Highly Precise Prediction of Micro- and Supra-pKa Based on 3D Descriptors Integrating Non-Covalent Interactions

Abstract

Accurate pKa prediction is crucial for understanding proton dissociation in complex molecular systems. However, existing models often face challenges in addressing subtle stereoelectronic effects and conformational flexibility. This study presents H-SPOC, a localized 3D descriptor that captures covalent and non-covalent interactions and incorporates solvent effects to predict site-specific pKa values accurately. H-SPOC was validated on multiple benchmark datasets, including SAMPL6, SAMPL7, and SAMPL8, where it outperformed state-of-the-art methods. H-SPOC also proved versatile across various applications, including aspirin's non-equilibrium conformations, glycine's microstate distributions, and the stereoelectronic anomalies of Janus Sponge and Meldrum's Acid. It addressed challenging supra-pKa predictions in crystalline environments and accurately correlated pKa with reaction rates, selectivity, tautomerism, and pharmacokinetic properties. With its chemically intuitive design and computational efficiency, H-SPOC provides an efficient framework for rapid and precise micro- and supra-pKa predictions, offering significant potential in drug discovery, catalysis, and materials science.