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 K-Dense-AI/scientific-agent-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. **Version note:** Examples target **deepchem 2.8.0** (PyPI stable, Apr 2024). Requires **Python 3.7–3.11** (`<3.12` on PyPI). Core utilities (loaders, featurizers, MoleculeNet) work without a DL backend; GNN and transformer models need the matching extra (`torch`, `tensorflow`, or `jax`). Install the backend framework first when using GPU builds. ## 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,
- Overview
- When to Use This Skill
- Core Capabilities
- Example Scripts
- 1. predictsolubility.py
- 2. graphneuralnetwork.py
- 3. transferlearning.py
- Common Patterns and Best Practices
- Pattern 1: Always Use Scaffold Splitting for Molecules
- Pattern 2: Normalize Features and Targets
- Pattern 3: Start Simple, Then Scale
- Pattern 4: Handle Imbalanced Data
- Pattern 5: Avoid Memory Issues
- Common Pitfalls
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 K-Dense-AI/scientific-agent-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 K-Dense-AI/scientific-agent-skills, a repository with 32,619 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.