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

Dmitry S. Karlov | Zendy; Sergey Sosnin | Zendy; Maxim V. Fedorov | Zendy; Petr Popov | Zendy

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

Author(s) -

Dmitry S. Karlov,

Sergey Sosnin,

Maxim V. Fedorov,

Petr Popov

Publication year - 2020

Publication title -

acs omega

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.779

H-Index - 40

ISSN - 2470-1343

DOI - 10.1021/acsomega.9b04162

Subject(s) - dissociation constant , convolutional neural network , correlation coefficient , protein ligand , graph , ligand (biochemistry) , artificial neural network , computer science , constant (computer programming) , artificial intelligence , function (biology) , dissociation (chemistry) , pattern recognition (psychology) , chemistry , mathematics , biological system , machine learning , biochemistry , theoretical computer science , biology , evolutionary biology , receptor , programming language

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 ( K d ), inhibition constant ( K i ), and half maximal inhibitory concentration (IC 50 ). 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 K deep .

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