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

• URL: http://arxiv.org/abs/2511.21438v1
• Date: Wed, 26 Nov 2025 14:28:19 GMT
• Title: Conversational no-code and multi-agentic disease module identification and drug repurposing prediction with ChatDRex
• Authors: Simon Süwer, Kester Bagemihl, Sylvie Baier, Lucia Dicunta, Markus List, Jan Baumbach, Andreas Maier, Fernando M. Delgado-Chaves,
• Abstract summary: ChatDRex is a multi-agent system that facilitates the execution of complex bioinformatic analyses.<n>It builds on the integrated systems medicine knowledge graph NeDRex.<n>By enabling physicians and researchers without computer science expertise to control complex analyses in natural language, ChatDRex democratizes access to bioinformatics.
• Score: 35.374587870360095
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A co-evolving agentic AI system for medical imaging analysis [14.925000849408683]
  "TissueLab" is a co-evolving agentic AI system that allows researchers to ask direct questions, automatically plan and generate explainable results, and conduct real-time analyses.<n>By standardizing inputs, outputs, and capabilities of diverse tools, the system determines when and how to invoke them to address research and clinical questions.<n>TissueLab achieves state-of-the-art performance compared with end-to-end vision-language models (VLMs) and other agentic AI systems such as GPT-5.
  arXiv  Detail & Related papers  (2025-09-24T16:15:28Z)
• OpenLens AI: Fully Autonomous Research Agent for Health Infomatics [22.16210485266852]
  OpenLens AI is a fully automated framework tailored to health informatics.<n>It integrates specialized agents for literature review, data analysis, code generation, and manuscript preparation.<n>The framework automates the entire research pipeline, producing publication-ready manuscripts.
  arXiv  Detail & Related papers  (2025-09-18T09:25:57Z)
• DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery [54.79763887844838]
  Large language models (LLMs) integrated with autonomous agents hold significant potential for advancing scientific discovery through automated reasoning and task execution.<n>We introduce DrugPilot, a LLM-based agent system with a parameterized reasoning architecture designed for end-to-end scientific in drug discovery.<n>DrugPilot significantly outperforms state-of-the-art agents such as ReAct and LoT, achieving task completion rates of 98.0%, 93.5%, and 64.0% for simple, multi-tool, and multi-turn scenarios, respectively.
  arXiv  Detail & Related papers  (2025-05-20T05:18:15Z)
• Advancing AI Research Assistants with Expert-Involved Learning [84.30323604785646]
  Large language models (LLMs) and large multimodal models (LMMs) promise to accelerate biomedical discovery, yet their reliability remains unclear.<n>We introduce ARIEL (AI Research Assistant for Expert-in-the-Loop Learning), an open-source evaluation and optimization framework.<n>We find that state-of-the-art models generate fluent but incomplete summaries, whereas LMMs struggle with detailed visual reasoning.
  arXiv  Detail & Related papers  (2025-05-03T14:21:48Z)
• LLM Agent Swarm for Hypothesis-Driven Drug Discovery [2.7036595757881323]
  PharmaSwarm is a unified multi-agent framework that orchestrates specialized "agents" to propose, validate, and refine hypotheses for novel drug targets and lead compounds.<n>By acting as an AI copilot, PharmaSwarm can accelerate translational research and deliver high-confidence hypotheses more efficiently than traditional pipelines.
  arXiv  Detail & Related papers  (2025-04-24T22:27:50Z)
• DrugAgent: Multi-Agent Large Language Model-Based Reasoning for Drug-Target Interaction Prediction [8.98329812378801]
  DrugAgent is a multi-agent system for drug-target interaction prediction.<n>It combines multiple specialized perspectives with transparent reasoning.<n>Our approach provides detailed, human-interpretable reasoning for each prediction.
  arXiv  Detail & Related papers  (2024-08-23T21:24:59Z)
• Simplicity within biological complexity [0.0]
  We survey the literature and argue for the development of a comprehensive framework for embedding of multi-scale molecular network data. Network embedding methods map nodes to points in low-dimensional space, so that proximity in the learned space reflects the network's topology-function relationships. We propose to develop a general, comprehensive embedding framework for multi-omic network data, from models to efficient and scalable software implementation.
  arXiv  Detail & Related papers  (2024-05-15T13:32:45Z)
• Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network [69.16939798838159]
  We propose EmerGNN, a graph neural network (GNN) that can effectively predict interactions for emerging drugs. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.
  arXiv  Detail & Related papers  (2023-11-15T06:34:00Z)
• SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design [64.69434941796904]
  We propose a novel setting and models for in-context drug synergy learning. We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets. Our goal is to predict additional drug synergy relationships in that context.
  arXiv  Detail & Related papers  (2023-06-19T17:03:46Z)
• A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective [4.746544835197422]
  We aim to help guide machine learning and knowledge graph practitioners who are interested in applying new techniques to the drug discovery field. We detail publicly available primary data sources containing information suitable for use in constructing various drug discovery focused knowledge graphs.
  arXiv  Detail & Related papers  (2021-02-19T17:49:38Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.