AI-assisted synthesis prediction. (December 2019)

• Record Type: Journal Article
• Authors: Johansson, Simon; Thakkar, Amol; Kogej, Thierry; Bjerrum, Esben; Genheden, Samuel; Bastys, Tomas; Kannas, Christos; Schliep, Alexander; Chen, Hongming; Engkvist, Ola
• Is Part Of: Drug discovery today. Volume 32/33(2019)
• Page Start: 65
• Page End: 72
• Publication Date: 2019-12
• Subjects: Drug development -- Periodicals; Physiology, Pathological -- Periodicals; Drugs -- Design -- Periodicals; 615.1
• DOI: 10.1016/j.ddtec.2020.06.002
• Languages: English
• ISSNs: 1740-6749

Abstract

Graphical abstract: Abstract : Application of AI technologies in synthesis prediction has developed very rapidly in recent years. We attempt here to give a comprehensive summary on the latest advancement on retro-synthesis planning, forward synthesis prediction as well as quantum chemistry-based reaction prediction models. Besides an introduction on the AI/ML models for addressing various synthesis related problems, the sources of the reaction datasets used in model building is also covered. In addition to the predictive models, the robotics based high throughput experimentation technology will be another crucial factor for conducting synthesis in an automated fashion. Some state-of-the-art of high throughput experimentation practices carried out in the pharmaceutical industry are highlighted in this chapter to give the reader a sense of how future chemistry will be conducted to make compounds faster and cheaper.