Automating AI Discovery for Biomedicine Through Knowledge Graphs And LLM Agents

The biomedical domain’s accelerating progress in understanding, early detection, and treatment of diseases has created an exponentially growing and overwhelming body of literature. Researchers must rely on this literature to find relevant information, but navigating this vast landscape has become increasingly challenging, especially for interdisciplinary AI-biomedicine researchers who need to stay current across both highly fast-paced fields. Despite the emergence of LLM systems, retrieving precise, domain-specific literature remains a significant challenge. This paper addresses these challenges by integrating knowledge graphs with scientific literature embedded in large language models to expedite biomedical discovery. We employ a novel strategy to discover the most relevant pathways between biomedical entities in knowledge graphs. These pathways are then leveraged by a multi-agent LLM system to formulate facts from literature, design AI predictors for understanding discovered pathways, and propose wet-lab experiments to validate AI predictions. This approach creates a comprehensive end-to-end methodology for biomedical discovery. Experiments with various biomedical entity pairs demonstrate the framework’s ability to identify highly relevant pathways and design plausible, complex AI predictors with wet lab validation experiments across diverse therapeutic areas. We developed Intelliscope, a web-based dashboard making this framework available to researchers worldwide. This first-of-its-kind platform could significantly accelerate scientific discoveries, potentially leading to breakthroughs in disease understanding, drug repurposing, and therapeutic development.