Machine intelligence for chemical reaction space

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 inte...

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Vydáno v:Wiley interdisciplinary reviews. Computational molecular science Ročník 12; číslo 5; s. e1604 - n/a
Hlavní autoři: Schwaller, Philippe, Vaucher, Alain C., Laplaza, Ruben, Bunne, Charlotte, Krause, Andreas, Corminboeuf, Clemence, Laino, Teodoro
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA Wiley Periodicals, Inc 01.09.2022
Wiley Subscription Services, Inc
Témata:
Algorithms
Artificial intelligence
Catalysts
Chemical reactions
Chemical synthesis
computer‐assisted synthesis planning
Data science
data‐driven approaches
Design optimization
machine intelligence
Machine learning
Pharmaceutical industry
Space exploration
ISSN:1759-0876, 1759-0884
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Abstract 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 Machine intelligence has emerged in the chemical reaction space and enables chemists to accelerate the synthesis and discovery of novel molecules.
AbstractList 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 LearningData Science > Computer Algorithms and ProgrammingData Science > Chemoinformatics
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 Machine intelligence has emerged in the chemical reaction space and enables chemists to accelerate the synthesis and discovery of novel molecules.
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
Author Laino, Teodoro
Vaucher, Alain C.
Laplaza, Ruben
Corminboeuf, Clemence
Krause, Andreas
Bunne, Charlotte
Schwaller, Philippe
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  orcidid: 0000-0001-8717-0456
  surname: Laino
  fullname: Laino, Teodoro
  organization: National Center for Competence in Research‐Catalysis (NCCR‐Catalysis)
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Snippet Discovering new reactions, optimizing their performance, and extending the synthetically accessible chemical space are critical drivers for major technological...
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SubjectTerms Algorithms
Artificial intelligence
Catalysts
Chemical reactions
Chemical synthesis
computer‐assisted synthesis planning
Data science
data‐driven approaches
Design optimization
machine intelligence
Machine learning
Pharmaceutical industry
Space exploration
Title Machine intelligence for chemical reaction space
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Volume 12
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