medchem

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

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:

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(smiles)
passes_cns = mc.rules.basic_rules.rule_of_cns(smiles)

Multiple Rules with RuleFilters:

import datamol as dm
import medchem as mc

# Load molecules
mols = [dm.to_mol(smiles) for smiles in smiles_list]

# Create filter with multiple rules
rfilter = mc.rules.RuleFilters(
    rule_list=[
        "rule_of_five",
        "rule_of_oprea",
        "rule_of_cns",
        "rule_of_leadlike_soft"
    ]
)

# Apply filters with parallelization
results = rfilter(
    mols=mols,
    n_jobs=-1,  # Use all CPU cores
    progress=True
)