RAG-Enhanced Collaborative LLM Agents for Drug Discovery

TL;DR

This paper introduces CLADD, a retrieval-augmented system of collaborative LLM agents that enhances drug discovery by dynamically integrating biomedical data without domain-specific fine-tuning, improving over existing methods.

Contribution

The paper presents a novel RAG-empowered multi-agent framework for drug discovery that handles data heterogeneity and ambiguity without requiring domain-specific fine-tuning.

Findings

Outperforms general-purpose and domain-specific LLMs in drug discovery tasks.

Effectively integrates multi-source biomedical data.

Demonstrates flexibility across various drug discovery applications.

Abstract

Recent advances in large language models (LLMs) have shown great potential to accelerate drug discovery. However, the specialized nature of biochemical data often necessitates costly domain-specific fine-tuning, posing major challenges. First, it hinders the application of more flexible general-purpose LLMs for cutting-edge drug discovery tasks. More importantly, it limits the rapid integration of the vast amounts of scientific data continuously generated through experiments and research. Compounding these challenges is the fact that real-world scientific questions are typically complex and open-ended, requiring reasoning beyond pattern matching or static knowledge retrieval.To address these challenges, we propose CLADD, a retrieval-augmented generation (RAG)-empowered agentic system tailored to drug discovery tasks. Through the collaboration of multiple LLM agents, CLADD dynamically retrieves information from biomedical knowledge bases, contextualizes query molecules, and integrates relevant evidence to generate responses - all without the need for domain-specific fine-tuning. Crucially, we tackle key obstacles in applying RAG workflows to biochemical data, including data heterogeneity, ambiguity, and multi-source integration. We demonstrate the flexibility and effectiveness of this framework across a variety of drug discovery tasks, showing that it outperforms general-purpose and domain-specific LLMs as well as traditional deep learning approaches. Our code is publicly available at https://github.com/Genentech/CLADD.