medchem

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Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.

AI & Automation 27,984 stars 2901 forks Updated today MIT

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Skill Content

# Medchem ## Overview Medchem is a Python library for molecular filtering and prioritization in drug discovery workflows. Apply hundreds of well-established and novel molecular filters, structural alerts, and medicinal chemistry rules to efficiently triage and prioritize compound libraries at scale. Rules and filters are context-specific—use as guidelines combined with domain expertise. ## When to Use This Skill This skill should be used when: - Applying drug-likeness rules (Lipinski, Veber, etc.) to compound libraries - Filtering molecules by structural alerts or PAINS patterns - Prioritizing compounds for lead optimization - Assessing compound quality and medicinal chemistry properties - Detecting reactive or problematic functional groups - Calculating molecular complexity metrics ## Installation ```bash uv pip install medchem ``` ## Core Capabilities ### 1. Medicinal Chemistry Rules Apply established drug-likeness rules to molecules using the `medchem.rules` module. **Available Rules:** - Rule of Five (Lipinski) - Rule of Oprea - Rule of CNS - Rule of leadlike (soft and strict) - Rule of three - Rule of Reos - Rule of drug - Rule of Veber - Golden triangle - PAINS filters **Single Rule Application:** ```python import medchem as mc # Apply Rule of Five to a SMILES string smiles = "CC(=O)OC1=CC=CC=C1C(=O)O" # Aspirin passes = mc.rules.basic_rules.rule_of_five(smiles) # Returns: True # Check specific rules passes_oprea = mc.rules.basic_rules.rule_of_oprea(smile...

Details

Author
davila7
Repository
davila7/claude-code-templates
Created
11 months ago
Last Updated
today
Language
Python
License
MIT

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