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AI 靶点发现共 18 篇
AI 靶点发现综述Nature

Towards the construction of a virtual yeast.

5 次引用趋势数据不足

为推进细胞生命的计算模拟,研究者提出“虚拟酵母”——一种由人工智能驱动的代理,以酿酒酵母为遗传可操作、数据丰富的模式系统,通过整合多模态生物学数据、机制推理与主动实验来模拟真核细胞行为。该系统将细胞复杂性分解为遗传、代谢和结构系统等八个以功能为中心的模块,每个模块由领域专用AI工具实现,并通过基于大语言模型的编排层协调。系统以机制知识、亚细胞结构和动态状态三大数据支柱为基础,在闭环学习流程中整合表征学习与生成建模,自主设计和执行实验。虚拟酵母可作为优化生物合成通路、支持多种细胞过程假设生成与优先级排序以及加速靶点发现的概念与操作平台,为构建虚拟真核细胞和推进合成生物学提供可推广的蓝图。

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暂未整理

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完整作者

Liujia Qian、Zizhuo Zhou、Peijie Zhou、Zhen Dong、Xudong Zhang、Zhenwu Dai、Zhangyang Gao、Siqi Sun、Kevin R Roy、Shuaiyao Wang、Nicola Zamboni、Charles Boone、Michael Costanzo、Jianzhe Li、Gianni Liti、Jia-Xing Yue、Markus Ralser、Evan Williams、Mattia Zampieri、Heng Jiang、Tailin Wu、Yalin Wang、Feiran Li、Joseph Schacherer、Rui Sun、Zhaoxing Li、Yaming Deng、Yi Chen、Zhiping Xie、Huiqiang Lou、Xiaowen Wang、Linhai Xie、Han Wen、Liangyi Chen、Kai Lei、George Rosenberger、Xue Cai、Yingrui Wang、Qi Xiao、Huaizong Shen、Gaowen Liu、Lei Ma、Brenda Andrews、Hui Lu、Kiryl Piatkevich、Yi Zhu、Lei Bai、Yizhi Cai、Yuping Chen、Weinan E、Ge Gao、Fuchu He、Luonan Chen、Stan Z Li、Hongwu Ma、Liang Qiao、Lars M Steinmetz、Leihan Tang、Tang Tang、Xiaofan Zhang、Jing Yang、Yifan Yang、Kaicheng Yu、Jianyang Zeng、Yefeng Zheng、Bowen Zhou、Tiannan Guo

