Defining and Exploring Chemical Spaces

Abstract

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.