Conversational Multi-Agentic Network-Driven Disease Module Identification and Drug Repurposing Prediction With ChatDRex: Platform Development Study

Abstract

Background: Repurposing approved drugs offers a time- and cost-effective alternative to traditional drug development. However, in silico prediction of repurposing candidates is challenging and requires the effective collaboration of specialists in various fields, including pharmacology, medicine, biology, and bioinformatics. Fragmented, specialized algorithms and tools often address only narrow aspects of the overall problem. Heterogeneous, unstructured data landscapes require the expertise of specialized users. Hence, diverse data sources might not integrate smoothly across workflows. Objective: We aimed to develop an integrated, natural language-driven system that enables domain experts to conduct complex network-based drug repurposing analyses by integrating heterogeneous biomedical data sources and specialized bioinformatics methods within a unified workflow. Methods: With ChatDRex, we present a conversation-based, multi-agent system that facilitates the execution of complex bioinformatic analyses aiming for network-based drug repurposing prediction. It builds on the integrated systems medicine knowledge graph NeDRexDB. ChatDRex provides natural language access to its extensive biomedical knowledge base. It integrates bioinformatics agents for network analysis, literature mining, and drug repurposing. These are complemented by agents that evaluate functional coherence for in silico validation. Its flexible multi-agent design assigns specific tasks to specialized agents, including query routing, data retrieval, algorithm execution, and result visualization. A dedicated reasoning module keeps the user in the loop and allows for hallucination detection. Results: ChatDRex operationalizes complex network-based drug repurposing workflows through conversational interaction, integrating biomedical knowledge access with algorithm execution and validation-oriented assessments. Its modular multi-agent architecture enables scalable task specialization across retrieval, analysis, validation, and visualization, while the reasoning module provides transparency and safeguards against unreliable outputs. Conclusions: By enabling researchers with limited computational expertise to control complex analyses with natural language, ChatDRex could democratize access to bioinformatics as an important resource for drug repurposing. It could enable clinical experts to generate hypotheses and explore drug repurposing opportunities, ultimately accelerating the discovery of novel therapies and advancing personalized medicine and translational research.

The authors' abstract, as published at the source. JMIR AI, 2026 · DOI ↗