esm2_t33_650M_UR50D() # load ESM-2 model + self.esm_model.to(device) + self.prot_tokenizer = alphabet.get_batch_converter() # load esm tokenizer + + data = [("target", prot_seq)] + # get tokenized protein + _, _, prot_tokens = self.prot_tokenizer(data) + prot_tokens = prot_tokens.to(device) + with torch.no_grad(): + results = self.esm_model.forward(prot_tokens, repr_layers=[33]) # Example with ESM-2 + prot_emb = results["representations"][33] + + self.prot_emb = prot_emb[0] + self.prot_emb = torch.mean(self.prot_emb, dim=0, keepdim=True).to(device) + + self.device = device + + def forward(self, input_seqs): + with torch.no_grad(): + scores = [] + for seq in input_seqs: + pep_tokens = self.pep_tokenizer(seq, return_tensors='pt', padding=True) + + with torch.no_grad(): + emb = self.pep_model(input_ids=pep_tokens['input_ids'], + attention_mask=pep_tokens['attention_mask'], + output_hidden_states=True) + + #emb = self.pep_model(input_ids=pep_tokens['input_ids'], attention_mask=pep_tokens['attention_mask']) + pep_emb = emb.last_hidden_state.squeeze(0).to(self.device) + pep_emb = torch.mean(pep_emb, dim=0, keepdim=True) + + score, logits = self.model.forward(self.prot_emb, pep_emb) + scores.append(score.item()) + return torch.tensor(scores) + + def __call__(self, input_seqs: list): + return self.forward(input_seqs) + +def unittest(): + amhr = 'MLGSLGLWALLPTAVEAPPNRRTCVFFEAPGVRGSTKTLGELLDTGTELPRAIRCLYSRCCFGIWNLTQDRAQVEMQGCRDSDEPGCESLHCDPSPRAHPSPGSTLFTCSCGTDFCNANYSHLPPPGSPGTPGSQGPQAAPGESIWMALVLLGLFLLLLLLLGSIILALLQRKNYRVRGEPVPEPRPDSGRDWSVELQELPELCFSQVIREGGHAVVWAGQLQGKLVAIKAFPPRSVAQFQAERALYELPGLQHDHIVRFITASRGGPGRLLSGPLLVLELHPKGSLCHYLTQYTSDWGSSLRMALSLAQGLAFLHEERWQNGQYKPGIAHRDLSSQNVLIREDGSCAIGDLGLALVLPGLTQPPAWTPTQPQGPAAIMEAGTQRYMAPELLDKTLDLQDWGMALRRADIYSLALLLWEILSRCPDLRPDSSPPPFQLAYEAELGNTPTSDELWALAVQERRRPYIPSTWRCFATDPDGLRELLEDCWDADPEARLTAECVQQRLAALAHPQESHPFPESCPRGCPPLCPEDCTSIPAPTILPCRPQRSACHFSVQQGPCSRNPQPACTLSPV' + tfr = 'MMDQARSAFSNLFGGEPLSYTRFSLARQVDGDNSHVEMKLAVDEEENADNNTKANVTKPKRCSGSICYGTIAVIVFFLIGFMIGYLGYCKGVEPKTECERLAGTESPVREEPGEDFPAARRLYWDDLKRKLSEKLDSTDFTGTIKLLNENSYVPREAGSQKDENLALYVENQFREFKLSKVWRDQHFVKIQVKDSAQNSVIIVDKNGRLVYLVENPGGYVAYSKAATVTGKLVHANFGTKKDFEDLYTPVNGSIVIVRAGKITFAEKVANAESLNAIGVLIYMDQTKFPIVNAELSFFGHAHLGTGDPYTPGFPSFNHTQFPPSRSSGLPNIPVQTISRAAAEKLFGNMEGDCPSDWKTDSTCRMVTSESKNVKLTVSNVLKEIKILNIFGVIKGFVEPDHYVVVGAQRDAWGPGAAKSGVGTALLLKLAQMFSDMVLKDGFQPSRSIIFASWSAGDFGSVGATEWLEGYLSSLHLKAFTYINLDKAVLGTSNFKVSASPLLYTLIEKTMQNVKHPVTGQFLYQDSNWASKVEKLTLDNAAFPFLAYSGIPAVSFCFCEDTDYPYLGTTMDTYKELIERIPELNKVARAAAEVAGQFVIKLTHDVELNLDYERYNSQLLSFVRDLNQYRADIKEMGLSLQWLYSARGDFFRATSRLTTDFGNAEKTDRFVMKKLNDRVMRVEYHFLSPYVSPKESPFRHVFWGSGSHTLPALLENLKLRKQNNGAFNETLFRNQLALATWTIQGAANALSGDVWDIDNEF' + gfap = 'MERRRITSAARRSYVSSGEMMVGGLAPGRRLGPGTRLSLARMPPPLPTRVDFSLAGALNAGFKETRASERAEMMELNDRFASYIEKVRFLEQQNKALAAELNQLRAKEPTKLADVYQAELRELRLRLDQLTANSARLEVERDNLAQDLATVRQKLQDETNLRLEAENNLAAYRQEADEATLARLDLERKIESLEEEIRFLRKIHEEEVRELQEQLARQQVHVELDVAKPDLTAALKEIRTQYEAMASSNMHEAEEWYRSKFADLTDAAARNAELLRQAKHEANDYRRQLQSLTCDLESLRGTNESLERQMREQEERHVREAASYQEALARLEEEGQSLKDEMARHLQEYQDLLNVKLALDIEIATYRKLLEGEENRITIPVQTFSNLQIRETSLDTKSVSEGHLKRNIVVKTVEMRDGEVIKESKQEHKDVM' + glp1 = 'MAGAPGPLRLALLLLGMVGRAGPRPQGATVSLWETVQKWREYRRQCQRSLTEDPPPATDLFCNRTFDEYACWPDGEPGSFVNVSCPWYLPWASSVPQGHVYRFCTAEGLWLQKDNSSLPWRDLSECEESKRGERSSPEEQLLFLYIIYTVGYALSFSALVIASAILLGFRHLHCTRNYIHLNLFASFILRALSVFIKDAALKWMYSTAAQQHQWDGLLSYQDSLSCRLVFLLMQYCVAANYYWLLVEGVYLYTLLAFSVLSEQWIFRLYVSIGWGVPLLFVVPWGIVKYLYEDEGCWTRNSNMNYWLIIRLPILFAIGVNFLIFVRVICIVVSKLKANLMCKTDIKCRLAKSTLTLIPLLGTHEVIFAFVMDEHARGTLRFIKLFTELSFTSFQGLMVAILYCFVNNEVQLEFRKSWERWRLEHLHIQRDSSMKPLKCPTSSLSSGATAGSSMYTATCQASCS' + glast = 'MTKSNGEEPKMGGRMERFQQGVRKRTLLAKKKVQNITKEDVKSYLFRNAFVLLTVTAVIVGTILGFTLRPYRMSYREVKYFSFPGELLMRMLQMLVLPLIISSLVTGMAALDSKASGKMGMRAVVYYMTTTIIAVVIGIIIVIIIHPGKGTKENMHREGKIVRVTAADAFLDLIRNMFPPNLVEACFKQFKTNYEKRSFKVPIQANETLVGAVINNVSEAMETLTRITEELVPVPGSVNGVNALGLVVFSMCFGFVIGNMKEQGQALREFFDSLNEAIMRLVAVIMWYAPVGILFLIAGKIVEMEDMGVIGGQLAMYTVTVIVGLLIHAVIVLPLLYFLVTRKNPWVFIGGLLQALITALGTSSSSATLPITFKCLEENNGVDKRVTRFVLPVGATINMDGTALYEALAAIFIAQVNNFELNFGQIITISITATAASIGAAGIPQAGLVTMVIVLTSVGLPTDDITLIIAVDWFLDRLRTTTNVLGDSLGAGIVEHLSRHELKNRDVEMGNSVIEENEMKKPYQLIAQDNETEKPIDSETKM' + ncam = 'LQTKDLIWTLFFLGTAVSLQVDIVPSQGEISVGESKFFLCQVAGDAKDKDISWFSPNGEKLTPNQQRISVVWNDDSSSTLTIYNANIDDAGIYKCVVTGEDGSESEATVNVKIFQKLMFKNAPTPQEFREGEDAVIVCDVVSSLPPTIIWKHKGRDVILKKDVRFIVLSNNYLQIRGIKKTDEGTYRCEGRILARGEINFKDIQVIVNVPPTIQARQNIVNATANLGQSVTLVCDAEGFPEPTMSWTKDGEQIEQEEDDEKYIFSDDSSQLTIKKVDKNDEAEYICIAENKAGEQDATIHLKVFAKPKITYVENQTAMELEEQVTLTCEASGDPIPSITWRTSTRNISSEEKASWTRPEKQETLDGHMVVRSHARVSSLTLKSIQYTDAGEYICTASNTIGQDSQSMYLEVQYAPKLQGPVAVYTWEGNQVNITCEVFAYPSATISWFRDGQLLPSSNYSNIKIYNTPSASYLEVTPDSENDFGNYNCTAVNRIGQESLEFILVQADTPSSPSIDQVEPYSSTAQVQFDEPEATGGVPILKYKAEWRAVGEEVWHSKWYDAKEASMEGIVTIVGLKPETTYAVRLAALNGKGLGEISAASEF' + + binding = BindingAffinity(amhr, device='cuda:0') + seq = ['N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCSC(F)F)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)O'] + + scores = binding(seq) + print(scores) + print(len(scores)) + +if __name__ == '__main__': + unittest() \ No newline at end of file diff --git a/smiles/scoring/functions/binding_utils.py b/smiles/scoring/functions/binding_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b66e0b9ada1893dc78f9bcbb8b65a684a43bdfe3 --- /dev/null +++ b/smiles/scoring/functions/binding_utils.py @@ -0,0 +1,291 @@ +from torch import nn +import pdb +import torch +import numpy as np + +def to_var(x): + if torch.cuda.is_available(): + x = x.cuda() + return x + +class MultiHeadAttentionSequence(nn.Module): + + def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1): + + super().__init__() + + self.n_head = n_head + self.d_model = d_model + self.d_k = d_k + self.d_v = d_v + + self.W_Q = nn.Linear(d_model, n_head*d_k) + self.W_K = nn.Linear(d_model, n_head*d_k) + self.W_V = nn.Linear(d_model, n_head*d_v) + self.W_O = nn.Linear(n_head*d_v, d_model) + + self.layer_norm = nn.LayerNorm(d_model) + + self.dropout = nn.Dropout(dropout) + + def forward(self, q, k, v): + + batch, len_q, _ = q.size() + batch, len_k, _ = k.size() + batch, len_v, _ = v.size() + + Q = self.W_Q(q).view([batch, len_q, self.n_head, self.d_k]) + K = self.W_K(k).view([batch, len_k, self.n_head, self.d_k]) + V = self.W_V(v).view([batch, len_v, self.n_head, self.d_v]) + + Q = Q.transpose(1, 2) + K = K.transpose(1, 2).transpose(2, 3) + V = V.transpose(1, 2) + + attention = torch.matmul(Q, K) + + attention = attention / np.sqrt(self.d_k) + + attention = F.softmax(attention, dim=-1) + + output = torch.matmul(attention, V) + + output = output.transpose(1, 2).reshape([batch, len_q, self.d_v*self.n_head]) + + output = self.W_O(output) + + output = self.dropout(output) + + output = self.layer_norm(output + q) + + return output, attention + +class MultiHeadAttentionReciprocal(nn.Module): + + + def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1): + + super().__init__() + + self.n_head = n_head + self.d_model = d_model + self.d_k = d_k + self.d_v = d_v + + self.W_Q = nn.Linear(d_model, n_head*d_k) + self.W_K = nn.Linear(d_model, n_head*d_k) + self.W_V = nn.Linear(d_model, n_head*d_v) + self.W_O = nn.Linear(n_head*d_v, d_model) + self.W_V_2 = nn.Linear(d_model, n_head*d_v) + self.W_O_2 = nn.Linear(n_head*d_v, d_model) + + self.layer_norm = nn.LayerNorm(d_model) + + self.dropout = nn.Dropout(dropout) + + self.layer_norm_2 = nn.LayerNorm(d_model) + + self.dropout_2 = nn.Dropout(dropout) + + def forward(self, q, k, v, v_2): + + batch, len_q, _ = q.size() + batch, len_k, _ = k.size() + batch, len_v, _ = v.size() + batch, len_v_2, _ = v_2.size() + + Q = self.W_Q(q).view([batch, len_q, self.n_head, self.d_k]) + K = self.W_K(k).view([batch, len_k, self.n_head, self.d_k]) + V = self.W_V(v).view([batch, len_v, self.n_head, self.d_v]) + V_2 = self.W_V_2(v_2).view([batch, len_v_2, self.n_head, self.d_v]) + + Q = Q.transpose(1, 2) + K = K.transpose(1, 2).transpose(2, 3) + V = V.transpose(1, 2) + V_2 = V_2.transpose(1,2) + + attention = torch.matmul(Q, K) + + + attention = attention /np.sqrt(self.d_k) + + attention_2 = attention.transpose(-2, -1) + + + + attention = F.softmax(attention, dim=-1) + + attention_2 = F.softmax(attention_2, dim=-1) + + + output = torch.matmul(attention, V) + + output_2 = torch.matmul(attention_2, V_2) + + output = output.transpose(1, 2).reshape([batch, len_q, self.d_v*self.n_head]) + + output_2 = output_2.transpose(1, 2).reshape([batch, len_k, self.d_v*self.n_head]) + + output = self.W_O(output) + + output_2 = self.W_O_2(output_2) + + output = self.dropout(output) + + output = self.layer_norm(output + q) + + output_2 = self.dropout(output_2) + + output_2 = self.layer_norm(output_2 + k) + + + return output, output_2, attention, attention_2 + + +class FFN(nn.Module): + + def __init__(self, d_in, d_hid, dropout=0.1): + super().__init__() + + self.layer_1 = nn.Conv1d(d_in, d_hid,1) + self.layer_2 = nn.Conv1d(d_hid, d_in,1) + self.relu = nn.ReLU() + self.layer_norm = nn.LayerNorm(d_in) + + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + + residual = x + output = self.layer_1(x.transpose(1, 2)) + + output = self.relu(output) + + output = self.layer_2(output) + + output = self.dropout(output) + + output = self.layer_norm(output.transpose(1, 2)+residual) + + return output + +class ConvLayer(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, padding, dilation): + super(ConvLayer, self).__init__() + self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, padding=padding, dilation=dilation) + self.relu = nn.ReLU() + + def forward(self, x): + out = self.conv(x) + out = self.relu(out) + return out + + +class DilatedCNN(nn.Module): + def __init__(self, d_model, d_hidden): + super(DilatedCNN, self).__init__() + self.first_ = nn.ModuleList() + self.second_ = nn.ModuleList() + self.third_ = nn.ModuleList() + + dilation_tuple = (1, 2, 3) + dim_in_tuple = (d_model, d_hidden, d_hidden) + dim_out_tuple = (d_hidden, d_hidden, d_hidden) + + for i, dilation_rate in enumerate(dilation_tuple): + self.first_.append(ConvLayer(dim_in_tuple[i], dim_out_tuple[i], kernel_size=3, padding=dilation_rate, + dilation=dilation_rate)) + + for i, dilation_rate in enumerate(dilation_tuple): + self.second_.append(ConvLayer(dim_in_tuple[i], dim_out_tuple[i], kernel_size=5, padding=2*dilation_rate, + dilation=dilation_rate)) + + for i, dilation_rate in enumerate(dilation_tuple): + self.third_.append(ConvLayer(dim_in_tuple[i], dim_out_tuple[i], kernel_size=7, padding=3*dilation_rate, + dilation=dilation_rate)) + + def forward(self, protein_seq_enc): + # pdb.set_trace() + protein_seq_enc = protein_seq_enc.transpose(1, 2) # protein_seq_enc's shape: B*L*d_model -> B*d_model*L + + first_embedding = protein_seq_enc + second_embedding = protein_seq_enc + third_embedding = protein_seq_enc + + for i in range(len(self.first_)): + first_embedding = self.first_[i](first_embedding) + + for i in range(len(self.second_)): + second_embedding = self.second_[i](second_embedding) + + for i in range(len(self.third_)): + third_embedding = self.third_[i](third_embedding) + + # pdb.set_trace() + + protein_seq_enc = first_embedding + second_embedding + third_embedding + + return protein_seq_enc.transpose(1, 2) + + +class ReciprocalLayerwithCNN(nn.Module): + + def __init__(self, d_model, d_inner, d_hidden, n_head, d_k, d_v): + super().__init__() + + self.cnn = DilatedCNN(d_model, d_hidden) + + self.sequence_attention_layer = MultiHeadAttentionSequence(n_head, d_hidden, d_k, d_v) + + self.protein_attention_layer = MultiHeadAttentionSequence(n_head, d_hidden, d_k, d_v) + + self.reciprocal_attention_layer = MultiHeadAttentionReciprocal(n_head, d_hidden, d_k, d_v) + + self.ffn_seq = FFN(d_hidden, d_inner) + + self.ffn_protein = FFN(d_hidden, d_inner) + + def forward(self, sequence_enc, protein_seq_enc): + # pdb.set_trace() # protein_seq_enc.shape = B * L * d_model + protein_seq_enc = self.cnn(protein_seq_enc) + prot_enc, prot_attention = self.protein_attention_layer(protein_seq_enc, protein_seq_enc, protein_seq_enc) + + seq_enc, sequence_attention = self.sequence_attention_layer(sequence_enc, sequence_enc, sequence_enc) + + prot_enc, seq_enc, prot_seq_attention, seq_prot_attention = self.reciprocal_attention_layer(prot_enc, seq_enc, seq_enc, prot_enc) + + prot_enc = self.ffn_protein(prot_enc) + + seq_enc = self.ffn_seq(seq_enc) + + return prot_enc, seq_enc, prot_attention, sequence_attention, prot_seq_attention, seq_prot_attention + + +class ReciprocalLayer(nn.Module): + + def __init__(self, d_model, d_inner, n_head, d_k, d_v): + + super().__init__() + + self.sequence_attention_layer = MultiHeadAttentionSequence(n_head, d_model, d_k, d_v) + + self.protein_attention_layer = MultiHeadAttentionSequence(n_head, d_model, d_k, d_v) + + self.reciprocal_attention_layer = MultiHeadAttentionReciprocal(n_head, d_model, d_k, d_v) + + self.ffn_seq = FFN(d_model, d_inner) + + self.ffn_protein = FFN(d_model, d_inner) + + def forward(self, sequence_enc, protein_seq_enc): + prot_enc, prot_attention = self.protein_attention_layer(protein_seq_enc, protein_seq_enc, protein_seq_enc) + + seq_enc, sequence_attention = self.sequence_attention_layer(sequence_enc, sequence_enc, sequence_enc) + + + prot_enc, seq_enc, prot_seq_attention, seq_prot_attention = self.reciprocal_attention_layer(prot_enc, seq_enc, seq_enc, prot_enc) + prot_enc = self.ffn_protein(prot_enc) + + seq_enc = self.ffn_seq(seq_enc) + + return prot_enc, seq_enc, prot_attention, sequence_attention, prot_seq_attention, seq_prot_attention \ No newline at end of file diff --git a/smiles/scoring/functions/hemolysis.py b/smiles/scoring/functions/hemolysis.py new file mode 100644 index 0000000000000000000000000000000000000000..1b203a69b009efad173fac1ba5549fabca06a69b --- /dev/null +++ b/smiles/scoring/functions/hemolysis.py @@ -0,0 +1,71 @@ +import sys +import os +sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..')) +import xgboost as xgb +import torch +import numpy as np +from transformers import AutoModelForMaskedLM +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +import warnings +import numpy as np +from rdkit.Chem import Descriptors, rdMolDescriptors +from rdkit import Chem, rdBase, DataStructs +from transformers import AutoModelForMaskedLM + + +rdBase.DisableLog('rdApp.error') +warnings.filterwarnings("ignore", category=DeprecationWarning) +warnings.filterwarnings("ignore", category=UserWarning) +warnings.filterwarnings("ignore", category=FutureWarning) + +class Hemolysis: + + def __init__(self, device): + self.predictor = xgb.Booster(model_file='./scoring/checkpoints/hemolysis-xgboost.json') + self.emb_model = AutoModelForMaskedLM.from_pretrained('aaronfeller/PeptideCLM-23M-all').roformer.to(device) + self.tokenizer = SMILES_SPE_Tokenizer('./smiles_tokenizer/new_vocab.txt', + './smiles_tokenizer/new_splits.txt') + + self.device = device + + def generate_embeddings(self, sequences): + embeddings = [] + for sequence in sequences: + tokenized = self.tokenizer(sequence, return_tensors='pt').to(self.device) + with torch.no_grad(): + output = self.emb_model(**tokenized) + # Mean pooling across sequence length + embedding = output.last_hidden_state.mean(dim=1).squeeze(0).cpu().numpy() + embeddings.append(embedding) + return np.array(embeddings) + + def get_scores(self, input_seqs: list): + scores = np.ones(len(input_seqs)) + features = self.generate_embeddings(input_seqs) + + if len(features) == 0: + return scores + + features = np.nan_to_num(features, nan=0.) + features = np.clip(features, np.finfo(np.float32).min, np.finfo(np.float32).max) + + features = xgb.DMatrix(features) + + probs = self.predictor.predict(features) + # return the probability of it being not hemolytic + return scores - probs + + def __call__(self, input_seqs: list): + scores = self.get_scores(input_seqs) + return torch.tensor(scores) + +def unittest(): + hemo = Hemolysis(device='cuda:6') + seq = ["N[C@@H](CC(=O)O)-N[C@@H](C[C@@H](C))C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@H](CO)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CO)C(=O)N[C@H](CO)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CCN)C(=O)N[C@@H](CCCC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)N)C(=O)O"] + + scores = hemo(input_seqs=seq) + print(scores) + + +if __name__ == '__main__': + unittest() \ No newline at end of file diff --git a/smiles/scoring/functions/nonfouling.py b/smiles/scoring/functions/nonfouling.py new file mode 100644 index 0000000000000000000000000000000000000000..090d57ca0aad88b8af5e6c34b06bd90bea374950 --- /dev/null +++ b/smiles/scoring/functions/nonfouling.py @@ -0,0 +1,69 @@ +import sys +import os +sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..')) +import xgboost as xgb +import torch +import numpy as np +from transformers import AutoModelForMaskedLM +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +import warnings +import numpy as np +from rdkit import Chem, rdBase, DataStructs +from transformers import AutoModelForMaskedLM + + +rdBase.DisableLog('rdApp.error') +warnings.filterwarnings("ignore", category=DeprecationWarning) +warnings.filterwarnings("ignore", category=UserWarning) +warnings.filterwarnings("ignore", category=FutureWarning) + +class Nonfouling: + + def __init__(self, device): + self.predictor = xgb.Booster(model_file='./scoring/checkpoints/nonfouling-xgboost.json') + self.emb_model = AutoModelForMaskedLM.from_pretrained('aaronfeller/PeptideCLM-23M-all').roformer.to(device) + self.tokenizer = SMILES_SPE_Tokenizer('./smiles_tokenizer/old_vocab.txt', + './smiles_tokenizer/old_splits.txt') + self.device = device + + def generate_embeddings(self, sequences): + embeddings = [] + for sequence in sequences: + tokenized = self.tokenizer(sequence, return_tensors='pt').to(self.device) + with torch.no_grad(): + output = self.emb_model(**tokenized) + # Mean pooling across sequence length + embedding = output.last_hidden_state.mean(dim=1).squeeze(0).cpu().numpy() + embeddings.append(embedding) + return np.array(embeddings) + + def get_scores(self, input_seqs: list): + scores = np.zeros(len(input_seqs)) + features = self.generate_embeddings(input_seqs) + + if len(features) == 0: + return scores + + features = np.nan_to_num(features, nan=0.) + features = np.clip(features, np.finfo(np.float32).min, np.finfo(np.float32).max) + + features = xgb.DMatrix(features) + + scores = self.predictor.predict(features) + # return the probability of it being not hemolytic + return scores + + def __call__(self, input_seqs: list): + scores = self.get_scores(input_seqs) + return torch.tensor(scores) + +def unittest(): + nf = Nonfouling(device='cuda:6') + seq = ["N[C@@H](CC(=O)O)-N[C@@H](C[C@@H](C))C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H]([C@H](CC)C)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@H](CO)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CO)C(=O)N[C@H](CO)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CCN)C(=O)N[C@@H](CCCC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)N)C(=O)O"] + + scores = nf(input_seqs=seq) + print(scores) + + +if __name__ == '__main__': + unittest() \ No newline at end of file diff --git a/smiles/scoring/functions/scoring_utils.py b/smiles/scoring/functions/scoring_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..642b6e9d49d652a14a2f39bdea01410eba30a911 --- /dev/null +++ b/smiles/scoring/functions/scoring_utils.py @@ -0,0 +1,111 @@ +import warnings +import numpy as np +from loguru import logger +from sklearn.ensemble import RandomForestRegressor +from rdkit.Chem import Descriptors, rdMolDescriptors +import joblib +from transformation import TransformFunction +from rdkit import Chem, rdBase, DataStructs +from rdkit.Chem import AllChem +from typing import List + + +def fingerprints_from_mol(molecule, radius=3, size=2048, hashed=False): + """ + Create ECFP fingerprint of a molecule + """ + if hashed: + fp_bits = AllChem.GetHashedMorganFingerprint(molecule, radius, nBits=size) + else: + fp_bits = AllChem.GetMorganFingerprintAsBitVect(molecule, radius, nBits=size) + fp_np = np.zeros((1,)) + DataStructs.ConvertToNumpyArray(fp_bits, fp_np) + return fp_np.reshape(1, -1) + + +def fingerprints_from_smiles(smiles: List, size=2048): + """ Create ECFP fingerprints of smiles, with validity check """ + fps = [] + valid_mask = [] + for i, smile in enumerate(smiles): + mol = Chem.MolFromSmiles(smile) + valid_mask.append(int(mol is not None)) + fp = fingerprints_from_mol(mol, size=size) if mol else np.zeros((1, size)) + fps.append(fp) + + fps = np.concatenate(fps, axis=0) if len(fps) > 0 else np.zeros((0, size)) + return fps, valid_mask + + +def getMolDescriptors(mol, missingVal=0): + """ calculate the full list of descriptors for a molecule """ + + values, names = [], [] + for nm, fn in Descriptors._descList: + try: + val = fn(mol) + except: + val = missingVal + values.append(val) + names.append(nm) + + custom_descriptors = {'hydrogen-bond donors': rdMolDescriptors.CalcNumLipinskiHBD, + 'hydrogen-bond acceptors': rdMolDescriptors.CalcNumLipinskiHBA, + 'rotatable bonds': rdMolDescriptors.CalcNumRotatableBonds,} + + for nm, fn in custom_descriptors.items(): + try: + val = fn(mol) + except: + val = missingVal + values.append(val) + names.append(nm) + return values, names + + +def get_pep_dps_from_smi(smi): + try: + mol = Chem.MolFromSmiles(smi) + except: + print(f"convert smi {smi} to molecule failed!") + mol = None + + dps, _ = getMolDescriptors(mol) + return np.array(dps) + + +def get_pep_dps(smi_list): + if len(smi_list) == 0: + return np.zeros((0, 211)) + return np.array([get_pep_dps_from_smi(smi) for smi in smi_list]) + + +"""def get_smi_from_helms(helm_seqs: list): + valid_idxes = [] + valid_smiles = [] + + for idx, helm in enumerate(helm_seqs): + # Ignore helm which cannot converted into molecules + try: + smi = get_cycpep_smi_from_helm(helm) + if smi: + valid_idxes.append(idx) + valid_smiles.append(smi) + except Exception as e: + # logger.debug(f'Error: {e} in helm {helm}') + pass + return valid_smiles, valid_idxes""" + + +def check_smi_validity(smiles: list): + valid_smi, valid_idx = [], [] + for idx, smi in enumerate(smiles): + try: + mol = Chem.MolFromSmiles(smi) if smi else None + if mol: + valid_smi.append(smi) + valid_idx.append(idx) + except Exception as e: + # logger.debug(f'Error: {e} in smiles {smi}') + pass + return valid_smi, valid_idx \ No newline at end of file diff --git a/smiles/scoring/functions/solubility.py b/smiles/scoring/functions/solubility.py new file mode 100644 index 0000000000000000000000000000000000000000..9cafef0da7f9a0371c09c5ba42d6f0b3f2ff41aa --- /dev/null +++ b/smiles/scoring/functions/solubility.py @@ -0,0 +1,64 @@ +import sys +import os +sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..')) +import xgboost as xgb +import torch +import numpy as np +from transformers import AutoModelForMaskedLM +from smiles.smiles_tokenizer.my_tokenizers import SMILES_SPE_Tokenizer +import warnings +import numpy as np +from rdkit import Chem, rdBase, DataStructs + + +rdBase.DisableLog('rdApp.error') +warnings.filterwarnings("ignore", category=DeprecationWarning) +warnings.filterwarnings("ignore", category=UserWarning) +warnings.filterwarnings("ignore", category=FutureWarning) + +class Solubility: + def __init__(self, device): + self.predictor = xgb.Booster(model_file='./scoring/checkpoints/solubility-xgboost.json') + self.emb_model = AutoModelForMaskedLM.from_pretrained('aaronfeller/PeptideCLM-23M-all').roformer.to(device) + self.tokenizer = SMILES_SPE_Tokenizer('./smiles_tokenizer/old_vocab.txt', + './smiles_tokenizer/old_splits.txt') + self.device = device + + def generate_embeddings(self, sequences): + embeddings = [] + for sequence in sequences: + tokenized = self.tokenizer(sequence, return_tensors='pt').to(self.device) + with torch.no_grad(): + output = self.emb_model(**tokenized) + # Mean pooling across sequence length + embedding = output.last_hidden_state.mean(dim=1).squeeze(0).cpu().numpy() + embeddings.append(embedding) + return np.array(embeddings) + + def get_scores(self, input_seqs: list): + scores = np.zeros(len(input_seqs)) + features = self.generate_embeddings(input_seqs) + + if len(features) == 0: + return scores + + features = np.nan_to_num(features, nan=0.) + features = np.clip(features, np.finfo(np.float32).min, np.finfo(np.float32).max) + + features = xgb.DMatrix(features) + + scores = self.predictor.predict(features) + return scores + + def __call__(self, input_seqs: list): + scores = self.get_scores(input_seqs) + return torch.tensor(scores) + +def unittest(): + solubility = Solubility(device='cuda:6') + seq = ["N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H]([C@H](O)C)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](C)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CC(=O)N)C(=O)N[C@@H](CCC(=O)N)C(=O)O"] + scores = solubility(input_seqs=seq) + print(scores) + +if __name__ == '__main__': + unittest() \ No newline at end of file diff --git a/smiles/smiles_tokenizer/__init__.py b/smiles/smiles_tokenizer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9d7d6e809cbb10e2065f5d3aa5db30520b8e5b33 --- /dev/null +++ b/smiles/smiles_tokenizer/__init__.py @@ -0,0 +1 @@ +"""SMILES tokenization utilities and vocabulary files.""" diff --git a/smiles/smiles_tokenizer/check_lys_tokens.py b/smiles/smiles_tokenizer/check_lys_tokens.py new file mode 100644 index 0000000000000000000000000000000000000000..5a69e06f40660cd2f95ebb38417cef5a9ff28b0f --- /dev/null +++ b/smiles/smiles_tokenizer/check_lys_tokens.py @@ -0,0 +1,18 @@ +from my_tokenizers import SMILES_SPE_Tokenizer + +# Initialize tokenizer with your vocab files +tokenizer = SMILES_SPE_Tokenizer( + vocab_file='new_vocab.txt', + spe_file='new_splits.txt' +) + +# Lysine side chain patterns +lys_patterns = [ + 'CCCCN', # Lysine side chain + '[C@@H](CCCCN)', # Lys with stereochemistry + '[C@H](CCCCN)', # Lys with opposite stereochemistry +] + +for pattern in lys_patterns: + tokens = tokenizer._tokenize(pattern) + print(f"{pattern} → {tokens}") \ No newline at end of file diff --git a/smiles/smiles_tokenizer/my_tokenizers.py b/smiles/smiles_tokenizer/my_tokenizers.py new file mode 100644 index 0000000000000000000000000000000000000000..4bcd57c8b958b54461553df2e0a6f60cee470e93 --- /dev/null +++ b/smiles/smiles_tokenizer/my_tokenizers.py @@ -0,0 +1,445 @@ +import collections +import logging +import os +import re +import codecs +import unicodedata +from typing import List, Optional +from transformers import PreTrainedTokenizer +from SmilesPE.tokenizer import SPE_Tokenizer +import torch + +def load_vocab(vocab_file): + """Loads a vocabulary file into a dictionary.""" + vocab = collections.OrderedDict() + with open(vocab_file, "r", encoding="utf-8") as reader: + tokens = reader.readlines() + for index, token in enumerate(tokens): + token = token.rstrip("\n") + vocab[token] = index + return vocab + +class Atomwise_Tokenizer(object): + """Run atom-level SMILES tokenization""" + + def __init__(self): + """ Constructs a atom-level Tokenizer. + """ + # self.regex_pattern = r"(\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\(|\)|\.|=|#|-|\+|\\|\/|:|~|@|\?|>>?|\*|\$|\%[0-9]{2}|[0-9])" + self.regex_pattern = r"(\([^\(\)]{0,4}\)|\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\(|\)|\.|=|#|-|\+|\\|\/\/?|:|~|@|\?|>>?