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medchem

k-dense-ai/scie…t-skills

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.

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安装命令

npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill medchem

SKILL.md

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.

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

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.

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

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