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name: model-zoo-pretraining description: "Select, instantiate, inspect, and troubleshoot DGL-LifeSci model-zoo architectures, pretrained models, readouts, molecule embeddings, and link prediction heads." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Model Zoo and Pretraining

Use this sub-skill when an agent needs to choose or instantiate dgllife.model architectures, inspect constructor compatibility, load pretrained models, produce molecule embeddings from pretrained GIN encoders, or wire link-prediction heads.

Use This For

• Selecting among dgllife.model.gnn, dgllife.model.readout, dgllife.model.model_zoo, and dgllife.model.load_pretrained entry points.
• Matching graph feature tensors such as g.ndata['h'], g.ndata['hv'], g.edata['e'], or pretrained categorical lists to constructor parameters.
• Diagnosing constructor signatures, missing root exports, zero-in-degree errors, and pretrained checkpoint download failures.
• Adapting molecule embedding or link-prediction patterns without running large dataset downloads.

Route Elsewhere

• For SMILES parsing, featurizers, graph construction, label masks, splitters, and dataset field names, use ../molecule-data-prep/SKILL.md.
• For property prediction training loops, metrics, masking, and model evaluation workflows, use ../property-prediction/SKILL.md after choosing the model here.
• For WLN reaction-center/ranking workflows and reaction-specific featurization, use ../reaction-prediction/SKILL.md; this sub-skill only covers shared constructors and pretrained names.

References

• Start with references/model-catalog.md for constructor families, feature dimensions, output expectations, pretrained names, molecule embedding, and link prediction usage.
• Use references/constructor-recipes.md for tiny CPU instantiation snippets and shape reasoning without data downloads.
• Use references/troubleshooting.md for import/install, optional dependency, data/config, CLI/API misuse, pretrained download, zero-in-degree, and version/export caveats.
• Run scripts/inspect_model_constructors.py to print installed constructor signatures and optionally instantiate tiny CPU-safe constructors.

Fast Workflow

1. Identify whether the user needs a GNN encoder, graph-level predictor, readout, pretrained model, molecule embedding encoder, or link-prediction head.
2. Check feature ownership: node and edge feature construction belongs to molecule-data-prep; this sub-skill only maps those tensor widths and field names into constructors.
3. Read references/model-catalog.md for the closest constructor family and any pretrained feature contract.
4. Validate the installed API with python scripts/inspect_model_constructors.py --constructors GCNPredictor,GATPredictor,MPNNPredictor before writing code that must run across DGL-LifeSci versions.
5. If pretrained checkpoints are needed, provide a no-network fallback plan because load_pretrained() downloads checkpoint files on first use.

Version Caveats

The verified installed package was dgllife 0.3.1 with DGL 1.1.3 and CPU Torch. The source includes GATv2Predictor, but one installed inspection found no GATv2Predictor attribute at dgllife.model; prefer GATv2 as the stable encoder and inspect submodule fallback availability before promising root-level GATv2Predictor imports.