Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure–activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of ‘big’ data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era.

机器智能通常表示为人工智能，是指计算机所展现的智能。在合理的药物发现历史中，各种机器智能方法已被用来指导传统的实验，这既昂贵又费时。在过去的几十年中，开发了机器学习工具，例如定量结构-活性关系（QSAR）建模，可以快速，廉价地从数百万种候选化合物中识别出潜在的生物活性分子。但是，当药物发现进入“大”数据时代时，机器学习方法演变为深度学习方法，这是处理现代药物发现方法所产生的大量数据的一种更强大，更有效的方法。这里，我们总结了机器学习的历史，并对最近开发的深度学习方法及其在合理药物发现中的应用提供了见识。我们认为，机器智能的这种发展现在为当前​​大数据时代的早期药物设计和发现提供了指南。