GraphDelta: MPNN scoring function for the affinity prediction of protein-ligand complexes

Dmitry S. Karlov, Sergey Sosnin, Maxim V. Fedorov, Petr Popov

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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 (Kd), inhibition constant (Ki), 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 Kdeep. ©

Original languageEnglish
Pages (from-to)5150-5159
Number of pages10
JournalACS Omega
Volume5
Issue number10
Early online date9 Mar 2020
DOIs
Publication statusPublished - 17 Mar 2020

Keywords

  • protein ligand scoring
  • protein molecules
  • protein data bank
  • neural networks

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