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文献精选 > 近年高分"AI药物发现"研究（IF≥30，近5年）

Artificial intelligence in cancer target identification and drug discovery. ​

文献信息 ​

DOI10.1038/s41392-022-00994-0
PMID35538061
期刊Signal transduction and targeted therapy
影响因子52.7
JCR 分区Q1
发表年份2022
被引次数131
关键词人工智能, 癌症靶点识别, 药物发现, 网络分析, 机器学习
文献类型Journal Article, Review
ISSN2059-3635
页码156
期号7(1)
作者Yujie You, Xin Lai, Yi Pan, Huiru Zheng, Julio Vera, Suran Liu, Senyi Deng, Le Zhang

一句话小结 ​

本文探讨了人工智能在识别新型抗癌靶点和药物发现中的应用，强调其通过生物网络分析量化细胞系统相互作用的重要性。研究表明，人工智能模型为揭示网络特征与癌症关系提供了定量框架，有助于识别潜在抗癌靶点及新药候选者。

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人工智能 · 癌症靶点识别 · 药物发现 · 网络分析 · 机器学习

摘要 ​

人工智能是一种先进的方法，用于识别新型抗癌靶点并从生物网络中发现新药，因为这些网络能够有效地保持并量化细胞系统组成部分之间的相互作用，这些组成部分与人类疾病（如癌症）相关。本文回顾并讨论了如何利用人工智能方法来识别新型抗癌靶点和发现药物。首先，我们描述了人工智能生物学分析在新型抗癌靶点研究中的应用范围。其次，我们回顾并讨论了常用的基于网络和基于机器学习的人工智能算法的基本原理和理论。最后，我们展示了人工智能方法在癌症靶点识别和药物发现中的应用。综上所述，人工智能模型为我们提供了一个定量框架，以研究网络特征与癌症之间的关系，从而有助于识别潜在的抗癌靶点并发现新型药物候选者。

英文摘要 ​

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

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主要研究问题 ​

  1. 人工智能在癌症靶点识别中的具体应用有哪些成功案例？
  2. 目前有哪些先进的机器学习算法被广泛应用于癌症药物发现中？
  3. 如何评估人工智能模型在癌症靶点识别中的准确性和可靠性？
  4. 人工智能技术在癌症药物发现过程中面临哪些主要挑战和局限性？
  5. 除了癌症，人工智能在其他疾病的靶点识别和药物发现中有何应用潜力？

核心洞察 ​

研究背景和目的 ​

随着癌症治疗的发展，靶向药物治疗因其高效、低副作用和抗药性弱等优点而受到广泛关注。然而，现有靶向治疗存在药物靶点有限、患者覆盖率低和缺乏替代应对方案等问题。因此，识别新的治疗靶点并评估其药物可行性成为当前癌症研究的重点。本文旨在综述如何利用人工智能（AI）技术识别新型抗癌靶点及发现新药物。

主要方法/材料/实验设计 ​

研究主要分为三个部分：

  1. 人工智能生物分析的范围：包括表观遗传学、基因组学、蛋白质组学和代谢组学等多组学技术。
  2. 常用的网络基础和机器学习基础的人工智能算法：详细介绍了网络分析算法（如最短路径算法、模块检测和节点中心性）和机器学习算法（如决策树和深度学习）。
  3. 应用实例：展示了人工智能在癌症靶点识别和药物发现中的应用。
Mermaid diagram

关键结果和发现 ​

  • 通过整合多组学数据，AI技术能够更好地理解癌症的复杂性，进而识别潜在的抗癌靶点。
  • 网络基础算法和机器学习算法在识别靶点和药物发现中表现出显著的效果，尤其是深度学习模型在处理复杂的生物数据方面具有优势。
  • 应用案例表明，AI技术能够有效预测药物靶点的可行性，并加速药物发现过程。

主要结论/意义/创新性 ​

人工智能为癌症靶点识别和药物发现提供了定量框架，通过整合网络特征与生物数据，能够深入探讨癌症的分子机制。该研究不仅为癌症治疗提供了新的思路，还为未来的研究方向指明了道路。

研究局限性和未来方向 ​

  • 当前的网络基础算法可能受到数据偏倚的影响，导致识别到的靶点多为已知靶点。
  • 机器学习算法在数据一致性和异构信息整合方面仍面临挑战。
  • 未来的研究应关注如何开发高效的特征选择应用，以解决数据偏倚问题，并提升AI模型的可解释性。

