Computation & Informatics, Relay Therapeutics, Cambridge, MA, USA.
Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
Expert Opin Drug Discov. 2021 Sep;16(9):937-947. doi: 10.1080/17460441.2021.1915982. Epub 2021 Apr 19.
: Artificial Intelligence (AI) has become a component of our everyday lives, with applications ranging from recommendations on what to buy to the analysis of radiology images. Many of the techniques originally developed for other fields such as language translation and computer vision are now being applied in drug discovery. AI has enabled multiple aspects of drug discovery including the analysis of high content screening data, and the design and synthesis of new molecules.: This perspective provides an overview of the application of AI in several areas relevant to drug discovery including property prediction, molecule generation, image analysis, and organic synthesis planning.: While a variety of machine learning methods are now being routinely used to predict biological activity and ADME properties, methods of representing molecules continue to evolve. Molecule generation methods are relatively new and unproven but hold the potential to access new, unexplored areas of chemical space. The application of AI in drug discovery will continue to benefit from dedicated research, as well as AI developments in other fields. With this pairing algorithmic advancements and high-quality data, the impact of AI in drug discovery will continue to grow in the coming years.
人工智能（AI）已经成为我们日常生活的一部分，应用范围从购买建议到放射学图像分析。许多最初为其他领域开发的技术，如语言翻译和计算机视觉，现在正在药物发现中得到应用。人工智能已经使药物发现的多个方面成为可能，包括高通量筛选数据的分析，以及新分子的设计和合成。
本文从多个角度概述了人工智能在药物发现中的应用，包括性质预测、分子生成、图像分析和有机合成规划。虽然现在已经有多种机器学习方法被常规用于预测生物活性和 ADME 性质，但分子表示方法仍在不断发展。分子生成方法相对较新且未经证实，但有可能进入化学空间中尚未探索的新领域。人工智能在药物发现中的应用将继续受益于专门的研究，以及其他领域的人工智能发展。随着这种配对算法的进步和高质量数据的出现，人工智能在药物发现中的影响将在未来几年继续增长。
