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Grzybowski, Bartosz A.
School of Natural Science
Research Interests
  • Nano science

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Synergy Between Expert and Machine-Learning Approaches Allows for Improved Retrosynthetic Planning

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Title
Synergy Between Expert and Machine-Learning Approaches Allows for Improved Retrosynthetic Planning
Author
Badowski, TomaszGajewska, Ewa P.Molga, KarolGrzybowski, Bartosz A.
Issue Date
2020-01
Publisher
WILEY-V C H VERLAG GMBH
Citation
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, v.59, no.2, pp.730
Abstract
When computers plan multistep syntheses, they can rely either on expert knowledge or information machine-extracted from large reaction repositories. Both approaches suffer from imperfect functions evaluating reaction choices: expert functions are heuristics based on chemical intuition, whereas machine learning (ML) relies on neural networks (NNs) that can make meaningful predictions only about popular reaction types. This paper shows that expert and ML approaches can be synergistic-specifically, when NNs are trained on literature data matched onto high-quality, expert-coded reaction rules, they achieve higher synthetic accuracy than either of the methods alone and, importantly, can also handle rare/specialized reaction types.
URI
https://scholarworks.unist.ac.kr/handle/201301/30588
URL
https://onlinelibrary.wiley.com/doi/full/10.1002/anie.201912083
DOI
10.1002/anie.201912083
ISSN
1433-7851
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SNS_Journal Papers
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