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文献精选 > 近年高分"AI药物发现"研究（IF≥30，近5年）

Rethinking drug design in the artificial intelligence era. ​

文献信息 ​

DOI10.1038/s41573-019-0050-3
PMID31801986
期刊Nature reviews. Drug discovery
影响因子101.8
JCR 分区Q1
发表年份2020
被引次数234
关键词人工智能, 药物发现, 小分子药物, 生物制药, 挑战
文献类型Journal Article, Review
ISSN1474-1776
页码353-364
期号19(5)
作者Petra Schneider, W Patrick Walters, Alleyn T Plowright, Norman Sieroka, Jennifer Listgarten, Robert A Goodnow, Jasmin Fisher, Johanna M Jansen, José S Duca, Thomas S Rush, Matthias Zentgraf, John Edward Hill, Elizabeth Krutoholow, Matthias Kohler, Jeff Blaney, Kimito Funatsu, Chris Luebkemann, Gisbert Schneider

一句话小结 ​

本文探讨了人工智能在小分子药物发现中的应用，尽管其潜力受到认可，但也面临诸多挑战。通过国际专家的观点，研究强调了应对这些挑战的重要性，以推动生物制药行业的创新和发展。

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人工智能 · 药物发现 · 小分子药物 · 生物制药 · 挑战

摘要 ​

人工智能（AI）工具在药物发现中越来越多地被应用。虽然一些支持者指出这些工具可能带来的巨大机遇，但另一些人则持怀疑态度，期待在药物发现项目中看到明确的影响。现实情况可能介于这两者之间，但显然，人工智能为参与的科学家以及生物制药行业及其传统的药物发现和开发流程带来了新的挑战。本文呈现了一组来自不同国家的国际专家对小分子药物发现中人工智能所面临的“重大挑战”及其应对方法的看法。

英文摘要 ​

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them.

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主要研究问题 ​

  1. 在人工智能时代，如何评估AI工具在药物设计中的实际效果与潜在价值？
  2. 目前在小分子药物发现中，AI技术面临的主要挑战有哪些？
  3. 不同国家或地区在AI辅助药物发现领域的研究进展和应用案例有哪些差异？
  4. AI如何改变传统药物开发流程，并对生物制药行业的未来产生影响？
  5. 针对AI在药物设计中的应用，行业内专家提出的解决方案有哪些具体实例？

核心洞察 ​

  1. 研究背景和目的
    随着人工智能（AI）工具在药物发现领域的逐步应用，生物制药行业正面临着新的挑战与机遇。一方面，支持者认为这些工具能够大幅提高药物发现的效率，推动新药研发的进展；另一方面，持怀疑态度的人士则期待能够看到AI在药物发现项目中的明确成效。该研究旨在探讨AI在小分子药物发现中的应用，分析其面临的重大挑战，并总结专家对这些挑战的看法及应对策略。

  2. 主要方法和发现
    文章通过收集和汇总国际专家的观点，详细探讨了AI在小分子药物发现过程中遇到的“重大挑战”。这些挑战包括数据的质量与可获取性、模型的可解释性、以及如何有效整合AI工具与传统药物发现流程等。专家们提出了一系列应对策略，包括加强跨学科合作、提升数据标准化水平，以及开发更具可解释性的AI模型，以便于科学家能够理解和信任AI的决策。

  3. 核心结论
    AI在药物发现领域的应用潜力虽巨大，但其落地实施仍需克服诸多挑战。专家们一致认为，未来药物开发的成功与否将取决于如何有效地将AI工具融入现有的研发流程，并同时确保这些工具能够提供可解释且可靠的结果。通过合理的策略与合作，AI有望在药物研发中发挥更为重要的作用。

  4. 研究意义和影响
    本研究的意义在于为生物制药行业提供了一个关于AI应用的全面视角，帮助业界理解当前技术的局限性与未来的发展方向。同时，研究强调了跨学科合作的重要性，推动了科学家、数据专家和临床医生之间的互动与交流。通过提出切实可行的解决方案，该研究为生物制药行业在AI时代的药物发现提供了新的思路和框架，可能推动新药研发流程的变革，提升药物研发的效率和成功率。

