medchem
Category: chemistry-and-drug-discovery · License: Apache-2.0 license · Version: 1.2 · Includes scripts
Tags: medicinal chemistry, drug-likeness, PAINS, structural alerts, compound filtering
When to use it
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.Example requests
Ask your agent in plain language, for example:- “How do I strip PAINS hits out of a SMILES list with medchem?”
- “I have hits.csv with ~40k compounds (columns: id, smiles) from a biochemical screen. I need to keep those passing Lipinski and Veber, drop anything with NIBR severity >= 10 or Brenk alerts, and keep the reasons for each rejection for the audit trail. It’s slow on my laptop. What should I run?”
- “I’m prioritizing a set of kinase inhibitor hits for lead optimization. I want only compounds that are CNS-friendly, have no reactive or toxic substructures, and contain a benzene ring or an amide. Can I express all of that as a single combined filter instead of chaining several steps?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole chemistry-and-drug-discovery category with /plugin install chemistry-and-drug-discovery@research-agent-skills.