Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms.
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ABSTRACT:
SUBMITTER: Seo S
PROVIDER: S-EPMC5831789 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
Publications
Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms.
Seo Sangmin S Choi Jonghwan J Ahn Soon Kil SK Kim Kil Won KW Kim Jaekwang J Choi Jaehyuck J Kim Jinho J Ahn Jaegyoon J
Computational and mathematical methods in medicine 20180130
We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or similarity of receptors or ligands. The experimental results show that these new features can be effective in predicting GPCR-ligand binding (average area under the curve [AUC] of 0.944), because they are thought to include hidden properties of good li ...[more]
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