---

name: molecular-graph-generation description: "Use DIG's 2D molecular graph generation stack for QM9/ZINC/MOSES datasets, GraphAF, GraphDF, GraphEBM, JTVAE, RDKit-based validity checks, and property-optimization workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Molecular Graph Generation

Use this sub-skill for DIG workflows that generate or optimize 2D molecular graphs and RDKit molecules.

Include

• dig.ggraph.dataset: QM9, ZINC250k, ZINC800, MOSES, PygDataset.
• dig.ggraph.method: GraphAF, GraphDF, GraphEBM, JTVAE, Generator.
• dig.ggraph.evaluation: RandGenEvaluator, PropOptEvaluator, ConstPropOptEvaluator.
• dig.ggraph.utils: check_chemical_validity, check_valency, calculate_min_plogp, reward_target_molecule_similarity, gen_mol_from_one_shot_tensor.
• 2D molecule generation, property optimization, and constrained optimization examples that use RDKit Mol/SMILES objects or one-shot atom/bond tensors.

Exclude

• 3D geometry generation and property evaluation: use ../three-d-graphs/SKILL.md.
• Self-supervised graph learning: use ../self-supervised-learning/SKILL.md.
• Explainability: use ../graph-explainability/SKILL.md.
• Graph OOD, augmentation, fairness, or large-scale loaders: route to sibling sub-skills.

Start Here

• Read references/workflows.md for the three primary generation workflows.
• Read references/api-reference.md for constructor names, method names, and helper functions.
• Read references/troubleshooting.md when datasets download unexpectedly, RDKit validity is low, or property-evaluator inputs are malformed.
• Run scripts/molecule_generation_smoke.py for a safe import-and-evaluator smoke check.

Core Workflows

• Random generation: load a QM9/ZINC250k dataset, train a generator with train_rand_gen, then generate molecules with run_rand_gen and evaluate with RandGenEvaluator.
• Property optimization: train or reuse a generator with train_prop_optim/train_prop_opt and score outputs with PropOptEvaluator.
• Constrained optimization: use train_const_prop_opt and run_const_prop_opt to optimize a source molecule while enforcing similarity thresholds with ConstPropOptEvaluator.
• One-shot conversion: use gen_mol_from_one_shot_tensor to convert model outputs back into RDKit molecules, then validate with the RDKit helpers.

Quick Validation

python scripts/molecule_generation_smoke.py --help
python scripts/molecule_generation_smoke.py

The smoke script only uses tiny in-memory molecules and tensors. It does not download datasets or train models.