Multi-Agent Orchestration for Knowledge Extraction and Retrieval: AI Expert System for GPCRs

Abstract

We present GPCR-Nexus, an AI-driven platform for integrated exploration of G protein-coupled receptor (GPCR) biology that unifies structured databases with unstructured scientific literature. The system combines a GPCR-ligand knowledge graph with vector-based semantic retrieval to enable comprehensive, up-to-date information access. Central to GPCR-Nexus is a multi-agent architecture in which specialized components coordinate query planning, evidence retrieval, validation, and synthesis. This design ensures that generated responses are grounded in verifiable sources while maintaining coherence across heterogeneous data modalities. By jointly leveraging curated databases and primary literature, GPCR-Nexus enables context-aware reasoning over molecular interactions, functional mechanisms, and disease associations. The platform produces citation-backed outputs with traceable evidence, addressing limitations of conventional database queries and standalone language models. We detail the system architecture, data integration strategy, and agent orchestration framework, and demonstrate its utility through representative query scenarios. GPCR-Nexus provides a scalable approach to combining structured and unstructured biomedical knowledge using agent-based AI, offering improved accuracy, interpretability, and coverage. This work establishes a foundation for trustworthy, AI-assisted knowledge synthesis in GPCR research and drug discovery.