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Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge

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Title
Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge
Author(s)
Strieth-Kalthoff, Felix; Szymkuć, Sara; Molga, Karol; Aspuru-Guzik, Alán; Glorius, Frank; Bartosz A. Grzybowski
Publication Date
2024-04
Journal
Journal of the American Chemical Society, v.146, no.16, pp.11005 - 11017
Publisher
American Chemical Society
Abstract
Rapid advancements in artificial intelligence (AI) have enabled breakthroughs across many scientific disciplines. In organic chemistry, the challenge of planning complex multistep chemical syntheses should conceptually be well-suited for AI. Yet, the development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of “hybrid” algorithms combining AI with expert knowledge. This Perspective examines possible causes of these shortcomings, extending beyond the established reasoning of insufficient quantities of reaction data. Drawing attention to the intricacies and data biases that are specific to the domain of synthetic chemistry, we advocate augmenting the unique capabilities of AI with the knowledge base and the reasoning strategies of domain experts. By actively involving synthetic chemists, who are the end users of any synthesis planning software, into the development process, we envision to bridge the gap between computer algorithms and the intricate nature of chemical synthesis.
URI
https://pr.ibs.re.kr/handle/8788114/15148
DOI
10.1021/jacs.4c00338
ISSN
0002-7863
Appears in Collections:
Center for Soft and Living Matter(첨단연성물질 연구단) > 1. Journal Papers (저널논문)
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