Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing machine learning models that can achieve this on the fly to the satisfaction of medicinal chemists remains a challenge owing to the enormous search space. Researchers have addressed de novo design of molecules by decomposing the problem into a series of tasks determined by design criteria. Here we provide a comprehensive overview of the current state of the art in molecular design using machine learning models as well as important design decisions, such as the choice of molecular representations, generative methods and optimization strategies. Subsequently, we present a collection of practical applications in which the reviewed methodologies have been experimentally validated, encompassing both academic and industrial efforts. Finally, we draw attention to the theoretical, computational and empirical challenges in deploying generative machine learning and highlight future opportunities to better align such approaches to achieve realistic drug discovery end points.

机器学习通过将分子生成和过滤步骤结合在一个架构中，利用药物化学家的经验和设计偏好，提供了一种加速早期药物发现的方法。然而，由于搜索空间巨大，设计能够动态实现这一目标并使药物化学家满意的机器学习模型仍然是一个挑战。研究人员通过将问题分解为一系列由设计标准确定的任务来解决分子的从头设计。在这里，我们全面概述了使用机器学习模型进行分子设计的最新技术以及重要的设计决策，例如分子表示、生成方法和优化策略的选择。随后，我们提出了一系列实际应用，其中所审查的方法已经过实验验证，涵盖学术和工业方面的努力。最后，我们提请人们注意部署生成机器学习的理论、计算和实证挑战，并强调未来更好地调整这些方法以实现现实的药物发现终点的机会。