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name: deepchem description: "Use DeepChem for molecular machine learning, data loading, featurization, model training, MoleculeNet, docking, and optional backend troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

DeepChem

Use this skill when a task involves the DeepChem Python package: molecular machine learning, drug-discovery or life-science datasets, molecule featurization, MoleculeNet loaders, model training/evaluation, structure/docking workflows, or optional TensorFlow/PyTorch/JAX/DQC dependency issues.

DeepChem is broad. Start here for routing, installation checks, and cross-cutting troubleshooting, then open the focused sub-skill for the workflow.

Quick Start

import deepchem as dc
print(dc.__version__)

For a minimal core install, expect numpy<2, pandas, scikit-learn, scipy>=1.10.1, sympy, joblib, and rdkit. Neural, docking, materials, and quantum workflows often need optional extras or external binaries.

Read references/installation.md when the request mentions installation, optional extras, backend warnings, CUDA, Colab, conda, or from-source setup. Read references/troubleshooting.md when imports succeed but DeepChem prints missing optional dependency warnings.

Route By Task

• Data loading, MoleculeNet, splitters, transformers, CSV/SDF/FASTA/image/numpy datasets: use sub-skills/data-and-molnet/.
• Featurizer selection, molecule fingerprints, graph features, RDKit descriptors, sequences, materials, polymers, or invalid SMILES diagnostics: use sub-skills/featurization/.
• Model fitting, metrics, predictions, save/restore, callbacks, hyperparameter search, or model-family selection: use sub-skills/model-training/.
• Protein-ligand structures, binding pockets, docking, PDB/PDBQT/SDF preparation, materials/DFT dependency triage, or external docking binaries: use sub-skills/docking-and-structure/.

If a user asks for an end-to-end molecular property workflow, combine the route order: data-and-molnet for loading/splitting, featurization for representation choice, then model-training for model/metric/evaluation. If a user asks for PDBBind, protein-ligand interactions, or docking scores, start in docking-and-structure, then route back to data/model sub-skills only after the structure-specific inputs are clear.

Common Workflows

• Tiny molecular ML baseline: load a CSV with CSVLoader, choose CircularFingerprint or RDKitDescriptors, split with RandomSplitter or ScaffoldSplitter, train a SklearnModel, and evaluate with Metric.
• MoleculeNet dataset: use dc.molnet.load_* only when downloads/cache are acceptable; otherwise build a small local fixture and follow data-and-molnet to avoid network surprises.
• Graph neural model plan: use featurization to confirm MolGraphConvFeaturizer output and model-training to check whether torch/tensorflow/jax/DGL extras are installed before selecting a neural model.
• Docking or pocket workflow: use docking-and-structure to check file formats and optional Vina/GNINA/OpenMM/PDBFixer/MDTraj dependencies before promising executable docking.

Bundled Checks

Run these skill-owned helpers from any environment where DeepChem is installed:

python sub-skills/data-and-molnet/scripts/load_tiny_csv_dataset.py
python sub-skills/data-and-molnet/scripts/split_tiny_dataset.py
python sub-skills/featurization/scripts/inspect_featurizer_outputs.py --smiles CCO c1ccccc1
python sub-skills/model-training/scripts/train_tiny_sklearn_model.py
python sub-skills/docking-and-structure/scripts/check_structure_dependencies.py

Use scripts/deepchem_environment_report.py for a cross-cutting import/version/optional dependency summary before debugging user environments.

Important References

• references/installation.md: install modes, extras, and dependency expectations.
• references/troubleshooting.md: cross-cutting import, optional dependency, data, and workflow failures.
• references/repo-provenance.md: source snapshot and evidence paths used to generate this skill.

Boundaries

This skill is for using DeepChem as a package, not for maintaining DeepChem releases, CI, Docker images, or legacy contrib/ experiments. It intentionally treats large benchmarks, notebooks, remote dataset downloads, and GPU training as opt-in workflows rather than default verification paths.