RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914095839576064 |
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| author | Qin, Lang Gan, Zijian Cao, Xu Jiang, Pengcheng Jiang, Yankai Han, Jiawei Wu, Kaishun Chen, Jintai |
| author_facet | Qin, Lang Gan, Zijian Cao, Xu Jiang, Pengcheng Jiang, Yankai Han, Jiawei Wu, Kaishun Chen, Jintai |
| contents | Computational drug repurposing for rare diseases is especially challenging when no prior associations exist between drugs and target diseases. Therefore, knowledge graph completion and message-passing GNNs have little reliable signal to learn and propagate, resulting in poor performance. We present RareAgent, a self-evolving multi-agent system that reframes this task from passive pattern recognition to active evidence-seeking reasoning. RareAgent organizes task-specific adversarial debates in which agents dynamically construct evidence graphs from diverse perspectives to support, refute, or entail hypotheses. The reasoning strategies are analyzed post hoc in a self-evolutionary loop, producing textual feedback that refines agent policies, while successful reasoning paths are distilled into transferable heuristics to accelerate future investigations. Comprehensive evaluations reveal that RareAgent improves the indication AUPRC by 18.1% over reasoning baselines and provides a transparent reasoning chain consistent with clinical evidence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05764 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases Qin, Lang Gan, Zijian Cao, Xu Jiang, Pengcheng Jiang, Yankai Han, Jiawei Wu, Kaishun Chen, Jintai Artificial Intelligence Multiagent Systems Computational drug repurposing for rare diseases is especially challenging when no prior associations exist between drugs and target diseases. Therefore, knowledge graph completion and message-passing GNNs have little reliable signal to learn and propagate, resulting in poor performance. We present RareAgent, a self-evolving multi-agent system that reframes this task from passive pattern recognition to active evidence-seeking reasoning. RareAgent organizes task-specific adversarial debates in which agents dynamically construct evidence graphs from diverse perspectives to support, refute, or entail hypotheses. The reasoning strategies are analyzed post hoc in a self-evolutionary loop, producing textual feedback that refines agent policies, while successful reasoning paths are distilled into transferable heuristics to accelerate future investigations. Comprehensive evaluations reveal that RareAgent improves the indication AUPRC by 18.1% over reasoning baselines and provides a transparent reasoning chain consistent with clinical evidence. |
| title | RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2510.05764 |