Machine learning (ML) has emerged as a general, problem-solving paradigm with many
applications in computer vision, natural language processing, digital safety, or medicine. By …

Conspectus Computer-aided synthesis planning (CASP) is focused on the goal of
accelerating the process by which chemists decide how to synthesize small molecule …

The recent synergy of machine learning (ML) with molecular synthesis has emerged as an
increasingly powerful platform in organic synthesis and catalysis. This merger has set the …

During the last decade, modern machine learning has found its way into synthetic chemistry.
Some long‐standing challenges, such as computer‐aided synthesis planning (CASP), have …

Recent advances in machine learning or artificial intelligence for vision and natural
language processing that have enabled the development of new technologies such as …

Recent years have witnessed a boom of machine learning (ML) applications in chemistry,
which reveals the potential of data‐driven prediction of synthesis performance. Digitalization …

Abstract Machine learning (ML) promises to tackle the grand challenges in chemistry and
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Conspectus In the domain of reaction development, one aims to obtain higher efficacies as
measured in terms of yield and/or selectivities. During the empirical cycles, an admixture of …

Data-driven chemistry has been described as the “future” of industrial organic synthesis that
“will increasingly help guide synthetic chemists through the toughest synthesis problems”, in …