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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 for natural product drug discovery. ​

文献信息 ​

DOI10.1038/s41573-023-00774-7
PMID37697042
期刊Nature reviews. Drug discovery
影响因子101.8
JCR 分区Q1
发表年份2023
被引次数94
关键词人工智能, 天然产物, 药物发现, 机器学习, 计算药物设计
文献类型Journal Article, Review, Research Support, N.I.H., Extramural, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't
ISSN1474-1776
页码895-916
期号22(11)
作者Michael W Mullowney, Katherine R Duncan, Somayah S Elsayed, Neha Garg, Justin J J van der Hooft, Nathaniel I Martin, David Meijer, Barbara R Terlouw, Friederike Biermann, Kai Blin, Janani Durairaj, Marina Gorostiola González, Eric J N Helfrich, Florian Huber, Stefan Leopold-Messer, Kohulan Rajan, Tristan de Rond, Jeffrey A van Santen, Maria Sorokina, Marcy J Balunas, Mehdi A Beniddir, Doris A van Bergeijk, Laura M Carroll, Chase M Clark, Djork-Arné Clevert, Chris A Dejong, Chao Du, Scarlet Ferrinho, Francesca Grisoni, Albert Hofstetter, Willem Jespers, Olga V Kalinina, Satria A Kautsar, Hyunwoo Kim, Tiago F Leao, Joleen Masschelein, Evan R Rees, Raphael Reher, Daniel Reker, Philippe Schwaller, Marwin Segler, Michael A Skinnider, Allison S Walker, Egon L Willighagen, Barbara Zdrazil, Nadine Ziemert, Rebecca J M Goss, Pierre Guyomard, Andrea Volkamer, William H Gerwick, Hyun Uk Kim, Rolf Müller, Gilles P van Wezel, Gerard J P van Westen, Anna K H Hirsch, Roger G Linington, Serina L Robinson, Marnix H Medema

一句话小结 ​

本研究探讨了计算组学与机器学习在药物发现中的协同作用，强调了这些技术在识别自然产物药物候选物方面的潜力。通过解决高质量数据集和算法验证策略等关键挑战，研究旨在推动新药设计的进展。

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人工智能 · 天然产物 · 药物发现 · 机器学习 · 计算药物设计

摘要 ​

计算组学技术的发展为获取自然产物的隐藏多样性提供了新手段，揭示了药物发现的新潜力。同时，机器学习等人工智能方法在计算药物设计领域取得了令人振奋的发展，促进了对生物活性的预测以及针对感兴趣分子靶点的新药设计。在这里，我们描述了这些发展之间当前和未来的协同作用，以有效识别自然产生的丰富分子中的药物候选物。我们还讨论了如何应对实现这些协同作用潜力的关键挑战，例如需要高质量的数据集来训练深度学习算法，以及适当的算法验证策略。

英文摘要 ​

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

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主要研究问题 ​

  1. 在自然产品药物发现中，如何确保机器学习模型的训练数据集具有高质量和多样性？
  2. 目前有哪些成功案例展示了人工智能在自然产品药物发现中的应用？
  3. 针对自然产品的药物发现，人工智能与传统药物发现方法相比，优势和劣势分别是什么？
  4. 在未来的发展中，哪些新的计算技术可能会进一步推动自然产品药物发现的进展？
  5. 目前在算法验证方面存在哪些主要挑战，如何有效解决这些问题以提升人工智能的应用效果？

核心洞察 ​

研究背景和目的 ​

随着计算组学技术的发展，科学家们能够更好地探索自然产品的多样性，这为新药发现提供了新的可能性。自然产品因其结构复杂性和生物活性，成为药物研发的重要来源。同时，人工智能（AI）特别是机器学习的进步，使得计算药物设计领域取得了显著进展。这项研究的目的在于探讨计算组学与人工智能之间的协同作用，如何有效地从自然界的丰富分子中识别潜在的药物候选物，并解决实现这一潜力所面临的关键挑战。

