What format do the molecules need to be in?

The skill primarily works with SMILES strings and RDKit-compatible molecule objects via the datamol library.

Does it detect specific problematic patterns like PAINS?

Yes, it includes filters for Pan-Assay Interference Compounds (PAINS), NIBR filters, and Lilly Demerits to identify potentially problematic structural alerts.

Can I process large chemical libraries efficiently?

Yes, the skill supports parallelization using n_jobs=-1, allowing for high-performance processing of thousands of molecules simultaneously.

What specific drug-likeness rules does this skill support?

It supports a wide range of rules including Lipinski's Rule of Five, Veber's rules, Oprea, CNS-likeness, and lead-like criteria (both soft and strict).

Is it possible to combine multiple filtering criteria?

Yes, you can use the Medchem Query Language or RuleFilters to combine various rules, alerts, and complexity constraints into a single workflow.

MedChem Molecular Triage

Name: MedChem Molecular Triage
Author: BbgnsurfTech

byBbgnsurfTech

•

데이터 과학 및 ML

Applies medicinal chemistry rules and structural filters to prioritize and triage molecular libraries for drug discovery.

소개

MedChem is a specialized Python skill designed for molecular filtering and prioritization within drug discovery workflows. It provides a comprehensive suite of established drug-likeness rules (such as Lipinski's Rule of Five and Veber's rules), structural alerts like PAINS patterns, and complexity metrics to assess synthetic accessibility. By enabling automated screening of large chemical libraries, this skill helps researchers and developers detect problematic functional groups, calculate molecular properties, and identify high-quality lead candidates efficiently and at scale.

주요 기능

Structural alert detection (PAINS, NIBR filters, Lilly Demerits)
Molecular complexity and synthetic accessibility scoring
Drug-likeness rule application (Lipinski, Veber, CNS, lead-like)
Medchem Query Language for complex filtering logic
3 GitHub stars
Functional group and chemical entity identification

사용 사례

Detecting reactive or problematic functional groups in chemical datasets
Lead optimization and refinement of candidate molecules
Initial triage of large compound libraries for drug discovery

소개

주요 기능

Structural alert detection (PAINS, NIBR filters, Lilly Demerits)
Molecular complexity and synthetic accessibility scoring
Drug-likeness rule application (Lipinski, Veber, CNS, lead-like)
Medchem Query Language for complex filtering logic
3 GitHub stars
Functional group and chemical entity identification

사용 사례

Detecting reactive or problematic functional groups in chemical datasets
Lead optimization and refinement of candidate molecules
Initial triage of large compound libraries for drug discovery