US EPA

7446564 

Journal Article 

Rapid and Accurate Prediction of pKa Values of C-H Acids Using Graph Convolutional Neural Networks 

Roszak, R; Beker, W; Molga, K; Grzybowski, BA 

2019 

Yes 

Journal of the American Chemical Society
ISSN: 0002-7863
EISSN: 1520-5126 

141 

43 

17142-17149 

English 

31633925 

10.1021/jacs.9b05895 

WOS:000493866300019 

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.