MolGpka: A Web Server for Small Molecule p Prediction Using a Graph-Convolutional Neural Network.
Journal:
Journal of chemical information and modeling
Published Date:
Jul 12, 2021
Abstract
p 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 p is vital during the drug discovery process. We present MolGpKa, a web server for p prediction using a graph-convolutional neural network model. The model works by learning p related chemical patterns automatically and building reliable predictors with learned features. ACD/p 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 p is well learned by the model. MolGpKa is a handy tool for the rapid estimation of p during the ligand design process. The MolGpKa server is freely available to researchers and can be accessed at https://xundrug.cn/molgpka.