molfeat
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
npx skills add majiayu000/claude-skill-registry --skill molfeat-k-dense-ai-scientific-agent-ski-2 --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.
# Molfeat - Molecular Featurization Hub ## Overview Molfeat is a comprehensive Python library for molecular featurization that unifies 100+ pre-trained embeddings and hand-crafted featurizers. Convert chemical structures (SMILES strings or RDKit molecules) into numerical representations for machine learning tasks including QSAR modeling, virtual screening, similarity searching, and deep learning applications. Features fast parallel processing, scikit-learn compatible transformers, and built-in caching. **Version note:** Examples target **molfeat 0.11.0** (PyPI stable, May 2025). Requires **Python 3.9–3.10** (`requires-python` caps below 3.11). Depends on **datamol ≥0.8.0** and **PyTorch ≥1.13**. Since 0.8.7, prefer datamol `Mol` objects over raw `rdkit.Chem.Mol`. Since 0.10.1, fingerprint calculators use RDKit's `rdFingerprintGenerator` API internally. Since 0.11.0, pretrained models load in memory and base models are set to PyTorch evaluation mode automatically. ## When to Use This Skill This skill should be used when working with: - **Molecular machine learning**: Building QSAR/QSPR models, property prediction - **Virtual screening**: Ranking compound libraries for biological act
- Overview
- When to Use This Skill
- Installation
- Core Concepts
- 1. Calculators (molfeat.calc)
- 2. Transformers (molfeat.trans)
- 3. Pretrained Transformers (molfeat.trans.pretrained)
- Quick Start Workflow
- Basic Featurization
- Save and Load Configuration
- Handle Errors Gracefully
- Choosing the Right Featurizer
- For Traditional Machine Learning (RF, SVM, XGBoost)
- For Deep Learning
uv pip install "molfeat==0.11.0" With all pip-installable optional dependencies uv pip install "molfeat[all]==0.11.0" grep -i "chembert" references/available_featurizers.md grep -i "pharmacophore" references/available_featurizers.md uv pip install "molfeat[transformer]==0.11.0" # For ChemBERTa/ChemGPT uv pip install "molfeat[dgl]==0.11.0" # For GIN models uv pip install "molfeat[graphormer]==0.11.0" # For Graphormer
What does the molfeat skill do?
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill molfeat-k-dense-ai-scientific-agent-ski-2 --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 majiayu000/claude-skill-registry, a repository with 534 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.