DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins. Issue 5 (May 2016)

• Record Type: Journal Article
• Authors: Jamali, Ali Akbar; Ferdousi, Reza; Razzaghi, Saeed; Li, Jiuyong; Safdari, Reza; Ebrahimie, Esmaeil
• Is Part Of: Drug discovery today. Volume 21:Issue 5(2016)
• Page Start: 718
• Page End: 724
• Publication Date: 2016-05
• Subjects: Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2016.01.007
• Languages: English
• ISSNs: 1359-6446

Abstract

Highlights: Developing a novel machine learning tool for prediction of potential druggable proteins. Remarkable high performance of employed models in prediction. Introducing new targets participating in crucial biological pathways. Abstract : Application of computational methods in drug discovery has received increased attention in recent years as a way to accelerate drug target prediction. Based on 443 sequence-derived protein features, we applied the most commonly used machine learning methods to predict whether a protein is druggable as well as to opt for superior algorithm in this task. In addition, feature selection procedures were used to provide the best performance of each classifier according to the optimum number of features. When run on all features, Neural Network was the best classifier, with 89.98% accuracy, based on a k-fold cross-validation test. Among all the algorithms applied, the optimum number of most-relevant features was 130, according to the Support Vector Machine-Feature Selection (SVM-FS) algorithm. This study resulted in the discovery of new drug target which potentially can be employed in cell signaling pathways, gene expression, and signal transduction. The DrugMiner web tool was developed based on the findings of this study to provide researchers with the ability to predict druggable proteins. DrugMiner is freely available atwww.DrugMiner.org .