Thermodynamics-Informed Accurate pKa Prediction and Protonation State Generation in PlayMolecule AI

Abstract

Accurate prediction of acid dissociation constants (p$K_{\mathrm{a}}$) and the determination of dominant protonation states is critical in drug discovery, influencing molecular properties such as solubility, permeability, and protein-ligand binding. We present Acep$K_{\mathrm{a}}$, an advanced application integrated into the PlayMolecule AI platform. Acep$K_{\mathrm{a}}$ is built upon the theoretically rigorous Uni-p$K_{\mathrm{a}}$ framework, which unifies statistical mechanics with representation learning. By modeling the complete protonation ensemble rather than treating p$K_a$ as a scalar regression target, Acep$K_{\mathrm{a}}$ ensures thermodynamic consistency across coupled ionization sites. We describe the application's enhanced architecture, which features a retrained Uni-Mol backbone achieving state-of-the-art performance on standard benchmarks. Furthermore, we detail critical engineering advancements. These include AceConfgen, a proprietary GPU-accelerated conformer generator that achieves a ~40x speed-up compared to NVIDIA's nvmolkit, a streamlined inference engine to directly protonate molecules, and a 3D-aware modality for applying protonation states to bound ligand poses. Finally, we discuss the integration of Acep$K_{\mathrm{a}}$ into the PlayMolecule AI ecosystem, a modern AI-assisted environment for molecular modelling and drug discovery.