Identification of protein-ligand binding site using multi-clustering and support vector machine

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
IECON Proceedings (Industrial Electronics Conference), 2016, pp. 939 - 944
Issue Date:
2016-12-21
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© 2016 IEEE.Multi-clustering has been widely used. It acts as a pre-training process for identifying protein-ligand binding in structure-based drug design. Then, the Support Vector Machine (SVM) is employed to classify the sites most likely for binding ligands. Three types of attributes are used, namely geometry-based, energy-based, and sequence conservation. Comparison is made on 198 drug-target protein complexes with LIGSITECSC, SURFNET, Fpocket, Q-SiteFinder, ConCavity, and MetaPocket. The results show an improved success rate of up to 86%.
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