Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques

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Author(s): S. Prasanthi (University of Hyderabad, India), S.Durga Bhavani (University of Hyderabad, India), T. Sobha Rani (University of Hyderabad, India)and Raju S. Bapi (University of Hyderabad, India)
Copyright: 2013
Pages: 11
Source title: Bioinformatics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-3604-0.ch050

Keywords: Bioinformatics / Medical Information Science Reference / Medicine & Healthcare

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Abstract

Vast majority of successful drugs or inhibitors achieve their activity by binding to, and modifying the activity of a protein leading to the concept of druggability. A target protein is druggable if it has the potential to bind the drug-like molecules. Hence kinase inhibitors need to be studied to understand the specificity of a kinase inhibitor in choosing a particular kinase target. In this paper we focus on human kinase drug target sequences since kinases are known to be potential drug targets. Also we do a preliminary analysis of kinase inhibitors in order to study the problem in the protein-ligand space in future. The identification of druggable kinases is treated as a classification problem in which druggable kinases are taken as positive data set and non-druggable kinases are chosen as negative data set. The classification problem is addressed using machine learning techniques like support vector machine (SVM) and decision tree (DT) and using sequence-specific features. One of the challenges of this classification problem is due to the unbalanced data with only 48 druggable kinases available against 509 non-drugggable kinases present at Uniprot. The accuracy of the decision tree classifier obtained is 57.65 which is not satisfactory. A two-tier architecture of decision trees is carefully designed such that recognition on the non-druggable dataset also gets improved. Thus the overall model is shown to achieve a final performance accuracy of 88.37. To the best of our knowledge, kinase druggability prediction using machine learning approaches has not been reported in literature.

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