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

[Image: see text] 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 ma...

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Autores principales: Karlov, Dmitry S., Sosnin, Sergey, Fedorov, Maxim V., Popov, Petr
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2020
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081425/
https://www.ncbi.nlm.nih.gov/pubmed/32201802
http://dx.doi.org/10.1021/acsomega.9b04162
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author Karlov, Dmitry S.
Sosnin, Sergey
Fedorov, Maxim V.
Popov, Petr
author_facet Karlov, Dmitry S.
Sosnin, Sergey
Fedorov, Maxim V.
Popov, Petr
author_sort Karlov, Dmitry S.
collection PubMed
description [Image: see text] 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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spelling pubmed-70814252020-03-20 graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein–Ligand Complexes Karlov, Dmitry S. Sosnin, Sergey Fedorov, Maxim V. Popov, Petr ACS Omega [Image: see text] 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). American Chemical Society 2020-03-09 /pmc/articles/PMC7081425/ /pubmed/32201802 http://dx.doi.org/10.1021/acsomega.9b04162 Text en Copyright © 2020 American Chemical Society This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License (http://pubs.acs.org/page/policy/authorchoice_ccbyncnd_termsofuse.html) , which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes.
spellingShingle Karlov, Dmitry S.
Sosnin, Sergey
Fedorov, Maxim V.
Popov, Petr
graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein–Ligand Complexes
title graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein–Ligand Complexes
title_full graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein–Ligand Complexes
title_fullStr graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein–Ligand Complexes
title_full_unstemmed graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein–Ligand Complexes
title_short graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein–Ligand Complexes
title_sort graphdelta: mpnn scoring function for the affinity prediction of protein–ligand complexes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081425/
https://www.ncbi.nlm.nih.gov/pubmed/32201802
http://dx.doi.org/10.1021/acsomega.9b04162
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