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deepchem
Builds molecular property prediction and MoleculeNet workflows with DeepChem, including SMILES featurization, scaffold or grouped holdouts, masked labels, graph models and explicit pretrained encoder transfer. Used for ADMET, toxicity, solubility and chemistry ML when DeepChem data/model contracts and scientific validation are needed.
skills/deepchem/SKILL.md
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SKILL.mdcore · 8,967 bytesSKILL.mdcore · 8,967 bytesSKILL.mdcore · 8,967 bytesSKILL.mdcore · 8,967 bytesSKILL.mdcore · 8,967 bytesreferences/api_reference.mddoc · 8,458 bytesreferences/core_capabilities.mddoc · 3,692 bytesreferences/review.mddoc · 5,325 bytesreferences/typical_workflows.mddoc · 3,171 bytesreferences/workflows.mddoc · 7,206 bytesscripts/_common.pyruns · 6,474 bytesscripts/graph_neural_network.pyruns · 9,569 bytesscripts/predict_solubility.pyruns · 6,945 bytesscripts/transfer_learning.pyruns · 11,161 bytes
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# DeepChem
## When to use
Use for molecular property prediction, SMILES/graph featurization, MoleculeNet
benchmarks and explicitly configured encoder transfer. The workflow also covers
DeepChem materials and sequence adapters when their data/model contracts are met.
Targets **DeepChem 2.8.0**, still the stable PyPI release at review. Nightly 2.8.1
builds have additional APIs and different constraints; do not mix `latest` docs
with a stable installation. The tested CPU stack and optional-backend limitations
are in [references/review.md](references/review.md).
## Workflow