DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration

• URL: http://arxiv.org/abs/2411.15692v2
• Date: Wed, 05 Mar 2025 10:54:30 GMT
• Title: DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration
• Authors: Sizhe Liu, Yizhou Lu, Siyu Chen, Xiyang Hu, Jieyu Zhao, Yingzhou Lu, Yue Zhao,
• Abstract summary: DrugAgent is a multi-agent framework that automates machine learning (ML) programming for drug discovery tasks.<n>Our results show that DrugAgent consistently outperforms leading baselines.
• Score: 24.65716292347949
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent progress in Large Language Models (LLMs) has drawn attention to their potential for accelerating drug discovery. However, a central problem remains: translating theoretical ideas into robust implementations in the highly specialized context of pharmaceutical research. This limitation prevents practitioners from making full use of the latest AI developments in drug discovery. To address this challenge, we introduce DrugAgent, a multi-agent framework that automates machine learning (ML) programming for drug discovery tasks. DrugAgent employs an LLM Planner that formulates high-level ideas and an LLM Instructor that identifies and integrates domain knowledge when implementing those ideas. We present case studies on three representative drug discovery tasks. Our results show that DrugAgent consistently outperforms leading baselines, including a relative improvement of 4.92% in ROC-AUC compared to ReAct for drug-target interaction (DTI). DrugAgent is publicly available at https://anonymous.4open.science/r/drugagent-5C42/.

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