Conversational No-code, Multi-agentic Disease Module Identification and Drug Repurposing Prediction with ChatDRex
Simon S\"uwer, Kester Bagemihl, Sylvie Baier, Lucia Dicunta, Markus List, Jan Baumbach, Andreas Maier, Fernando M. Delgado-Chaves

TL;DR
ChatDRex is a conversational multi-agent system that simplifies complex bioinformatics analyses for drug repurposing, making advanced network-based predictions accessible to clinicians and researchers without programming expertise.
Contribution
It introduces a novel, natural language-driven multi-agent platform integrating diverse bioinformatics tools for drug repurposing prediction based on a comprehensive knowledge graph.
Findings
Enables non-experts to perform complex bioinformatics analyses.
Integrates multiple specialized agents for network analysis, literature mining, and validation.
Facilitates hypothesis generation and exploration of drug repurposing opportunities.
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. Heterogeneous, unstructured data landscapes require the expertise of specialized users. 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 KG). ChatDRex provides natural language access to its…
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Bioinformatics and Genomic Networks · Computational Drug Discovery Methods
