Gaussian Process Regression Models for the Prediction of Hydrogen Bond Acceptor Strengths.

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 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 , not far off the expected error of 2 kJ mol 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 , 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 values.