If you're doing molecular machine learning, this gives you 100+ featurizers in one library. It handles the annoying stuff: converting SMILES to ECFP fingerprints, RDKit descriptors, or embeddings from pretrained models like ChemBERTa. The transformer API is scikit-learn compatible with built-in parallelization, and you can save/reload configs for reproducibility. One catch: it only works on Python 3.9-3.10, not 3.11+, which is frustrating if you're on a newer stack. The optional dependencies are a maze (DGL, graphormer, transformers) so expect some conda-forge wrestling for GNN models. But for QSAR pipelines or virtual screening, it beats rolling your own featurization every time.
- skills.sh installs
- 2K
- GitHub stars
- 56.6K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add k-dense-ai/scientific-agent-skills --skill molfeat --agent claude-codeInstalls into .claude/skills of the current project.
Files
Molfeat — Small-molecule featurization
When to use
Use this skill to turn SMILES or RDKit molecules into fingerprints, descriptors, pharmacophores, or pretrained embeddings for molecular machine learning and similarity search. Compare representations on the same molecular split and assay endpoint.
This skill targets Molfeat 1.0.0. The previous 0.11 runtime guidance is obsolete:
1.x supports modern Python and removes DGL/DGLLife, legacy Graphormer, and protein
adapters. Historical model-store cards can still name removed adapters. The tagged
1.0 migration guide
and source take precedence over older pages still served at the documentation's
stable URL.
Installation
Create an isolated environment. Core examples were executed on Python 3.13.3/macOS Apple Silicon with Molfeat 1.0.0, datamol 0.13.0, RDKit 2026.03.6, and PyTorch 2.14.1.
uv venv --python 3.13 .venv-molfeat
uv pip install --python .venv-molfeat/bin/python "molfeat==1.0.0"
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- Repository
- github.com/k-dense-ai/scientific-agent-skills
- Categories
- AI & Agent Building, Data Science & ML
- First seen
- Jun 3, 2026
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