Efficient Black-Box Prediction of Hydrogen-Bond-Donor and Acceptor Strength

Hydrogen-bond strength is a critical determinant of physicochemical properties and binding affinity in drug discovery, but computationally predicting the strength of different hydrogen-bond donors acceptors remains challenging and inaccessible to non-experts. Here, we report a robust black-box workflow for predicting site-specific hydrogen-bond basicity and acidity in organic molecules with minimal computational cost. Our approach begins with rapid conformer generation and optimization with neural network potentials, followed by a single density-functional-theory calculation of the electrostatic potential. We then calibrate these results against an extensive reference set of experimentally determined pKBHX and pKα, achieving high accuracy in both cases. We illustrate the power of this tool in multiple published drug discovery programs, highlighting how per-site pKBHX tuning can improve bioavailability, minimize efflux, and enhance selectivity.