Deep learning (DL) algorithms are a subset of machine learning algorithms with the aim of modeling complex mapping between a set of elements and their classes. In parallel to the advance in revealing the molecular bases of diseases, a notable innovation has been undertaken to apply DL in data/libraries management, reaction optimizations, differentiating uncertainties, molecule constructions, creating metrics from qualitative results, and prediction of structures or interactions. From source identification to lead discovery and medicinal chemistry of the drug candidate, drug delivery, and modification, the challenges can be subjected to artificial intelligence algorithms to aid in the generation and interpretation of data. Discovery and design approach, both demand automation, large data management and data fusion by the advance in high‐throughput mode. The application of DL can accelerate the exploration of drug mechanisms, finding novel indications for existing drugs (drug repositioning), drug development, and preclinical and clinical studies. The impact of DL in the workflow of drug discovery, design, and their complementary tools are highlighted in this review. Additionally, the type of DL algorithms used for this purpose, and their pros and cons along with the dominant directions of future research are presented.

中文翻译：

---

药物发现中深度学习算法的范围

深度学习（DL）算法是机器学习算法的子集，旨在对一组元素及其类之间的复杂映射进行建模。在揭示疾病分子基础的同时，人们进行了一项显着的创新，将DL应用于数据/图书馆管理，反应优化，区分不确定性，分子构造，从定性结果创建度量以及预测结构或相互作用。从源识别到候选药物的先导发现和药物化学，药物输送和修饰，这些挑战都可以通过人工智能算法来解决，以帮助生成和解释数据。发现和设计方法都需要自动化，通过高吞吐量模式的发展实现大数据管理和数据融合。DL的应用可以加快药物作用机理的探索，为现有药物（药物重新定位），药物开发以及临床前和临床研究寻找新的适应症。本文将重点介绍DL在药物发现，设计及其补充工具的工作流程中的影响。此外，还介绍了用于此目的的DL算法的类型及其优缺点以及未来研究的主要方向。本评论重点介绍了它们及其补充工具。此外，还介绍了用于此目的的DL算法的类型及其优缺点以及未来研究的主要方向。本评论重点介绍了它们及其补充工具。此外，还介绍了用于此目的的DL算法的类型及其优缺点以及未来研究的主要方向。