Domain-Knowledge Augmented Multi-Agent Collaborative Reasoning Protein-Disease Mapping

Music City Center 

Understanding protein-disease relationships is crucial for uncovering disease mechanisms, identifying biomarkers, and accelerating drug discovery. However, researchers currently spend significant time and effort manually reasoning over fragmented biomedical data to extract meaningful insights. Existing methods often lack efficient integration of diverse biological perspectives, making it challenging to derive comprehensive conclusions. To address this, we propose a multi-agent framework that automates the reasoning process for protein-disease mapping. Given a user-specified disease, the system retrieves top-associated proteins and employs specialized reasoning agents to analyze key aspects such as existing data evidence, protein function, and disease biology. Additional agents explore gene-disease associations, protein-protein interactions, and protein-drug relationships, synthesizing multi-source biomedical data. An aggregation agent ensures coherence, while a natural language generation agent translates findings into human-readable reports.By automating complex reasoning and reducing manual effort, our framework enhances the interpretability of disease mechanisms, facilitates hypothesis generation, and supports precision medicine and drug discovery.