Individual skill
medchem
Filters and triages small-molecule libraries with the Python medchem library (datamol-io, v2.0.5) on top of RDKit and datamol. Covers drug-likeness rules (Lipinski, Veber, CNS, lead-like) via RuleFilters, structural alerts (ChEMBL-derived sets, NIBR, PAINS, Brenk), chemical group detection, ZINC-based complexity thresholds, scaffold constraints, and the medchem query language (QueryFilter). Use when screening a compound library for drug-likeness, removing PAINS or other structural-alert compounds, prioritizing hits for hit-to-lead or lead optimization, detecting functional groups, or combining criteria in one query. Not for computing general descriptors or fingerprints; use RDKit directly.
plugins/KalarisLabs/research-agent-skills/skills/medchem/SKILL.md
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# Medchem
## Overview
Medchem is a Python library from [datamol-io](https://github.com/datamol-io/medchem) 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