deepchem
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
- Version
- 1.5
- License
- MIT license
- Compatibility
- Requires Python 3.7–3.11 (PyPI 2.8.0 caps at <3.12). Install PyTorch, TensorFlow, or JAX before the matching deepchem extra. RDKit is a core dependency.
Pinned to revision 70a605012eee, so it is the text this page describes rather than whatever the author pushed since.
Pre-approved tools experimental
Experimental field. Support varies between clients, so this list is what the author declared, not what your client will enforce.
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Files
- skills/deepchem/SKILL.md
- skills/deepchem/references/api_reference.md
- skills/deepchem/references/core_capabilities.md
- skills/deepchem/references/typical_workflows.md
- skills/deepchem/references/workflows.md
- skills/deepchem/scripts/graph_neural_network.py
- skills/deepchem/scripts/predict_solubility.py
- skills/deepchem/scripts/transfer_learning.py
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