作者机构

  • Affiliated Hangzhou First People's Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, School of Future Biomedicine, Westlake University, Hangzhou, China.
  • Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
  • Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, China.
  • Center for Machine Learning Research, Peking University, Beijing, China.
  • AI for Science Institute, Beijing, China.
  • National Engineering Laboratory for Big Data Analysis and Applications, Beijing, China.
  • Center for Data Science, Peking University, Beijing, China.
  • Shanghai Artificial Intelligence Laboratory, Shanghai, China.
  • Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China.
  • Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA.
  • Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  • Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
  • The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
  • Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
  • Institute of Research on Cancer and Ageing of Nice (IRCAN), Faculté de Médecine, Nice, France.
  • State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Department of Biochemistry, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité, Berlin, Germany.
  • Center for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Max Planck Institute for Molecular Genetics, Berlin, Germany.
  • Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
  • Department of Biomedicine, University of Basel, Basel, Switzerland.
  • Department of Artificial Intelligence, School of Engineering, Westlake University, Hangzhou, China.
  • Biomedical Research Core Facilities, Westlake University, Hangzhou, China.
  • Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
  • Université de Strasbourg, CNRS, Inserm, IGBMC UMR 7104-UMR-S 1258, Illkirch, France.
  • State Key Laboratory of Microbial Metabolism and Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
  • South China Hospital, Health Science Center, Guangdong Key Laboratory of Genome Instability and Disease Prevention, Shenzhen University School of Medicine, Shenzhen, China.
  • State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics Driven Cancer Precision Medicine (Chinese Academy of Medical Sciences), Beijing, China.
  • International Academy of Phronesis Medicine (Guangdong), Guangzhou, China.
  • DP Technology Co. Ltd., Beijing, China.
  • Beijing Advanced Center of RNA Biology (BEACON), Peking University, Beijing, China.
  • State Key Laboratory of Medical Proteomics, Beijing, China.
  • State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, Center for Life Sciences, College of Future Technology, Peking University, Beijing, China.
  • Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.
  • Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.
  • Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, China.
  • Bruker Switzerland AG, Faellanden, Switzerland.
  • Zhejiang Key Laboratory of Structural Biology, School of Life Sciences, Westlake University, Hangzhou, China.
  • Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • National Biomedical Imaging Center, College of Future Technology, Peking University, Beijing, China.
  • State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
  • SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute Shanghai Jiao Tong University, Shanghai, China.
  • Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai, China.
  • School of Life Sciences, Westlake University, Hangzhou, China.
  • Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China.
  • Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.
  • Generative and Synthetic Genomics, Wellcome Sanger Institute, Cambridge, UK.
  • Shenzhen Institute of Synthetic Biology, Chinese Academy of Sciences, Shenzhen, China.
  • Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • School of Mathematical Sciences, Peking University, Beijing, China.
  • State Key Laboratory of Gene Function and Modulation Research, Biomedical Pioneering Innovative Center (BIOPIC) and Beijing Advanced Innovation Center for Genomics (ICG), School of Life Sciences, Center for Bioinformatics (CBI), Peking University, Beijing, China.
  • School of Mathematical Sciences and School of AI, Shanghai Jiao Tong University, Shanghai, China.
  • AI Laboratory, Research Center for Industries of the Future, Westlake University, Hangzhou, China.
  • Biodesign Center, State Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
  • National Center of Technology Innovation for Synthetic Biology, Tianjin, China.
  • Department of Chemistry, Fudan University, Shanghai, China.
  • Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
  • DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany.
  • Stanford Genome Technology Center, Palo Alto, CA, USA.
  • Center for Interdisciplinary Studies, Westlake University, Hangzhou, China.
  • Wuhan Metware Biotechnology Co. Ltd., Wuhan, China.
  • Guangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, China.
  • State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences Beijing, Beijing Institute of Lifeomics, Beijing, China.
  • School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, China.
  • Autolab, Westlake University, Hangzhou, China.
  • School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, China.
  • Medical Artificial Intelligence Laboratory, Westlake University, Hangzhou, China.
  • Department of Electronic Engineering, Tsinghua University, Beijing, China.
  • Affiliated Hangzhou First People's Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, School of Future Biomedicine, Westlake University, Hangzhou, China. guotiannan@westlake.edu.cn.
  • Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China. guotiannan@westlake.edu.cn.
  • Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou, China. guotiannan@westlake.edu.cn.

技术路线

多模态生物数据与机理知识、亚细胞结构、动态状态三类数据支柱 → 细胞复杂性分解为八个功能模块(遗传、代谢、结构系统),各模块为领域AI工具并由大语言模型调度层协调

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/29 12:32
AI 靶点发现基础研究Cell

AI-driven discovery of GPNMB CAR T cells as a multi-cancer therapy.

8 次引用趋势数据不足

研究开发了一种人工智能驱动的CAR-T细胞靶点发现方法,通过整合人皮肤癌与健康组织的单细胞RNA测序数据集筛选候选靶点,并利用公共数据集优化肿瘤组成、组织特异性及临床可行性,再以大语言模型优先排序和提名治疗潜力靶点。糖蛋白非转移性黑色素瘤蛋白B(GPNMB)为最常被提名的靶点,其在血液肿瘤和实体瘤中的表达得到验证。研究构建了靶向人GPNMB的CAR-T细胞,在小鼠单核母细胞白血病、黑色素瘤和结直肠腺癌模型中显示出强效抗肿瘤活性。研究建立了可扩展的CAR-T细胞靶点发现流程,并支持将GPNMB靶向CAR-T细胞作为多癌种治疗手段的转化。

为什么值得关注

暂未整理

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完整作者

Daniel J Baker、Leon M Frommer、Ugur Uslu、Kisha K Patel、Daniel Zhu、Nils W Engel、James M George、Wencao Zhao、Samuel I Kim、Lisa Sun、Christopher Roselle、Philipp C Rommel、Regina M Young、Jonathan A Epstein、Sikander Hayat、Zoltan Arany、Carl H June

作者机构

  • Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: bakerdj@upenn.edu.
  • Department of Medicine 2, RWTH Aachen University, Medical Faculty, Aachen, Germany.
  • Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Dermatology, Venereology and Allergology, University Hospital Würzburg, 97080 Würzburg, Germany.
  • Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
  • Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA.
  • Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Institute for Regenerative Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Cell and Developmental Biology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
  • Department of Medicine 2, RWTH Aachen University, Medical Faculty, Aachen, Germany; Cardiovascular Research Institute, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: shayat@ukaachen.de.
  • Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: zarany@upenn.edu.
  • Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Parker Institute for Cancer Immunotherapy at University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: cjune@upenn.edu.

