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Molecular Machine Learning: The Future of Synthetic Chemistry?
Author(s) -
Pflüger Philipp M.,
Glorius Frank
Publication year - 2020
Publication title -
angewandte chemie international edition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.831
H-Index - 550
eISSN - 1521-3773
pISSN - 1433-7851
DOI - 10.1002/anie.202008366
Subject(s) - casp , computer science , artificial intelligence , data science , chemistry , management science , engineering , protein structure prediction , biochemistry , protein structure
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 been successfully addressed, while other issues have barely been touched. This Viewpoint poses the question of whether current trends can persist in the long term and identifies factors that may lead to an (un)productive development. Thereby, specific risks of molecular machine learning (MML) are discussed. Furthermore, possible sustainable developments are suggested, such as explainable artificial intelligence (exAI) for synthetic chemistry. This Viewpoint will illuminate chances for possible newcomers and aims to guide the community into a discussion about current as well as future trends.