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Rapid Report (Bioinformatics/Computational biology/Molecular modeling)

Classification of HDAC8 Inhibitors and Non-Inhibitors Using Support Vector Machines
Guang Ping Cao1, Sundarapandian Thangapandian1, Shalini John1 and Keun Woo Lee1,*
1Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), Jinju, Republic of Korea
*Corresponding author
  Received : March 26, 2012
  Accepted : March 30, 2012
  Published : March 30, 2012
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Synopsis

Introduction: Histone deacetylases (HDAC) are a class of enzymes that remove acetyl groups from 琯-N-acetyl lysine amino acids of histone proteins. Their action is opposite to that of histone acetyltransferase that adds acetyl groups to these lysines. Only few HDAC inhibitors are approved and used as anti-cancer therapeutics. Thus, discovery of new and potential HDAC inhibitors are necessary in the effective treatment of cancer.
Materials and methods: This study proposed a method using support vector machine (SVM) to classify HDAC8 inhibitors and non-inhibitors in early-phase virtual compound filtering and screening. The 100 experimentally known HDAC8 inhibitors including 52 inhibitors and 48 non-inhibitors were used in this study. A set of molecular descriptors was calculated for all compounds in the dataset using ADRIANA.Code of Molecular Networks. Different kernel functions available from SVM Tools of free support vector machine software and training and test sets of varying size were used in model generation and validation.
Results and conclusion: The best model obtained using kernel functions has shown 75% of accuracy on test set prediction. The other models have also displayed good prediction over the test set compounds. The results of this study can be used as simple and effective filters in the drug discovery process.

Keyword: histone deacetylase 8, support vector machine, ADRIANA.Code, libSVM Tool, classification model, drug discovery process
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