Individual skill
medchem
Applies medicinal chemistry filters for compound triage, using drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.
skills/medchem/SKILL.md
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- Last checked
- Nov 10, 2026
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- Repository last updated
- Nov 9, 2026
- License
- MIT
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- ai-scientist Repository topic
- bioinformatics Repository topic
- chemoinformatics Repository topic
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- clinical-research Repository topic
- computational-biology Repository topic
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- drug-discovery Repository topic
- engineering Derived category
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Read excerpt
# 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.
**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.
## 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