<i>In silico</i> prediction of p<i>K</i> _a values using explainable deep learning methods.

ABSTRACT: Negative logarithm of the acid dissociation constant (pK _a) significantly influences the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of molecules and is a crucial indicator in drug research. Given the rapid and accurate characteristics of computational methods, their role in predicting drug properties is increasingly important. Although many pK _a prediction models currently exist, they often focus on enhancing model precision while neglecting interpretability. In this study, we present GraFpK _a, a pK _a prediction model using graph neural networks (GNNs) and molecular fingerprints. The results show that our acidic and basic models achieved mean absolute errors (MAEs) of 0.621 and 0.4

SUBMITTER: Yang C 

PROVIDER: S-EPMC12268062 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature