MolGpka: A Web Server for Small Molecule p Ka Prediction Using a Graph-Convolutional Neural Network

Abstract

pK_a is an important property in the lead optimization process since the charge state of a molecule in physiologic pH plays a critical role in its biological activity, solubility, membrane permeability, metabolism, and toxicity. Accurate and fast estimation of small molecule pK_a is vital during the drug discovery process. We present MolGpKa, a web server for pK_a prediction using a graph-convolutional neural network model. The model works by learning pK_a related chemical patterns automatically and building reliable predictors with learned features. ACD/pK_a data for 1.6 million compounds from the ChEMBL database was used for model training. We found that the performance of the model is better than machine learning models built with human-engineered fingerprints. Detailed analysis shows that the substitution effect on pK_a is well learned by the model. MolGpKa is a handy tool for the rapid estimation of pK_a during the ligand design process. The MolGpKa server is freely available to researchers and can be accessed at https://xundrug.cn/molgpka.