Title: Multi-view spectral clustering and its chemical application

Authors: Adeshola A. Adefioye; Xinhai Liu; Bart De Moor

Addresses: ESAT-SCD/IBBT-K.U.Leuven Future Health Department, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Box 2446, 3001 Leuven, Belgium ' ESAT-SCD/IBBT-K.U.Leuven Future Health Department, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Box 2446, 3001 Leuven, Belgium ' ESAT-SCD/IBBT-K.U.Leuven Future Health Department, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Box 2446, 3001 Leuven, Belgium

Abstract: Clustering is an unsupervised method that allows researchers to group objects and gather information about their relationships. In chemoinformatics, clustering enables hypotheses to be drawn about a compound's biological, chemical and physical property in comparison to another. We introduce a novel improved spectral clustering algorithm, proposed for chemical compound clustering, using multiple data sources. Tensor-based spectral methods, used in this paper, provide chemically appropriate and statistically significant results when attempting to cluster compounds from both the GSK-Chembl Malaria data set and the Zinc database. Spectral clustering algorithms based on the tensor method give robust results on the mid-size compound sets used here. The goal of this paper is to present the clustering of chemical compounds, using a tensor-based multi-view method which proves of value to the medicinal chemistry community. Our findings show compounds of extremely different chemotypes clustering together, this is a hint to the chemogenomics nature of our method.

Keywords: chemoinformatics; medicinal chemistry; chemogenomics; drug design; malaria; TCAMS data set; Zinc data set; multi-view spectral clustering; tensor method; chemical compounds.

DOI: 10.1504/IJCBDD.2013.052200

International Journal of Computational Biology and Drug Design, 2013 Vol.6 No.1/2, pp.32 - 49

Published online: 20 Feb 2013 *

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