Medchem

If you're triaging compound libraries in drug discovery, this gives you the standard filters in one place. Run Lipinski/Veber/CNS rules, flag PAINS and structural alerts, or build composite queries like "CNS-like AND no PAINS AND TPSA under 90" with the built-in query language. It wraps datamol and RDKit with a functional API that returns DataFrames or boolean masks, so you can chain filters over thousands of molecules without writing boilerplate. The NIBR filters and complexity percentiles are handy for screening deck curation. One catch: the Lilly demerit filter needs separate conda binaries. Best used as a first-pass filter before you apply target-specific knowledge, not as a final decision layer.

skills.sh installs: 2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill medchem --agent claude-code

Installs into .claude/skills of the current project.

Files

Medchem

Overview

Medchem is a Python library from datamol-io for molecular filtering and prioritization in drug discovery. Apply literature-derived drug-likeness rules, named alert catalogs, complexity thresholds, chemical-group detection, and a custom query language to triage compound libraries at scale. Filters are context-specific guidelines — combine with domain expertise and target knowledge.

Verified runtime: medchem 2.1.1, datamol 0.13.0, RDKit 2026.3.6; Python 3.11+. RuleFilters and structural classes return pandas DataFrames. Filters run locally; installation and documentation lookup need network access. Lilly native execution was not tested in this review.