|\*|\$|\%[0-9]{2}|[0-9])" + + self.regex = re.compile(self.regex_pattern) + + def tokenize(self, text): + """ Basic Tokenization of a SMILES. + """ + tokens = [token for token in self.regex.findall(text)] + return tokens + +class SMILES_SPE_Tokenizer(PreTrainedTokenizer): + r""" + Constructs a SMILES tokenizer. Based on SMILES Pair Encoding (https://github.com/XinhaoLi74/SmilesPE). + This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the methods. Users + should refer to the superclass for more information regarding methods. + Args: + vocab_file (:obj:`string`): + File containing the vocabulary. + spe_file (:obj:`string`): + File containing the trained SMILES Pair Encoding vocabulary. + unk_token (:obj:`string`, `optional`, defaults to "[UNK]"): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + sep_token (:obj:`string`, `optional`, defaults to "[SEP]"): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences + for sequence classification or for a text and a question for question answering. + It is also used as the last token of a sequence built with special tokens. + pad_token (:obj:`string`, `optional`, defaults to "[PAD]"): + The token used for padding, for example when batching sequences of different lengths. + cls_token (:obj:`string`, `optional`, defaults to "[CLS]"): + The classifier token which is used when doing sequence classification (classification of the whole + sequence instead of per-token classification). It is the first token of the sequence when built with + special tokens. + mask_token (:obj:`string`, `optional`, defaults to "[MASK]"): + The token used for masking values. This is the token used when training this model with masked language + modeling. This is the token which the model will try to predict. + """ + + def __init__(self, vocab_file, spe_file, + unk_token="[UNK]", + sep_token="[SEP]", + pad_token="[PAD]", + cls_token="[CLS]", + mask_token="[MASK]", + **kwargs): + if not os.path.isfile(vocab_file): + raise ValueError("Can't find a vocabulary file at path '{}'.".format(vocab_file)) + if not os.path.isfile(spe_file): + raise ValueError("Can't find a SPE vocabulary file at path '{}'.".format(spe_file)) + + self.vocab = load_vocab(vocab_file) + self.spe_vocab = open(spe_file, 'r', encoding='utf-8') + self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()]) + self.spe_tokenizer = SPE_Tokenizer(self.spe_vocab) + + super().__init__( + unk_token=unk_token, + sep_token=sep_token, + pad_token=pad_token, + cls_token=cls_token, + mask_token=mask_token, + **kwargs) + + @property + def vocab_size(self): + return len(self.vocab) + + def get_vocab(self): + return dict(self.vocab, **self.added_tokens_encoder) + + def _tokenize(self, text): + return self.spe_tokenizer.tokenize(text).split(' ') + + def _convert_token_to_id(self, token): + """ Converts a token (str) in an id using the vocab. """ + return self.vocab.get(token, self.vocab.get(self.unk_token)) + + # changed encode and decode functions + def encode(self, token_array): + token_ids = [] + token_ids.append(2) + for token in token_array: + id = self._convert_token_to_id(token) + token_ids.append(id) + token_ids.append(3) + token_ids = torch.tensor([token_ids]) + attn_mask = torch.ones_like(token_ids) + return {'input_ids': token_ids, 'attention_mask': attn_mask} + + def decode(self, token_ids, skip_special_tokens=True): + token_ids = token_ids.squeeze(0).cpu().tolist() + token_array = [] + for idx in token_ids: + if idx == 3: # Stop decoding when token ID 3 is encountered + break + if skip_special_tokens and idx in self.all_special_ids: + continue + token = self._convert_id_to_token(idx) + token_array.append(token) + sequence = "".join(token_array) + return sequence + + def batch_decode(self, batch_token_ids, skip_special_tokens=True): + sequences = [] + for token_ids in batch_token_ids: + sequences.append(self.decode(token_ids)) + return sequences + + def get_token_split(self, token_ids): + if isinstance(token_ids, torch.Tensor): + token_ids = token_ids.cpu().tolist() + + # import pdb + # pdb.set_trace() + token_array = [] + for seq_ids in token_ids: + seq_array = [] + if isinstance(seq_ids, torch.Tensor): + seq_ids = seq_ids.cpu().tolist() + for id in seq_ids: + token = self._convert_id_to_token(id) + seq_array.append(token) + token_array.append(seq_array) + + return token_array + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + return self.ids_to_tokens.get(index, self.unk_token) + + def convert_tokens_to_string(self, tokens): + """ Converts a sequence of tokens (string) in a single string. """ + out_string = " ".join(tokens).replace(" ##", "").strip() + return out_string + + def build_inputs_with_special_tokens( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks + by concatenating and adding special tokens. + A BERT sequence has the following format: + - single sequence: ``[CLS] X [SEP]`` + - pair of sequences: ``[CLS] A [SEP] B [SEP]`` + Args: + token_ids_0 (:obj:`List[int]`): + List of IDs to which the special tokens will be added + token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`): + Optional second list of IDs for sequence pairs. + Returns: + :obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens. + """ + if token_ids_1 is None: + return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + cls = [self.cls_token_id] + sep = [self.sep_token_id] + return cls + token_ids_0 + sep + token_ids_1 + sep + + def get_special_tokens_mask( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False + ) -> List[int]: + """ + Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer ``prepare_for_model`` method. + Args: + token_ids_0 (:obj:`List[int]`): + List of ids. + token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`): + Set to True if the token list is already formatted with special tokens for the model + Returns: + :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + + if already_has_special_tokens: + if token_ids_1 is not None: + raise ValueError( + "You should not supply a second sequence if the provided sequence of " + "ids is already formated with special tokens for the model." + ) + return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0)) + + if token_ids_1 is not None: + return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + return [1] + ([0] * len(token_ids_0)) + [1] + + def create_token_type_ids_from_sequences( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Creates a mask from the two sequences passed to be used in a sequence-pair classification task. + A BERT sequence pair mask has the following format: + :: + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + if token_ids_1 is None, only returns the first portion of the mask (0's). + Args: + token_ids_0 (:obj:`List[int]`): + List of ids. + token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`): + Optional second list of IDs for sequence pairs. + Returns: + :obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given + sequence(s). + """ + sep = [self.sep_token_id] + cls = [self.cls_token_id] + if token_ids_1 is None: + return len(cls + token_ids_0 + sep) * [0] + return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + + def save_vocabulary(self, vocab_path): + """ + Save the sentencepiece vocabulary (copy original file) and special tokens file to a directory. + Args: + vocab_path (:obj:`str`): + The directory in which to save the vocabulary. + Returns: + :obj:`Tuple(str)`: Paths to the files saved. + """ + index = 0 + if os.path.isdir(vocab_path): + vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES["vocab_file"]) + else: + vocab_file = vocab_path + with open(vocab_file, "w", encoding="utf-8") as writer: + for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]): + if index != token_index: + logger.warning( + "Saving vocabulary to {}: vocabulary indices are not consecutive." + " Please check that the vocabulary is not corrupted!".format(vocab_file) + ) + index = token_index + writer.write(token + "\n") + index += 1 + return (vocab_file,) + +class SMILES_Atomwise_Tokenizer(PreTrainedTokenizer): + r""" + Constructs a SMILES tokenizer. Based on SMILES Pair Encoding (https://github.com/XinhaoLi74/SmilesPE). + This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the methods. Users + should refer to the superclass for more information regarding methods. + Args: + vocab_file (:obj:`string`): + File containing the vocabulary. + unk_token (:obj:`string`, `optional`, defaults to "[UNK]"): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + sep_token (:obj:`string`, `optional`, defaults to "[SEP]"): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences + for sequence classification or for a text and a question for question answering. + It is also used as the last token of a sequence built with special tokens. + pad_token (:obj:`string`, `optional`, defaults to "[PAD]"): + The token used for padding, for example when batching sequences of different lengths. + cls_token (:obj:`string`, `optional`, defaults to "[CLS]"): + The classifier token which is used when doing sequence classification (classification of the whole + sequence instead of per-token classification). It is the first token of the sequence when built with + special tokens. + mask_token (:obj:`string`, `optional`, defaults to "[MASK]"): + The token used for masking values. This is the token used when training this model with masked language + modeling. This is the token which the model will try to predict. + """ + + def __init__( + self, + vocab_file, + unk_token="[UNK]", + sep_token="[SEP]", + pad_token="[PAD]", + cls_token="[CLS]", + mask_token="[MASK]", + **kwargs + ): + super().__init__( + unk_token=unk_token, + sep_token=sep_token, + pad_token=pad_token, + cls_token=cls_token, + mask_token=mask_token, + **kwargs, + ) + + if not os.path.isfile(vocab_file): + raise ValueError( + "Can't find a vocabulary file at path '{}'.".format(vocab_file) + ) + self.vocab = load_vocab(vocab_file) + self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()]) + self.tokenizer = Atomwise_Tokenizer() + + @property + def vocab_size(self): + return len(self.vocab) + + def get_vocab(self): + return dict(self.vocab, **self.added_tokens_encoder) + + + def _tokenize(self, text): + return self.tokenizer.tokenize(text) + + def _convert_token_to_id(self, token): + """ Converts a token (str) in an id using the vocab. """ + return self.vocab.get(token, self.vocab.get(self.unk_token)) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + return self.ids_to_tokens.get(index, self.unk_token) + + def convert_tokens_to_string(self, tokens): + """ Converts a sequence of tokens (string) in a single string. """ + out_string = " ".join(tokens).replace(" ##", "").strip() + return out_string + + def build_inputs_with_special_tokens( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks + by concatenating and adding special tokens. + A BERT sequence has the following format: + - single sequence: ``[CLS] X [SEP]`` + - pair of sequences: ``[CLS] A [SEP] B [SEP]`` + Args: + token_ids_0 (:obj:`List[int]`): + List of IDs to which the special tokens will be added + token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`): + Optional second list of IDs for sequence pairs. + Returns: + :obj:`List[int]`: list of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens. + """ + if token_ids_1 is None: + return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + cls = [self.cls_token_id] + sep = [self.sep_token_id] + return cls + token_ids_0 + sep + token_ids_1 + sep + + def get_special_tokens_mask( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False + ) -> List[int]: + """ + Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer ``prepare_for_model`` method. + Args: + token_ids_0 (:obj:`List[int]`): + List of ids. + token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`): + Set to True if the token list is already formatted with special tokens for the model + Returns: + :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + + if already_has_special_tokens: + if token_ids_1 is not None: + raise ValueError( + "You should not supply a second sequence if the provided sequence of " + "ids is already formated with special tokens for the model." + ) + return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0)) + + if token_ids_1 is not None: + return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + return [1] + ([0] * len(token_ids_0)) + [1] + + def create_token_type_ids_from_sequences( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Creates a mask from the two sequences passed to be used in a sequence-pair classification task. + A BERT sequence pair mask has the following format: + :: + 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 + | first sequence | second sequence | + if token_ids_1 is None, only returns the first portion of the mask (0's). + Args: + token_ids_0 (:obj:`List[int]`): + List of ids. + token_ids_1 (:obj:`List[int]`, `optional`, defaults to :obj:`None`): + Optional second list of IDs for sequence pairs. + Returns: + :obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given + sequence(s). + """ + sep = [self.sep_token_id] + cls = [self.cls_token_id] + if token_ids_1 is None: + return len(cls + token_ids_0 + sep) * [0] + return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + + def save_vocabulary(self, vocab_path): + """ + Save the sentencepiece vocabulary (copy original file) and special tokens file to a directory. + Args: + vocab_path (:obj:`str`): + The directory in which to save the vocabulary. + Returns: + :obj:`Tuple(str)`: Paths to the files saved. + """ + index = 0 + if os.path.isdir(vocab_path): + vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES["vocab_file"]) + else: + vocab_file = vocab_path + with open(vocab_file, "w", encoding="utf-8") as writer: + for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]): + if index != token_index: + logger.warning( + "Saving vocabulary to {}: vocabulary indices are not consecutive." + " Please check that the vocabulary is not corrupted!".format(vocab_file) + ) + index = token_index + writer.write(token + "\n") + index += 1 + return (vocab_file,) diff --git a/smiles/smiles_tokenizer/new_splits.txt b/smiles/smiles_tokenizer/new_splits.txt new file mode 100644 index 0000000000000000000000000000000000000000..e48784ed46b8515935fe27feaeff177b63e5da5b --- /dev/null +++ b/smiles/smiles_tokenizer/new_splits.txt @@ -0,0 +1,159 @@ +c 1 +c 2 +c 3 +c 4 +c 5 +c 6 +c 7 +c 8 +c 9 +( c1 +( c2 +c1 ) +c2 ) +n 1 +n 2 +n 3 +n 4 +n 5 +n 6 +n 7 +n 8 +n 9 +( n1 +( n2 +n1 ) +n2 ) +O 1 +O 2 +O 3 +O 4 +O 5 +O 6 +O 7 +O 8 +O 9 +( O1 +( O2 +O2 ) +O2 ) += O += C += c += N += n +=C C +=C N +=C c +=c c +=N C +=N c +=n C +=n c +# N +# C +#N C +#C C +#C N +#N N +( C +C ) +( O +O ) +( N +N ) +Br c +( =O +(=O ) +C (=O) +C =O +C =N +C #N +C #C +C C +CC C +CC N +CC O +CC S +CC c +CC n +C N +CN C +CN c +C O +CO C +CO N +CO c +C S +CS C +CS S +CS c +C c +Cl c +C n +F c +N C +NC C +NC c +N N +N O +N c +N n +O C +OC C +OC O +OC c +O N +O O +O c +S C +SC C +SC c +S S +S c +c c +cc c +cc n +cc o +cc s +cc cc +c n +cn c +cn n +c o +co c +c s +cs c +cs n +n c +nc c +nc n +nc o +nc s +n n +nn c +nn n +n o +no c +no n +n s +ns c +ns n +o c +oc c +o n +s c +sc c +sc n +s n +N P +P N +C P +P C +N S +S N +C S +S C +S P +P S +C I \ No newline at end of file diff --git a/smiles/smiles_tokenizer/new_vocab.txt b/smiles/smiles_tokenizer/new_vocab.txt new file mode 100644 index 0000000000000000000000000000000000000000..1127d24d4cea8480d413e57949b4c6742dfd5fe9 --- /dev/null +++ b/smiles/smiles_tokenizer/new_vocab.txt @@ -0,0 +1,587 @@ +[PAD] +[UNK] +[CLS] +[SEP] +[MASK] +# +% +( +) ++ +- +/ +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 += +@ +A +B +Br +Brc +C +CC +CCC +CCN +CCO +CCS +CCc +CCn +CN +CNC +CNc +CO +COC +CON +COc +CS +CSC +CSS +CSc +Cc +Cl +Clc +Cn +F +Fc +H +I +K +L +M +N +NC +NCC +NCc +NN +NO +Nc +Nn +O +OC +OCC +OCO +OCc +ON +OO +Oc +P +R +S +SC +SCC +SCc +SS +Sc +T +X +Z +[ +\\ +(/ +] +a +b +c +cc +ccc +cccc +ccn +cco +ccs +cn +cnc +cnn +co +coc +cs +csc +csn +e +g +i +l +n +nc +ncc +ncn +nco +ncs +nn +nnc +nnn +no +noc +non +ns +nsc +nsn +o +oc +occ +on +p +r +s +sc +scc +scn +sn +t +c1 +c2 +c3 +c4 +c5 +c6 +c7 +c8 +c9 +n1 +n2 +n3 +n4 +n5 +n6 +n7 +n8 +n9 +O1 +O2 +O3 +O4 +O5 +O6 +O7 +O8 +O9 +(c1 +(c2 +c1) +c2) +(n1 +(n2 +n1) +n2) +(O1 +(O2 +O2) +=O +=C +=c +=N +=n +=CC +=CN +=Cc +=cc +=NC +=Nc +=nC +=nc +#C +#CC +#CN +#N +#NC +#NN +(C +C) +(O +O) +(N +N) +NP +PN +CP +PC +NS +SN +SP +PS +C(=O) +(/Br) +(/C#N) +(/C) +(/C=N) +(/C=O) +(/CBr) +(/CC) +(/CCC) +(/CCF) +(/CCN) +(/CCO) +(/CCl) +(/CI) +(/CN) +(/CO) +(/CS) +(/Cl) +(/F) +(/I) +(/N) +(/NC) +(/NCC) +(/NO) +(/O) +(/OC) +(/OCC) +(/S) +(/SC) +(=C) +(=C/C) +(=C/F) +(=C/I) +(=C/N) +(=C/O) +(=CBr) +(=CC) +(=CCF) +(=CCN) +(=CCO) +(=CCl) +(=CF) +(=CI) +(=CN) +(=CO) +(=C\\C) +(=C\\F) +(=C\\I) +(=C\\N) +(=C\\O) +(=N) +(=N/C) +(=N/N) +(=N/O) +(=NBr) +(=NC) +(=NCC) +(=NCl) +(=NN) +(=NO) +(=NOC) +(=N\\C) +(=N\\N) +(=N\\O) +(=O) +(=S) +(B) +(Br) +(C#C) +(C#CC) +(C#CI) +(C#CO) +(C#N) +(C#SN) +(C) +(C=C) +(C=CF) +(C=CI) +(C=N) +(C=NN) +(C=NO) +(C=O) +(C=S) +(CBr) +(CC#C) +(CC#N) +(CC) +(CC=C) +(CC=O) +(CCBr) +(CCC) +(CCCC) +(CCCF) +(CCCI) +(CCCN) +(CCCO) +(CCCS) +(CCCl) +(CCF) +(CCI) +(CCN) +(CCNC) +(CCNN) +(CCNO) +(CCO) +(CCOC) +(CCON) +(CCS) +(CCSC) +(CCl) +(CF) +(CI) +(CN) +(CN=O) +(CNC) +(CNCC) +(CNCO) +(CNN) +(CNNC) +(CNO) +(CNOC) +(CO) +(COC) +(COCC) +(COCI) +(COCN) +(COCO) +(COF) +(CON) +(COO) +(CS) +(CSC) +(CSCC) +(CSCF) +(CSO) +(Cl) +(F) +(I) +(N) +(N=N) +(N=NO) +(N=O) +(N=S) +(NBr) +(NC#N) +(NC) +(NC=N) +(NC=O) +(NC=S) +(NCBr) +(NCC) +(NCCC) +(NCCF) +(NCCN) +(NCCO) +(NCCS) +(NCCl) +(NCNC) +(NCO) +(NCS) +(NCl) +(NN) +(NN=O) +(NNC) +(NO) +(NOC) +(O) +(OC#N) +(OC) +(OC=C) +(OC=O) +(OC=S) +(OCBr) +(OCC) +(OCCC) +(OCCF) +(OCCI) +(OCCN) +(OCCO) +(OCCS) +(OCCl) +(OCF) +(OCI) +(OCO) +(OCOC) +(OCON) +(OCSC) +(OCl) +(OI) +(ON) +(OO) +(OOC) +(OOCC) +(OOSN) +(OSC) +(P) +(S) +(SC#N) +(SC) +(SCC) +(SCCC) +(SCCF) +(SCCN) +(SCCO) +(SCCS) +(SCCl) +(SCF) +(SCN) +(SCOC) +(SCSC) +(SCl) +(SI) +(SN) +(SN=O) +(SO) +(SOC) +(SOOO) +(SS) +(SSC) +(SSCC) +([At]) +([O-]) +([O]) +([S-]) +(\\Br) +(\\C#N) +(\\C) +(\\C=N) +(\\C=O) +(\\CBr) +(\\CC) +(\\CCC) +(\\CCO) +(\\CCl) +(\\CF) +(\\CN) +(\\CNC) +(\\CO) +(\\COC) +(\\Cl) +(\\F) +(\\I) +(\\N) +(\\NC) +(\\NCC) +(\\NN) +(\\NO) +(\\NOC) +(\\O) +(\\OC) +(\\OCC) +(\\ON) +(\\S) +(\\SC) +(\\SCC) +[Ag+] +[Ag-4] +[Ag] +[Al-3] +[Al] +[As+] +[AsH3] +[AsH] +[As] +[At] +[B-] +[B@-] +[B@@-] +[BH-] +[BH2-] +[BH3-] +[B] +[Ba] +[Br+2] +[BrH] +[Br] +[C+] +[C-] +[C@@H] +[C@@] +[C@H] +[C@] +[CH-] +[CH2] +[CH3] +[CH] +[C] +[CaH2] +[Ca] +[Cl+2] +[Cl+3] +[Cl+] +[Cs] +[FH] +[F] +[H] +[He] +[I+2] +[I+3] +[I+] +[IH] +[I] +[K] +[Kr] +[Li+] +[LiH] +[MgH2] +[Mg] +[N+] +[N-] +[N@+] +[N@@+] +[N@@] +[N@] +[NH+] +[NH-] +[NH2+] +[NH3] +[NH] +[N] +[Na] +[O+] +[O-] +[OH+] +[OH2] +[OH] +[O] +[P+] +[P@+] +[P@@+] +[P@@] +[P@] +[PH2] +[PH] +[P] +[Ra] +[Rb] +[S+] +[S-] +[S@+] +[S@@+] +[S@@] +[S@] +[SH+] +[SH2] +[SH] +[S] +[Se+] +[Se-2] +[SeH2] +[SeH] +[Se] +[Si@] +[SiH2] +[SiH] +[Si] +[SrH2] +[TeH] +[Te] +[Xe] +[Zn+2] +[Zn-2] +[Zn] +[b-] +[c+] +[c-] +[cH-] +[cH] +[c] +[n+] +[n-] +[nH] +[n] +[o+] +[s+] +[se+] +[se] +[te+] +[te] \ No newline at end of file diff --git a/third_party/SMILESToPeptide/LICENSE-p2smi b/third_party/SMILESToPeptide/LICENSE-p2smi new file mode 100644 index 0000000000000000000000000000000000000000..55b62d54d00c69da4266297f8792f951970a0462 --- /dev/null +++ b/third_party/SMILESToPeptide/LICENSE-p2smi @@ -0,0 +1,18 @@ +MIT License + +Copyright (c) 2025 Aaron Feller + +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. + +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. \ No newline at end of file diff --git a/third_party/SMILESToPeptide/__init__.py b/third_party/SMILESToPeptide/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3bf14850c598c8a607a714ed2f11b6ec50fbf341 --- /dev/null +++ b/third_party/SMILESToPeptide/__init__.py @@ -0,0 +1,6 @@ +"""Vendored peptide-to-SMILES conversion helpers used by CupOFMoCA.""" + +from .aminoacid_selective import one_letter_to_three_letter +from .peptide_to_smiles import aa_to_smiles + +__all__ = ["aa_to_smiles", "one_letter_to_three_letter"] diff --git a/third_party/SMILESToPeptide/aminoacid_selective.py b/third_party/SMILESToPeptide/aminoacid_selective.py new file mode 100644 index 0000000000000000000000000000000000000000..2418afa5fced4e39e63a51e42aeca224b8187775 --- /dev/null +++ b/third_party/SMILESToPeptide/aminoacid_selective.py @@ -0,0 +1,588 @@ +#!/usr/bin/env python +"""Definitions and properties of amino-acids for p2smi""" + +# Natural Amino-acids: + +specific_aminos = { + "Fmoc-Aib-OH": { + "Code": "Aib", + "Formula": "C28H29NO5", + "Letter": "Ŷ", + "MolWeight": 221.141578848, + "SMILES": "CC(C)(N)C(=O)O", + "cterm": "NC(C)(C)C(=O)[*:2]", + "disulphide": False, + "ester": False, + "nterm": "[*:1]NC(C)(C)C(=O)O", + }, + "Fmoc-Asp(OtBu)-(Dmb)Gly-OH": { + "Code": "Dtg", + "Formula": "C28H29NO5", + "Letter": "Ĝ", + "MolWeight": 221.141578848, + "SMILES": "N[C@@H](CC(=O)OC(C)(C)C)C(=O)N(CC1=C(C=C(C=C1)OC)OC)CC(=O)O", + "nterm": "[*:1]N[C@@H](CC(=O)OC(C)(C)C)C(=O)N(CC1=C(C=C(C=C1)OC)OC)CC(=O)O", + "cterm": "N[C@@H](CC(=O)OC(C)(C)C)C(=O)N(CC1=C(C=C(C=C1)OC)OC)CC(=O)[*:2]", + "disulphide": False, + "ester": False, + }, + "Fmoc-Cys(Mmt)-OH": { + "Code": "Cmt", + "Formula": "C28H29NO5", + "Letter": "Ĉ", + "MolWeight": 221.141578848, + "SMILES": "COC1=CC=C(C=C1)C(C2=CC=CC=C2)(C3=CC=CC=C3)SC[C@@H](N)C(=O)O", + "nterm": "N([*:1])[C@@H](CSC(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=C(OC)C=C3)C(=O)O", + "cterm": "N[C@@H](CSC(C1=CC=CC=C1)(C2=CC=CC=C2)C3=CC=C(OC)C=C3)C(=O)[*:2]", + "disulphide": False, + "ester": False, + }, + "Fmoc-Glu(OAll)-OH": { + "Code": "Eal", + "Formula": "C28H29NO5", + "Letter": "Ė", + "MolWeight": 221.141578848, + "SMILES": "C=CCOC(=O)CC[C@@H](N)C(=O)O", + "nterm": "[*:1]N[C@@H](CCC(=O)OCC=C)C(=O)O", + "cterm": "N[C@@H](CCC(=O)OCC=C)C(=O)[*:2]", + "disulphide": False, + "ester": False, + }, + "Fmoc-Lys(palmitoyl-Glu-OtBu)-OH": { + "Code": "Kpg", + "Formula": "C28H29NO5", + "Letter": "Ƙ", + "MolWeight": 221.141578848, + "SMILES": "N[C@@H](CCCNC(=O)CCC[C@@H](NC(=O)CCCCCCCCCCCCCCCC)C(=O)OC(C)(C)C)C(=O)O", + "nterm": "[*:1]N[C@@H](CCCN(C(=O)CCC[C@@H](NC(=O)CCCCCCCCCCCCCCCC)C(=O)OC(C)(C)C))C(=O)O", + "cterm": "N[C@@H](CCCN(C(=O)CCC[C@@H](NC(=O)CCCCCCCCCCCCCCCC)C(=O)OC(C)(C)C))C(=O)[*:2]", + "disulphide": False, + "ester": False, + }, + "Fmoc-Thr(PO(OBzl)OH)-OH": { + "Code": "Tpb", + "Formula": "C28H29NO5", + "Letter": "Ṯ", + "MolWeight": 221.141578848, + "SMILES": "N[C@@H]([C@H](C)OP(=O)(O)OCc1ccccc1)C(=O)O", + "disulphide": False, + "ester": False, + "nterm": "[*:1]N[C@@H]([C@H](C)OP(=O)(O)OCC1=CC=CC=C1)C(=O)O", + "cterm": "N[C@@H]([C@H](C)OP(=O)(O)OCC1=CC=CC=C1)C(=O)[*:2]" + }, + "Fmoc-Cycloleucine": { + "Code": "Cyl", + "Formula": "C28H29NO5", + "Letter": "Ċ", + "MolWeight": 221.141578848, + "SMILES": "NC1(CCCC1)C(=O)O", + "nterm": "[*:1]NC1(CCCC1)C(=O)O", + "cterm": "NC1(CCCC1)C(=O)[*:2]", + "disulphide": False, + "ester": False, + }, + "Fmoc-N-Me-Ala-OH": { + "Code": "Nma", + "Formula": "C28H29NO5", + "Letter": "Ṃ", + "MolWeight": 221.141578848, + "SMILES": "CN([C@@H](C)C(=O)O)", + "cterm": "N(C)[C@@H](C)C(=O)[*:2]", + "disulphide": False, + "ester": False, + "nterm": "[*:1]N(C)[C@@H](C)C(=O)O", + }, + "Fmoc-N-Me-Leu-OH": { + "Code": "Nml", + "Formula": "C28H29NO5", + "Letter": "Ŀ", + "MolWeight": 221.141578848, + "SMILES": "CN([C@@H](CC(C)C))C(=O)O", + "cterm": "CN([C@@H](CC(C)C)C(=O))[*:2]", + "disulphide": False, + "ester": False, + "nterm": "[*:1]N(C)[C@@H](CC(C)C)C(=O)O", + }, + "Fmoc-Nle-OH": { + "Code": "Nle", + "Formula": "C28H29NO5", + "Letter": "Ł", + "MolWeight": 221.141578848, + "SMILES": "N[C@@H](CCCC)C(=O)O", + "nterm": "[*:1]N[C@@H](CCCC)C(=O)O", + "cterm": "N[C@@H](CCCC)C(=O)[*:2]", + "disulphide": False, + "ester": False, + }, + "N-Fmoc-L-homophenylalanine": { + "Code": "Hph", + "Formula": "C28H29NO5", + "Letter": "Ĥ", + "MolWeight": 221.141578848, + "SMILES": "N[C@@H](CCC1=CC=CC=C1)C(=O)O", + "nterm": "[*:1]N[C@@H](CCC1=CC=CC=C1)C(=O)O", + "cterm": "N[C@@H](CCC1=CC=CC=C1)C(=O)[*:2]", + "disulphide": False, + "ester": False, + }, + "Glycine": { + "Code": "Gly", + "Formula": "C2H5NO2", + "Letter": "G", + "MolWeight": "75.07", + "SMILES": "NCC(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "L-Alanine": { + "Code": "Ala", + "Formula": "C3H7NO2", + "Letter": "A", + "MolWeight": "89.09", + "SMILES": "N[C@@H](C)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "L-Arginine": { + "Code": "Arg", + "Formula": "C6H14N4O2", + "Letter": "R", + "MolWeight": "174.20", + "SMILES": "N[C@@H](CCCNC(=N)N)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": "N[C@@H](CCCNC(=N*)N)C(=O)O", + }, + "L-Asparagine": { + "Code": "Asn", + "Formula": "C4H8N2O3", + "Letter": "N", + "MolWeight": "132.12", + "SMILES": "N[C@@H](CC(=O)N)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": "N[C@@H](CC(=O)N*)C(=O)O", + }, + "L-Aspartic_Acid": { + "Code": "Asp", + "Formula": "C4H7NO4", + "Letter": "D", + "MolWeight": "133.10", + "SMILES": "N[C@@H](CC(=O)O)C(=O)O", + "cterm": "N[C@@H](CC*(=O))C(=O)O", + "disulphide": False, + "ester": False, + "nterm": False, + }, + "L-Cysteine": { + "Code": "Cys", + "Formula": "C3H7NO2S", + "Letter": "C", + "MolWeight": "121.16", + "SMILES": "N[C@@H](CS)C(=O)O", + "cterm": False, + "disulphide": "N[C@@H](CS*)C(=O)O", + "ester": False, + "nterm": False, + }, + "L-Glutamic_Acid": { + "Code": "Glu", + "Formula": "C5H9NO4", + "Letter": "E", + "MolWeight": "147.13", + "SMILES": "N[C@@H](CCC(=O)O)C(=O)O", + "cterm": "N[C@@H](CCC*(=O))C(=O)O", + "disulphide": False, + "ester": False, + "nterm": False, + }, + "L-Glutamine": { + "Code": "Gln", + "Formula": "C5H10N2O3", + "Letter": "Q", + "MolWeight": "146.15", + "SMILES": "N[C@@H](CCC(=O)N)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": "N[C@@H](CCC(=O)N*)C(=O)O", + }, + "L-Histidine": { + "Code": "His", + "Formula": "C6H9N3O2", + "Letter": "H", + "MolWeight": "155.16", + "SMILES": "N[C@@H](CC1=CNC=N1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "L-Isoleucine": { + "Code": "Ile", + "Formula": "C6H13NO2", + "Letter": "I", + "MolWeight": "131.18", + "SMILES": "N[C@@H]([C@H](CC)C)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "L-Leucine": { + "Code": "Leu", + "Formula": "C6H13NO2", + "Letter": "L", + "MolWeight": "131.18", + "SMILES": "N[C@@H](CC(C)C)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "L-Lysine": { + "Code": "Lys", + "Formula": "C6H12N2O2", + "Letter": "K", + "MolWeight": "146.19", + "SMILES": "N[C@@H](CCCCN)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": "N[C@@H](CCCCN*)C(=O)O", + }, + "L-Methionine": { + "Code": "Met", + "Formula": "C5H11NO2S", + "Letter": "M", + "MolWeight": "149.21", + "SMILES": "N[C@@H](CCSC)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "L-Phenylalanine": { + "Code": "Phe", + "Formula": "C9H11NO2", + "Letter": "F", + "MolWeight": "165.19", + "SMILES": "N[C@@H](Cc1ccccc1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "L-Proline": { + "Code": "Pro", + "Formula": "C5H9NO2", + "Letter": "P", + "MolWeight": "115.13", + "SMILES": "N1[C@@H](CCC1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "L-Serine": { + "Code": "Ser", + "Formula": "C3H7NO2", + "Letter": "S", + "MolWeight": "105.09", + "SMILES": "N[C@@H](CO)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": "N[C@@H](CO*)C(=O)O", + "nterm": False, + }, + "L-Threonine": { + "Code": "Thr", + "Formula": "C4H9NO3", + "Letter": "T", + "MolWeight": "119.12", + "SMILES": "N[C@@H]([C@H](O)C)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": "N[C@@H]([C@H](O*)C)C(=O)O", + "nterm": False, + }, + "L-Tryptophan": { + "Code": "Trp", + "Formula": "C11H12N2O2", + "Letter": "W", + "MolWeight": "204.23", + "SMILES": "N[C@@H](CC(=CN2)C1=C2C=CC=C1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "L-Tyrosine": { + "Code": "Tyr", + "Formula": "C9H11NO3", + "Letter": "Y", + "MolWeight": "181.19", + "SMILES": "N[C@@H](Cc1ccc(O)cc1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": "N[C@@H](Cc1ccc(O*)cc1)C(=O)O", + "nterm": False, + }, + "L-Valine": { + "Code": "Val", + "Formula": "C5H11NO2", + "Letter": "V", + "MolWeight": "117.15", + "SMILES": "N[C@@H](C(C)C)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "D-Alanine": { + "Code": "ala", + "Formula": "C3H7NO2", + "Letter": "a", + "MolWeight": "89.09", + "SMILES": "N[C@H](C)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "D-Arginine": { + "Code": "arg", + "Formula": "C6H14N4O2", + "Letter": "r", + "MolWeight": "174.20", + "SMILES": "N[C@H](CCCNC(=N)N)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": "N[C@H](CCCNC(=N*)N)C(=O)O", + }, + "D-Asparagine": { + "Code": "asn", + "Formula": "C4H8N2O3", + "Letter": "n", + "MolWeight": "132.12", + "SMILES": "N[C@H](CC(=O)N)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": "N[C@H](CC(=O)N*)C(=O)O", + }, + "D-Aspartic_Acid": { + "Code": "asp", + "Formula": "C4H7NO4", + "Letter": "d", + "MolWeight": "133.10", + "SMILES": "N[C@H](CC(=O)O)C(=O)O", + "cterm": "N[C@H](CC*(=O))C(=O)O", + "disulphide": False, + "ester": False, + "nterm": False, + }, + "D-Cysteine": { + "Code": "cys", + "Formula": "C3H7NO2S", + "Letter": "c", + "MolWeight": "121.16", + "SMILES": "N[C@H](CS)C(=O)O", + "cterm": False, + "disulphide": "N[C@H](CS*)C(=O)O", + "ester": False, + "nterm": False, + }, + "D-Glutamic_Acid": { + "Code": "glu", + "Formula": "C5H9NO4", + "Letter": "e", + "MolWeight": "147.13", + "SMILES": "N[C@H](CCC(=O)O)C(=O)O", + "cterm": "N[C@H](CCC*(=O))C(=O)O", + "disulphide": False, + "ester": False, + "nterm": False, + }, + "D-Glutamine": { + "Code": "gln", + "Formula": "C5H10N2O3", + "Letter": "q", + "MolWeight": "146.15", + "SMILES": "N[C@H](CCC(=O)N)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": "N[C@H](CCC(=O)N*)C(=O)O", + }, + "D-Histidine": { + "Code": "his", + "Formula": "C6H9N3O2", + "Letter": "h", + "MolWeight": "155.16", + "SMILES": "N[C@H](CC1=CNC=N1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "D-Isoleucine": { + "Code": "ile", + "Formula": "C6H13NO2", + "Letter": "i", + "MolWeight": "131.18", + "SMILES": "N[C@H]([C@@H](CC)C)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "D-Leucine": { + "Code": "leu", + "Formula": "C6H13NO2", + "Letter": "l", + "MolWeight": "131.18", + "SMILES": "N[C@H](CC(C)C)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "D-Lysine": { + "Code": "lys", + "Formula": "C6H12N2O2", + "Letter": "k", + "MolWeight": "146.19", + "SMILES": "N[C@H](CCCCN)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": "N[C@H](CCCCN*)C(=O)O", + }, + "D-Methionine": { + "Code": "met", + "Formula": "C5H11NO2S", + "Letter": "m", + "MolWeight": "149.21", + "SMILES": "N[C@H](CCSC)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "D-Phenylalanine": { + "Code": "phe", + "Formula": "C9H11NO2", + "Letter": "f", + "MolWeight": "165.19", + "SMILES": "N[C@H](Cc1ccccc1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "D-Proline": { + "Code": "pro", + "Formula": "C5H9NO2", + "Letter": "p", + "MolWeight": "115.13", + "SMILES": "N1[C@H](CCC1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "D-Serine": { + "Code": "ser", + "Formula": "C3H7NO2", + "Letter": "s", + "MolWeight": "105.09", + "SMILES": "N[C@H](CO)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": "N[C@H](CO*)C(=O)O", + "nterm": False, + }, + "D-Tryptophan": { + "Code": "trp", + "Formula": "C11H12N2O2", + "Letter": "w", + "MolWeight": "204.23", + "SMILES": "N[C@H](CC(=CN2)C1=C2C=CC=C1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "D-Tyrosine": { + "Code": "tyr", + "Formula": "C9H11NO3", + "Letter": "y", + "MolWeight": "181.19", + "SMILES": "N[C@H](Cc1ccc(O)cc1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": "N[C@H](Cc1ccc(O*)cc1)C(=O)O", + "nterm": False, + }, + "D-Valine": { + "Code": "val", + "Formula": "C5H11NO2", + "Letter": "v", + "MolWeight": "117.15", + "SMILES": "N[C@H](C(C)C)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "D-Threonine": { + "Code": "thr", + "Formula": "C4H9NO3", + "Letter": "t", + "MolWeight": "119.12", + "SMILES": "N[C@H]([C@@H](O)C)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": "N[C@H]([C@@H](O*)C)C(=O)O", + "nterm": False, + }, +} + +letter_to_code = { + props["Letter"]: props["Code"] + for props in specific_aminos.values() +} + +def one_letter_to_three_letter(seq, sep='-'): + """ + Convert a 1-letter amino acid sequence into a dash-separated 3-letter sequence. + + Examples + -------- + one_letter_to_three_letter("AGAĤ") -> "Ala-Gly-Ala-Hph" + one_letter_to_three_letter("AĜAĤ") -> "Ala-Dtg-Ala-Hph" + """ + # If seq is a string, strip separators/whitespace and split into characters + if isinstance(seq, str): + # Remove common separators but keep all amino-acid letters, including Unicode + cleaned = seq.replace('-', '').replace(' ', '') + letters = list(cleaned) + else: + # If already an iterable of letters + letters = list(seq) + + codes = [] + for aa in letters: + if aa not in letter_to_code: + raise ValueError(f"Unknown amino-acid letter: {aa!r}") + codes.append(letter_to_code[aa]) + + return sep.join(codes) \ No newline at end of file diff --git a/third_party/SMILESToPeptide/peptide_to_smiles.py b/third_party/SMILESToPeptide/peptide_to_smiles.py new file mode 100644 index 0000000000000000000000000000000000000000..244a3e57a0f7eaa568cbd5841ef3a9b401e64b36 --- /dev/null +++ b/third_party/SMILESToPeptide/peptide_to_smiles.py @@ -0,0 +1,1387 @@ +import os +import re +import pandas as pd +from io import StringIO +import rdkit +from rdkit import Chem +from rdkit.Chem import AllChem, Draw +import numpy as np +from PIL import Image, ImageDraw, ImageFont +import matplotlib.pyplot as plt +import matplotlib.patches as patches +from io import BytesIO +import tempfile +from rdkit import Chem +from .swisssidechain import all_aminos +from .aminoacid_selective import specific_aminos, one_letter_to_three_letter +import pdb + +def _internal_from_cterm(cterm: str) -> str: + s = cterm.strip() + s = re.sub(r'C\(=O\)\[\*:\s*2\]\s*$', '', s) # drop trailing carbonyl anchor + s = re.sub(r'^\[\*:\s*1\]', '', s) # drop leading anchor + s = re.sub(r'^\(?N\)?', '', s) # drop leading N + return s + +def _internal_from_nterm(nterm: str) -> str: + s = nterm.strip() + s = re.sub(r'^\[\*:\s*1\]', '', s) # drop leading anchor + s = re.sub(r'^\(?N\)?', '', s) # drop leading N + s = re.sub(r'C\(=O\)O\s*$', '', s) # drop trailing COOH + return s + +def _chirality_agnostic_regex(literal_smiles: str) -> re.Pattern: + """ + Make a regex that matches the literal SMILES but ignores stereo/ring digit specifics. + - Escapes all chars + - Makes '@' optional (so [C@@H] / [C@H] / [CH] all match) + - Allows any ring digit where a digit appears + """ + esc = re.escape(literal_smiles) + + # make any '@' optional (two steps to handle @@) + esc = esc.replace(r'\@\@', r'\@?\@?') + esc = esc.replace(r'\@', r'\@?') + + # allow any ring digit(s) where digits appear + esc = re.sub(r'\\\d+', r'\\d+', esc) + + return re.compile(esc) + +class PeptideAnalyzer: + def __init__(self): + self.bond_patterns = [ + #(r'OC\(=O\)', 'ester'), # Ester bond + (r'N\(C\)C\(=O\)', 'n_methyl'), # N-methylated peptide bond + (r'N[0-9]C\(=O\)', 'proline'), # Proline peptide bond + (r'NC\(=O\)', 'peptide'), # Standard peptide bond + (r'C\(=O\)N\(C\)', 'n_methyl_reverse'), # Reverse N-methylated + (r'C\(=O\)N[12]?', 'peptide_reverse') # Reverse peptide bond + ] + self.complex_residue_patterns = [ + (r'\[C[@]H\]\(CCCNC\(=O\)CCC\[C@@H\]\(NC\(=O\)CCCCCCCCCCCCCCCC\)C\(=O\)OC\(C\)\(C\)C\)', 'Kpg'), + (r'CCCCCCCCCCCCCCCCC\(=O\)N\[C@H\]\(CCCC\(=O\)NCCC\[C@@H\]', 'Kpg'), + (r'\[C@*H\]\(CSC\(c\d+ccccc\d+\)\(c\d+ccccc\d+\)c\d+ccc\(OC\)cc\d+\)', 'Cmt'), + (r'CSC\(c.*?c.*?OC\)', 'Cmt'), + (r'COc.*?ccc\(C\(SC', 'Cmt'), + (r'c2ccccc2\)c2ccccc2\)cc', 'Cmt'), + # Glu(OAll) + (r'C=CCOC\(=O\)CC\[C@@H\]', 'Eal'), + (r'\(C\)OP\(=O\)\(O\)OCc\d+ccccc\d+', 'Tpb'), + #(r'COc\d+ccc\(C\(SC\[C@@H\]\d+.*?\)\(c\d+ccccc\d+\)c\d+ccccc\d+\)cc\d+', 'Cmt-cyclic'), + + # Dtg - Asp(OtBu)-(Dmb)Gly + (r'CN\(Cc\d+ccc\(OC\)cc\d+OC\)C\(=O\)\[C@H\]\(CC\(=O\)OC\(C\)\(C\)C\)', 'Dtg'), + (r'C\(=O\)N\(CC\d+=C\(C=C\(C=C\d+\)OC\)OC\)CC\(=O\)', 'Dtg'), + (r'N\[C@@H\]\(CC\(=O\)OC\(C\)\(C\)C\)C\(=O\)N\(CC\d+=C\(C=C\(C=C\d+\)OC\)OC\)CC\(=O\)', 'Dtg'), + ] + # Three to one letter code mapping + self.three_to_one = { + 'Ala': 'A', 'Cys': 'C', 'Asp': 'D', 'Glu': 'E', + 'Phe': 'F', 'Gly': 'G', 'His': 'H', 'Ile': 'I', + 'Lys': 'K', 'Leu': 'L', 'Met': 'M', 'Asn': 'N', + 'Pro': 'P', 'Gln': 'Q', 'Arg': 'R', 'Ser': 'S', + 'Thr': 'T', 'Val': 'V', 'Trp': 'W', 'Tyr': 'Y', + 'ala': 'a', 'cys': 'c', 'asp': 'd', 'glu': 'e', + 'phe': 'f', 'gly': 'g', 'his': 'h', 'ile': 'i', + 'lys': 'k', 'leu': 'l', 'met': 'm', 'asn': 'n', + 'pro': 'p', 'gln': 'q', 'arg': 'r', 'ser': 's', + 'thr': 't', 'val': 'v', 'trp': 'w', 'tyr': 'y', 'Cmt-cyclic': 'Ĉ', + 'Aib': 'Ŷ', 'Dtg': 'Ĝ', 'Cmt': 'Ĉ', 'Eal': 'Ė', 'Nml': "Ŀ", 'Nma': 'Ṃ', + 'Kpg': 'Ƙ', 'Tpb': 'Ṯ', 'Cyl': 'Ċ', 'Nle': 'Ł', 'Hph': 'Ĥ', 'Cys-Cys': 'CC', 'cys-cys': 'cc', + } + + self._build_swisssidechain_lookups() + + def _build_swisssidechain_lookups(self): + self.exact_smiles_lookup = {} + self.clean_smiles_lookup = {} + self.uaa_internal_exact = {} + self.uaa_internal_patterns = [] + for uaa_name, uaa_data in specific_aminos.items(): + code = uaa_data["Code"] + smiles = uaa_data.get("SMILES", "") + nterm = uaa_data.get("nterm", "") + cterm = uaa_data.get("cterm", "") + letter = uaa_data.get("Letter") + + # keep existing full-aa lookups + if smiles: + self.exact_smiles_lookup[smiles] = code + clean = self._remove_stereochemistry(smiles) + self.clean_smiles_lookup.setdefault(clean, []).append(code) + + internal = "" + if cterm: + internal = _internal_from_cterm(cterm) + elif nterm: + internal = _internal_from_nterm(nterm) + + if internal: + self.exact_smiles_lookup[internal] = code + clean_int = self._remove_stereochemistry(internal) + self.clean_smiles_lookup.setdefault(clean_int, []).append(code) + + self.uaa_internal_exact[code] = internal + self.uaa_internal_patterns.append((_chirality_agnostic_regex(internal), code)) + + if letter: + self.three_to_one[code] = letter + + for uaa_name, uaa_data in all_aminos.items(): + code = uaa_data["Code"] + smiles = uaa_data.get("SMILES", "") + nterm = uaa_data.get("nterm", "") + cterm = uaa_data.get("cterm", "") + letter = uaa_data.get("Letter") + + # keep existing full-aa lookups + if smiles: + self.exact_smiles_lookup[smiles] = code + clean = self._remove_stereochemistry(smiles) + self.clean_smiles_lookup.setdefault(clean, []).append(code) + + internal = "" + if cterm: + internal = _internal_from_cterm(cterm) + elif nterm: + internal = _internal_from_nterm(nterm) + + if internal: + self.exact_smiles_lookup[internal] = code + clean_int = self._remove_stereochemistry(internal) + self.clean_smiles_lookup.setdefault(clean_int, []).append(code) + + self.uaa_internal_exact[code] = internal + self.uaa_internal_patterns.append((_chirality_agnostic_regex(internal), code)) + + if letter: + self.three_to_one[code] = letter + + def _remove_stereochemistry(self, smiles): + """Remove stereochemistry from SMILES""" + cleaned = smiles + stereochemistry_patterns = [ + '[C@@H]', '[C@H]', '[C@@]', '[C@]', + '[S@@]', '[S@]', '[N@@]', '[N@]', + '@@', '@' + ] + for pattern in stereochemistry_patterns: + cleaned = cleaned.replace(pattern, pattern.replace('@@', '').replace('@', '').replace('[', '').replace(']', '')) + return cleaned + + def preprocess_complex_residues(self, smiles): + complex_positions = [] + + for pattern, residue_type in self.complex_residue_patterns: + for match in re.finditer(pattern, smiles): + if not any(pos['start'] <= match.start() < pos['end'] or + pos['start'] < match.end() <= pos['end'] for pos in complex_positions): + complex_positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': residue_type, + 'pattern': match.group() + }) + + for rgx, code in getattr(self, 'uaa_internal_patterns', []): + for match in rgx.finditer(smiles): + if not any(pos['start'] <= match.start() < pos['end'] or + pos['start'] < match.end() <= pos['end'] for pos in complex_positions): + complex_positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': code, # e.g., 'Dtg' + 'pattern': match.group() + }) + + complex_positions.sort(key=lambda x: x['start']) + if not complex_positions: + return smiles, [] + + preprocessed_smiles = smiles + offset = 0 + protected_residues = [] + + for pos in complex_positions: + start = pos['start'] + offset + end = pos['end'] + offset + complex_part = preprocessed_smiles[start:end] + + # keep your stereo sanity check (OK to keep) + if not ('[C@H]' in complex_part or '[C@@H]' in complex_part): + # Dtg internal often *does* have [C@@H], so it will pass. + # If you find UAAs without explicit stereo, you may relax this guard. + pass + + placeholder = f"COMPLEX_RESIDUE_{len(protected_residues)}" + preprocessed_smiles = preprocessed_smiles[:start] + placeholder + preprocessed_smiles[end:] + offset += len(placeholder) - (end - start) + + protected_residues.append({ + 'placeholder': placeholder, + 'type': pos['type'], + 'content': complex_part + }) + + return preprocessed_smiles, protected_residues + + def split_on_bonds(self, smiles, protected_residues=None): + """Split SMILES into segments based on peptide bonds, with improved handling of protected residues""" + positions = [] + used = set() + + # Handle protected complex residues if any + if protected_residues: + for residue in protected_residues: + match = re.search(residue['placeholder'], smiles) + if match: + positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': 'complex', + 'pattern': residue['placeholder'], + 'residue_type': residue['type'], + 'content': residue['content'] + }) + used.update(range(match.start(), match.end())) + + # Find all peptide bonds + bond_positions = [] + + # Find Gly pattern first + gly_pattern = r'NCC\(=O\)' + for match in re.finditer(gly_pattern, smiles): + if not any(p in range(match.start(), match.end()) for p in used): + bond_positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': 'gly', + 'pattern': match.group() + }) + used.update(range(match.start(), match.end())) + + for pattern, bond_type in self.bond_patterns: + for match in re.finditer(pattern, smiles): + if not any(p in range(match.start(), match.end()) for p in used): + bond_positions.append({ + 'start': match.start(), + 'end': match.end(), + 'type': bond_type, + 'pattern': match.group() + }) + used.update(range(match.start(), match.end())) + + bond_positions.sort(key=lambda x: x['start']) + + all_positions = positions + bond_positions + all_positions.sort(key=lambda x: x['start']) + + segments = [] + + if all_positions and all_positions[0]['start'] > 0: + segments.append({ + 'content': smiles[0:all_positions[0]['start']], + 'bond_after': all_positions[0]['pattern'] if all_positions[0]['type'] != 'complex' else None, + 'complex_after': all_positions[0]['pattern'] if all_positions[0]['type'] == 'complex' else None + }) + + for i in range(len(all_positions)-1): + current = all_positions[i] + next_pos = all_positions[i+1] + + if current['type'] == 'complex': + segments.append({ + 'content': current['content'], + 'bond_before': all_positions[i-1]['pattern'] if i > 0 and all_positions[i-1]['type'] != 'complex' else None, + 'bond_after': next_pos['pattern'] if next_pos['type'] != 'complex' else None, + 'complex_type': current['residue_type'] + }) + elif current['type'] == 'gly': + segments.append({ + 'content': 'NCC(=O)', + 'bond_before': all_positions[i-1]['pattern'] if i > 0 and all_positions[i-1]['type'] != 'complex' else None, + 'bond_after': next_pos['pattern'] if next_pos['type'] != 'complex' else None + }) + else: + content = smiles[current['end']:next_pos['start']] + if content and next_pos['type'] != 'complex': + segments.append({ + 'content': content, + 'bond_before': current['pattern'], + 'bond_after': next_pos['pattern'] if next_pos['type'] != 'complex' else None + }) + + # Last segment + if all_positions and all_positions[-1]['end'] < len(smiles): + if all_positions[-1]['type'] == 'complex': + segments.append({ + 'content': all_positions[-1]['content'], + 'bond_before': all_positions[-2]['pattern'] if len(all_positions) > 1 and all_positions[-2]['type'] != 'complex' else None, + 'complex_type': all_positions[-1]['residue_type'] + }) + else: + segments.append({ + 'content': smiles[all_positions[-1]['end']:], + 'bond_before': all_positions[-1]['pattern'] + }) + + return segments + def is_peptide(self, smiles): + """Check if the SMILES represents a peptide structure""" + mol = Chem.MolFromSmiles(smiles) + if mol is None: + return False + + # Look for peptide bonds: NC(=O) pattern + peptide_bond_pattern = Chem.MolFromSmarts('[NH][C](=O)') + if mol.HasSubstructMatch(peptide_bond_pattern): + return True + + # Look for N-methylated peptide bonds: N(C)C(=O) pattern + n_methyl_pattern = Chem.MolFromSmarts('[N;H0;$(NC)](C)[C](=O)') + if mol.HasSubstructMatch(n_methyl_pattern): + return True + + return False + + def is_cyclic(self, smiles): + # Check for C-terminal carboxyl + if smiles.endswith('C(=O)O'): + return False, [], [] + + # Find all numbers used in ring closures + ring_numbers = re.findall(r'(?:^|[^c])[0-9](?