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Bartosz Andrzej Grzybowski
첨단연성물질 연구단
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Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

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Title
Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?
Author(s)
Skoraczynski, G; Dittwald, P; Miasojedow, B; Szymkuc, S; Gajewska, EP; Bartosz Grzybowski; A. Gambin
Publication Date
2017-12
Journal
SCIENTIFIC REPORTS, v.7, no.1, pp.3582 -
Publisher
NATURE PUBLISHING GROUP
Abstract
As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest -and hope -that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited -in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted. Improving the performance of machine-learning methods calls for the development of fundamentally new chemical descriptors. © The Author(s) 2017
URI
http://pr.ibs.re.kr/handle/8788114/3636
DOI
10.1038/s41598-017-02303-0
ISSN
2045-2322
Appears in Collections:
Center for Soft and Living Matter(첨단연성물질 연구단) > Journal Papers (저널논문)
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