技术路线

单细胞RNA测序数据(人类皮肤癌与健康组织)整合→AI驱动候选靶点筛选,结合公共数据集优化肿瘤组成、组织特异性与临床可行性→大语言模型优先排序与提名→GPNMB跨血液与实体瘤表达验证→构建人源GPNMB CAR T细胞→单核细胞白血病、黑色素瘤和结直肠腺癌小鼠模型抗肿瘤活性验证

最近 5 个完整年度引用趋势

0110202102022020230202402025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/29 12:32
AI 靶点发现基础研究Nature

Accelerating scientific discovery with Co-Scientist.

157 次引用引用上升

研究旨在加速科学发现,研究人员开发了基于Gemini的多智能体人工智能系统Co-Scientist,用于结构化科学思维与假说生成。该系统根据研究目标和已有科学证据提出可验证的新颖研究假说,其设计包括多智能体架构与异步任务执行框架,以及通过锦标赛式进化过程实现假说生成的自我改进。自动评估显示,测试时计算扩展可持续提升假说质量。研究在药物重定位、新靶点发现和抗菌药物耐药机制解释三个生物医学应用中开展验证;Co-Scientist识别出急性髓系白血病的新药物重定位候选物和协同联合疗法,并经体外实验验证。真实世界验证表明,Co-Scientist有望加速科学发现。

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完整作者

Juraj Gottweis、Wei-Hung Weng、Alexander Daryin、Tao Tu、Petar Sirkovic、Artiom Myaskovsky、Grzegorz Glowaty、Felix Weissenberger、Alessio Orlandi、Dan Popovici、Anil Palepu、Keran Rong、Ryutaro Tanno、Khaled Saab、Fan Zhang、Jacob Blum、Andrew Carroll、Kavita Kulkarni、Nenad Tomašev、Dina Zverinski、Ivor Rendulic、Elahe Vedadi、Florian Hasler、Luka Rimanic、Marina Boia、Ivan Budiselic、Ben Feinstein、Mathias Bellaiche、Tom Sheffer、Jan Freyberg、Jeremy Ratcliff、Ottavia Bertolli、Katherine Chou、Avinatan Hassidim、Burak Gokturk、Amin Vahdat、Yuan Guan、Vikram Dhillon、Eeshit Dhaval Vaishnav、Byron Lee、Tiago R D Costa、José R Penadés、Gary Peltz、Yossi Matias、James Manyika、Demis Hassabis、Yunhan Xu、Pushmeet Kohli、Annalisa Pawlosky、Alan Karthikesalingam、Vivek Natarajan

作者机构

  • Google Cloud AI Research, Zurich, Switzerland. juro@google.com.
  • Google DeepMind, Mountain View, CA, USA. ckbjimmy@google.com.
  • Google Cloud AI Research, Zurich, Switzerland.
  • Google DeepMind, Mountain View, CA, USA.
  • Google Research, Mountain View, CA, USA.
  • Stanford University School of Medicine, Palo Alto, CA, USA.
  • Houston Methodist Hospital, Houston, TX, USA.
  • Sequome, San Francisco, CA, USA.
  • Fleming Initiative and Imperial College London, London, UK.
  • Google DeepMind, Mountain View, CA, USA. pushmeet@google.com.
  • Google Research, Mountain View, CA, USA. apawlosky@google.com.
  • Google DeepMind, Mountain View, CA, USA. alankarthi@google.com.
  • Google DeepMind, Mountain View, CA, USA. natviv@google.com.

技术路线

研究目标与科学证据输入→多智能体(基于Gemini)异步任务执行与锦标赛演化生成/批判/优化假设→自动化评估(测试时算力扩展)→三个生物医学应用(药物重定位、新靶点发现、抗菌素耐药机制解释)→急性髓系白血病候选药物与协同组合的体外实验验证

最近 5 个完整年度引用趋势

0153002021020220202302024302025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/29 12:32
AI 靶点发现基础研究Nucleic acids research

ARKbase: Antimicrobial Resistance Knowledgebase1.0.

