MolGpka: A Web Server for Small Molecule p<i>K</i>_a Prediction Using a Graph-Convolutional Neural Network

pKa 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 pKa is vital
during the drug discovery process. We present MolGpKa, a web server
for pKa prediction using a graph-convolutional
neural network model. The model works by learning pKa related chemical patterns automatically and building
reliable predictors with learned features. ACD/pKa 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 pKa is well learned by the model. MolGpKa
is a handy tool for the rapid estimation of pKa during the ligand design process. The MolGpKa server is freely
available to researchers and can be accessed at https://xundrug.cn/molgpka.