In Silico Prediction of pKa Values Using Explainable Deep Learning Methods

pK_a significantly influences the 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 0.402, respectively, on the test set, demonstrating good predictive performance. Notably, to improve interpretability, GraFpK_a also incorporates Integrated Gradients (IGs), providing a clearer visual description of the atoms significantly affecting the pK_a values. The high reliability and interpretability of GraFpK_a ensure accurate pK_a predictions while also facilitating a deeper understanding of the relationship between molecular structure and pK_a values, making it a valuable tool in the field of pK_a prediction.