Machine learning the ropes: principles, applications and directions in synthetic chemistry

Chemical Society Reviews
Felix Strieth-KalthoffFrank Glorius

Abstract

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 recognizing complex patterns in data, ML bears the potential to modernise the way how many chemical challenges are approached. In this review, an introduction to ML is given from the perspective of synthetic chemistry: starting from the fundamentals regarding algorithms and best-practice workflows, the review covers different applications of machine learning in synthesis planning, property prediction, molecular design, and reactivity prediction. In particular, different approaches of representing and utilizing organic molecules will be discussed - providing synthetic chemists both with the understanding and the tools required to apply machine learning in the context of their research, and pointers for further studying.

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Citations

Mar 4, 2021·ACS Central Science·Toryn DaltonFrank Glorius
Mar 12, 2021·Chemical Reviews·Oliver T UnkeKlaus-Robert Müller
Apr 27, 2021·Analytica Chimica Acta·Lucas B AyresCarlos D Garcia
Jul 9, 2021·The Journal of Organic Chemistry·A Filipa AlmeidaTiago Rodrigues
Jul 16, 2021·Journal of Chemical Information and Modeling·Zachary J BaumQiongqiong Zhou
Aug 7, 2021·Journal of the American Chemical Society·Ruiyang LyuYiying Wu
Aug 16, 2021·The Journal of Chemical Physics·Stefan HeinenO Anatole von Lilienfeld
Sep 28, 2021·Briefings in Bioinformatics·Jingxin DongXiangxiang Zeng
Oct 13, 2021·Journal of the American Chemical Society·Marcus ReisFrank A Leibfarth
Sep 25, 2021·Angewandte Chemie·Sara SzymkućBartosz A Grzybowski

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Software Mentioned

Synthia
ECFP4
ML
CASP
Chematica
Scifinder
SMILES
Reaxys

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