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Proceedings of the AAAI Conference on Artificial Intelligence

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RAG-Enhanced Collaborative LLM Agents for Drug Discovery
Namkyeong Le · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Namkyeong Lee Korea Advanced Institute of Science & Technology
  • Edward De Brouwer Genentech
  • Ehsan Hajiramezanali Genentech
  • Tommaso Biancalani Genentech
  • Chanyoung Park Korea Advanced Institute of Science & Technology
  • Gabriele Scalia Genentech

DOI:

https://doi.org/10.1609/aaai.v40i1.37020

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 critical challenges. First, it hinders the application of more flexible general-purpose LLMs in 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.

How to Cite

Lee, N., De Brouwer, E., Hajiramezanali, E., Biancalani, T., Park, C., & Scalia, G. (2026). RAG-Enhanced Collaborative LLM Agents for Drug Discovery. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 561–569. https://doi.org/10.1609/aaai.v40i1.37020

Issue

Section

AAAI Technical Track on Application Domains I

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