title: Drug/nondrug classification using Support Vector Machines with various feature selection strategies.
abstract: In conjunction with the advance in computer technology, virtual screening of small molecules has been started to use in drug discovery. Since there are thousands of compounds in early-phase of drug discovery, a fast classification method, which can distinguish between active and inactive molecules, can be used for screening large compound collections. In this study, we used Support Vector Machines (SVM) for this type of classification task. SVM is a powerful classification tool that is becoming increasingly popular in various machine-learning applications. The data sets consist of 631 compounds for training set and 216 compounds for a separate test set. In data pre-processing step, the Pearson's correlation coefficient used as a filter to eliminate redundant features. After application of the correlation filter, a single SVM has been applied to this reduced data set. Moreover, we have investigated the performance of SVM with different feature selection strategies, including SVM-Recursive Feature Elimination, Wrapper Method and Subset Selection. All feature selection methods generally represent better performance than a single SVM while Subset Selection outperforms other feature selection methods. We have tested SVM as a classification tool in a real-life drug discovery problem and our results revealed that it could be a useful method for classification task in early-phase of drug discovery.
authors.affiliation: Hacettepe University, Faculty of Medicine, Department of Biostatistics, 06100 Sihhiye, Ankara, Turkey. Electronic address: selcuk.korkmaz@hacettepe.edu.tr.
authors.forename: Selcuk
authors.initials: S
authors.lastname: Korkmaz
authors.affiliation: Hacettepe University, Faculty of Medicine, Department of Biostatistics, 06100 Sihhiye, Ankara, Turkey.
authors.forename: Gokmen
authors.initials: G
authors.lastname: Zararsiz
authors.affiliation: Hacettepe University, Faculty of Medicine, Department of Biostatistics, 06100 Sihhiye, Ankara, Turkey.
authors.forename: Dincer
authors.initials: D
authors.lastname: Goksuluk
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D015195:Drug Design; D009363:Neoplasm Proteins / Q000737:chemistry* / Q000145:classification*; D010363:Pattern Recognition, Automated / Q000379:methods; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000145:classification*; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods; D060388:