Conversational no-code and multi-agentic disease module identification and drug repurposing prediction with ChatDRex

2025-11-26

AI Summary

Drug repurposing faces challenges including fragmented interdisciplinary collaboration, heterogeneous and unstructured data, tool fragmentation, and limited accessibility for non-computational researchers lacking bioinformatics expertise. Method: We propose ChatDRex, a conversational multi-agent system built upon the NeDRex biomedical knowledge graph, integrating natural language processing, network science, and literature mining to enable interactive, hallucination-resistant reasoning. Contribution/Results: ChatDRex is the first system to support end-to-end, interpretable inference—from natural language queries to disease module identification and drug repositioning hypothesis generation—without requiring programming. It empowers clinicians and domain experts to independently conduct full analytical workflows, substantially lowering technical barriers, enhancing cross-disciplinary collaboration efficiency, and accelerating personalized drug discovery in translational medicine.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Multiagent Systems: Modeling other Agents

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Repurposing approved drugs offers a time-efficient 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, and heterogeneous, unstructured data landscapes require specialized users to be involved. Hence, these data services do not integrate smoothly across workflows. 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 NeDRex. ChatDRex provides natural language access to its extensive biomedical KG and integrates bioinformatics agents for network analysis and drug repurposing, complemented by agents for functional coherence evaluation for in silico validation, as well as agents for literature mining and for discussing the obtained results in a scientific context. 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. By enabling physicians and researchers without computer science expertise to control complex analyses in natural language, ChatDRex democratizes access to bioinformatics as an important resource for drug repurposing. It enables clinical experts to generate hypotheses and explore drug repurposing opportunities, ultimately accelerating the discovery of novel therapies and advancing personalized medicine and translational research.

Problem

• Predicting drug repurposing candidates through computational methods
• Integrating fragmented algorithms and heterogeneous biomedical data sources
• Enabling non-experts to perform complex bioinformatics analyses via natural language

Innovation

• Conversational multi-agent system for drug repurposing
• Natural language access to biomedical knowledge graph
• Specialized agents handle query routing and analysis