Acep$K_{\rm a}$: Thermodynamics-Informed p$K_{\rm a}$ Prediction and Protonation-State Generation in PlayMolecule AI

Abstract

The acid dissociation constants (p$K_{\rm a}$) and the protonation states that they determine govern solubility, permeability, and protein--ligand binding, making their accurate prediction essential in drug discovery. We present Acep$K_{\rm a}$, an application in the PlayMolecule AI platform that implements the Uni-p$K_{\rm a}$ framework, which couples statistical mechanics with representation learning. Rather than treating p$K_{\rm a}$ as a scalar regression target, Acep$K_{\rm a}$ models the complete protonation ensemble, enforcing thermodynamic consistency across coupled ionization sites. The application is built on an independently retrained Uni-Mol backbone that matches state-of-the-art accuracy on standard public benchmarks. We further describe three engineering contributions: AceConfgen, a GPU-accelerated conformer generator approximately 7 times faster than other GPU implementations and more than an order of magnitude faster than multithreaded RDKit; a streamlined inference engine that protonates molecules directly; and a 3D-aware mode that applies predicted protonation states to bound ligand poses. Acep$K_{\rm a}$ supports library-scale prediction and provides a validated, ready-to-use implementation of this methodology, available at open.playmolecule.org.