What is the Medchem Query Language?

It is a specialized syntax that allows users to combine complex filtering criteria using logical operators, such as 'rule_of_five AND NOT common_alerts'.

Can this skill handle large compound libraries?

Yes, Medchem is designed for scale and supports parallel processing (n_jobs=-1) to handle datasets with thousands of molecules efficiently.

What drug-likeness rules can Medchem apply?

Medchem supports several established rules including Lipinski's Rule of Five, Veber's rule, CNS-likeness, Oprea, and lead-like criteria (both soft and strict).

Does it identify problematic structural patterns?

Yes, it includes specialized filters for PAINS (Pan-Assay Interference Compounds), NIBR (Novartis) alerts, and Lilly Demerits to flag reactive or undesirable molecules.

Can I calculate synthetic accessibility with this skill?

While it doesn't provide a single SA score, it calculates multiple molecular complexity metrics (Bertz, Whitlock, Barone) that serve as strong proxies for synthetic accessibility.

Medchem Tools

Name: Medchem Tools
Author: Sologa

bySologa

データサイエンスとML

Applies medicinal chemistry filters and drug-likeness rules to prioritize compound libraries and assess molecular quality.

概要

Medchem is a specialized toolkit designed for drug discovery workflows, enabling researchers to efficiently triage compound libraries using hundreds of established medicinal chemistry rules and filters. It provides comprehensive support for drug-likeness criteria like Lipinski's Rule of Five, detects problematic structural alerts (PAINS, NIBR, Lilly Demerits), and calculates molecular complexity metrics to approximate synthetic accessibility. Whether used for initial library screening or lead optimization, Medchem streamlines the prioritization process through parallelized processing and a powerful query language, ensuring high-quality compound selection based on rigorous chemical property constraints and functional group detection.

主な機能

Comprehensive drug-likeness rules (Lipinski, Veber, CNS, etc.)
Advanced structural alert filtering including PAINS and NIBR filters
Parallelized batch processing for large-scale chemical datasets
Molecular complexity and synthetic accessibility metric calculation
Chemical group and functional group detection using SMARTS patterns
0 GitHub stars

ユースケース

Lead optimization through strict property filtering and complexity assessment
Identification and filtering of reactive or problematic functional groups
Initial triage of large compound collections for drug-likeness

概要

主な機能

Comprehensive drug-likeness rules (Lipinski, Veber, CNS, etc.)
Advanced structural alert filtering including PAINS and NIBR filters
Parallelized batch processing for large-scale chemical datasets
Molecular complexity and synthetic accessibility metric calculation
Chemical group and functional group detection using SMARTS patterns
0 GitHub stars

ユースケース

Lead optimization through strict property filtering and complexity assessment
Identification and filtering of reactive or problematic functional groups
Initial triage of large compound collections for drug-likeness