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Bartosz Andrzej Grzybowski
첨단연성물질 연구단
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Rapid and Accurate Prediction of p Ka Values of C-H Acids Using Graph Convolutional Neural Networks

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Title
Rapid and Accurate Prediction of p Ka Values of C-H Acids Using Graph Convolutional Neural Networks
Author(s)
Rafał Roszak; Wiktor Beker; Karol Molga; Bartosz A. Grzybowski
Publication Date
2019-10
Journal
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, v.141, no.43, pp.17142 - 17149
Publisher
AMER CHEMICAL SOC
Abstract
© 2019 American Chemical Society.The ability to estimate the acidity of C-H groups within organic molecules in non-aqueous solvents is important in synthetic planning to correctly predict which protons will be abstracted in reactions such as alkylations, Michael additions, or aldol condensations. This Article describes the use of the so-called graph convolutional neural networks (GCNNs) to perform such predictions on the time scales of milliseconds and with accuracy comparing favorably with state-of-the-art solutions, including commercial ones. The crux of the method is to train GCNNs using descriptors that reflect not only topological but also chemical properties of atomic environments. The model is validated against adversarial controls, supplemented by the discussion of realistic synthetic problems (on which it correctly predicts the most acidic protons in >90% of cases), and accompanied by a Web application intended to aid the community in everyday synthetic planning
URI
https://pr.ibs.re.kr/handle/8788114/6860
ISSN
0002-7863
Appears in Collections:
Center for Soft and Living Matter(첨단연성물질 연구단) > Journal Papers (저널논문)
Files in This Item:
2019_Bartosz_JACS_Rapid and Accurate Prediction of p Ka Values of C-H Acids Using Graph Convolutional Neural Networks.pdfDownload

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