PharmAgents: Building a Virtual Pharma with Large Language Model Agents

• URL: http://arxiv.org/abs/2503.22164v2
• Date: Mon, 31 Mar 2025 16:26:42 GMT
• Title: PharmAgents: Building a Virtual Pharma with Large Language Model Agents
• Authors: Bowen Gao, Yanwen Huang, Yiqiao Liu, Wenxuan Xie, Wei-Ying Ma, Ya-Qin Zhang, Yanyan Lan,
• Abstract summary: We introduce PharmAgents, a virtual pharmaceutical ecosystem driven by multi-agent collaboration.<n>The system integrates explainable, LLM-driven agents equipped with specialized machine learning models and computational tools.<n>It identifies potential therapeutic targets, discovers promising lead compounds, enhances binding affinity and key molecular properties, and performs in silico analyses of toxicity and synthetic feasibility.
• Score: 19.589707628042422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The discovery of novel small molecule drugs remains a critical scientific challenge with far-reaching implications for treating diseases and advancing human health. Traditional drug development--especially for small molecule therapeutics--is a highly complex, resource-intensive, and time-consuming process that requires multidisciplinary collaboration. Recent breakthroughs in artificial intelligence (AI), particularly the rise of large language models (LLMs), present a transformative opportunity to streamline and accelerate this process. In this paper, we introduce PharmAgents, a virtual pharmaceutical ecosystem driven by LLM-based multi-agent collaboration. PharmAgents simulates the full drug discovery workflow--from target discovery to preclinical evaluation--by integrating explainable, LLM-driven agents equipped with specialized machine learning models and computational tools. Through structured knowledge exchange and automated optimization, PharmAgents identifies potential therapeutic targets, discovers promising lead compounds, enhances binding affinity and key molecular properties, and performs in silico analyses of toxicity and synthetic feasibility. Additionally, the system supports interpretability, agent interaction, and self-evolvement, enabling it to refine future drug designs based on prior experience. By showcasing the potential of LLM-powered multi-agent systems in drug discovery, this work establishes a new paradigm for autonomous, explainable, and scalable pharmaceutical research, with future extensions toward comprehensive drug lifecycle management.

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