AI-assisted synthesis prediction
Reviewartikel, 2020

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.

Författare

Simon Johansson

AstraZeneca AB

Chalmers, Data- och informationsteknik, Data Science

Amol Thakkar

AstraZeneca AB

Universität Bern

Thierry Kogej

AstraZeneca AB

Esben Jannik Bjerrum

AstraZeneca AB

Samuel Genheden

AstraZeneca AB

Tomas Bastys

AstraZeneca AB

Christos Kannas

AstraZeneca AB

Alexander Schliep

Göteborgs universitet

Hongming Chen

Guangdong Laboratory

Ola Engkvist

AstraZeneca AB

Drug Discovery Today: Technologies

1740-6749 (ISSN)

Vol. In Press

Ämneskategorier

Energiteknik

Bioinformatik (beräkningsbiologi)

Annan samhällsbyggnadsteknik

DOI

10.1016/j.ddtec.2020.06.002

Mer information

Senast uppdaterat

2020-08-18