With the development of computer-assisted techniques, research communities including biochemistry and deep learning have been devoted into the drug discovery field for over a decade. Various applications of deep learning have drawn great attention in drug discovery, such as molecule generation, molecular property prediction, retrosynthesis prediction, and reaction prediction. While most existing surveys only focus on one of the applications, limiting the view of researchers in the community. In this paper, we present a comprehensive review on the aforementioned four aspects, and discuss the relationships among different applications. The latest literature and classical benchmarks are presented for better understanding the development of variety of approaches. We commence by summarizing the molecule representation format in these works, followed by an introduction of recent proposed approaches for each of the four tasks. Furthermore, we review a variety of commonly used datasets and evaluation metrics and compare the performance of deep learning-based models. Finally, we conclude by identifying remaining challenges and discussing the future trend for deep learning methods in drug discovery.

翻译：随着计算机辅助技术的发展，包括生物化学和深度学习在内的研究团体已致力于药物发现领域超过十年。深度学习的多种应用已在药物发现领域引起广泛关注，例如分子生成、分子属性预测、逆合成预测以及反应预测。尽管现有综述大多仅聚焦于其中某一应用，限制了领域研究者的视野。本文针对上述四个方面进行系统性综述，并探讨不同应用间的关联。为便于理解各类方法的发展脉络，我们呈现了最新文献和经典基准数据集。首先总结这些工作中采用的分子表征格式，继而分别介绍四个任务的最新方法。此外，我们回顾了多种常用数据集与评估指标，并比较了基于深度学习的模型性能。最后，通过指出现存挑战与讨论深度学习在药物发现中的未来趋势作为总结。