# Deepchem

Molecular ML with diverse featurizers and pre-built datasets.

- Repository: https://github.com/LeonChaoX/qinyan-academic-skills (996 stars)
- Category: Data and ML
- License: MIT license
- Last updated: 2026-07-20
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add LeonChaoX/qinyan-academic-skills --skill deepchem -g -a claude-code -y`
Codex: `npx skills add LeonChaoX/qinyan-academic-skills --skill deepchem -g -a codex -y`
Cursor: `npx skills add LeonChaoX/qinyan-academic-skills --skill deepchem -g -a cursor -y`
Gemini CLI: `npx skills add LeonChaoX/qinyan-academic-skills --skill deepchem -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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:

```python
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 identifiers
- `SDFLoader`: Molecular structure files
- `FASTALoader`: Protein/DNA sequences
- `ImageLoader`: Molecular images
- `JsonLoader`: JSON-formatted datasets

### 2. Molecular Featurization

The rest: https://github.com/LeonChaoX/qinyan-academic-skills/blob/HEAD/skills/06-化学信息与药物发现/deepchem/SKILL.md