Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA.
Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA.
Brief Bioinform. 2022 Jan 17;23(1). doi: 10.1093/bib/bbab430.
Artificial intelligence (AI) has been transforming the practice of drug discovery in the past decade. Various AI techniques have been used in many drug discovery applications, such as virtual screening and drug design. In this survey, we first give an overview on drug discovery and discuss related applications, which can be reduced to two major tasks, i.e. molecular property prediction and molecule generation. We then present common data resources, molecule representations and benchmark platforms. As a major part of the survey, AI techniques are dissected into model architectures and learning paradigms. To reflect the technical development of AI in drug discovery over the years, the surveyed works are organized chronologically. We expect that this survey provides a comprehensive review on AI in drug discovery. We also provide a GitHub repository with a collection of papers (and codes, if applicable) as a learning resource, which is regularly updated.
人工智能（AI）在过去十年中改变了药物发现的实践。各种 AI 技术已被用于许多药物发现应用中，例如虚拟筛选和药物设计。在本综述中，我们首先概述了药物发现并讨论了相关应用，这些应用可归结为两个主要任务，即分子性质预测和分子生成。然后，我们介绍了常见的数据资源、分子表示和基准平台。作为综述的主要部分，我们将 AI 技术分为模型架构和学习范例。为了反映 AI 在药物发现中的技术发展，我们按时间顺序组织了调查的作品。我们希望本综述提供对药物发现中 AI 的全面回顾。我们还提供了一个带有论文（如果适用，则带有代码）集合的 GitHub 存储库，作为学习资源，该资源会定期更新。
