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review article

Machine intelligence for chemical reaction space

Schwaller, Philippe
•
Vaucher, Alain C.
•
Laplaza, Ruben  
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March 7, 2022
Wiley Interdisciplinary Reviews-Computational Molecular Science

Discovering new reactions, optimizing their performance, and extending the synthetically accessible chemical space are critical drivers for major technological advances and more sustainable processes. The current wave of machine intelligence is revolutionizing all data-rich disciplines. Machine intelligence has emerged as a potential game-changer for chemical reaction space exploration and the synthesis of novel molecules and materials. Herein, we will address the recent development of data-driven technologies for chemical reaction tasks, including forward reaction prediction, retrosynthesis, reaction optimization, catalysts design, inference of experimental procedures, and reaction classification. Accurate predictions of chemical reactivity are changing the R&D processes and, at the same time, promoting an accelerated discovery scheme both in academia and across chemical and pharmaceutical industries. This work will help to clarify the key contributions in the fields and the open challenges that remain to be addressed. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Data Science > Computer Algorithms and Programming Data Science > Chemoinformatics

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Type
review article
DOI
10.1002/wcms.1604
Web of Science ID

WOS:000765648800001

Author(s)
Schwaller, Philippe
Vaucher, Alain C.
Laplaza, Ruben  
Bunne, Charlotte
Krause, Andreas
Corminboeuf, Clemence  
Laino, Teodoro
Date Issued

2022-03-07

Publisher

WILEY

Published in
Wiley Interdisciplinary Reviews-Computational Molecular Science
Article Number

e1604

Subjects

Chemistry, Multidisciplinary

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Mathematical & Computational Biology

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Chemistry

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Mathematical & Computational Biology

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artificial intelligence

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chemical reactions

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computer-assisted synthesis planning

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data-driven approaches

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machine intelligence

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reaction prediction

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organic-chemistry

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molecular design

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neural-networks

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knowledge-base

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line notation

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computer

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optimization

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retrosynthesis

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representation

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LCMD  
Available on Infoscience
March 28, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/186659
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