Deep learning bears promise for drug discovery, including advanced image analysis, prediction of molecular structure and function, and automated generation of innovative chemical entities with bespoke properties. Despite the growing number of successful prospective applications, the underlying mathematical models often remain elusive to interpretation by the human mind. There is a demand for ‘explainable’ deep learning methods to address the need for a new narrative of the machine language of the molecular sciences. This Review summarizes the most prominent algorithmic concepts of explainable artificial intelligence, and forecasts future opportunities, potential applications as well as several remaining challenges. We also hope it encourages additional efforts towards the development and acceptance of explainable artificial intelligence techniques.

深度学习为药物发现提供了希望，包括高级图像分析，分子结构和功能的预测以及具有定制属性的创新化学实体的自动生成。尽管成功的前瞻性应用程序数量不断增加，但基本的数学模型通常仍然难以理解人的思维。需要“可解释的”深度学习方法来解决对分子科学机器语言的新叙述的需求。这篇综述总结了可解释人工智能的最杰出的算法概念，并预测了未来的机会，潜在的应用以及尚存的一些挑战。我们也希望它鼓励为开发和接受可解释的人工智能技术做出更多的努力。