Machine learning the ropes: principles, applications and directions in synthetic chemistry. (16th July 2020)

• Record Type: Journal Article
• Authors: Strieth-Kalthoff, Felix; Sandfort, Frederik; Segler, Marwin H. S.; Glorius, Frank
• Is Part Of: Chemical Society reviews. Volume 49:Number 17(2020)
• Page Start: 6154
• Page End: 6168
• Publication Date: 2020-07-16
• Subjects: Chemistry -- Periodicals; 540
• DOI: 10.1039/c9cs00786e
• Languages: English
• ISSNs: 0306-0012

Abstract

Abstract : Chemists go ML! This tutorial review provides easy access to the fundamentals of machine learning from a synthetic chemist's perspective. Its diverse applications for molecular design, synthesis planning, or reactivity prediction are summarized. 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.