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name: molecular-workflows description: "Plan TorchDrug molecular property prediction, molecular pretraining and finetuning, molecule generation, and retrosynthesis workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Molecular Workflows

Use this sub-skill when a user needs to build or debug a TorchDrug workflow for small molecules or chemical reactions.

When To Use

• Build property prediction pipelines for molecule datasets such as ClinTox, BACE, or custom SMILES/label datasets.
• Plan molecular self-supervised pretraining with InfoGraph, AttributeMasking, or related pretraining tasks, then finetune with PropertyPrediction.
• Set up ZINC250k molecule generation with GCPNGeneration or GraphAF-style AutoregressiveGeneration, including PPO finetuning for QED or penalized logP.
• Set up USPTO50k retrosynthesis with G2Gs: center identification, synthon completion, and the combined Retrosynthesis beam-search pipeline.
• Interpret molecular metrics, generated SMILES, beam-search candidates, and common chemistry-specific failures.

Route Elsewhere

• Raw data.Graph, data.Molecule, packed batches, masks, collators, and custom data object construction belong in the graph-data sub-skill.
• GNN layer internals, custom model/layer design, compiled extensions, and representation output shapes belong in the layers-and-extensions sub-skill.
• Generic core.Engine checkpointing, save/load, logging, scheduler, distributed settings, and config serialization belong in the training-engine sub-skill.
• Protein structure, protein function, residue, sequence, and contact workflows belong in the protein-workflows sub-skill.
• Knowledge graph reasoning and entity/relation ranking workflows belong in the knowledge-graphs sub-skill.

References

• Property and pretraining covers ClinTox/BACE property prediction, custom molecule datasets, split choices, feature compatibility, GIN, InfoGraph, AttributeMasking, and finetuning.
• Generation and retrosynthesis covers ZINC250k GCPN/GraphAF setup, PPO objective caveats, USPTO50k reaction/synthon modes, G2Gs training, and beam-search output interpretation.
• Troubleshooting covers dataset downloads, RDKit and invalid SMILES, feature mismatches, criterion/metric choices, generation validity and resampling, retrosynthesis split seeds, and compute expectations.

Safe Planner

Run python scripts/plan_molecular_workflow.py --workflow property, pretrain, generation, or retrosynthesis from this sub-skill directory to print a no-download, no-training checklist and minimal API skeleton.