# Prompt-to-Pill: Multi-Agent Drug Discovery and Clinical Simulation Pipeline

## TL;DR

This paper introduces a modular AI framework for drug discovery and clinical simulation, demonstrating a full pipeline from molecule generation to virtual patient recruitment.

## Contribution

A novel multi-agent LLM-based system for end-to-end drug discovery and clinical trial simulation is proposed and implemented.

## Key findings

- The Prompt-to-Pill framework successfully simulated drug discovery and clinical trial phases for the DPP4 target.
- Integration of generative and predictive LLMs enabled molecule creation, ADMET evaluation, and virtual patient screening.
- The system demonstrated the feasibility of using AI to streamline drug development workflows in silico.

## Abstract

This study presents a proof-of-concept, comprehensive, modular framework for AI-driven drug discovery (DD) and clinical trial simulation, spanning from target identification to virtual patient recruitment. Synthesized from a systematic analysis of 51 large language model (LLM)-based systems, the proposed Prompt-to-Pill architecture and corresponding implementation leverages a multi-agent system (MAS) divided into DD, preclinical and clinical phases, coordinated by a central Orchestrator. Each phase comprises specialized LLM for molecular generation, toxicity screening, docking, trial design, and patient matching. To demonstrate the full pipeline in practice, the well-characterized target Dipeptidyl Peptidase 4 (DPP4) was selected as a representative use case. The process begins with generative molecule creation and proceeds through ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluation, structure-based docking, and lead optimization. Clinical-phase agents then simulate trial generation, patient eligibility screening using electronic health records (EHRs), and predict trial outcomes. By tightly integrating generative, predictive, and retrieval-based LLM components, this architecture bridges drug discovery and preclinical phase with virtual clinical development, offering a demonstration of how LLM-based agents can operationalize the drug development workflow in silico.

The implementation and code are available at: https://github.com/ChatMED/Prompt-to-Pill.