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Bartosz Andrzej Grzybowski
첨단연성물질 연구단
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Synergy Between Expert and Machine-Learning Approaches Allows for Improved Retrosynthetic Planning

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dc.contributor.authorTomasz Badowski-
dc.contributor.authorEwa P. Gajewska-
dc.contributor.authorKarol Molga-
dc.contributor.authorBartosz A. Grzybowski-
dc.date.available2020-03-18T08:17:42Z-
dc.date.created2019-12-16-
dc.date.issued2020-01-
dc.identifier.issn1433-7851-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/7020-
dc.description.abstractWhen 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. c.2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim-
dc.languageENG-
dc.publisherWILEY-V C H VERLAG GMBH-
dc.titleSynergy Between Expert and Machine-Learning Approaches Allows for Improved Retrosynthetic Planning-
dc.typeArticle-
dc.type.rimsA-
dc.identifier.wosid000497490400001-
dc.identifier.scopusid2-s2.0-85075534968-
dc.contributor.affiliatedAuthorBartosz A. Grzybowski-
dc.identifier.bibliographicCitationANGEWANDTE CHEMIE-INTERNATIONAL EDITION, v.59, no.2, pp.725 - 730-
Appears in Collections:
Center for Soft and Living Matter(첨단연성물질 연구단) > Journal Papers (저널논문)
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