Agent skill · research science · k-dense-ai
medchem
Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.
Why this skill is useful
Provides domain-specific filtering rules and metrics for medicinal chemistry that the AI wouldn't reliably generate on its own.
What it needs
Requires datamol, python installed locally. About 5k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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. Version note: Examples target medchem 2.0.5 (PyPI stable, Nov 2024). Requires Python ≥3.9. Depends on datamol and RDKit (installed automatically). RuleFilters and structural filter classes return pandas DataFrames. Lilly demerits require optional native binaries (mamba install lilly-medchem-rules). When to Use This Skill This skill should be used when: Applying drug-likeness rules (Lipinski, Veber, CNS, lead-like) to compound libraries Filtering molecules by structural alerts, PAINS, or NIBR screening-deck rules Prioritizing compounds for hit-to-lead or lead optimization Calculating complexity metrics against ZINC-derived thresholds Detecting functional groups or named substructure catalogs Building multi-criteria filters with the medchem query language Installation Optional — Eli Lilly demerit filter (requires conda-forge native binaries): Core Capabilities 1. Medicinal Chemistry Rules Apply established drug-likeness rules via medchem.rules. List available rules: Single rule on one molecule: Multiple rules with RuleFilters (returns a DataFrame): Use keepprops=True to include computed descriptors (mw, clogp, tpsa, etc.) in the result. 2. Structural Alert Filters Detect problematic patterns with medchem.structural. Both classes return DataFrames with passfilter, status, and reasons columns. Common alerts (ChEMBL-derived rule sets): NIBR filters (Novartis screening-deck curation): Compounds with severity >= 10 are excluded by default (see NIBR paper). 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/medchem