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Gaussian Process Regression Models for the Prediction of Hydrogen Bond Acceptor Strengths
Author(s) -
Bauer Christoph A.,
Schneider Gisbert,
Göller Andreas H.
Publication year - 2019
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201800115
Subject(s) - hydrogen bond , acceptor , steric effects , chemistry , quantitative structure–activity relationship , computational chemistry , linear regression , test set , molecule , statistical physics , computer science , artificial intelligence , physics , machine learning , stereochemistry , quantum mechanics , organic chemistry
We present two approaches for the computation of hydrogen bond acceptor strengths, one by machine‐learning and one by a composite quantum‐mechanical protocol, both based on the well‐established pK BHX scale and dataset. The QM calculations after a necessary linear fit reproduce the complexation free energies in solution with an RMSE of 2.6 kJ mol −1 , not far off the expected error of 2 kJ mol −1 obtained from the comparison of experimental data from two different sources. The second approach is by Gaussian Process Regression (GPR) machine‐learning. We describe the hydrogen bond acceptor atoms by a radial atomic reactivity descriptor that encodes their electronic and steric environment. The performance of the GPR model on an external test set corresponds to 3.3 kJ mol −1 , which is also close to the experimental error. We apply the GPR model built on experimental data to model the hydrogen bond acceptor strengths of a series of hydrogen bond acceptor sites of 10 phosphodiesterase 10 A inhibitors. The predicted values correlate well with the experimentally measured IC 50 values.