Multi-agent system for automated drug discovery pipelines
MADD (Multi-Agent Drug Discovery) is a multi-agent system that automates key stages of the drug discovery pipeline — target identification, molecule generation, property prediction (ADMET), docking simulation, and lead optimization. Specialized agents collaborate to propose, evaluate, and refine drug candidates, reducing the manual effort in early-stage drug discovery research.
Target Protein
↓
Target Analysis Agent (binding site, druggability)
↓
Molecule Generation Agent (de novo design)
↓
Property Prediction Agent (ADMET screening)
↓
Docking Agent (binding affinity estimation)
↓
Optimization Agent (lead optimization cycle)
↓
Report Agent (candidate ranking + rationale)
from madd import DrugDiscoveryPipeline
pipeline = DrugDiscoveryPipeline(
llm_provider="anthropic",
tools=["rdkit", "autodock_vina", "admet_predictor"],
)
# Run discovery pipeline
results = pipeline.discover(
target_protein="6LU7", # PDB ID (SARS-CoV-2 Mpro)
target_site="active_site",
constraints={
"molecular_weight": (200, 500), # Lipinski
"logP": (-0.4, 5.6),
"hbd": (0, 5),
"hba": (0, 10),
"tpsa": (0, 140),
},
num_candidates=100,
optimization_rounds=3,
)
# Top candidates
for i, mol in enumerate(results.top_candidates[:5]):
print(f"\nCandidate {i+1}: {mol.smiles}")
print(f" Docking score: {mol.docking_score:.2f} kcal/mol")
print(f" QED: {mol.qed:.3f}")
print(f" Synthetic accessibility: {mol.sa_score:.2f}")
print(f" ADMET: {mol.admet_summary}")
from madd.agents import ADMETAgent
admet = ADMETAgent()
# Predict ADMET properties for a molecule
props = admet.predict("CC(=O)Oc1ccccc1C(=O)O") # Aspirin
print(f"Absorption: {props.absorption}")
print(f"Distribution: {props.distribution}")
print(f"Metabolism: {props.metabolism}")
print(f"Excretion: {props.excretion}")
print(f"Toxicity: {props.toxicity}")
print(f"BBB penetration: {props.bbb_penetration}")
print(f"CYP inhibition: {props.cyp_inhibition}")
print(f"hERG liability: {props.herg_risk}")
from madd.agents import MolGenAgent
gen = MolGenAgent(method="reinforcement_learning")
# Generate molecules targeting a binding site
molecules = gen.generate(
target_pdb="6LU7",
binding_site="active_site",
num_molecules=500,
diversity_threshold=0.5, # Tanimoto diversity
constraints={
"drug_likeness": True, # Lipinski + Veber
"novelty": True, # Not in ChEMBL
},
)
print(f"Generated: {len(molecules)}")
print(f"Drug-like: {sum(1 for m in molecules if m.is_drug_like)}")
print(f"Novel: {sum(1 for m in molecules if m.is_novel)}")
from madd.agents import OptimizationAgent
optimizer = OptimizationAgent()
# Optimize a lead compound
optimized = optimizer.optimize(
lead_smiles="c1ccc(-c2ncc(F)c(N)n2)cc1",
objectives=[
("docking_score", "minimize"),
("qed", "maximize"),
("sa_score", "minimize"),
("solubility", "maximize"),
],
num_iterations=50,
keep_scaffold=True, # Maintain core structure
)
for mol in optimized.pareto_front[:5]:
print(f"SMILES: {mol.smiles}")
print(f" Docking: {mol.docking_score:.2f}")
print(f" QED: {mol.qed:.3f}")