From the start of a synthetic chemist's training, experiments are conducted based on recipes
from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist …

Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …

Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …

Therapeutics machine learning is an emerging field with incredible opportunities for
innovatiaon and impact. However, advancement in this field requires formulation of …

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 …

Applications of machine learning (ML) to synthetic chemistry rely on the assumption that
large numbers of literature-reported examples should enable construction of accurate and …

Discovering new reactions, optimizing their performance, and extending the synthetically
accessible chemical space are critical drivers for major technological advances and more …

Assessing the outcomes of chemical reactions in a quantitative fashion has been a
cornerstone across all synthetic disciplines. Classically approached through empirical …

Organic chemistry is replete with complex relationships: for example, how a reactant's
structure relates to the resulting product formed; how reaction conditions relate to yield; how …

Machine-learning methods have great potential to accelerate the identification of reaction
conditions for chemical transformations. A tool that gives substrate-adaptive conditions for …