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2026年8月13日星期四
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AI生物学家XunZi揭示疾病修饰靶点

XunZi, an AI biologist, reveals disease-modifying targets.

期刊
Nature Biomedical Engineering
PMID
42552442
原文
PubMed ↗
发布日期

作者

  • Xinhe Huang — Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Junhong Qin — Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Pediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.
  • Fei Tang — Department of Neurology and State Key Laboratory of Biotherapy, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
  • Chenyu Yang — Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Linhua Xu — Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Pediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.
  • Junfen Wei — Department of Neurology and State Key Laboratory of Biotherapy, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
  • Dan Liu — Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Kang Chen — Department of Neurology and State Key Laboratory of Biotherapy, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
  • Chi Zhang — Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Miaomiao Chen — Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Yujie Gou — Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Jiayi Zhang — Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Yongze Ma — Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Lin Zhao — Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Pediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.
  • Yingfeng Tu — Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Pediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.
  • Peng Lei — Department of Neurology and State Key Laboratory of Biotherapy, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China. peng.lei@scu.edu.cn.
  • Yu Xue — Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China. xueyu@hust.edu.cn.
  • Da Jia — Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Pediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China. Jiada@scu.edu.cn.

作者单位

  • Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Pediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China.
  • Department of Neurology and State Key Laboratory of Biotherapy, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
  • Department of Neurology and State Key Laboratory of Biotherapy, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China. peng.lei@scu.edu.cn.
  • Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China. xueyu@hust.edu.cn.
  • Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Pediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China. Jiada@scu.edu.cn.

摘要

中文

生物医学中的假设生成受到人类认知局限性的制约，难以从碎片化的生物医学知识和多模态数据源中综合见解。在此，我们介绍XunZi，一位整合逻辑推理和多模态数据融合的AI生物学家，能够自主生成具有可测试机制的全新治疗靶点假设。XunZi已接受2440万篇论文和613.6 TB多源数据的训练，涵盖21,008个人类基因和5,850种疾病，并在多种疾病背景下在准确性和可解释性上优于现有方法。在帕金森病（PD）中，复杂机制和有限靶点阻碍了治疗开发，XunZi在多种模型中识别出CHK2和IRAK4激酶的异常激活。Chk2的药理学或遗传学抑制可挽救PD小鼠多巴胺能神经元丢失和运动缺陷。我们进一步展示了XunZi在非小细胞肺癌等疾病中的广泛适用性。XunZi建立了一个范式转变的框架，将碎片化的生物医学知识和数据转化为可操作的疗法。

English

Hypothesis generation in biomedicine is constrained by human cognitive limitations in synthesizing insights from fragmented biomedical knowledge and multimodal data sources. Here we introduce XunZi, an AI biologist that integrates logical reasoning and multimodal data fusion to autonomously generate de novo therapeutic target hypotheses with testable mechanisms. XunZi has been trained on 24.4 million publications and 613.6 TB of multisource data spanning 21,008 human genes and 5,850 diseases, and outperforms existing methods in both accuracy and interpretability across diverse disease contexts. In Parkinson's disease (PD), where complex mechanisms and limited targets hamper therapy development, XunZi identifies aberrant activation of CHK2 and IRAK4 kinases across multiple models. Pharmacological or genetic inhibition of Chk2 rescues dopaminergic neuron loss and motor deficits in PD mice. We further demonstrate XunZi's broad versatility in diseases such as non-small-cell lung cancer. XunZi establishes a paradigm-shifting framework to translate fragmented biomedical knowledge and data into actionable therapeutics.

分类与指标

研究类型
AI/ML
病种
肺癌
JCR 分区
Q1
影响因子
26.3
新锐分区
1区