OriGene: A Self-Evolving Virtual Disease Biologist Automating Therapeutic Target Discovery

作者：Zhongyue Zhang, Zijie Qiu, Yingcheng Wu, Sitan Li, Dingyan Wang, Yong Liu, Zhuomin Zhou, Yiwen Hu, Yuhan Chen, Duo An, Yongbo Wang, Haijun Yu, Zhenyi Zhong, C.-Y. Ou, Zichen Wang, Feng Tang, J Chen, Runmin Ma, Jun Li, Xinyu Wang, Wei Lu, HaoMing Xue, Wenxin Zhang, Zhi-Jian Wei, Runze Ma, Zhanpeng Shi, Ke Wang, Qingwu Liu, Bo Dong, Yuexi He, Tinghai Liu, Jiayu Gu, Shuotong Song, Qili Feng, Jian Zhang, Bo Zhang, Luyi Tian, Lei Bai, Qiang Gao, Siqi Sun, Shuangjia Zheng · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2025 · DOI：10.1101/2025.06.03.657658 · 被引用次数：9 · 研究领域：Genetics, Bioinformatics, and Biomedical Research、vaccines and immunoinformatics approaches、Computational Drug Discovery Methods

Here, we present OriGene, a self-evolving multi-agent system that functions as a virtual disease biologist, systematically identifying original and mechanistically grounded therapeutic targets at scale. OriGene’s architecture integrates over 600 specialized tools through a Model Context Protocol (MCP), enabling it to reason across diverse data modalities including genomics, protein networks, pharmacology, clinical records and literature evidence, to generate and prioritize target discovery hypotheses. We implemented a strategy combining a knowledge graph-based Tool RAG with an advanced agent selection mechanism to enable dynamic, context-aware tool deployment. Through a self-evolving framework, OriGene continuously integrates human and experimental feedback to iteratively refine its core thinking templates, tool composition, and analytical protocols, thereby enhancing both accuracy and adaptability over time. To comprehensively evaluate its performance, we established TRQA, an original benchmark comprising over 1,900 expert-level question-answer pairs spanning a wide range of diseases and target classes. OriGene consistently outperforms human experts, leading research agents, and state-of-the-art large language models in accuracy, recall, and robustness, particularly under conditions of data sparsity or noise. Critically, OriGene nominated previously underexplored therapeutic targets for liver (GPR160) and colorectal cancer (ARG2), which demonstrated significant anti-tumor ac...