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name: molecule-data-prep description: "Prepare molecular and protein-ligand data for DGL-LifeSci by validating SMILES/CSV inputs, constructing graphs, selecting featurizers and datasets, and applying supported split/evaluation helpers." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Molecule Data Prep

Use this sub-skill when a task needs DGL-LifeSci data ingestion or graph preparation before model training or inference:

• Convert SMILES or RDKit molecules into DGL graphs with dgllife.utils constructors.
• Choose atom/bond featurizers and check feature field names and dimensions.
• Load custom CSV or unlabeled SMILES inputs with MoleculeCSVDataset or UnlabeledSMILES.
• Select built-in MoleculeNet, Alchemy, or PDBBind-style dataset classes without writing training loops.
• Split datasets and use support utilities such as Meter and EarlyStopping for prepared data pipelines.
• Validate tiny fixtures offline with the bundled input validator.

Start Here

1. Read references/data-formats.md to decide the expected input schema and dataset class.
2. Read references/api-reference.md for verified constructor signatures, feature fields, graph shapes, and splitter gotchas.
3. Run scripts/validate_molecule_inputs.py --help and then validate the user's tiny CSV or TXT fixture before building a full pipeline.
4. If validation fails, use references/troubleshooting.md to diagnose invalid SMILES, missing labels, import errors, cache mismatches, or split failures.

Quick Commands

Validate a labeled CSV and report graph shape summaries:

python scripts/validate_molecule_inputs.py \
  --input molecules.csv --format csv --smiles-column smiles --tasks task1,task2 \
  --require-labels --graph bigraph --max-rows 50

Validate an inference TXT file without labels:

python scripts/validate_molecule_inputs.py \
  --input smiles.txt --format txt --graph complete --max-rows 25

Routing Boundaries

Stay in this sub-skill for data schemas, graph construction, featurizers, splits, dataset classes, and tiny fixture validation.

Route elsewhere when the task asks for:

• Property model architecture, batching, loss functions, or full train/eval loops: use property-prediction.
• Pretrained GIN checkpoints, load_pretrained, or model-zoo selection: use model-zoo-pretraining.
• WLN reaction center/ranking datasets or reaction-specific atom-map handling: use reaction-prediction.
• Binding-affinity model architecture details after PDBBind-style complex graphs are prepared: use ../binding-affinity/SKILL.md.

Bundled Resources

• references/api-reference.md: verified DGL-LifeSci signatures, graph constructors, featurizers, splitters, Meter, and EarlyStopping notes.
• references/data-formats.md: CSV, TXT, SMILES, built-in dataset, and PDBBind-style data expectations.
• references/troubleshooting.md: import/install, optional dependency, invalid data, feature-shape, split, cache, and helper failure fixes.
• scripts/validate_molecule_inputs.py: safe offline validator for CSV/TXT SMILES fixtures and optional graph shape summaries.