Conversational Multi-Agentic Network-Driven Disease Module Identification and Drug Repurposing Prediction With ChatDRex: Platform Development Study
5 affiliations
- 1Institute for Computational Systems Biomedicine, University of Hamburg, Albert-Einstein-Ring 8-10, Hamburg, Hamburg, Germany, +49 40 42838-7635
- 2Data Science of Systems Biology, School of Life Sciences, Technical University of Munich, München, Bavaria, Germany
- 3Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- 4Munich Data Science Institute, Technical University of Munich,, Garching, Germany
- 5Institute for Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
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.
Related
- Development of a Translational Assay for Identification and Quantitation of Orthologous Proteins to Aid Drug Development in Preclinical Models.
- Development and Evaluation of a Capillary Column GC-FID Identification Test for Sage Oil and Sage Water Listed in the Japanese Standards of Quasi-Drug Ingredients
- From Black Box to Dialogue: An MCP Server for Tanabe–Sugano Diagrams as a Reference Case for Conversational Scientific Tools
- Repurposing HIV Protease Inhibitors as Antifungal Chemosensitizers Against Multidrug Resistant Candida auris: Promise, Limitations, and Translational Challenges
- Repurposing of Milciclib as a Potential Inhibitor of Toxoplasma gondii in Human Trophoblasts and Placental Explants
BibTeX
@article{suwer2026conversational,
title = {{Conversational Multi-Agentic Network-Driven Disease Module Identification and Drug Repurposing Prediction With ChatDRex: Platform Development Study}},
author = {Simon Süwer and Kester Bagemihl and Sylvie Baier and Lucia Dicunta and Markus List and Jan Baumbach and Fernando Miguel Delgado-Chaves and Andreas Maier},
journal = {JMIR AI},
year = {2026},
month = nov,
doi = {10.2196/93862},
url = {https://doi.org/10.2196/93862},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Conversational Multi-Agentic Network-Driven Disease Module Identification and Drug Repurposing Prediction With ChatDRex: Platform Development Study
AU - Simon Süwer
AU - Kester Bagemihl
AU - Sylvie Baier
AU - Lucia Dicunta
AU - Markus List
AU - Jan Baumbach
AU - Fernando Miguel Delgado-Chaves
AU - Andreas Maier
PY - 2026
DA - 2026/11/03
JO - JMIR AI
DO - 10.2196/93862
UR - https://doi.org/10.2196/93862
AB - 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.
SN - 2817-1705
ER -