Acep K a: Thermodynamics-Informed p K a Prediction and Protonation-State Generation in PlayMolecule AI

Abstract

The acid dissociation constants (pKa) and the protonation states that they determine govern solubility, permeability, and protein–ligand binding, making their accurate prediction essential in drug discovery. We present AcepKa, an application on the PlayMolecule AI platform that implements the Uni-pKa framework, which couples statistical mechanics with representation learning. Rather than treating pKa as a scalar regression target, AcepKa 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. AcepKa supports library-scale prediction and provides a validated, ready-to-use implementation of this methodology, available at https://open.playmolecule.org.