CupOFMoCA for PROTACs
This folder contains the PROTAC linker-generation code, model weights, validity classifiers, target builders, paper configurations, and reported outputs for CupOFMoCA: Coupled Objective-Guided Discrete Flows for Molecular Conjugate Assembly.
The PROTAC workflow holds the warhead and E3-ligand subgraphs fixed and edits the linker subgraph with a DeFoG discrete flow-matching model. CupOFMoCA applies a hard assembly gate and three soft property objectives during sampling.
The main README provides the shared project overview and a quickstart for both conjugate modalities. This guide covers the PROTAC targets, objective functions, commands, artifacts, evaluation programs, and molecular-dynamics workflow in greater detail.
Model and objectives
| Artifact | Role |
|---|---|
checkpoints/protac_linker_finetune/epoch=99-v1.ckpt |
DeFoG graph generator fine-tuned for 100 epochs on 2,070 PROTAC-DB linker graphs |
src/classifier/protac_linker_clf.pkl |
generic linker validity and quality classifier |
src/classifier/protac_linker_clf_ErlotinibVHL.pkl |
EGFR-VHL target-specific classifier |
src/classifier/protac_linker_clf_BRD9VHL.pkl |
BRD9-VHL target-specific classifier |
src/classifier/protac_linker_clf_CDK6CRBN.pkl |
CDK6-CRBN target-specific classifier |
The generator applies the validity classifier as a hard mask before exponential tilting. Three soft scores define the weighted objective used for the reported runs.
| Position | Score | Paper weight |
|---|---|---|
| 1 | synthetic accessibility | 4.0 |
| 2 | PROTAC-DB chemotype closeness across logP, molecular weight, rotatable bonds, and ether count | 2.0 |
| 3 | linker connectivity | 4.0 |
The --weights option accepts these values in the table order. The hard validity gate
acts before weighted scoring and sits outside the three-value objective vector.
Installation
Run the following commands from this folder.
conda env create -f environment.yml
conda activate defog
pip install -e src/defog
python scripts/verify_release.py
The tested environment uses Python 3.9, PyTorch 2.4.0, and CUDA 12.1. A CUDA GPU is recommended for full generation experiments.
Target systems
| Reported system | --protac_module |
Warhead | E3 ligand |
|---|---|---|---|
| EGFR-VHL | ErlotinibVHL |
gefitinib-piperazine from MS39 | VH032 |
| BRD9-VHL | BRD9VHL |
BI-7273-piperazine | VH032 |
| CDK6-CRBN | CDK6CRBN |
palbociclib-piperazine | pomalidomide |
Each module under src/protac/ defines the fixed molecular fragments, reactive SMARTS,
and assembly builder for its system.
Run generation
The following command generates 100 EGFR-VHL candidates with seed 42 and the reported
configuration. Run it from the scripts/ directory.
cd scripts
python generate.py \
--checkpoint ../checkpoints/protac_linker_finetune/epoch=99-v1.ckpt \
--protac_module ErlotinibVHL \
--fix_termini cooh \
--fast_scoring \
--num_samples 100 \
--num_batches 1 \
--num_steps 1500 \
--warmup_steps 100 \
--weights 4.0 2.0 4.0 \
--seed 42 \
--output_file ./protac_linkers_egfrvhl.smi \
--output_dir ../generation_logs
Use BRD9VHL or CDK6CRBN as the module name for the other reported target systems.
Seeds 42, 123, and 456 reproduce the paper's three generation batches. The complete
configuration in configs/paper.json records 100 candidates per seed, 1,500 sampling
steps, 100 warmup steps, guidance strength 5.0, top-k 50, weighted position sampling,
and the objective weights shown above.
The generator writes assembled PROTAC SMILES to the selected --output_file and detailed
sampling records under --output_dir. Run the help command for the full set of graph,
guidance, scoring, and output controls.
python generate.py --help
Evaluate generated candidates
evaluate_linkers.py assembles each candidate with the selected anchors and computes
structural validity, linker properties, assembled-PROTAC properties, and available
degradation scores.
python evaluate_linkers.py protac_linkers_egfrvhl.smi \
--protac-module ErlotinibVHL \
--out protac_linkers_egfrvhl_scored.csv
batch_report.py compares one or more generation-summary files and can save a figure.
python batch_report.py ../generation_logs/*_summary.csv \
--protac-module ErlotinibVHL \
--out batch_report.png
The paper evaluates assembly validity, predicted degradation, molecular diversity,
training-set similarity, and physicochemical properties for EGFR-VHL, BRD9-VHL, and
CDK6-CRBN. results/README.md maps the archived outputs to the paper
tables and records the aggregation conventions.