4 次引用趋势数据不足

抗菌药物耐药(AMR)被视为“静默大流行”,是紧迫的全球健康挑战之一。为此,研究者开发了ARKbase,一个面向世界卫生组织细菌重点病原体的整合型、经人工整理并增值的AMR知识库。该知识库包含数据库模块、洞察模块和比较分析模块三个核心部分,提供经整理的数据以及利用定制流程分析所得数据。数据库模块收录经整理的参考基因组、泛基因组及抗菌药物敏感性试验(AST)谱;洞察模块含14个子模块,提供耐药基因、毒力因子、结构注释、宿主—病原互作、生物合成基因簇、已知抗生素、蛋白—蛋白互作、药物靶点、共靶点、药物—靶点互作、机器学习模型及抗生素暴露转录组数据集等深度注释;比较分析模块提供简易界面,用于比较不同重点病原体的耐药基因、毒力因子和药物靶点。数据经质量核查,AST按CLSI标准、基因组序列按EUCAST指南整理,并遵循FAIR数据原则。ARKbase是首个同时聚焦WHO重点病原体和AWaRe分类的综合知识库,有助于以综合证据方法整体理解AMR。

为什么值得关注

AI 辅助短评,基于标题和摘要整理

ARKbase整合了WHO优先病原体的基因组、药敏及多组学注释,模块化设计便于比较ARG、毒力因子和药物靶点。其遵循CLSI、EUCAST和FAIR原则进行数据质控,但摘要未说明任何实验验证或独立基准测试。作为知识库,其实际效用取决于数据覆盖度、更新机制及用户评估,需进一步验证其分析流程的准确性。

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完整作者

Simran Gambhir、Shweta Pandey、Harsh Bajetha、Jasleen Kaur、Ankita Das、Gnanasekar Pranavathiyani、Rupali Aggarwal、Upasana Maity、Suhani Dange、Vipin Singh、Mayur Zarkar、Raghav Sankhdher、Bhupender Singh、Shivani Seth、Anshu Bhardwaj

作者机构

  • Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh 160036, India.
  • Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
  • UM-DAE Centre for Excellence in Basic Sciences, School of Biological Sciences, University of Mumbai Kalina Campus, Mumbai 400098, India.
  • Life Sciences Department, J.C. Bose University of Science and Technology, YMCA, Sector 6, Mathura road, Faridabad Haryana 121006, India.
  • Independent Researcher, IMTECH Housing, Sector 39A, Chandigarh 160036, India.

技术路线

数据/样本:聚焦WHO细菌优先病原体的参考基因组、泛基因组、AST谱、转录组数据集及多类注释资源(ARG、毒力因子、结构、宿主-病原互作、BGC、药物靶点等) → 分析:使用自建流程进行数据分析和注释,并按CLSI AST标准、EUCAST基因组序列指南及FAIR原则进行质量检查与人工整理

最近 5 个完整年度引用趋势

0110202102022020230202412025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/29 12:32
AI 靶点发现方法学eLife

Harnessing AlphaFold to reveal hERG channel conformational state secrets.

14 次引用引用上升

为设计安全、选择性且有效的新疗法,需深入理解药物靶点的结构与功能,而跨膜离子通道蛋白离散构象状态的解析尤为困难。KV11.1(hERG)承载主要心脏复极电流Ikr,是药物安全性筛查中重要的抗靶点,其失活态药物相互作用与心律失常风险升高相关,药物还可能在通道关闭时被捕获。本研究显示,引入精心选择的结构模板可引导AlphaFold预测走向不同功能状态。该靶向建模通过与实验数据比较得到验证,包括状态依赖性结构特征、分子对接药物相互作用及分子动力学模拟的离子传导特性。AlphaFold不仅预测了阻止离子传导的hERG失活机制,还揭示了解释失活期间药物结合增强的新分子特征。利用AlphaFold推导的状态可改善计算筛查与实验药物亲和力的一致性,弥补传统单状态模型的不足。通过映射关闭、开放和失活状态的残基相互作用网络,研究识别出驱动状态转换的关键残基,并得到既往诱变研究验证。该方法为整合深度学习蛋白结构预测与实验验证建立了新基准,并提供了广泛适用的策略。

