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.
npx skills add LeonChaoX/qinyan-academic-skills --skill deepchem --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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: ```pyth
- Overview
- When to Use This Skill
- Core Capabilities
- 1. Molecular Data Loading and Processing
- 2. Molecular Featurization
- 3. Data Splitting
- 4. Model Selection and Training
- 5. MoleculeNet Benchmarks
- 6. Transfer Learning
- 7. Model Evaluation
- 8. Making Predictions
- Typical Workflows
- Workflow A: Quick Benchmark Evaluation
- Workflow B: Custom Data Prediction
Use Delaney benchmark python scripts/predict_solubility.py Use custom data python scripts/predict_solubility.py \ Train GCN on Tox21 python scripts/graph_neural_network.py --model gcn --dataset tox21 Train AttentiveFP on custom data python scripts/graph_neural_network.py \ Fine-tune ChemBERTa on BBBP python scripts/transfer_learning.py --model chemberta --dataset bbbp
What does the deepchem skill do?
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.
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-skills --skill deepchem --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From LeonChaoX/qinyan-academic-skills, a repository with 759 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.