Can I use this for high-throughput screening (HTS) data?

Yes, the skill is designed for scale and supports parallelized processing using all available CPU cores to handle large datasets efficiently.

How do I filter molecules based on custom chemical properties?

You can use the Medchem Query Language or the Constraints module to define specific ranges for Molecular Weight, LogP, TPSA, and rotatable bond counts.

Does it detect Pan-Assay Interference Compounds (PAINS)?

Yes, it includes dedicated PAINS filters along with common structural alerts from ChEMBL, Novartis (NIBR), and Eli Lilly to identify potentially problematic molecules.

What drug-likeness rules does this skill support?

It supports a wide variety of established rules including Lipinski's Rule of Five, Veber's rules, Rule of Oprea, Rule of Lead-like, Rule of Three, and Rule of CNS.

Medicinal Chemistry Toolkit

Name: Medicinal Chemistry Toolkit
Author: K-Dense-AI

byK-Dense-AI

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2,188

データサイエンスとML

Applies medicinal chemistry rules and structural filters to prioritize drug-like compounds in molecular discovery workflows.

概要

The Medchem skill provides a comprehensive suite of tools for medicinal chemists and computational scientists to triage molecular libraries within Claude. It implements industry-standard drug-likeness rules such as Lipinski’s Rule of Five and Veber’s rules, detects Pan-Assay Interference Compounds (PAINS), and identifies problematic structural alerts using NIBR and Lilly Demerits. By integrating these scientific filters directly into the coding environment, it enables efficient compound prioritization, lead optimization assessment, and sophisticated chemical group detection using a specialized query language or high-performance Pythonic API.

主な機能

Implementation of classic drug-likeness rules including Lipinski, Veber, and CNS rules
2,188 GitHub stars
High-performance parallelized batch filtering for large-scale virtual screening
Molecular complexity metrics to approximate synthetic accessibility
Chemical group and functional group identification using curated SMARTS patterns
Advanced structural alert detection including PAINS, NIBR filters, and Lilly Demerits

ユースケース

Initial triage of large compound libraries to identify drug-like candidates
Automated detection of reactive or problematic functional groups in chemical datasets
Lead optimization filtering to ensure candidates meet strict property constraints

概要

主な機能

Implementation of classic drug-likeness rules including Lipinski, Veber, and CNS rules
2,188 GitHub stars
High-performance parallelized batch filtering for large-scale virtual screening
Molecular complexity metrics to approximate synthetic accessibility
Chemical group and functional group identification using curated SMARTS patterns
Advanced structural alert detection including PAINS, NIBR filters, and Lilly Demerits

ユースケース

Initial triage of large compound libraries to identify drug-like candidates
Automated detection of reactive or problematic functional groups in chemical datasets
Lead optimization filtering to ensure candidates meet strict property constraints