/medchem

name: medchem
description: Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.
license: Apache-2.0 license
metadata:
    skill-author: K-Dense Inc.

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
)

**Result Format:** Results are returned as dictionaries with pass/fail status and detailed information for each rule.

2. Structural Alert Filters

Detect potentially problematic structural patterns using the `medchem.structural` module.

**Available Filters:**

1. **Common Alerts** - General structural alerts derived from ChEMBL curation and literature 2. **NIBR Filters** - Novartis Institutes for BioMedical Research filter set 3. **Lilly Demerits** - Eli Lilly's demerit-based system (275 rules, molecules rejected at >100 demerits)

**Common Alerts:**

import medchem as mc

# Create filter
alert_filter = mc.structural.CommonAlertsFilters()

# Check single molecule
mol = dm.to_mol("c1ccccc1")
has_alerts, details = alert_filter.check_mol(mol)

# Batch filtering with parallelization
results = alert_filter(
    mols=mol_list,
    n_jobs=-1,
    progress=True
)

**NIBR Filters:**

import medchem as mc

# Apply NIBR filters
nibr_filter = mc.structural.NIBRFilters()
results = nibr_filter(mols=mol_list, n_jobs=-1)

**Lilly Demerits:**

import medchem as mc

# Calculate Lilly demerits
lilly = mc.structural.LillyDemeritsFilters()
results = lilly(mols=mol_list, n_jobs=-1)

# Each result includes demerit score and whether it passes (≤100 demerits)

3. Functional API for High-Level Operations

The `medchem.functional` module provides convenient functions for common workflows.

**Quick Filtering:**

import medchem as mc

# Apply NIBR filters to a list
filter_ok = mc.functional.nibr_filter(
    mols=mol_list,
    n_jobs=-1
)

# Apply common alerts
alert_results = mc.functional.common_alerts_filter(
    mols=mol_list,
    n_jobs=-1
)

4. Chemical Groups Detection

Identify specific chemical groups and functional groups using `medchem.groups`.

**Available Groups:**

• Hinge binders
• Phosphate binders
• Michael acceptors
• Reactive groups
• Custom SMARTS patterns

**Usage:**

import medchem as mc

# Create group detector
group = mc.groups.ChemicalGroup(groups=["hinge_binders"])

# Check for matches
has_matches = group.has_match(mol_list)

# Get detailed match information
matches = group.get_matches(mol)

5. Named Catalogs

Access curated collections of chemical structures through `medchem.catalogs`.

**Available Catalogs:**

• Functional groups
• Protecting groups
• Common reagents
• Standard fragments

**Usage:**

import medchem as mc

# Access named catalogs
catalogs = mc.catalogs.NamedCatalogs

# Use catalog for matching
catalog = catalogs.get("functional_groups")
matches = catalog.get_matches(mol)

6. Molecular Complexity

Calculate complexity metrics that approximate synthetic accessibility using `medchem.complexity`.

**Common Metrics:**

• Bertz complexity
• Whitlock complexity
• Barone complexity

**Usage:**

import medchem as mc

# Calculate complexity
complexity_score = mc.complexity.calculate_complexity(mol)

# Filter by complexity threshold
complex_filter = mc.complexity.ComplexityFilter(max_complexity=500)
results = complex_filter(mols=mol_list)

7. Constraints Filtering

Apply custom property-based constraints using `medchem.constraints`.

**Example Constraints:**

• Molecular weight ranges
• LogP bounds
• TPSA limits
• Rotatable bond counts

**Usage:**

import medchem as mc

# Define constraints
constraints = mc.constraints.Constraints(
    mw_range=(200, 500),
    logp_range=(-2, 5),
    tpsa_max=140,
    rotatable_bonds_max=10
)

# Apply constraints
results = constraints(mols=mol_list, n_jobs=-1)

8. Medchem Query Language

Use a specialized query language for