madd-drug-discovery-guide

name	madd-drug-discovery-guide
description	Multi-agent system for automated drug discovery pipelines
metadata	{"openclaw":{"emoji":"💊","category":"domains","subcategory":"pharma","keywords":["drug discovery","multi-agent","molecular design","ADMET","virtual screening","pharma AI"],"source":"https://github.com/sb-ai-lab/MADD"}}

MADD: Multi-Agent Drug Discovery Guide

Overview

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.

Agent Pipeline

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)

Usage

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=,
    constraints={
        : (, ),    
        : (-, ),
        : (, ),
        : (, ),
        : (, ),
    },
    num_candidates=,
    optimization_rounds=,
)


 i, mol  (results.top_candidates[:]):
    ()
    ()
    ()
    ()
    ()