Literature DB >> 33386096

AI-assisted synthesis prediction.

Simon Johansson1, Amol Thakkar2, Thierry Kogej3, Esben Bjerrum3, Samuel Genheden3, Tomas Bastys3, Christos Kannas3, Alexander Schliep4, Hongming Chen5, Ola Engkvist3.   

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.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Year:  2020        PMID: 33386096     DOI: 10.1016/j.ddtec.2020.06.002

Source DB:  PubMed          Journal:  Drug Discov Today Technol        ISSN: 1740-6749


  5 in total

1.  Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias.

Authors:  Dávid Péter Kovács; William McCorkindale; Alpha A Lee
Journal:  Nat Commun       Date:  2021-03-16       Impact factor: 14.919

2.  Prediction of the Chemical Context for Buchwald-Hartwig Coupling Reactions.

Authors:  Samuel Genheden; Agnes Mårdh; Gustav Lahti; Ola Engkvist; Simon Olsson; Thierry Kogej
Journal:  Mol Inform       Date:  2022-02-22       Impact factor: 4.050

3.  Combining generative artificial intelligence and on-chip synthesis for de novo drug design.

Authors:  Francesca Grisoni; Berend J H Huisman; Alexander L Button; Michael Moret; Kenneth Atz; Daniel Merk; Gisbert Schneider
Journal:  Sci Adv       Date:  2021-06-11       Impact factor: 14.136

4.  Predicting enzymatic reactions with a molecular transformer.

Authors:  David Kreutter; Philippe Schwaller; Jean-Louis Reymond
Journal:  Chem Sci       Date:  2021-05-25       Impact factor: 9.825

Review 5.  Fragment-based drug discovery: opportunities for organic synthesis.

Authors:  Jeffrey D St Denis; Richard J Hall; Christopher W Murray; Tom D Heightman; David C Rees
Journal:  RSC Med Chem       Date:  2020-12-24
  5 in total

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