=[A-Z@\(\)])', smiles) + + # Aromatic ring numbers + aromatic_matches = re.findall(r'c[0-9](?:ccccc|c\[nH\]c)[0-9]', smiles) + aromatic_cycles = [] + for match in aromatic_matches: + numbers = re.findall(r'[0-9]', match) + aromatic_cycles.extend(numbers) + + peptide_cycles = [n for n in ring_numbers if n not in aromatic_cycles] + + is_cyclic = len(peptide_cycles) > 0 and not smiles.endswith('C(=O)O') + return is_cyclic, peptide_cycles, aromatic_cycles + + + def clean_terminal_carboxyl(self, segment): + """Remove C-terminal carboxyl only if it's the true terminus""" + content = segment['content'] + + # Only clean if: + # 1. Contains C(=O)O + # 2. No bond_after exists (meaning it's the last segment) + if 'C(=O)O' in content and not segment.get('bond_after'): + # Remove C(=O)O pattern regardless of position + cleaned = re.sub(r'\(C\(=O\)O\)', '', content) + # Remove any leftover empty parentheses + cleaned = re.sub(r'\(\)', '', cleaned) + return cleaned + return content + + def identify_residue(self, segment): + if 'complex_type' in segment: + return segment['complex_type'], [] + + # If this was protected by dynamic UAA shielding + if segment.get('complex_type') in self.uaa_internal_exact: + return segment['complex_type'], [] + + content = self.clean_terminal_carboxyl(segment) + mods = self.get_modifications(segment) + + if content.startswith('COc1ccc(C(SC[C@@H]'): + print("DIRECT MATCH: Found Cmt at beginning") + return 'Cmt', mods + + if '[C@@H]3CCCN3C2=O)(c2ccccc2)c2ccccc2)cc' in content: + print("DIRECT MATCH: Found Pro at end") + return 'Pro', mods + + # Eal - Glu(OAll) + if 'CCC(=O)OCC=C' in content or 'CC(=O)OCC=C' in content or 'C=CCOC(=O)CC' in content: + return 'Eal', mods + + # Proline (P) + if any([ + (segment.get('bond_after', '').startswith(f'N{n}C(=O)') and 'CCC' in content and + any(f'[C@@H]{n}' in content or f'[C@H]{n}' in content for n in '123456789')) + for n in '123456789' + ]) or any([(segment.get('bond_before', '').startswith(f'C(=O)N{n}') and 'CCC' in content and + any(f'CCC{n}' for n in '123456789')) + for n in '123456789' + ]) or any([ + (f'CCCN{n}' in content and content.endswith('=O') and + any(f'[C@@H]{n}' in content or f'[C@H]{n}' in content for n in '123456789')) + for n in '123456789' + ]) or any([ + # CCC[C@H]n + (content == f'CCC[C@H]{n}' and segment.get('bond_before', '').startswith(f'C(=O)N{n}')) or + (content == f'CCC[C@@H]{n}' and segment.get('bond_before', '').startswith(f'C(=O)N{n}')) or + # N-terminal Pro with any ring number + (f'N{n}CCC[C@H]{n}' in content) or + (f'N{n}CCC[C@@H]{n}' in content) + for n in '123456789' + ]): + return 'Pro', mods + + # D-Proline (p) + if ('N1[C@H](CCC1)' in content): + return 'pro', mods + + # Tryptophan (W) + if re.search(r'c[0-9]c\[nH\]c[0-9]ccccc[0-9][0-9]', content) and \ + 'c[nH]c' in content.replace(' ', ''): + if '[C@H](CC' in content: # D-form + return 'trp', mods + return 'Trp', mods + + # Lysine (K) + if '[C@@H](CCCCN)' in content or '[C@H](CCCCN)' in content: + if '[C@H](CCCCN)' in content: # D-form + return 'lys', mods + return 'Lys', mods + + # Arginine (R) + if '[C@@H](CCCNC(=N)N)' in content or '[C@H](CCCNC(=N)N)' in content: + if '[C@H](CCCNC(=N)N)' in content: # D-form + return 'arg', mods + return 'Arg', mods + + if content == 'C' and segment.get('bond_before') and segment.get('bond_after'): + if ('C(=O)N' in segment['bond_before'] or 'NC(=O)' in segment['bond_before'] or 'N(C)C(=O)' in segment['bond_before']) and \ + ('NC(=O)' in segment['bond_after'] or 'C(=O)N' in segment['bond_after'] or 'N(C)C(=O)' in segment['bond_after']): + return 'Gly', mods + + if 'CNC' in content and any(f'C{i}=' in content for i in range(1, 10)): + return 'Gly', mods #'CNC1=O' + if not segment.get('bond_before') and segment.get('bond_after'): + if content == 'C' or content == 'NC': + if ('NC(=O)' in segment['bond_after'] or 'C(=O)N' in segment['bond_after'] or 'N(C)C(=O)' in segment['bond_after']): + return 'Gly', mods + + # Leucine patterns (L/l) + if 'CC(C)C[C@H]' in content or 'CC(C)C[C@@H]' in content or '[C@@H](CC(C)C)' in content or '[C@H](CC(C)C)' in content or (('N[C@H](CCC(C)C)' in content or 'N[C@@H](CCC(C)C)' in content) and segment.get('bond_before') is None): + if '[C@H](CC(C)C)' in content or 'CC(C)C[C@H]' in content: # D-form + return 'leu', mods + return 'Leu', mods + + # Threonine patterns (T/t) + if '[C@@H]([C@@H](C)O)' in content or '[C@H]([C@H](C)O)' in content or '[C@@H]([C@H](C)O)' in content or '[C@H]([C@@H](C)O)' in content: + if '[C@H]([C@@H](C)O)' in content: # D-form + return 'thr', mods + return 'Thr', mods + + if re.search(r'\[C@H\]\(CCc\d+ccccc\d+\)', content) or re.search(r'\[C@@H\]\(CCc\d+ccccc\d+\)', content): + return 'Hph', mods + + # Phenylalanine patterns (F/f) + if re.search(r'\[C@H\]\(Cc\d+ccccc\d+\)', content) or re.search(r'\[C@@H\]\(Cc\d+ccccc\d+\)', content): + if re.search(r'\[C@H\]\(Cc\d+ccccc\d+\)', content): # D-form + return 'phe', mods + return 'Phe', mods + + if ('CC(C)[C@@H]' in content or 'CC(C)[C@H]' in content or + '[C@H](C(C)C)' in content or '[C@@H](C(C)C)' in content or + 'C(C)C[C@H]' in content or 'C(C)C[C@@H]' in content): + if not any(p in content for p in ['CC(C)C[C@H]', 'CC(C)C[C@@H]', 'CCC(=O)']): + if '[C@H]' in content and not '[C@@H]' in content: # D-form + return 'val', mods + return 'Val', mods + + # Isoleucine patterns (I/i) + if (any(['CC[C@@H](C)' in content, '[C@@H](C)CC' in content, '[C@@H](CC)C' in content, + 'C(C)C[C@@H]' in content, '[C@@H]([C@H](C)CC)' in content, '[C@H]([C@@H](C)CC)' in content, + '[C@@H]([C@@H](C)CC)' in content, '[C@H]([C@H](C)CC)' in content, + 'C[C@H](CC)[C@@H]' in content, 'C[C@@H](CC)[C@H]' in content, + 'C[C@H](CC)[C@H]' in content, 'C[C@@H](CC)[C@@H]' in content, + 'CC[C@H](C)[C@@H]' in content, 'CC[C@@H](C)[C@H]' in content, + 'CC[C@H](C)[C@H]' in content, 'CC[C@@H](C)[C@@H]' in content]) + and 'CC(C)C' not in content): # Exclude valine pattern + if any(['[C@H]([C@@H](CC)C)' in content, '[C@H](CC)C' in content, + '[C@H]([C@@H](C)CC)' in content, '[C@H]([C@H](C)CC)' in content, + 'C[C@@H](CC)[C@H]' in content, 'C[C@H](CC)[C@H]' in content, + 'CC[C@@H](C)[C@H]' in content, 'CC[C@H](C)[C@H]' in content]): + # D-form + return 'ile', mods + return 'Ile', mods + + # Tpb - Thr(PO(OBzl)OH) + if re.search(r'\(C\)OP\(=O\)\(O\)OCc[0-9]ccccc[0-9]', content) or 'OP(=O)(O)OCC' in content: + return 'Tpb', mods + + # Alanine patterns (A/a) + if ('[C@H](C)' in content or '[C@@H](C)' in content): + if not any(p in content for p in ['C(C)C', 'COC', 'CN(', 'C(C)O', 'CC[C@H]', 'CC[C@@H]']): + if '[C@H](C)' in content: # D-form + return 'ala', mods + return 'Ala', mods + + # Tyrosine patterns (Y/y) + if re.search(r'Cc[0-9]ccc\(O\)cc[0-9]', content): + if '[C@H](Cc1ccc(O)cc1)' in content: # D-form + return 'tyr', mods + return 'Tyr', mods + + # Serine patterns (S/s) + if '[C@H](CO)' in content or '[C@@H](CO)' in content: + if not ('C(C)O' in content or 'COC' in content): + if '[C@H](CO)' in content: # D-form + return 'ser', mods + return 'Ser', mods + + if 'CSSC' in content: + # cysteine-cysteine bridge + if re.search(r'\[C@@H\].*CSSC.*\[C@@H\]', content) or re.search(r'\[C@H\].*CSSC.*\[C@H\]', content): + if '[C@H]' in content and not '[C@@H]' in content: # D-form + return 'cys-cys', mods + return 'Cys-Cys', mods + + # N-terminal amine group + if '[C@@H](N)CSSC' in content or '[C@H](N)CSSC' in content: + if '[C@H](N)CSSC' in content: # D-form + return 'cys-cys', mods + return 'Cys-Cys', mods + + # C-terminal carboxyl + if 'CSSC[C@@H](C(=O)O)' in content or 'CSSC[C@H](C(=O)O)' in content: + if 'CSSC[C@H](C(=O)O)' in content: # D-form + return 'cys-cys', mods + return 'Cys-Cys', mods + + # Cysteine patterns (C/c) + if '[C@H](CS)' in content or '[C@@H](CS)' in content: + if '[C@H](CS)' in content: # D-form + return 'cys', mods + return 'Cys', mods + + # Methionine patterns (M/m) + if ('CCSC' in content) or ("CSCC" in content): + if '[C@H](CCSC)' in content: # D-form + return 'met', mods + elif '[C@H]' in content: + return 'met', mods + return 'Met', mods + + # Glutamine patterns (Q/q) + if (content == '[C@@H](CC' or content == '[C@H](CC' and segment.get('bond_before')=='C(=O)N' and segment.get('bond_after')=='C(=O)N') or ('CCC(=O)N' in content) or ('CCC(N)=O' in content): + if '[C@H](CCC(=O)N)' in content: # D-form + return 'gln', mods + return 'Gln', mods + + # Asparagine patterns (N/n) + if (content == '[C@@H](C' or content == '[C@H](C' and segment.get('bond_before')=='C(=O)N' and segment.get('bond_after')=='C(=O)N') or ('CC(=O)N' in content) or ('CCN(=O)' in content) or ('CC(N)=O' in content): + if '[C@H](CC(=O)N)' in content: # D-form + return 'asn', mods + return 'Asn', mods + + # Glutamic acid patterns (E/e) + if ('CCC(=O)O' in content): + if '[C@H](CCC(=O)O)' in content: # D-form + return 'glu', mods + return 'Glu', mods + + # Aspartic acid patterns (D/d) + if ('CC(=O)O' in content): + if '[C@H](CC(=O)O)' in content: # D-form + return 'asp', mods + return 'Asp', mods + + if re.search(r'Cc\d+c\[nH\]cn\d+', content) or re.search(r'Cc\d+cnc\[nH\]\d+', content): + if '[C@H]' in content: # D-form + return 'his', mods + return 'His', mods + if 'C2(CCCC2)' in content or 'C1(CCCC1)' in content or re.search(r'C\d+\(CCCC\d+\)', content): + return 'Cyl', mods + + if ('N[C@@H](CCCC)' in content or '[C@@H](CCCC)' in content or 'CCCC[C@@H]' in content or + 'N[C@H](CCCC)' in content or '[C@H](CCCC)' in content) and 'CC(C)' not in content: + return 'Nle', mods + + if 'C(C)(C)(N)' in content: + return 'Aib', mods + + if 'C(C)(C)' in content and 'OC(C)(C)C' not in content: + if (segment.get('bond_before') and segment.get('bond_after') and + any(bond in segment['bond_before'] for bond in ['C(=O)N', 'NC(=O)', 'N(C)C(=O)']) and + any(bond in segment['bond_after'] for bond in ['NC(=O)', 'C(=O)N', 'N(C)C(=O)'])): + return 'Aib', mods + + # Dtg - Asp(OtBu)-(Dmb)Gly + if 'CC(=O)OC(C)(C)C' in content and 'CC1=C(C=C(C=C1)OC)OC' in content: + return 'Dtg', mods + + + # Kpg - Lys(palmitoyl-Glu-OtBu) + if 'CCCNC(=O)' in content and 'CCCCCCCCCCCC' in content: + return 'Kpg', mods + + #======================Other UAAs from the SwissSidechain========================================== + # ADD SWISSSIDECHAIN MATCHING AT THE VERY END - only if nothing else matched + if content in self.exact_smiles_lookup: + return self.exact_smiles_lookup[content], mods + + # Look up without stereochemistry differences) + content_clean = self._remove_stereochemistry(content) + if content_clean in self.clean_smiles_lookup: + matches = self.clean_smiles_lookup[content_clean] + if len(matches) == 1: + return matches[0], mods + else: + # Prefer L-forms (non-D prefixed codes) over D-forms + l_forms = [m for m in matches if not m.startswith('D')] + if l_forms: + return l_forms[0], mods + return matches[0], mods + + return None, mods + + def get_modifications(self, segment): + """Get modifications based on bond types and segment content - fixed to avoid duplicates""" + mods = [] + + # Check for N-methylation in any form, but only add it once + # Check both bonds and segment content for N-methylation patterns + if ((segment.get('bond_after') and + ('N(C)' in segment['bond_after'] or segment['bond_after'].startswith('C(=O)N(C)'))) or + ('N(C)C(=O)' in segment['content'] or 'N(C)C1=O' in segment['content']) or + (segment['content'].endswith('N(C)C(=O)') or segment['content'].endswith('N(C)C1=O'))): + mods.append('N-Me') + + # Check for O-linked modifications + #if segment.get('bond_after') and 'OC(=O)' in segment['bond_after']: + #mods.append('O-linked') + + return mods + + def analyze_structure(self, smiles, verbose=False): + logs = [] + preprocessed_smiles, protected_residues = self.preprocess_complex_residues(smiles) + + is_cyclic, peptide_cycles, aromatic_cycles = self.is_cyclic(smiles) + + segments = self.split_on_bonds(preprocessed_smiles, protected_residues) + + sequence = [] + for i, segment in enumerate(segments): + if verbose: + logs.append(f"\nSegment {i}:") + logs.append(f" Content: {segment.get('content','None')}") + logs.append(f" Bond before: {segment.get('bond_before','None')}") + logs.append(f" Bond after: {segment.get('bond_after','None')}") + + pdb.set_trace() + residue, mods = self.identify_residue(segment) + if residue: + if mods: + sequence.append(f"{residue}({','.join(mods)})") + else: + sequence.append(residue) + else: + logs.append(f"Warning: Could not identify residue in segment: {segment.get('content', 'None')}") + + three_letter = '-'.join(sequence) + + one_letter = ''.join(self.three_to_one.get(aa.split('(')[0], 'X') for aa in sequence) + + if is_cyclic: + three_letter = f"cyclo({three_letter})" + one_letter = f"cyclo({one_letter})" + + return { + 'three_letter': three_letter, + 'one_letter': one_letter, + 'is_cyclic': is_cyclic, + 'residues': sequence, + 'details': "\n".join(logs) + } + + def get_uaa_information(self): + uaa_info = """ + ## Supported Non-Standard Amino Acids (UAAs) (Common) + + - **Kpg** - Lys(palmitoyl-Glu-OtBu) + - **Cmt** - Cys(Mmt) + - **Eal** - Glu(OAll) + - **Tpb** - Thr(PO(OBzl)OH) + - **Dtg** - Asp(OtBu)-(Dmb)Gly + - **Aib** - α-Aminoisobutyric acid + - **Nle** - Norleucine + - **Hph** - Homophenylalanine + - **Cyl** - Cycloleucine + - **Nml** - N-methylleucine + - **Nma** - N-methylalanine + + ### Special Cases: + - **Cys-Cys** - Disulfide-bridged cysteine dimer + --- + + ## Three-to-One Letter Code Mapping + + ### Standard Amino Acids: + **L-amino acids:** A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y + **D-amino acids:** a, c, d, e, f, g, h, i, k, l, m, n, p, q, r, s, t, v, w, y + + ### UAA Single Letter Codes: + | UAA | Code | UAA | Code | UAA | Code | + |-----|------|-----|------|-----|------| + | Aib | Ŷ | Dtg | Ĝ | Cmt | Ĉ | + | Eal | Ė | Nml | Ŀ | Nma | Ṃ | + | Kpg | Ƙ | Tpb | Ṯ | Cyl | Ċ | + | Nle | Ł | Hph | Ĥ | | | + + ### Special Cases: + - **Cys-Cys:** CC (L-form) or cc (D-form) + + ## For other mappings, please refer to the [SwissSideChain webside](https://www.swisssidechain.ch/browse/family/table.php?family=all) + """ + return uaa_info + +def annotate_cyclic_structure(mol, sequence): + """Create structure visualization""" + AllChem.Compute2DCoords(mol) + + drawer = Draw.rdMolDraw2D.MolDraw2DCairo(2000, 2000) + + drawer.drawOptions().addAtomIndices = False + drawer.DrawMolecule(mol) + drawer.FinishDrawing() + + img = Image.open(BytesIO(drawer.GetDrawingText())) + draw = ImageDraw.Draw(img) + try: + small_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 60) + except OSError: + try: + small_font = ImageFont.truetype("arial.ttf", 60) + except OSError: + print("Warning: TrueType fonts not available, using default font") + small_font = ImageFont.load_default() + + seq_text = f"Sequence: {sequence}" + bbox = draw.textbbox((1000, 100), seq_text, font=small_font) + padding = 10 + draw.rectangle([bbox[0]-padding, bbox[1]-padding, + bbox[2]+padding, bbox[3]+padding], + fill='white', outline='white') + draw.text((1000, 100), seq_text, + font=small_font, fill='black', anchor="mm") + + return img + +def create_enhanced_linear_viz(sequence, smiles): + """"Linear visualization""" + analyzer = PeptideAnalyzer() + + fig = plt.figure(figsize=(15, 10)) + gs = fig.add_gridspec(2, 1, height_ratios=[1, 2]) + ax_struct = fig.add_subplot(gs[0]) + ax_detail = fig.add_subplot(gs[1]) + + if sequence.startswith('cyclo('): + residues = sequence[6:-1].split('-') + else: + residues = sequence.split('-') + + segments = analyzer.split_on_bonds(smiles) + + print(f"Number of residues: {len(residues)}") + print(f"Number of segments: {len(segments)}") + + ax_struct.set_xlim(0, 10) + ax_struct.set_ylim(0, 2) + + num_residues = len(residues) + spacing = 9.0 / (num_residues - 1) if num_residues > 1 else 9.0 + + y_pos = 1.5 + for i in range(num_residues): + x_pos = 0.5 + i * spacing + + rect = patches.Rectangle((x_pos-0.3, y_pos-0.2), 0.6, 0.4, + facecolor='lightblue', edgecolor='black') + ax_struct.add_patch(rect) + + if i < num_residues - 1: + segment = segments[i] if i < len(segments) else None + if segment: + bond_type = 'ester' if 'O-linked' in segment.get('bond_after', '') else 'peptide' + is_n_methylated = 'N-Me' in segment.get('bond_after', '') + + bond_color = 'red' if bond_type == 'ester' else 'black' + linestyle = '--' if bond_type == 'ester' else '-' + + ax_struct.plot([x_pos+0.3, x_pos+spacing-0.3], [y_pos, y_pos], + color=bond_color, linestyle=linestyle, linewidth=2) + + mid_x = x_pos + spacing/2 + bond_label = f"{bond_type}" + if is_n_methylated: + bond_label += "\n(N-Me)" + ax_struct.text(mid_x, y_pos+0.1, bond_label, + ha='center', va='bottom', fontsize=10, + color=bond_color) + + ax_struct.text(x_pos, y_pos-0.5, residues[i], + ha='center', va='top', fontsize=14) + + ax_detail.set_ylim(0, len(segments)+1) + ax_detail.set_xlim(0, 1) + + segment_y = len(segments) + for i, segment in enumerate(segments): + y = segment_y - i + + # Check if this is a bond or residue + residue, mods = analyzer.identify_residue(segment) + if residue: + text = f"Residue {i+1}: {residue}" + if mods: + text += f" ({', '.join(mods)})" + color = 'blue' + else: + text = f"Bond {i}: " + if 'O-linked' in segment.get('bond_after', ''): + text += "ester" + elif 'N-Me' in segment.get('bond_after', ''): + text += "peptide (N-methylated)" + else: + text += "peptide" + color = 'red' + + ax_detail.text(0.05, y, text, fontsize=12, color=color) + ax_detail.text(0.5, y, f"SMILES: {segment.get('content', '')}", fontsize=10, color='gray') + + # If cyclic, add connection indicator + if sequence.startswith('cyclo('): + ax_struct.annotate('', xy=(9.5, y_pos), xytext=(0.5, y_pos), + arrowprops=dict(arrowstyle='<->', color='red', lw=2)) + ax_struct.text(5, y_pos+0.3, 'Cyclic Connection', + ha='center', color='red', fontsize=14) + + ax_struct.set_title("Peptide Structure Overview", pad=20) + ax_detail.set_title("Segment Analysis Breakdown", pad=20) + + for ax in [ax_struct, ax_detail]: + ax.set_xticks([]) + ax.set_yticks([]) + ax.axis('off') + + plt.tight_layout() + return fig + +class PeptideStructureGenerator: + """Generate 3D structures of peptides using different embedding methods""" + + @staticmethod + def prepare_molecule(smiles): + """Prepare molecule with proper hydrogen handling""" + mol = Chem.MolFromSmiles(smiles, sanitize=False) + if mol is None: + raise ValueError("Failed to create molecule from SMILES") + + for atom in mol.GetAtoms(): + atom.UpdatePropertyCache(strict=False) + + # Sanitize with reduced requirements + Chem.SanitizeMol(mol, + sanitizeOps=Chem.SANITIZE_FINDRADICALS| + Chem.SANITIZE_KEKULIZE| + Chem.SANITIZE_SETAROMATICITY| + Chem.SANITIZE_SETCONJUGATION| + Chem.SANITIZE_SETHYBRIDIZATION| + Chem.SANITIZE_CLEANUPCHIRALITY) + + mol = Chem.AddHs(mol) + return mol + + @staticmethod + def get_etkdg_params(attempt=0): + """Get ETKDG parameters""" + params = AllChem.ETKDGv3() + params.randomSeed = -1 + params.maxIterations = 200 + params.numThreads = 4 # Reduced for web interface + params.useBasicKnowledge = True + params.enforceChirality = True + params.useExpTorsionAnglePrefs = True + params.useSmallRingTorsions = True + params.useMacrocycleTorsions = True + params.ETversion = 2 + params.pruneRmsThresh = -1 + params.embedRmsThresh = 0.5 + + if attempt > 10: + params.bondLength = 1.5 + (attempt - 10) * 0.02 + params.useExpTorsionAnglePrefs = False + + return params + + def generate_structure_etkdg(self, smiles, max_attempts=20): + """Generate 3D structure using ETKDG without UFF optimization""" + success = False + mol = None + + for attempt in range(max_attempts): + try: + mol = self.prepare_molecule(smiles) + params = self.get_etkdg_params(attempt) + + if AllChem.EmbedMolecule(mol, params) == 0: + success = True + break + except Exception as e: + continue + + if not success: + raise ValueError("Failed to generate structure with ETKDG") + + return mol + + def generate_structure_uff(self, smiles, max_attempts=20): + """Generate 3D structure using ETKDG followed by UFF optimization""" + best_mol = None + lowest_energy = float('inf') + + for attempt in range(max_attempts): + try: + test_mol = self.prepare_molecule(smiles) + params = self.get_etkdg_params(attempt) + + if AllChem.EmbedMolecule(test_mol, params) == 0: + res = AllChem.UFFOptimizeMolecule(test_mol, maxIters=2000, + vdwThresh=10.0, confId=0, + ignoreInterfragInteractions=True) + + if res == 0: + ff = AllChem.UFFGetMoleculeForceField(test_mol) + if ff: + current_energy = ff.CalcEnergy() + if current_energy < lowest_energy: + lowest_energy = current_energy + best_mol = Chem.Mol(test_mol) + except Exception: + continue + + if best_mol is None: + raise ValueError("Failed to generate optimized structure") + + return best_mol + + @staticmethod + def mol_to_sdf_bytes(mol): + """Convert RDKit molecule to SDF file bytes""" + sio = StringIO() + writer = Chem.SDWriter(sio) + writer.write(mol) + writer.close() + + return sio.getvalue().encode('utf-8') + +class PeptideEncoder: + # map one-letter <-> three-letter + one_to_three = { + 'A':'Ala','C':'Cys','D':'Asp','E':'Glu','F':'Phe','G':'Gly','H':'His','I':'Ile', + 'K':'Lys','L':'Leu','M':'Met','N':'Asn','P':'Pro','Q':'Gln','R':'Arg','S':'Ser', + 'T':'Thr','V':'Val','W':'Trp','Y':'Tyr', + 'a':'ala','c':'cys','d':'asp','e':'glu','f':'phe','g':'gly','h':'his','i':'ile', + 'k':'lys','l':'leu','m':'met','n':'asn','p':'pro','q':'gln','r':'arg','s':'ser', + 't':'thr','v':'val','w':'trp','y':'tyr' + } + + # L-form uses [C@@H], D-form uses [C@H]. + SEG_L = { + 'Ala': '[C@@H](C)', + 'Gly': 'C', # your analyzer treats bare 'C' (or 'NC') as Gly in context + 'Val': '[C@@H](C(C)C)', + 'Leu': '[C@@H](CC(C)C)', + 'Ile': '[C@@H]([C@H](C)CC)', + 'Ser': '[C@@H](CO)', + 'Thr': '[C@@H]([C@@H](C)O)', + 'Cys': '[C@@H](CS)', + 'Met': '[C@@H](CCSC)', + 'Phe': '[C@@H](Cc1ccccc1)', + 'Tyr': '[C@@H](Cc1ccc(O)cc1)', + 'Trp': '[C@@H](Cc1c[nH]c2ccccc12)', + 'His': '[C@@H](Cc1c[nH]cn1)', + 'Asp': '[C@@H](CC(=O)O)', + 'Glu': '[C@@H](CCC(=O)O)', + 'Asn': '[C@@H](CC(=O)N)', + 'Gln': '[C@@H](CCC(=O)N)', + 'Lys': '[C@@H](CCCCN)', + 'Arg': '[C@@H](CCCNC(=N)N)', + 'Pro': 'CC[C@H]2CN2' # only used if not doing ring-number closure + } + # D-forms: flip chirality tag to [C@H] + SEG_D = {k.lower(): v.replace('[C@@H]', '[C@H]').replace('[C@H]2','[C@@H]2') for k, v in SEG_L.items()} + + UAA_SEG = { + 'Aib': 'C(C)(C)', # alpha,alpha-dimethyl gly (detected as Aib when bracketed by peptide bonds) + 'Nle': '[C@@H](CCCC)', # norleucine ~ Lys w/o terminal amine + 'Hph': '[C@@H](CCc1ccccc1)', # homophenylalanine + 'Cyl': 'C1(CCCC1)', # cycloleucine + + } + + def __init__(self): + self.ssc_code_to_internal = {} + for name, data in specific_aminos.items(): + code = data["Code"] + cterm = data.get("cterm", "") + nterm = data.get("nterm", "") + internal = "" + if cterm: + internal = _internal_from_cterm(cterm) + elif nterm: + internal = _internal_from_nterm(nterm) + if internal: + self.ssc_code_to_internal[code] = internal + for name, data in all_aminos.items(): + code = data["Code"] + cterm = data.get("cterm", "") + nterm = data.get("nterm", "") + internal = "" + if cterm: + internal = _internal_from_cterm(cterm) + elif nterm: + internal = _internal_from_nterm(nterm) + if internal: + self.ssc_code_to_internal[code] = internal + + def _segment_for(self, code): + if code in self.SEG_L: return self.SEG_L[code] + if code in self.SEG_D: return self.SEG_D[code] + if code in self.UAA_SEG: return self.UAA_SEG[code] + + if code in self.ssc_code_to_internal: + return self.ssc_code_to_internal[code] + + cap = code[:1].upper() + code[1:].lower() + if cap in self.SEG_L: return self.SEG_L[cap] + raise ValueError(f"Unknown residue code: {code}") + + def _is_one_letter_seq(self, seq: str) -> bool: + """Check if the input string looks like a one-letter code sequence.""" + if "-" not in seq: + return True + + def _norm_token(self, tok): + """Normalize tokens like 'A', 'a', 'Ala', 'ala', 'Ala(N-Me)' -> (code, n_me_flag)""" + n_me = False + tok = tok.strip() + if tok in self.one_to_three: + base = self.one_to_three[tok] + else: + m = re.match(r'^([A-Za-z\-]+)(\((.*?)\))?$', tok) + if not m: + return tok, n_me + base = m.group(1) + mods = m.group(3) or "" + if 'N-Me' in mods or 'Nme' in mods or 'NME' in mods: + n_me = True + return base, n_me + + def _bond_for(self, n_me=False, pro_ring=False, ring_idx=1): + """Return the INTER-RESIDUE bond token your parser recognizes.""" + if pro_ring: + return f'C(=O)N{ring_idx}' + return 'N(C)C(=O)' if n_me else 'NC(=O)' + + def _split_tokens(self, seq): + if isinstance(seq, (list, tuple)): + return list(seq) + seq = seq.strip() + + if self._is_one_letter_seq(seq): + return list(seq) + + import re + return [t for t in re.split(r'-(?![^()]*\))', seq) if t] + + + def encode(self, seq, cyclic=False, use_proline_ring=True): + """ + Encode a peptide to a SMILES string using the same grammar your analyzer expects. + + Args: + seq: list of tokens or a string like: + 'Ala-Gly-Phe', 'A-G-F', 'Ala(N-Me)-Leu-Ser', 'Aib-Nle-Arg' + D-forms: 'ala-gly', or 'a-g' + cyclic: if True, connect C-terminus back to N-terminus (macrocycle) + use_proline_ring: if True, do ring-number closure for Pro (N{digit} ... [C@H]{digit}) + """ + toks = self._split_tokens(seq) + res, mods = [], [] + for t in toks: + base, n_me = self._norm_token(t) # your existing parser for "(N-Me)" + res.append(base) + mods.append(n_me) + + # Build segments + segs = [self._segment_for(r) for r in res] + + # Proline ring bookkeeping + # We only do the special N{digit}...