AI-assisted synthesis prediction
Review article, 2019
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.
Author
Simon Johansson
AstraZeneca AB
Chalmers, Computer Science and Engineering (Chalmers), Data Science
Amol Thakkar
AstraZeneca AB
University of Bern
Thierry Kogej
AstraZeneca AB
Esben Jannik Bjerrum
AstraZeneca AB
Samuel Genheden
AstraZeneca AB
Tomas Bastys
AstraZeneca AB
Christos Kannas
AstraZeneca AB
Alexander Schliep
University of Gothenburg
Hongming Chen
Guangdong Laboratory
Ola Engkvist
AstraZeneca AB
Published in
Drug Discovery Today: Technologies
17406749 (eISSN)
Vol. 32-33 p. 65-72Categorizing
Subject Categories (SSIF 2011)
Energy Engineering
Bioinformatics (Computational Biology)
Other Civil Engineering
Identifiers
DOI
10.1016/j.ddtec.2020.06.002