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name: property-prediction description: "Build DGL-LifeSci molecular property prediction workflows for custom CSVs, MoleculeNet/Alchemy/PubChem datasets, multitask tasks, metrics, model configs, and safe training or inference planning." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Property Prediction

Use this sub-skill when the task is to plan, configure, debug, or prototype molecular graph property prediction with DGL-LifeSci.

Read First

• references/workflows.md: choose a property workflow, map data columns to tasks, plan CLI/API execution, and avoid unsafe long-running downloads or training.
• references/model-configs.md: choose predictors, featurizers, metrics, dataset/model combinations, and JSON hyperparameter keys.
• references/troubleshooting.md: fix label/mask, metric, split, cache/download, CPU/GPU, config-key, and CLI/API issues.
• scripts/build_property_config.py: create or validate small JSON config files for common property predictors without training or downloading data.

Route Here When

• The user has a custom CSV of SMILES plus one or more molecular properties and needs a classification, regression, multitask, or inference plan.
• The user wants MoleculeNet classification/regression, Alchemy quantum property prediction, PubChem aromaticity prediction, OGB graph property prediction, or pretrain-GNN finetuning guidance.
• The user needs a model config for GCN, GAT, Weave, MPNN, AttentiveFP, NF, or supervised-pretrained GIN property workflows.
• The user asks how to choose metrics, splits, masks, n_tasks, featurizers, or safe short validation before expensive training.

Delegate Elsewhere

• Use ../molecule-data-prep/ for raw SMILES cleaning, graph construction, custom featurizer design, dataset schema validation, split implementation details, or RDKit/DGL graph debugging.
• Use ../model-zoo-pretraining/ for architecture internals, pretrained model catalog details, load_pretrained, GIN pretraining setup, and predictor implementation choices beyond property workflow configuration.
• Avoid this sub-skill for reaction prediction, binding affinity, generative models, molecule embeddings, or link prediction unless they only provide context for what not to use.

Standard Handoff

1. Identify task type: binary/multilabel classification, regression, quantum regression, aromatic atom count regression, or multitask ADME-style regression.
2. Confirm data shape: SMILES column, task columns, missing-label policy, classification label values, and whether masks are needed.
3. Pick workflow and model family using references/workflows.md and references/model-configs.md.
4. Create or validate a config with scripts/build_property_config.py for quick planning, then separately plan training/inference commands or API code.
5. Run small smoke checks first: import dgllife, featurize 1-3 SMILES, instantiate the predictor with n_tasks, and compute one forward pass before downloads or full epochs.

Safe Execution Defaults

• Treat full training, hyperparameter search, dataset downloads, and pretrained checkpoint downloads as long-running or networked work; ask before running them.
• Prefer CPU smoke checks and num_workers=0 or 1 when debugging featurization, masks, metrics, and config files.
• Keep generated configs and result directories outside package source trees.
• Record dataset cache locations explicitly in user project paths instead of relying on accidental working-directory caches.