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文献精选 > 近年高分"AI药物发现"研究（IF≥30，近5年）

Scientific discovery in the age of artificial intelligence. ​

文献信息 ​

DOI10.1038/s41586-023-06221-2
PMID37532811
期刊Nature
影响因子48.5
JCR 分区Q1
发表年份2023
被引次数245
关键词人工智能, 科学发现, 自监督学习, 几何深度学习, 生成式AI
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
ISSN0028-0836
页码47-60
期号620(7972)
作者Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai, Debora Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Veličković, Max Welling, Linfeng Zhang, Connor W Coley, Yoshua Bengio, Marinka Zitnik

一句话小结 ​

本文探讨了人工智能在科学研究中的应用进展，特别是自监督学习和几何深度学习如何提高数据处理的准确性和效率，同时生成性AI在药物和蛋白质设计中的潜力。尽管取得了显著成就，研究指出数据质量和管理等核心问题仍需解决，以推动科学理解和AI创新的进一步发展。

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人工智能 · 科学发现 · 自监督学习 · 几何深度学习 · 生成式AI

摘要 ​

人工智能（AI）正日益被整合到科学发现中，以增强和加速研究，帮助科学家生成假设、设计实验、收集和解释大数据集，并获得可能无法通过传统科学方法单独获得的见解。在这里，我们审视过去十年的突破性进展，包括自监督学习，这种方法允许模型在大量未标记的数据上进行训练，以及几何深度学习，该方法利用科学数据的结构知识来提高模型的准确性和效率。生成性AI方法可以通过分析多种数据模式（包括图像和序列）来创造设计，例如小分子药物和蛋白质。我们讨论了这些方法如何在科学过程中帮助科学家，以及尽管取得了这样的进展，仍然存在的核心问题。AI工具的开发者和用户需要更好地理解何时需要改进这些方法，同时，数据质量和管理不善所带来的挑战依然存在。这些问题跨越科学学科，需要开发基础算法方法，以促进科学理解或自主获取科学知识，使其成为AI创新的关键关注领域。

英文摘要 ​

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

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主要研究问题 ​

  1. 在人工智能辅助的科学发现中，如何评估自监督学习的效果与传统学习方法的对比？
  2. 几何深度学习在处理科学数据结构时，具体应用案例有哪些，它们如何提升研究效率？
  3. 生成式人工智能在药物设计中面临哪些技术挑战，这些挑战如何影响最终产品的开发？
  4. 数据质量和管理问题对科学研究的影响有多大，如何通过算法改进来解决这些问题？
  5. 不同科学领域在应用人工智能时遇到的共性问题是什么，这些问题如何促进跨学科的合作与创新？

核心洞察 ​

研究背景和目的 ​

近年来，人工智能（AI）在科学发现中的应用日益增多，旨在加速研究进程，帮助科学家生成假设、设计实验、收集和解释大规模数据集。本文旨在探讨过去十年中AI技术的突破，特别是自监督学习和几何深度学习的应用，以及生成性AI在小分子药物和蛋白质设计中的潜力。

主要方法/材料/实验设计 ​

本研究分析了多种AI技术在科学研究中的应用，具体方法包括：

  1. 自监督学习：允许模型在大量未标记数据上进行训练，提升模型的泛化能力。
  2. 几何深度学习：利用科学数据的结构知识来增强模型的准确性和效率。
  3. 生成性AI：通过分析多种数据模态（如图像和序列）来创造设计，如小分子药物和蛋白质。

以下是技术路线的流程图展示：

Mermaid diagram

关键结果和发现 ​

  • 自监督学习和几何深度学习的结合显著提升了模型在处理科学数据时的表现。
  • 生成性AI能够有效分析和设计新型药物及蛋白质，显示出其在药物发现领域的潜力。
  • 尽管技术取得了进展，研究中仍然存在数据质量差和数据管理不善等挑战。

主要结论/意义/创新性 ​

本文强调了AI技术在科学研究中的重要性，特别是在加速假设生成和实验设计方面的应用。研究指出，AI工具的开发者和用户需要更好地理解这些方法的局限性和改进空间。此外，提出了在科学研究中开发基础算法的重要性，以便推动科学理解或自主获取知识。

研究局限性和未来方向 ​

  • 局限性：研究未能深入探讨AI工具在不同科学领域的具体应用差异，且未能全面评估数据质量对AI模型性能的影响。
  • 未来方向：建议未来研究应集中于改进数据管理策略和提高数据质量，同时探索更多跨学科的AI应用，以促进科学发现。
部分内容描述
研究背景和目的探讨AI在科学发现中的应用及其潜力
主要方法自监督学习、几何深度学习、生成性AI
关键结果提升模型表现，显示药物设计潜力
主要结论强调AI工具的重要性及改进空间
研究局限性数据质量和管理问题
未来方向改进数据管理，探索跨学科应用

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引用本文的文献 ​

  1. AI tools as science policy advisers? The potential and the pitfalls. - Chris Tyler;K L Akerlof;Alessandro Allegra;Zachary Arnold;Henriette Canino;Marius A Doornenbal;Josh A Goldstein;David Budtz Pedersen;William J Sutherland - Nature (2023)
  2. PLOS Biology at 20: Reflecting on the road we've traveled. - Hemai Parthasarathy;Theodora Bloom;Emma Ganley - PLoS biology (2023)
  3. A focus on harnessing big data and artificial intelligence: revolutionizing drug discovery from traditional Chinese medicine sources. - Mingyu Li;Jian Zhang - Chemical science (2023)
  4. From beasts to bytes: Revolutionizing zoological research with artificial intelligence. - Yu-Juan Zhang;Zeyu Luo;Yawen Sun;Junhao Liu;Zongqing Chen - Zoological research (2023)
  5. Advances in Designing Essential Oil Nanoformulations: An Integrative Approach to Mathematical Modeling with Potential Application in Food Preservation. - Monisha Soni;Arati Yadav;Akash Maurya;Somenath Das;Nawal Kishore Dubey;Abhishek Kumar Dwivedy - Foods (Basel, Switzerland) (2023)
  6. Hypotheses devised by AI could find 'blind spots' in research. - Matthew Hutson - Nature (2023)
  7. Artificial intelligence for science-bridging data to wisdom. - Yongjun Xu;Fei Wang;Zhulin An;Qi Wang;Zhao Zhang - Innovation (Cambridge (Mass.)) (2023)
  8. ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs. - Zhiling Zheng;Oufan Zhang;Ha L Nguyen;Nakul Rampal;Ali H Alawadhi;Zichao Rong;Teresa Head-Gordon;Christian Borgs;Jennifer T Chayes;Omar M Yaghi - ACS central science (2023)
  9. Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery. - Xiaoning Qian;Byung-Jun Yoon;Raymundo Arróyave;Xiaofeng Qian;Edward R Dougherty - Patterns (New York, N.Y.) (2023)
  10. Rational Design of Flexible Mechanical Force Sensors for Healthcare and Diagnosis. - Hang Zhang;Yihui Zhang - Materials (Basel, Switzerland) (2023)

... (235 更多 篇文献)

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