The archived weight-ablation outputs preserve an earlier five-slot representation of the objective. The active terms correspond to synthetic accessibility, chemotype closeness, and connectivity with weights 4, 2, and 4. The current generator accepts those three live weights directly.
Optional evaluation components
DegradeMaster supplies predicted degradation scores, and EGFR-PROPK supplies the EGFR pharmacokinetic columns. The fetch program retrieves both components at the commits used for the paper.
cd ..
python scripts/fetch_third_party.py
Generation uses the bundled checkpoint, builders, and classifiers. The optional
components extend evaluation after generation. Their upstream licensing and pinned
revisions are recorded in NOTICE and scripts/fetch_third_party.py.
DegradeMaster uses data derived from overlapping PROTAC databases, which makes its reported degradation analysis an in-domain model evaluation. Interpret these scores as computational prioritization measurements within that setting.
Training data
The generator starts from the released DeFoG GuacaMol checkpoint and was fine-tuned on
2,070 PROTAC-DB linker graphs. The validation and test partitions contain 258 and 260
graphs, respectively. data/linker.csv provides the derived linker table used by this
release. Appendix H describes the architecture, optimization procedure, data processing,
and scorers.
Reported outputs
The results/ directory contains the generation outputs and scored summaries used in the
paper's PROTAC tables.
results/
ablation_all/ EGFR-VHL constraint, guidance, and fine-tuning studies
ablation_targets/ BRD9-VHL and CDK6-CRBN target studies
README.md table mapping and aggregation details
The reported runs use seeds 42, 123, and 456 with 100 candidates per seed. The archived
files contain the per-seed results, pooled summaries, and scored per-molecule tables
described in results/README.md.
Molecular dynamics
The md/ folder contains the explicit-solvent workflow used to analyze generated PROTAC
conformations in water and chloroform. The analyses cover radius of gyration, three-
dimensional polar surface area, intramolecular hydrogen bonds, ternary span, and
conformational-state populations.
Create the dedicated environment and inspect the MD guide before launching trajectories.
conda env create -f md/environment_md.yml
conda activate protacmd
cd md
python protac_md.py --solvent chloroform --replicas 3 --prod-ns 50 --gpu 0
python analyze_conformations.py \
--dir egfrvhl_rank4/chloroform \
--name egfrvhl_rank4
md/README.md documents the complete workflow, and
md/reference_protocol.md records the simulation protocol.
Artifact verification
The integrity checker validates byte counts, SHA-256 hashes, and portability-sensitive source paths.
python scripts/verify_release.py
docs/artifacts.json provides the machine-readable manifest, and
docs/ARTIFACTS.md lists each artifact's role, size, and checksum.
The checker also reports the availability of the optional evaluation components.
PyTorch loads the bundled generator checkpoint through its serialized checkpoint interface. Use weights from a trusted release location and verify their hashes before loading.
Research scope
The released paper configurations cover three target and E3-ligase systems with fixed anchor chemistry. All generated structures and objective values are computational predictions. Synthesis, biochemical and cellular testing, toxicology, and safety assessment establish the experimental properties of candidates selected for further study.
Hardware, CUDA, and PyTorch revisions can introduce numerical variation across repeated runs.
Repository structure
checkpoints/ fine-tuned DeFoG generator weights
configs/paper.json settings used for the reported runs
data/linker.csv PROTAC-DB-derived linker table
docs/ artifact documentation and checksums
md/ explicit-solvent molecular-dynamics workflow
results/ generation outputs used in the paper tables
scripts/
generate.py guided linker generation
evaluate_linkers.py candidate assembly and scoring
batch_report.py multi-run summaries and plots
fetch_third_party.py pinned optional evaluation components
verify_release.py release integrity checker
src/classifier/ validity gate, analyzers, and classifiers
src/defog/ DeFoG discrete flow model
src/protac/ target-specific warhead and E3 builders
Citation and license
Citation metadata is available in CITATION.cff. The CupOFMoCA code is
released under Apache-2.0 through LICENSE and NOTICE. The bundled DeFoG code retains
its MIT license under src/defog/LICENSE. Optional upstream components retain their own
terms.