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

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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
Skoraczynski, G.Dittwald, P.Miasojedow, B.Szymkuc, S.Gajewska, E. P.Grzybowski, Bartosz A.Gambin, A.
Issue Date
201706
Publisher
NATURE PUBLISHING GROUP
Citation
SCIENTIFIC REPORTS, v.7, no., pp.3582 -
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.
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DOI
http://dx.doi.org/10.1038/s41598-017-02303-0
ISSN
2045-2322
Appears in Collections:
SNS_Journal Papers
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