graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein–Ligand Complexes

Abstract

In this work, we present graph-convolutional neural networks for the prediction of binding constants of protein-ligand complexes. We derived the model using multi task learning, where the target variables are the dissociation constant (<i>K</i> _d), inhibition constant (<i>K</i> _i), and half maximal inhibitory concentration (IC₅₀). Being rigorously trained on the PDBbind dataset, the model achieves the Pearson correlation coefficient of 0.87 and the RMSE value of 1.05 in pK units, outperforming recently developed 3D convolutional neural network model <i>K</i> _deep.