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name: featurization description: "Choose, run, validate, and troubleshoot DeepChem featurizers for molecules, graphs, descriptors, sequences, materials, polymers, and complexes." disable-model-invocation: true metadata: disco-role: operating license: MIT

DeepChem Featurization

Use this sub-skill when the user needs to select or debug deepchem.feat transformations before loading data into models. Route dataset loading, MolNet presets, splitting, and CSV/SDF loader mechanics to ../data-and-molnet/; route model construction/training to ../model-training/; route full docking pose generation to ../docking-and-structure/.

Quick Router

• Use CircularFingerprint for fixed-size ECFP/Morgan bit vectors or count vectors for classical ML, similarity, and quick baselines.
• Use RDKitDescriptors for interpretable 2D physicochemical descriptors; use MordredDescriptors only when the optional Mordred package is available and high-dimensional descriptors are desired.
• Use MolGraphConvFeaturizer for graph neural network inputs as GraphData; set use_edges=True when the downstream model consumes edge attributes and use_chirality=True when stereochemistry matters.
• Use CoulombMatrix or CoulombMatrixEig for coordinate-aware molecular representations where max_atoms and conformer handling are explicit.
• Use FASTAFeaturizer for FASTA files and biosequence extraction; use tokenizer wrappers such as HuggingFaceFeaturizer only when transformer tokenizers are installed and intentionally selected.
• Use material featurizers such as ElementPropertyFingerprint, SineCoulombMatrix, CGCNNFeaturizer, and ElemNetFeaturizer for composition strings or pymatgen structures.
• Use polymer, complex, and docking-adjacent featurizers only for feature extraction; route pose generation and docking workflows elsewhere.

Minimal Patterns

import deepchem as dc

smiles = ["CCO", "c1ccccc1"]
features = dc.feat.CircularFingerprint(size=1024).featurize(smiles)
print(features.shape)  # (2, 1024)

graphs = dc.feat.MolGraphConvFeaturizer(use_edges=True).featurize(["CCO"])
graph = graphs[0]
print(graph.node_features.shape, graph.edge_index.shape, graph.edge_features.shape)

descriptors = dc.feat.RDKitDescriptors(use_bcut2d=False).featurize(["CCO"])
print(descriptors.shape)

For mixed-validity batches, always inspect per-entry outputs before passing features to a model. Failed molecular featurizations usually appear as empty arrays, and heterogeneous feature shapes can force an object array.

Validation Checklist

1. Confirm input type: SMILES/RDKit Mol for molecular featurizers, FASTA file path for FASTAFeaturizer, pymatgen structure or structure dict for structure featurizers, composition string for composition featurizers.
2. Print type(feature) and shape-like attributes for a tiny batch before scaling up.
3. Check failures: empty arrays, warnings about failed datapoints, missing optional dependency warnings, or object arrays from heterogeneous outputs.
4. Match feature family to model family: vectors for scikit-learn/dense models, GraphData for graph models, sequence/tokenizer outputs for sequence models, structure/composition features for materials models.
5. Keep featurizer settings identical between train/validation/test/inference.

Bundled References

• references/featurizer-guide.md: selection guidance by input, task, and model family.
• references/api-reference.md: common signatures, output contracts, and validation snippets.
• references/troubleshooting.md: failure modes for invalid SMILES, optional dependencies, graph shape mismatches, Coulomb max_atoms, and descriptor normalization.
• scripts/inspect_featurizer_outputs.py: tiny CLI to inspect SMILES and optional FASTA outputs without needing dataset loaders.

Helper Script

Run the bundled helper from this sub-skill directory or by path:

python scripts/inspect_featurizer_outputs.py --smiles CCO 'C[C@H](O)Cl' bad_smiles --featurizers circular molgraph rdkit

Add --fasta path/to/file.fasta --featurizers fasta to inspect FASTA extraction when the sequence dependency is installed.