主要方法和发现 ​

研究中描述了当前和未来在计算组学和人工智能领域的协同发展。通过整合高通量的组学数据，研究团队能够更全面地了解自然产品的化学结构及其生物活性。同时，利用机器学习算法，研究人员能够预测分子的生物活性，并进行针对特定分子靶点的de novo药物设计。关键发现包括：高质量的数据集对训练深度学习算法至关重要，而合适的算法验证策略也是实现有效药物发现的关键因素。

核心结论 ​

整合计算组学和人工智能技术，可以显著提升自然产品药物发现的效率和成功率。通过构建高质量的数据集和验证机制，可以克服当前面临的挑战，从而更有效地识别出具有潜力的药物候选物。这一研究表明，利用现代计算技术和数据科学，可以更深入地挖掘自然产品的药用价值。

研究意义和影响 ​

本研究为自然产品药物发现提供了一种新的视角，强调了计算技术与人工智能的结合在现代药物研发中的重要性。通过推动这两者的协同作用，可以加速新药的开发过程，降低研发成本，并提高药物的成功率。这一研究不仅为药物发现开辟了新的途径，也为生物医药领域的未来发展提供了重要的理论基础和实践指导。

参考文献 ​

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引用本文的文献 ​

  1. Predicting success in Cu-catalyzed C-N coupling reactions using data science. - Mohammad H Samha;Lucas J Karas;David B Vogt;Emmanuel C Odogwu;Jennifer Elward;Jennifer M Crawford;Janelle E Steves;Matthew S Sigman - Science advances (2024)
  2. Antimicrobial resistance crisis: could artificial intelligence be the solution? - Guang-Yu Liu;Dan Yu;Mei-Mei Fan;Xu Zhang;Ze-Yu Jin;Christoph Tang;Xiao-Fen Liu - Military Medical Research (2024)
  3. Research progress of plant-derived natural products in thyroid carcinoma. - Qiujing Du;Weidong Shen - Frontiers in chemistry (2023)
  4. Natural products in osteoarthritis treatment: bridging basic research to clinical applications. - Shunzheng Fang;Bin Zhang;Wei Xiang;Liujie Zheng;Xiaodong Wang;Song Li;Tongyi Zhang;Daibo Feng;Yunquan Gong;Jinhui Wu;Jing Yuan;Yaran Wu;Yizhen Zhu;Enli Liu;Zhenhong Ni - Chinese medicine (2024)
  5. The antibiotic crisis: On the search for novel antibiotics and resistance mechanisms. - Marc W Van Goethem;Ramona Marasco;Pei-Ying Hong;Daniele Daffonchio - Microbial biotechnology (2024)
  6. Effectiveness of molecular fingerprints for exploring the chemical space of natural products. - Davide Boldini;Davide Ballabio;Viviana Consonni;Roberto Todeschini;Francesca Grisoni;Stephan A Sieber - Journal of cheminformatics (2024)
  7. Promiscuous, persistent and problematic: insights into current enterococcal genomics to guide therapeutic strategy. - David Hourigan;Ewelina Stefanovic;Colin Hill;R Paul Ross - BMC microbiology (2024)
  8. Harnessing regulatory networks in Actinobacteria for natural product discovery. - Hannah E Augustijn;Anna M Roseboom;Marnix H Medema;Gilles P van Wezel - Journal of industrial microbiology & biotechnology (2024)
  9. Advancing diabetes treatment: the role of mesenchymal stem cells in islet transplantation. - Lisha Mou;Tony Bowei Wang;Xinyu Wang;Zuhui Pu - Frontiers in immunology (2024)
  10. Combinatorial biosynthesis for the engineering of novel fungal natural products. - Elizabeth Skellam;Sanjeevan Rajendran;Lei Li - Communications chemistry (2024)

... (84 更多 篇文献)

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