Journal
ACS OMEGA
Volume 5, Issue 10, Pages 5150-5159Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acsomega.9b04162
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Funding
- Russian Science Foundation (RSF) [18-74-00117]
- Russian Science Foundation [18-74-00117] Funding Source: Russian Science Foundation
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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 (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 K-deep.
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