What medicinal chemistry rules are included?

The skill includes several established standards such as Lipinski's Rule of Five, Veber's rules, Oprea, CNS-like rules, lead-like filters, and Reos rules.

Does this support parallel processing for large libraries?

Yes, most filtering functions allow for parallelization using the n_jobs parameter, making it suitable for processing thousands of molecules efficiently.

Can I filter by structural alerts?

Yes, it supports comprehensive structural alert sets including PAINS (Pan-Assay Interference Compounds), Novartis (NIBR) filters, and Eli Lilly demerit systems.

Can I define custom filtering logic?

Yes, you can use the Medchem Query Language for complex logic or the constraints module to set specific ranges for molecular weight, LogP, TPSA, and other properties.

How do I calculate synthetic accessibility or complexity?

The skill includes the complexity module which provides metrics like Bertz, Whitlock, and Barone complexity scores to estimate molecular synthetic difficulty.

Medchem Molecular Filtering

Name: Medchem Molecular Filtering
Author: Activer007

byActiver007

Ciencia de Datos y ML

Applies medicinal chemistry rules, structural alerts, and drug-likeness filters to prioritize compound libraries for drug discovery.

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Medchem is a specialized Python library designed for molecular filtering and prioritization, enabling researchers to triage large chemical libraries efficiently. It provides a comprehensive suite of established rules like Lipinski's Rule of Five, structural alert systems like PAINS and NIBR filters, and advanced complexity metrics to identify high-quality lead candidates. This skill automates the detection of problematic functional groups and applies medicinal chemistry best practices, making it an essential tool for lead optimization and chemical library management.

Características Principales

Functional group and reactive moiety identification
Molecular complexity and synthetic accessibility analysis
Advanced structural alert detection (PAINS, NIBR, Lilly)
Specialized Medchem Query Language for custom logic
Drug-likeness rule application (Lipinski, Veber, CNS)
0 GitHub stars

Casos de Uso

Lead optimization and chemical quality assessment
Triage and filtering of large virtual compound libraries
Detection of reactive or toxic functional groups

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Características Principales

Functional group and reactive moiety identification
Molecular complexity and synthetic accessibility analysis
Advanced structural alert detection (PAINS, NIBR, Lilly)
Specialized Medchem Query Language for custom logic
Drug-likeness rule application (Lipinski, Veber, CNS)
0 GitHub stars

Casos de Uso

Lead optimization and chemical quality assessment
Triage and filtering of large virtual compound libraries
Detection of reactive or toxic functional groups