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

TL;DR

The paper introduces Acep$K_{a}$, a thermodynamics-informed AI tool for accurate p$K_{a}$ prediction and protonation state generation, enhancing drug discovery processes with state-of-the-art performance and efficiency.

Contribution

It presents a novel thermodynamics-based framework, Uni-p$K_{a}$, integrated into PlayMolecule AI, with engineering innovations like GPU-accelerated conformer generation and a direct inference engine.

Findings

Achieved state-of-the-art performance on standard benchmarks.

Implemented GPU-accelerated conformer generator with 40x speed-up.

Ensured thermodynamic consistency across ionization sites.

Abstract

Accurate prediction of acid dissociation constants (p$K_{\rm a}$) 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$K_{\rm a}$, an advanced application integrated into the PlayMolecule AI platform. Acep$K_{\rm a}$ is built upon the theoretically rigorous Uni-p$K_{\rm a}$ 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$K_{\rm a}$ 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$K_{\rm a}$ into the PlayMolecule AI ecosystem, a modern AI-assisted environment for molecular modelling and drug discovery.