Designing functional molecules with desirable properties is often a challenging, multi-objective optimization. For decades, there have been computational approaches to facilitate this process through the simulation of physical processes, the prediction of molecular properties using structure–property relationships, and the selection or generation of molecular structures. This review provides an overview of some algorithmic approaches to defining and exploring chemical spaces that have the potential to operationalize the process of molecular discovery. We emphasize the potential roles of machine learning and the consideration of synthetic feasibility, which is a prerequisite to ‘closing the loop’. We conclude by summarizing important directions for the future development and evaluation of these methods.

设计具有所需特性的功能分子通常是一项具有挑战性的多目标优化。数十年来，已经出现了通过物理过程模拟，使用结构-性质关系预测分子特性以及选择或生成分子结构的计算方法来促进该过程。这篇综述概述了一些定义和探索化学空间的算法方法，这些方法有可能使分子发现过程实用化。我们强调了机器学习的潜在作用以及对综合可行性的考虑，这是“闭环”的前提。最后，我们总结了这些方法的未来发展和评估的重要方向。