{digit} closure when a bond *into* Pro occurs. + bonds = [] + for i in range(len(segs)-1): + next_is_pro = res[i+1] in ('Pro','pro') + if use_proline_ring and next_is_pro: + bonds.append(self._bond_for(n_me=mods[i], pro_ring=True, ring_idx=1)) + # Make the Pro segment end with the matching ring digit + segs[i+1] = 'CCC[C@H]1' if res[i+1]=='Pro' else 'CCC[C@@H]1' + else: + bonds.append(self._bond_for(n_me=mods[i], pro_ring=False)) + + # Assemble linear chain + # [segment0] + bond0 + [segment1] + bond1 + ... + [segmentN-1] + C(=O)O + out = [] + for i, s in enumerate(segs): + out.append(s) + if i < len(bonds): + out.append(bonds[i]) + if cyclic: + core = ''.join(out) + + # Find N-terminus (first 'N' in the string). Assumes segments start with backbone N. + n_idx = core.find('N') + if n_idx == -1: + raise ValueError("Cannot find N-terminus 'N' to form cyclic peptide.") + + # Insert ring digit '2' after that N: N -> N2 + core = core[:n_idx + 1] + '2' + core[n_idx + 1:] + + # Add C-terminal carbonyl with matching ring closure digit '2' + core += 'C(=O)2' + + return core + else: + out.append('C(=O)O') + + return ''.join(out) + +def process_input( + smiles_input=None, + file_obj=None, + #show_linear=False, + show_segment_details=False, + generate_3d=False, + use_uff=False +): + """Actual Execution Command.""" + analyzer = PeptideAnalyzer() + temp_dir = tempfile.mkdtemp() if generate_3d else None + structure_files = [] + + # Retrieve UAA information + uaa_info = analyzer.get_uaa_information() + + # Handle direct SMILES input + if smiles_input: + smiles = smiles_input.strip() + + if not analyzer.is_peptide(smiles): + return "Error: Input SMILES does not appear to be a peptide structure.", None, None, [] + + try: + # Preprocess to protect complex residues + pre_smiles, protected_residues = analyzer.preprocess_complex_residues(smiles) + # Report protected residues in summary if any + protected_info = None + if protected_residues: + protected_info = [res['type'] for res in protected_residues] + + mol = Chem.MolFromSmiles(smiles) + if mol is None: + return "Error: Invalid SMILES notation.", None, None, [] + + if generate_3d: + generator = PeptideStructureGenerator() + + try: + # Generate ETKDG structure + mol_etkdg = generator.generate_structure_etkdg(smiles) + etkdg_path = os.path.join(temp_dir, "structure_etkdg.sdf") + writer = Chem.SDWriter(etkdg_path) + writer.write(mol_etkdg) + writer.close() + structure_files.append(etkdg_path) + + # Generate UFF structure if requested + if use_uff: + mol_uff = generator.generate_structure_uff(smiles) + uff_path = os.path.join(temp_dir, "structure_uff.sdf") + writer = Chem.SDWriter(uff_path) + writer.write(mol_uff) + writer.close() + structure_files.append(uff_path) + + except Exception as e: + return f"Error generating 3D structures: {str(e)}", None, None, [] + + analysis = analyzer.analyze_structure(smiles, verbose=show_segment_details) + three_letter = analysis['three_letter'] + one_letter = analysis['one_letter'] + is_cyclic = analysis['is_cyclic'] + details = analysis.get('details', "") + + img_cyclic = annotate_cyclic_structure(mol, three_letter) + + summary = "Summary:\n" + summary += f"Sequence: {three_letter}\n" + summary += f"One-letter code: {one_letter}\n" + summary += f"Is Cyclic: {'Yes' if is_cyclic else 'No'}\n" + + # Add segment details if requested + if show_segment_details and details: + summary += "\n" + "="*50 + "\n" + summary += "SEGMENT ANALYSIS:\n" + summary += "="*50 + "\n" + summary += details + "\n" + + detected_uaas = [aa for aa in analysis['residues'] if aa not in [ + 'Ala', 'Cys', 'Asp', 'Glu', 'Phe', 'Gly', 'His', 'Ile', 'Lys', 'Leu', + 'Met', 'Asn', 'Pro', 'Gln', 'Arg', 'Ser', 'Thr', 'Val', 'Trp', 'Tyr', + 'ala', 'cys', 'asp', 'glu', 'phe', 'gly', 'his', 'ile', 'lys', 'leu', + 'met', 'asn', 'pro', 'gln', 'arg', 'ser', 'thr', 'val', 'trp', 'tyr' + ]] + + if detected_uaas: + summary += f"\nDetected UAAs: {', '.join(set(detected_uaas))}\n" + + if structure_files: + summary += "\n3D Structures Generated:\n" + for filepath in structure_files: + summary += f"- {os.path.basename(filepath)}\n" + + #return summary, img_cyclic, img_linear, structure_files if structure_files else None + return summary, img_cyclic, uaa_info + + except Exception as e: + #return f"Error processing SMILES: {str(e)}", None, None, [] + return f"Error processing SMILES: {str(e)}", None, uaa_info + # Handle file input + if file_obj is not None: + try: + if hasattr(file_obj, 'name'): + with open(file_obj.name, 'r') as f: + content = f.read() + else: + content = file_obj.decode('utf-8') if isinstance(file_obj, bytes) else str(file_obj) + + output_text = "" + for line in content.splitlines(): + smiles = line.strip() + if not smiles: + continue + + if not analyzer.is_peptide(smiles): + output_text += f"Skipping non-peptide SMILES: {smiles}\n" + continue + + try: + result = analyzer.analyze_structure(smiles) + + output_text += f"\nSummary for SMILES: {smiles}\n" + output_text += f"Sequence: {result['three_letter']}\n" + output_text += f"One-letter code: {result['one_letter']}\n" + output_text += f"Is Cyclic: {'Yes' if result['is_cyclic'] else 'No'}\n" + output_text += "-" * 50 + "\n" + except Exception as e: + output_text += f"Error processing SMILES: {smiles} - {str(e)}\n" + output_text += "-" * 50 + "\n" + + return output_text, None, uaa_info + + except Exception as e: + #return f"Error processing file: {str(e)}", None, None, [] + return f"Error processing file: {str(e)}", None, uaa_info + + return ( + output_text or "No analysis done.", + img_cyclic if 'img_cyclic' in locals() else None, uaa_info + #img_linear if 'img_linear' in locals() else None, + #structure_files if structure_files else [] + ) + +def aa_to_smiles( + seq_input: str, + show_segment_details: bool = False, + use_proline_ring: bool = True, + cyclic: bool = True +): + """ + Encode a peptide sequence to SMILES, then analyze back with PeptideAnalyzer for round-trip. + """ + if not seq_input or not seq_input.strip(): + return "Please enter a peptide sequence.", None, None + + try: + enc = PeptideEncoder() # make sure this class is defined in your file + smiles = enc.encode(seq_input.strip(), cyclic=cyclic, use_proline_ring=use_proline_ring) + + analyzer = PeptideAnalyzer() + # pre-check it's a peptide + if not analyzer.is_peptide(smiles): + return "Internal error: generated SMILES did not look like a peptide.", None, None + + # analyze round-trip + # analysis = analyzer.analyze_structure(smiles, verbose=show_segment_details) + # three_letter = analysis['three_letter'] + # one_letter = analysis['one_letter'] + # is_cyclic = analysis['is_cyclic'] + # details = analysis.get('details', "") + + # img = annotate_cyclic_structure(Chem.MolFromSmiles(smiles), three_letter) + + # summary = [] + # summary.append("Peptide → SMILES") + # summary.append("-" * 50) + # summary.append(f"Input sequence: {seq_input}") + # summary.append(f"Generated SMILES:\n{smiles}") + # summary.append("") + # summary.append("Round-trip check (SMILES → sequence):") + # summary.append(f"Sequence: {three_letter}") + # summary.append(f"One-letter code: {one_letter}") + # summary.append(f"Is Cyclic: {'Yes' if is_cyclic else 'No'}") + + # if show_segment_details and details: + # summary.append("\n" + "="*50) + # summary.append("SEGMENT ANALYSIS") + # summary.append("="*50) + # summary.append(details) + + # # UAA report + # detected_uaas = [aa for aa in analysis['residues'] if aa not in [ + # 'Ala', 'Cys', 'Asp', 'Glu', 'Phe', 'Gly', 'His', 'Ile', 'Lys', 'Leu', + # 'Met', 'Asn', 'Pro', 'Gln', 'Arg', 'Ser', 'Thr', 'Val', 'Trp', 'Tyr', + # 'ala', 'cys', 'asp', 'glu', 'phe', 'gly', 'his', 'ile', 'lys', 'leu', + # 'met', 'asn', 'pro', 'gln', 'arg', 'ser', 'thr', 'val', 'trp', 'tyr' + # ]] + # if detected_uaas: + # summary.append(f"\nDetected UAAs (round-trip): {', '.join(sorted(set(detected_uaas)))}") + + # return "\n".join(summary), img, smiles + return smiles + + except Exception as e: + # return f"Error: {str(e)}", None, None + return f"Error: {str(e)}" + +def main(): + # peptide = 'CŶVTNVCALŶG' + # three_letter_peptide = one_letter_to_three_letter(peptide) + # print(three_letter_peptide) + # print('--------------------------------------------------') + # smiles = aa_to_smiles(three_letter_peptide, cyclic=True) + # print(smiles) + + seqs = ['CŶVTNVCALŶG', 'AVVVVCCŶRŶG', 'AŶMVKŶCKNŶG', 'STKYLEŶŶŶŶG', 'LĊCWŶRĊDYIG', 'ƘŶCQECCLŶŶG'] + + three_letter_seqs = [one_letter_to_three_letter(seq) for seq in seqs] + smiles_seqs = [aa_to_smiles(three_letter_seq, cyclic=True) for three_letter_seq in three_letter_seqs] + print(smiles_seqs) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/third_party/SMILESToPeptide/swisssidechain.py b/third_party/SMILESToPeptide/swisssidechain.py new file mode 100644 index 0000000000000000000000000000000000000000..a61a07bdb9dd052c5df54e5daaada96ed40f4da2 --- /dev/null +++ b/third_party/SMILESToPeptide/swisssidechain.py @@ -0,0 +1,4526 @@ +"""Definitions and properties of amino-acids adapted from p2smi (https://github.com/AaronFeller/p2smi)""" + +# Natural Amino-acids: + +all_aminos = { + "Phenylglycine": { + "Code": "PG", + "Formula": "C8H9NO2", + "Letter": "Ḁ", + "MolWeight": 151.063328528, + "SMILES": "N[C@@H](c1ccccc1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "4-methoxy-Phenylalanine": { + "Code": "0A1", + "Formula": "C10H13NO3", + "Letter": "Ạ", + "MolWeight": 195.089543276, + "SMILES": "N[C@@H](Cc1ccc(OC)cc1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "7-hydroxy-l-tryptophan": { + "Code": "0AF", + "Formula": "C11H12N2O3", + "Letter": "Ả", + "MolWeight": 220.084792244, + "SMILES": "N[C@@H](Cc1c[nH]c2c1cccc2O)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "4-carbamimidoyl-l-phenylalanine": { + "Code": "0BN", + "Formula": "C10H13N3O2", + "Letter": "Ấ", + "MolWeight": 207.100776656, + "SMILES": "N[C@@H](Cc1ccc(cc1)C(=N)N)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": "N[C@@H](Cc1ccc(cc1)C(=N*)N)C(=O)O", + }, + "4-chloro-Phenylalanine": { + "Code": "4CP", + "Formula": "C9H10ClNO2", + "Letter": "Ầ", + "MolWeight": 199.04000624, + "SMILES": "N[C@@H](Cc1ccc(cc1)Cl)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "2-Allyl-glycine": { + "Code": "2AG", + "Formula": "C7H11NO5", + "Letter": "Ẫ", + "MolWeight": 189.063722452, + "SMILES": "N[C@@H](CCCC(C(=O)O)=O)C(=O)O", + "cterm": "N[C@@H](CCCC(C*(=O))=O)C(=O)O", + "disulphide": False, + "ester": False, + "nterm": False, + }, + "3-methyl-aspartic-acid": { + "Code": "2AS", + "Formula": "C5H9NO4", + "Letter": "Ậ", + "MolWeight": 147.053157768, + "SMILES": "N[C@H]([C@H](C)(C(=O)O))C(=O)O", + "cterm": "N[C@H]([C@H](C)(C*(=O)))C(=O)O", + "disulphide": False, + "ester": False, + "nterm": False, + }, + "s-(difluoromethyl)-homocysteine": { + "Code": "2FM", + "Formula": "C5H9F2NO2S", + "Letter": "Ắ", + "MolWeight": 185.032205968, + "SMILES": "N[C@@H](CCSC(F)F)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "2-fluoro-l-histidine": { + "Code": "2HF", + "Formula": "C6H12FN3O2", + "Letter": "Ằ", + "MolWeight": 177.091354844, + "SMILES": "N[C@@H](C[C@@H]1CN[C@@H](N1)F)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "2-fluoro-l-histidine(1)": { + "Code": "2HF1", + "Formula": "C6H8FN3O2", + "Letter": "Ẳ", + "MolWeight": 173.060054716, + "SMILES": "N[C@@H](Cc1cnc(F)N1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "2-fluoro-l-histidine(2)": { + "Code": "2HF2", + "Formula": "C6H8FN3O2", + "Letter": "Ẵ", + "MolWeight": 173.060054716, + "SMILES": "N[C@@H](Cc1c[nH]c(n1)F)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "l-2-amino-6-methylene-pimelic-acid": { + "Code": "2NP", + "Formula": "C8H13NO4", + "Letter": "Ặ", + "MolWeight": 187.084457896, + "SMILES": "N[C@@H](CCCC(=C)C(=O)O)C(=O)O", + "cterm": "N[C@@H](CCCC(=C)C*(=O))C(=O)O", + "disulphide": False, + "ester": False, + "nterm": False, + }, + "3-(4H-thieno[3,2-b]pyrrol-6-yl)-L-alanine": { + "Code": "32T", + "Formula": "C9H10N2O2S", + "Letter": "Ḃ", + "MolWeight": 210.04629856, + "SMILES": "N[C@H](Cc1c[nH]c2c1scc2)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "3-cyano-phenylalanine": { + "Code": "3CF", + "Formula": "C10H10N2O2", + "Letter": "Ḅ", + "MolWeight": 190.07422756, + "SMILES": "N[C@@H](Cc1cccc(C#N)c1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "(2s)-amino(3,5-dihydroxyphenyl)-ethanoic-acid": { + "Code": "3FG", + "Formula": "C8H9NO4", + "Letter": "Ḇ", + "MolWeight": 183.053157768, + "SMILES": "N[C@@H](c1cc(O)cc(c1)O)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": "N[C@@H](c1cc(O*)cc(c1)O)C(=O)O", + "nterm": False, + }, + "4-hydroxy-glutamic-acid": { + "Code": "3GL", + "Formula": "C5H9NO5", + "Letter": "Ḉ", + "MolWeight": 163.048072388, + "SMILES": "N[C@@H](C[C@@H](C(=O)O)O)C(=O)O", + "cterm": "N[C@@H](C[C@@H](C*(=O))O)C(=O)O", + "disulphide": False, + "ester": False, + "nterm": False, + }, + "3-Chloro-tyrosine": { + "Code": "3MY", + "Formula": "C9H10ClNO3", + "Letter": "Ḋ", + "MolWeight": 215.03492086, + "SMILES": "N[C@H](Cc1ccc(c(c1)Cl)O)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "4-Bromo-phenylalanine": { + "Code": "4BF", + "Formula": "C9H10BrNO2", + "Letter": "Ḍ", + "MolWeight": 242.98949066, + "SMILES": "N[C@@H](Cc1ccc(cc1)Br)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "4-cyano-phenylalanine": { + "Code": "4CF", + "Formula": "C10H10N2O2", + "Letter": "Ḏ", + "MolWeight": 190.07422756, + "SMILES": "N[C@@H](Cc1ccc(cc1)C#N)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "nitrilo-l-methionine": { + "Code": "4CY", + "Formula": "C5H8N2O2S", + "Letter": "Ḑ", + "MolWeight": 160.030648496, + "SMILES": "N[C@@H](CCSC#N)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "4-fluoro-tryptophan": { + "Code": "4FW", + "Formula": "C11H11FN2O2", + "Letter": "Ḓ", + "MolWeight": 222.080455812, + "SMILES": "N[C@@H](Cc1c[nH]c2c1c(F)ccc2)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "4-hydroxymethyl-phenylalanine": { + "Code": "4HMP", + "Formula": "C10H13NO3", + "Letter": "Ḕ", + "MolWeight": 195.089543276, + "SMILES": "N[C@@H](Cc1ccc(CO)cc1)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "4-hydroxy-tryptophan": { + "Code": "4HT", + "Formula": "C11H12N2O3", + "Letter": "Ḗ", + "MolWeight": 220.084792244, + "SMILES": "N[C@@H](Cc1c[nH]c2c1c(O)ccc2)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": "N[C@@H](Cc1c[nH]c2c1c(O*)ccc2)C(=O)O", + "nterm": False, + }, + "4-amino-l-tryptophan": { + "Code": "4IN", + "Formula": "C11H13N3O2", + "Letter": "Ḙ", + "MolWeight": 219.100776656, + "SMILES": "N[C@@H](Cc1c[nH]c2c1c(N)ccc2)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "4-methyl-phenylalanine": { + "Code": "4PH", + "Formula": "C10H13NO2", + "Letter": "Ḛ", + "MolWeight": 179.094628656, + "SMILES": "N[C@@H](Cc1ccc(cc1)C)C(=O)O", + "cterm": False, + "disulphide": False, + "ester": False, + "nterm": False, + }, + "6-carboxylysine": { + "Code": "6CL", + "Formula": "C7H14N2O4", + "Letter": 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