通过这项研究，作者希望推动人工智能在癌症靶点识别和药物发现领域的应用，促进新药物的开发和临床应用。

参考文献 ​

  1. MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. - Tongxin Wang;Wei Shao;Zhi Huang;Haixu Tang;Jie Zhang;Zhengming Ding;Kun Huang - Nature communications (2021)
  2. Maximum entropy methods for extracting the learned features of deep neural networks. - Alex Finnegan;Jun S Song - PLoS computational biology (2017)
  3. Multi-omics approaches to disease. - Yehudit Hasin;Marcus Seldin;Aldons Lusis - Genome biology (2017)
  4. Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics. - Nikolaos Perakakis;Alireza Yazdani;George E Karniadakis;Christos Mantzoros - Metabolism: clinical and experimental (2018)
  5. PREDICT: a method for inferring novel drug indications with application to personalized medicine. - Assaf Gottlieb;Gideon Y Stein;Eytan Ruppin;Roded Sharan - Molecular systems biology (2011)
  6. A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes. - Feixiong Cheng;Junfei Zhao;Michaela Fooksa;Zhongming Zhao - Journal of the American Medical Informatics Association : JAMIA (2016)
  7. Identifying novel genes and chemicals related to nasopharyngeal cancer in a heterogeneous network. - Zhandong Li;Lifeng An;Hao Li;ShaoPeng Wang;You Zhou;Fei Yuan;Lin Li - Scientific reports (2016)
  8. Identification of key regulators in prostate cancer from gene expression datasets of patients. - Irengbam Rocky Mangangcha;Md Zubbair Malik;Ömer Küçük;Shakir Ali;R K Brojen Singh - Scientific reports (2019)
  9. Bioinformatic analysis of chromatin organization and biased expression of duplicated genes between two poplars with a common whole-genome duplication. - Le Zhang;Jingtian Zhao;Hao Bi;Xiangyu Yang;Zhiyang Zhang;Yutao Su;Zhenghao Li;Lei Zhang;Brian J Sanderson;Jianquan Liu;Tao Ma - Horticulture research (2021)
  10. A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome. - Susan Dina Ghiassian;Jörg Menche;Albert-László Barabási - PLoS computational biology (2015)

引用本文的文献 ​

  1. Applications of artificial intelligence multiomics in precision oncology. - Ruby Srivastava - Journal of cancer research and clinical oncology (2023)
  2. An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection. - Fubo Ma;Ming Xiao;Lin Zhu;Wen Jiang;Jizhe Jiang;Peng-Fei Zhang;Kang Li;Min Yue;Le Zhang - Frontiers in genetics (2022)
  3. Integration of artificial intelligence and precision oncology in Latin America. - Liliana Sussman;Juan Esteban Garcia-Robledo;Camila Ordóñez-Reyes;Yency Forero;Andrés F Mosquera;Alejandro Ruíz-Patiño;Diego F Chamorro;Andrés F Cardona - Frontiers in medical technology (2022)
  4. A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer. - Le Zhang;Shiwei Fan;Julio Vera;Xin Lai - Computational and structural biotechnology journal (2023)
  5. Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda. - Erik Karger;Marko Kureljusic - Pharmaceuticals (Basel, Switzerland) (2022)
  6. A Computer Simulation of SARS-CoV-2 Mutation Spectra for Empirical Data Characterization and Analysis. - Ming Xiao;Fubo Ma;Jun Yu;Jianghang Xie;Qiaozhen Zhang;Peng Liu;Fei Yu;Yuming Jiang;Le Zhang - Biomolecules (2022)
  7. Discovering hematoma-stimulated circuits for secondary brain injury after intraventricular hemorrhage by spatial transcriptome analysis. - Le Zhang;Jiayidaer Badai;Guan Wang;Xufang Ru;Wenkai Song;Yujie You;Jiaojiao He;Suna Huang;Hua Feng;Runsheng Chen;Yi Zhao;Yujie Chen - Frontiers in immunology (2023)
  8. Artificial intelligence for drug discovery: Resources, methods, and applications. - Wei Chen;Xuesong Liu;Sanyin Zhang;Shilin Chen - Molecular therapy. Nucleic acids (2023)
  9. Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers. - Bernardo Pereira Cabral;Luiza Amara Maciel Braga;Shabbir Syed-Abdul;Fabio Batista Mota - Current oncology (Toronto, Ont.) (2023)
  10. Editorial: Medical knowledge-assisted machine learning technologies in individualized medicine. - Feng Gao;William C Cho;Xin Gao;Wei Wang - Frontiers in molecular biosciences (2023)

... (121 更多 篇文献)

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