MedDiscovery: An Autonomous Multi-Agent System for End-to-End Biomedical Hypothesis Generation and Drug Formulation Design

We present MedDiscovery, a multi-agent cognitive architecture that autonomously generates complete drug development pipelines from a single disease target input. Unlike existing AI systems that produce isolated research hypotheses, MedDiscovery delivers an integrated R&D workflow comprising: (1) comprehensive literature synthesis across 500+ scientific sources, (2) novel therapeutic hypotheses with cross-domain reasoning spanning 4+ scientific fields, (3) SPIRIT-compliant clinical trial protocols achieving 100% checklist compliance, and (4) manufacturing-ready drug formulation recipes with complete bills of materials, CAS numbers, supplier information, and quality control specifications. We demonstrate this capability through a Type 1 Diabetes case study, where MedDiscovery generated a Layer-3 disruptive hypothesis targeting ZAP-70/Lck dual inhibition via pancreas-targeted nanoparticles (Novelty Score: 0.77, Feasibility: GREEN). The system produced three distinct drug formulation approaches: (1) BetaGuard-ImmunoNP, a multi-layered polymeric nanoparticle with 17 ingredients for IV infusion; (2) TCR-Silence-NP, an siRNA-loaded lipid nanoparticle with 20 ingredients for subcutaneous injection; and (3) GlucoZAP-NP1, a glucose-responsive PLGA formulation with 16 ingredients and complete cost estimation ($250/dose). Each recipe includes detailed standard operating procedures, equipment specifications, and in-process quality controls. This work demonstrates that current AI capabilities can extend beyond hypothesis generation to encompass the full translational research pipeline, potentially reducing early-stage drug development timelines from years to days while maintaining scientific rigor through built-in adversarial validation mechanisms.