为什么值得关注

编辑短评

本文提出以结构模板引导AlphaFold预测hERG通道离散构象,并与实验数据、对接和分子动力学模拟比较验证,进而用于计算筛选及残基网络分析。其思路对状态依赖性药物安全评估有潜在价值,但验证主要依赖计算与既往数据比对,缺乏新实验结构解析;筛选改善幅度与普适性需独立评估。

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完整作者

Khoa Ngo、Pei-Chi Yang、Vladimir Yarov-Yarovoy、Colleen E Clancy、Igor Vorobyov

作者机构

  • Center for Precision Medicine and Data Science, University of California, Davis, Davis, United States.
  • Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States.
  • Department of Anesthesiology and Pain Medicine, University of California, Davis, Davis, United States.
  • Department of Pharmacology, University of California, Davis, Davis, United States.

技术路线

结构模板引导AlphaFold预测hERG通道不同构象状态→与实验数据(状态依赖性结构特征、分子对接药物相互作用、分子动力学离子传导特性)比较验证→利用预测状态进行计算筛选并与实验药物亲和力比较→映射闭合/开放/失活态的残基相互作用网络,关键残基由既往突变研究验证

最近 5 个完整年度引用趋势

0240202102022020230202442025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/30 06:00
AI 靶点发现综述Cancer cell

New horizons at the interface of artificial intelligence and translational cancer research.

55 次引用引用上升

人工智能正日益成为分析多组学数据的计算策略应用于癌症研究。单细胞与空间 profiling 技术的进步显著加深了对肿瘤生物学的理解,人工智能方法现被用于加速转化研究,包括靶点发现、生物标志物识别、患者分层和治疗反应预测。然而,人工智能融入临床工作流程仍然有限,既带来挑战也带来机遇。本综述讨论人工智能在多组学分析和转化肿瘤学中的应用,强调其在推进生物学发现和为临床决策提供信息方面的作用。重点关注领域包括细胞异质性、肿瘤微环境相互作用和人工智能辅助诊断。综述探讨了可重复性、人工智能模型可解释性和临床整合等挑战,并关注应对这些障碍的策略。这些进展共同凸显了人工智能与多组学在提升精准肿瘤学和推动癌症护理进步方面的潜力。

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完整作者

Josephine Yates、Eliezer M Van Allen

作者机构

  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Institute for Machine Learning, Department of Computer Science, ETH Zürich, Zurich, Switzerland; ETH AI Center, ETH Zurich, Zurich, Switzerland; Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Medical Sciences, Harvard University, Boston, MA, USA; Parker Institute for Cancer Immunotherapy, Dana-Farber Cancer Institute, Boston, MA, USA. Electronic address: eliezerm_vanallen@dfci.harvard.edu.

技术路线

多组学数据集(单细胞与空间 profiling 数据) → AI 计算分析(细胞异质性、肿瘤微环境互作、靶点发现、生物标志物识别、患者分层、治疗反应预测、AI 辅助诊断)

最近 5 个完整年度引用趋势

0101902021020220202302024192025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/29 12:33
AI 靶点发现临床研究Nature biotechnology

A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.

347 次引用引用上升

特发性肺纤维化(IPF)是一种侵袭性间质性肺病,死亡率高,既往候选药物靶点未能在临床转化为有效疗法。研究利用预测性人工智能方法识别TRAF2和NCK相互作用激酶(TNIK)为抗纤维化靶点,并据此生成小分子TNIK抑制剂INS018_055。该化合物具有良好成药性,经口服、吸入或局部给药在体内多器官显示抗纤维化活性,并在多项体内研究中验证了抗炎作用。一项随机、双盲、安慰剂对照I期临床试验(NCT05154240)纳入78名健康受试者,验证了其安全性、耐受性和药代动力学;中国另一项I期试验(CTR20221542)也显示可比的安全性和药代动力学特征。从靶点发现到临床前候选药物提名约历时18个月,展示了生成式AI驱动药物发现流程的能力。

