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name: generative-models description: "Plan and troubleshoot DGL-LifeSci molecular generative workflows with DGMG, JTVAE/JTNNVAE, vocabulary handling, reconstruction, pretraining, generation, and safe input validation." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Generative Models

Use this sub-skill when an agent needs to plan, adapt, or debug DGL-LifeSci molecular generation workflows built around dgllife.model.DGMG, dgllife.model.JTNNVAE, dgllife.utils.JTVAEVocab, JTVAE MolTree utilities, or JTVAE datasets/collators.

Use This For

• Choosing between DGMG autoregressive graph generation and JTVAE/JTNNVAE junction-tree variational autoencoding.
• Preparing SMILES text inputs, DGMG atom/bond vocabularies, JTVAE vocabulary files, reconstruction inputs, and checkpoint expectations.
• Planning DGMG train/eval/generate or JTVAE pretrain/vae-train/reconstruct workflows without launching long training jobs.
• Diagnosing RDKit parseability, vocabulary mismatch, invalid generated molecules, checkpoint shape errors, and CPU/GPU/runtime issues.

Route Elsewhere

• For generic SMILES cleaning, CSV schemas, graph featurizers, and molecule dataset basics, use ../molecule-data-prep/SKILL.md.
• For generic model-zoo constructor inspection or pretrained model loading outside DGMG/JTVAE, use ../model-zoo-pretraining/SKILL.md.
• For property-prediction metrics, split strategy, training loops, and supervised evaluation, use ../property-prediction/SKILL.md.

References

• Read references/workflows.md for DGMG/JTVAE planning, API entry points, CLI-style arguments, outputs, and safe/unsafe execution classes.
• Read references/data-formats.md for SMILES files, DGMG atom/bond types, JTVAE vocabulary/MolTree expectations, and checkpoint contracts.
• Read references/troubleshooting.md for import/install, optional dependencies, RDKit sensitivity, vocabulary mismatch, invalid molecules, checkpoint shape, and long-runtime failures.
• Run scripts/validate_generative_inputs.py to validate small SMILES fixtures and optional JTVAE vocabulary tokens without training or downloading data.

Fast Workflow

1. Classify the task: DGMG for direct graph-generation decisions; JTVAE/JTNNVAE for junction-tree encoding, reconstruction, and latent sampling.
2. Validate user SMILES or vocabulary files with python scripts/validate_generative_inputs.py --smiles-file molecules.txt --max-rows 100 before planning model code.
3. For JTVAE, add --vocab-file vocab.txt or --derive-jtvae-vocab to detect malformed vocabulary tokens, duplicates, and missing MolTree vocabulary tokens.
4. Check references/workflows.md for constructor signatures, expected checkpoint naming, and whether a requested action is safe as a smoke check or requires explicit user approval.
5. Use references/troubleshooting.md when RDKit, DGL, Torch, vocabulary, checkpoint, generation validity, or runtime symptoms appear.

Version Notes

The verified installed package was dgllife 0.3.1 with DGL 1.1.3, CPU Torch, RDKit, scikit-learn, NumPy/SciPy, and pandas importable. Verified generative signatures included DGMG(atom_types, bond_types, node_hidden_size=128, num_prop_rounds=2, dropout=0.2) and JTNNVAE(vocab, hidden_size, latent_size, depth, stereo=True).