Deepchem
Molecular ML with diverse featurizers and pre-built datasets.
Install
npx skills add LeonChaoX/qinyan-academic-skills --skill deepchem -g -a claude-code -y- Repository
- LeonChaoX/qinyan-academic-skills996
- Category
- Data and ML
- Updated
- 4mo ago
- License
- MIT license
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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:
import deepchem as dc
# Load CSV with SMILES
featurizer = dc.feat.CircularFingerprint(radius=2, size=2048)
loader = dc.data.CSVLoader(
tasks=['solubility', 'toxicity'],
feature_field='smiles',
featurizer=featurizer
)
dataset = loader.create_dataset('molecules.csv')
# Load SDF files
loader = dc.data.SDFLoader(tasks=['activity'], featurizer=featurizer)
dataset = loader.create_dataset('compounds.sdf')
# Load protein sequences
loader = dc.data.FASTALoader()
dataset = loader.create_dataset('proteins.fasta')
Key Loaders:
CSVLoader: Tabular data with molecular identifiersSDFLoader: Molecular structure filesFASTALoader: Protein/DNA sequencesImageLoader: Molecular imagesJsonLoader: JSON-formatted datasets
