Multi agent large language models for biomedical hypothesis generation in drug combination discovery

Recent advancements in large language models (LLMs) have demonstrated their potential in scientific reasoning, but their ability to open-ended hypotheses in data-scarce domains remains underexplored. Here, we introduce <b>Co</b>mbinatorial <b>A</b>lzheimer's Disease <b>T</b>herapeutic <b>E</b>fficacy <b>D</b>ecision (Coated-LLM), an AI-driven framework that is inspired by scientific collaboration to predict efficacious combinatorial therapy when data-driven prediction is infeasible. Coated-LLM employs multiple specialized LLM agents-<i>Researcher</i>, <i>Reviewer</i> <i>s</i>, and <i>Moderator</i>-to systematically generate and evaluate hypotheses through several in-context learning techniques. Using Alzheimer's disease (AD) as a test case, Coated-LLM outperformed traditional knowledge-based methods (accuracy: 0.74 vs. 0.52), with external validation achieving an accuracy of 0.82. In addition, a drug combination predicted from Coated-LLM was experimentally validated to significantly reduce amyloid aggregation <i>in vitro</i>. These findings highlight the potential of our framework to augment human reasoning in complex scientific reasoning tasks, offering a scalable approach for hypothesis generation in biomedical research.