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

Multiple Rules with RuleFilters:

python

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:

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

Common Alerts:

python

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:

python

import medchem as mc

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

Lilly Demerits:

python

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:

python

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:

python

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:

python

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:

python

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:

python

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 complex filtering criteria.

Query Examples:

# Molecules passing Ro5 AND not having common alerts
"rule_of_five AND NOT common_alerts"

# CNS-like molecules with low complexity
"rule_of_cns AND complexity < 400"

# Leadlike molecules without Lilly demerits
"rule_of_leadlike AND lilly_demerits == 0"

Usage:

python

import medchem as mc

# Parse and apply query
query = mc.query.parse("rule_of_five AND NOT common_alerts")
results = query.apply(mols=mol_list, n_jobs=-1)

Workflow Patterns

Pattern 1: Initial Triage of Compound Library

Filter a large compound collection to identify drug-like candidates.

python

import datamol as dm
import medchem as mc
import pandas as pd

# Load compound library
df = pd.read_csv("compounds.csv")
mols = [dm.to_mol(smi) for smi in df["smiles"]]

# Apply primary filters
rule_filter = mc.rules.RuleFilters(rule_list=["rule_of_five", "rule_of_veber"])
rule_results = rule_filter(mols=mols, n_jobs=-1, progress=True)

# Apply structural alerts
alert_filter = mc.structural.CommonAlertsFilters()
alert_results = alert_filter(mols=mols, n_jobs=-1, progress=True)

# Combine results
df["passes_rules"] = rule_results["pass"]
df["has_alerts"] = alert_results["has_alerts"]
df["drug_like"] = df["passes_rules"] & ~df["has_alerts"]

# Save filtered compounds
filtered_df = df[df["drug_like"]]
filtered_df.to_csv("filtered_compounds.csv", index=False)

Pattern 2: Lead Optimization Filtering

Apply stricter criteria during lead optimization.

python

import medchem as mc

# Create comprehensive filter
filters = {
    "rules": mc.rules.RuleFilters(rule_list=["rule_of_leadlike_strict"]),
    "alerts": mc.structural.NIBRFilters(),
    "lilly": mc.structural.LillyDemeritsFilters(),
    "complexity": mc.complexity.ComplexityFilter(max_complexity=400)
}

# Apply all filters
results = {}
for name, filt in filters.items():
    results[name] = filt(mols=candidate_mols, n_jobs=-1)

# Identify compounds passing all filters
passes_all = all(r["pass"] for r in results.values())

Pattern 3: Identify Specific Chemical Groups

Find molecules containing specific functional groups or scaffolds.

python

import medchem as mc

# Create group detector for multiple groups
group_detector = mc.groups.ChemicalGroup(
    groups=["hinge_binders", "phosphate_binders"]
)

# Screen library
matches = group_detector.get_all_matches(mol_list)

# Filter molecules with desired groups
mol_with_groups = [mol for mol, match in zip(mol_list, matches) if match]

Best Practices

Context Matters: Don't blindly apply filters. Understand the biological target and chemical space.
Combine Multiple Filters: Use rules, structural alerts, and domain knowledge together for better decisions.
Use Parallelization: For large datasets (>1000 molecules), always use
```
n_jobs=-1
```
for parallel processing.
Iterative Refinement: Start with broad filters (Ro5), then apply more specific criteria (CNS, leadlike) as needed.
Document Filtering Decisions: Track which molecules were filtered out and why for reproducibility.
Validate Results: Remember that marketed drugs often fail standard filters—use these as guidelines, not absolute rules.
Consider Prodrugs: Molecules designed as prodrugs may intentionally violate standard medicinal chemistry rules.

Resources

references/api_guide.md

Comprehensive API reference covering all medchem modules with detailed function signatures, parameters, and return types.

references/rules_catalog.md

Complete catalog of available rules, filters, and alerts with descriptions, thresholds, and literature references.

scripts/filter_molecules.py

Production-ready script for batch filtering workflows. Supports multiple input formats (CSV, SDF, SMILES), configurable filter combinations, and detailed reporting.

Usage:

bash

python scripts/filter_molecules.py input.csv --rules rule_of_five,rule_of_cns --alerts nibr --output filtered.csv

Documentation

Official documentation: https://medchem-docs.datamol.io/ GitHub repository: https://github.com/datamol-io/medchem

medchem

NPX Install

Tags

SKILL.md Content

Medchem

Overview

When to Use This Skill

Installation

Core Capabilities

1. Medicinal Chemistry Rules

2. Structural Alert Filters

3. Functional API for High-Level Operations

4. Chemical Groups Detection

5. Named Catalogs

6. Molecular Complexity

7. Constraints Filtering

8. Medchem Query Language

Workflow Patterns

Pattern 1: Initial Triage of Compound Library

Pattern 2: Lead Optimization Filtering

Pattern 3: Identify Specific Chemical Groups

Best Practices

Resources

references/api_guide.md

references/rules_catalog.md

scripts/filter_molecules.py

Documentation