Machine Learning for Chemical Synthesis

Abstract

The synthesis of new molecules is essential for progress in various sectors within the chemical industry and academia. Medicinal and materials chemistry are two examples. Searching through vast regions of chemical space for routes to new molecules is a time-consuming process carried out by expert synthetic chemists. The use of machine learning and artificial intelligence for synthetic chemistry is rapidly expanding, the aim being to reduce the timelines of chemical syntheses. Tools, which predict products of chemical reactions and design retrosynthetic routes, are attracting particular attention. Emerging computer-aided synthesis design (CASD) programs are not intended to replace synthetic chemists but to aid them in everyday decision making. The incorporation of condition optimisation and reaction performance is highly desirable. Combining such tools with an automated synthesis testing module holds much promise for the future of reaction condition optimisation. To achieve the desired progress in, and acceptance of CASD, there are a few challenges that need to be addressed.