Highly Precise Prediction of Micro‐ and Supra‐p K a Based on 3D Descriptors Integrating Non‐Covalent Interactions

Accurate pK_a 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 pK_a 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 2-acetoxybenzoic acid (the main components of aspirin) non-equilibrium conformations, glycine's microstate distributions, and the stereoelectronic anomalies of Janus Sponge and Meldrum's Acid. It addressed challenging supra-pK_a predictions in crystalline environments and accurately correlated pK_a 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-pK_a predictions, offering significant potential in drug discovery, catalysis, and materials science.