FROGENT: An End-to-End Full-process Drug Design Multi-Agent System

TL;DR

FROGENT is an autonomous, multi-agent system leveraging large language models to streamline and unify the entire drug discovery process, significantly improving efficiency and accuracy over existing fragmented tools.

Contribution

This work introduces FROGENT, a novel end-to-end multi-agent framework that integrates planning, reasoning, and tool use for comprehensive drug design automation.

Findings

Outperforms six advanced ReAct-style agents on eight benchmarks.

Demonstrates practical application across small-molecule and peptide design.

Achieves substantial improvements in efficiency and accuracy.

Abstract

Drug discovery is a complex, multi-step pipeline that remains heavily dependent on manual, experience-driven operations; meanwhile, existing customized artificial intelligence tools are fragmented across web applications, desktop software, and code libraries, resulting in incompatible interfaces and inefficient, burdensome workflows. To overcome these challenges, we propose FROGENT, a full-process drug design multi-agent system that leverages the planning, reasoning, and tool-use capabilities of large language models (LLMs) to unify drug discovery within a closed-loop and autonomous framework. FROGENT is a collaborative multi-agent system comprising a central Orchestrate Agent for strategic workflow coordination and three distributed agents, Retrieve, Forge, and Gauge, that employ dynamic biochemical databases, extensible tool libraries, and task-specific computational models via the Model Context Protocol. This architecture enables end-to-end execution of complex drug discovery pipelines, covering target identification, small-molecule generation, peptide optimization, and retrosynthetic planning. Across eight benchmarks spanning core drug discovery tasks, FROGENT consistently outperforms six increasingly advanced ReAct-style agents. Case studies further demonstrate its practicality and generalization across real-world small-molecule and peptide design scenarios. Overall, FROGENT not only achieves substantial gains in efficiency and accuracy, but also demonstrates the potential of LLM-based agentic systems to autonomously orchestrate drug development pipelines, reducing, or even replacing, reliance on manual, experience-driven human intervention.