呼吁发起一项由行业主导的倡议，对机器学习在实际药物研发中的应用进行严格评估

IF 18.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Nature Machine Intelligence Pub Date : 2024-10-04 DOI:10.1038/s42256-024-00911-w

Cas Wognum, Jeremy R. Ash, Matteo Aldeghi, Raquel Rodríguez-Pérez, Cheng Fang, Alan C. Cheng, Daniel J. Price, Djork-Arné Clevert, Ola Engkvist, W. Patrick Walters

引用次数: 0

摘要

机器学习（ML）正在推动药物发现领域令人兴奋的创新，但我们需要注意这一应用领域的特殊情况。与其他领域拥有由数百万实例组成的适用数据集不同，药物发现领域已发布的数据集通常是异构的、不平衡的、有噪声的，而且生成成本高昂1。此外，ML 在药物发现中的应用非常多，需要熟悉多个科学学科，并为昂贵或耗时的实验等重大决策提供信息。由于缺乏标准化的、与领域相适应的数据集、指南和工具来评估和比较各种方法，导致人们认为的进展与实际影响之间的差距越来越大，从而延误了 ML 在药物发现领域的应用。为了弥合这一差距，我们相信，在真实世界中开展工作的业界科学家的独特专业知识对于制定针对药物发现的基准协议至关重要。为此，我们已经与来自十家生物技术和制药公司的代表建立了独特的合作关系，但我们相信，要解决此类重大挑战，还需要开放科学、跨行业和跨学科的努力。无偏见评估的黄金标准是盲目的前瞻性基准，即在新生成的测试集上对不同方法进行评估，测试集只有在结果公布后才会公开。CASP （结构预测批判性评估）2 是药物发现领域的一个流行范例，它通过系统地识别社区中有价值的创新，促成了蛋白质结构预测的一场革命3。然而，药物发现领域的数据采集既昂贵又耗时，这限制了盲基准对一般研究界的可及性和可用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Machine Intelligence Multiple-

CiteScore

36.90

自引率

2.10%

发文量

127

期刊介绍： Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.

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