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

ACL ARR 2025 February Submission3834 Authors

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 the anonymous link https://anonymous.4open.science/r/drugagent-5C42/.

Paper Type: Short

Research Area: NLP Applications

Research Area Keywords: healthcare applications, clinical NLP, code generation and understanding

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 3834