KaMLs for Predicting Protein p<i>K</i>_a Values and Ionization States: Are Trees All You Need?

ABSTRACT: Despite its importance in understanding biology and computer-aided drug discovery, the accurate prediction of protein ionization states remains a formidable challenge. Physics-based approaches struggle to capture the small, competing contributions in the complex protein environment, while machine learning (ML) is hampered by the scarcity of experimental data. Here, we report the development of pK_a ML (KaML) models based on decision trees and graph attention networks (GAT), exploiting physicochemical understanding and a new experiment pK_a database (PKAD-3) enriched with highly shifted pK_a's. KaML-CBtree significantly outperforms the current state of the art in predicting pK_a values and ionization states across all six t

SUBMITTER: Shen M 

PROVIDER: S-EPMC12323819 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature