Medchem

---
name: medchem
description: 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.
license: Apache-2.0 license
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.9+ and datamol (installed with medchem). Optional Lilly demerit filter requires separate `lilly-medchem-rules` conda package.
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---

# 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
- Calculating complexity metrics against ZINC-derived thresholds
- Detecting functional groups or named substructure catalogs… load the full skill through Skill Me