参考文献 ​

  1. Trends in clinical success rates. - Katarzyna Smietana;Marcin Siatkowski;Martin Møller - Nature reviews. Drug discovery (2016)
  2. 2018 FDA drug approvals. - Asher Mullard - Nature reviews. Drug discovery (2019)
  3. Computer-assisted drug design. - A J Hopfinger - Journal of medicinal chemistry (1985)
  4. Computer-assisted rational drug design. - Y C Martin - Methods in enzymology (1991)
  5. Computer-Aided Drug Design Methods. - Wenbo Yu;Alexander D MacKerell - Methods in molecular biology (Clifton, N.J.) (2017)
  6. Computer Aided Drug Design: Success and Limitations. - Mohammad Hassan Baig;Khurshid Ahmad;Sudeep Roy;Jalaluddin Mohammad Ashraf;Mohd Adil;Mohammad Haris Siddiqui;Saif Khan;Mohammad Amjad Kamal;Ivo Provazník;Inho Choi - Current pharmaceutical design (2016)
  7. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. - Xin Yang;Yifei Wang;Ryan Byrne;Gisbert Schneider;Shengyong Yang - Chemical reviews (2019)
  8. Opportunities and obstacles for deep learning in biology and medicine. - Travers Ching;Daniel S Himmelstein;Brett K Beaulieu-Jones;Alexandr A Kalinin;Brian T Do;Gregory P Way;Enrico Ferrero;Paul-Michael Agapow;Michael Zietz;Michael M Hoffman;Wei Xie;Gail L Rosen;Benjamin J Lengerich;Johnny Israeli;Jack Lanchantin;Stephen Woloszynek;Anne E Carpenter;Avanti Shrikumar;Jinbo Xu;Evan M Cofer;Christopher A Lavender;Srinivas C Turaga;Amr M Alexandari;Zhiyong Lu;David J Harris;Dave DeCaprio;Yanjun Qi;Anshul Kundaje;Yifan Peng;Laura K Wiley;Marwin H S Segler;Simina M Boca;S Joshua Swamidass;Austin Huang;Anthony Gitter;Casey S Greene - Journal of the Royal Society, Interface (2018)
  9. Why and how have drug discovery strategies in pharma changed? What are the new mindsets? - Serge Mignani;Scot Huber;Helena Tomás;João Rodrigues;Jean-Pierre Majoral - Drug discovery today (2016)
  10. Artificial Intelligence in Drug Design-The Storm Before the Calm? - Allan M Jordan - ACS medicinal chemistry letters (2018)

引用本文的文献 ​

  1. Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope. - Neann Mathai;Johannes Kirchmair - International journal of molecular sciences (2020)
  2. Mechanism of baricitinib supports artificial intelligence-predicted testing in COVID-19 patients. - Justin Stebbing;Venkatesh Krishnan;Stephanie de Bono;Silvia Ottaviani;Giacomo Casalini;Peter J Richardson;Vanessa Monteil;Volker M Lauschke;Ali Mirazimi;Sonia Youhanna;Yee-Joo Tan;Fausto Baldanti;Antonella Sarasini;Jorge A Ross Terres;Brian J Nickoloff;Richard E Higgs;Guilherme Rocha;Nicole L Byers;Douglas E Schlichting;Ajay Nirula;Anabela Cardoso;Mario Corbellino; - EMBO molecular medicine (2020)
  3. Strategies in Translating the Therapeutic Potentials of Host Defense Peptides. - Darren Shu Jeng Ting;Roger W Beuerman;Harminder S Dua;Rajamani Lakshminarayanan;Imran Mohammed - Frontiers in immunology (2020)
  4. Genome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncology. - Adrián Bazaga;Dan Leggate;Hendrik Weisser - Scientific reports (2020)
  5. Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. - Linlin Zhao;Heather L Ciallella;Lauren M Aleksunes;Hao Zhu - Drug discovery today (2020)
  6. Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery. - Izumi V Hinkson;Benjamin Madej;Eric A Stahlberg - Frontiers in pharmacology (2020)
  7. Mechanisms of Action for Small Molecules Revealed by Structural Biology in Drug Discovery. - Qingxin Li;CongBao Kang - International journal of molecular sciences (2020)
  8. Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques. - Rodrigo A Nava Lara;Jesús A Beltrán;Carlos A Brizuela;Gabriel Del Rio - Pharmaceuticals (Basel, Switzerland) (2020)
  9. Enhancing scientific discoveries in molecular biology with deep generative models. - Romain Lopez;Adam Gayoso;Nir Yosef - Molecular systems biology (2020)
  10. Crowdsourcing and open innovation in drug discovery: recent contributions and future directions. - David C Thompson;Jörg Bentzien - Drug discovery today (2020)

... (224 更多 篇文献)

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