Agent skill · research science · aiskillstore
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.
Why this skill is useful
Provides specialized molecular featurization methods and loaders for diverse chemical data formats that the AI wouldn't reliably generate on its own.
What it needs
Requires deepchem installed locally. About 8k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
DeepChem Overview DeepChem is a comprehensive Python library for applying machine learning to chemistry, materials science, and biology. Enable molecular property prediction, drug discovery, materials design, and biomolecule analysis through specialized neural networks, molecular featurization methods, and pretrained models. When to Use This Skill This skill should be used when: Loading and processing molecular data (SMILES strings, SDF files, protein sequences) Predicting molecular properties (solubility, toxicity, binding affinity, ADMET properties) Training models on chemical/biological datasets Using MoleculeNet benchmark datasets (Tox21, BBBP, Delaney, etc.) Converting molecules to ML-ready features (fingerprints, graph representations, descriptors) Implementing graph neural networks for molecules (GCN, GAT, MPNN, AttentiveFP) Applying transfer learning with pretrained models (ChemBERTa, GROVER, MolFormer) Predicting crystal/materials properties (bandgap, formation energy) Analyzing protein or DNA sequences Core Capabilities 1. Molecular Data Loading and Processing DeepChem provides specialized loaders for various chemical data formats: Key Loaders: CSVLoader: Tabular data with molecular identifiers SDFLoader: Molecular structure files FASTALoader: Protein/DNA sequences ImageLoader: Molecular images JsonLoader: JSON-formatted datasets 2. Molecular Featurization Convert molecules into numerical representations for ML models. Decision Tree for Featurizer Selection Example Featurization Selection Guide: Small datasets (<1K): CircularFingerprint or RDKitDescriptors Medium datasets (1K-100K): CircularFingerprint or graph featurizers Large datasets (>100K): Graph featurizers (MolGraphConvFeaturizer, DMPNNFeaturizer) Transfer learning: Pretrained model featurizers (GroverFeaturizer) See references/apireference.md for complete featurizer documentation. 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/deepchem--ee0ffe