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

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Rapid and Accurate Prediction of pK(a) Values of C-H Acids Using Graph Convolutional Neural Networks

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Title
Rapid and Accurate Prediction of pK(a) Values of C-H Acids Using Graph Convolutional Neural Networks
Author
Roszak, RafalBeker, WiktorMolga, KarolGrzybowski, Bartosz A.
Issue Date
2019-10
Publisher
AMER CHEMICAL SOC
Citation
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, v.141, no.43, pp.17142 - 17149
Abstract
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://scholarworks.unist.ac.kr/handle/201301/30844
URL
https://pubs.acs.org/doi/abs/10.1021/jacs.9b05895
DOI
10.1021/jacs.9b05895
ISSN
0002-7863
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