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完整作者

Feng Ren、Alex Aliper、Jian Chen、Heng Zhao、Sujata Rao、Christoph Kuppe、Ivan V Ozerov、Man Zhang、Klaus Witte、Chris Kruse、Vladimir Aladinskiy、Yan Ivanenkov、Daniil Polykovskiy、Yanyun Fu、Eugene Babin、Junwen Qiao、Xing Liang、Zhenzhen Mou、Hui Wang、Frank W Pun、Pedro Torres-Ayuso、Alexander Veviorskiy、Dandan Song、Sang Liu、Bei Zhang、Vladimir Naumov、Xiaoqiang Ding、Andrey Kukharenko、Evgeny Izumchenko、Alex Zhavoronkov

作者机构

  • Insilico Medicine Shanghai Ltd., Shanghai, China.
  • Insilico Medicine AI Limited, Abu Dhabi, UAE.
  • Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong SAR, China.
  • Department of Clinical Pharmacology, Affiliated Xiaoshan Hospital, Hangzhou Normal University, Hangzhou, China.
  • Insilico Medicine US Inc., New York, NY, USA.
  • Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany.
  • Department of Nephrology, University Clinic RWTH Aachen, Aachen, Germany.
  • Insilico Medicine Canada Inc, Montreal, Quebec, Canada.
  • Department of Cancer and Cellular Biology, Lewis Katz School of Medicine, Temple University, PA, USA.
  • Division of Nephrology, Zhongshan Hospital Shanghai Medical College, Fudan University, Shanghai, China.
  • Section of Hematology and Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA.
  • Insilico Medicine AI Limited, Abu Dhabi, UAE. alex@insilico.com.
  • Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong SAR, China. alex@insilico.com.
  • Insilico Medicine US Inc., New York, NY, USA. alex@insilico.com.
  • Insilico Medicine Canada Inc, Montreal, Quebec, Canada. alex@insilico.com.

技术路线

AI预测靶点→AI驱动生成小分子INS018_055→体内外抗纤维化/抗炎活性评估→随机双盲安慰剂对照I期临床试验(NCT05154240,78名健康受试者)安全性/耐受性/药代动力学验证→中国I期试验(CTR20221542)验证安全性/药代动力学

最近 5 个完整年度引用趋势

0681360202102022020235320241362025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/29 12:33
AI 靶点发现临床研究Nucleic acids research

The next-generation Open Targets Platform: reimagined, redesigned, rebuilt.

428 次引用引用平稳

Open Targets平台是一个利用公开数据系统辅助药物靶点发现与优先排序的开源资源。自上次更新以来,研究团队对该平台进行了重新构想、设计和构建,以简化数据整合与协调、扩展用户探索数据的方式并改善用户体验。基因—疾病因果证据得到增强和扩展,以更好地捕捉罕见病、常见病和体细胞疾病中的疾病因果关系。在靶点和药物注释方面,平台新增了有助于评估靶点安全性和可成药性的功能,包括遗传约束、PROTAC可成药性评估和AlphaFold结构预测。平台还引入了新的机器学习应用,用于从已发表文献、临床试验信息和药物标签中提取知识。新引入的技术和框架将便于添加新功能,并创建适应用户需求的平台实例。新的社区论坛、扩展的培训材料和外联计划支持用户应对多种使用场景。

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完整作者

David Ochoa、Andrew Hercules、Miguel Carmona、Daniel Suveges、Jarrod Baker、Cinzia Malangone、Irene Lopez、Alfredo Miranda、Carlos Cruz-Castillo、Luca Fumis、Manuel Bernal-Llinares、Kirill Tsukanov、Helena Cornu、Konstantinos Tsirigos、Olesya Razuvayevskaya、Annalisa Buniello、Jeremy Schwartzentruber、Mohd Karim、Bruno Ariano、Ricardo Esteban Martinez Osorio、Javier Ferrer、Xiangyu Ge、Sandra Machlitt-Northen、Asier Gonzalez-Uriarte、Shyamasree Saha、Santosh Tirunagari、Chintan Mehta、Juan María Roldán-Romero、Stuart Horswell、Sarah Young、Maya Ghoussaini、David G Hulcoop、Ian Dunham、Ellen M McDonagh

作者机构

  • Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.
  • European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.
  • Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK.
  • GlaxoSmithKline plc, GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, SG1 2NY, UK.

技术路线

公开数据 → 数据集成与统一

最近 5 个完整年度引用趋势

068136020210202255202313220241362025

不含 2026 年,仅展示最近 5 个完整年度。

数据来源:openalex更新时间:2026/10/29 12:33
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