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 …

Machine Learning (ML) techniques face significant challenges when predicting advanced
chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction …

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 …

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

Enabled by data management and digitalization adoption in chemistry, machine learning
(ML) is accelerating chemistry through automated data analysis, materials embeddings …

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 …

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 Numerous disciplines, such as image recognition and language translation,
have been revolutionized by using machine learning (ML) to leverage big data. In organic …

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

Accurate prediction of reaction yield is the holy grail for computer-assisted synthesis
prediction, but current models have failed to generalize to large literature datasets. To …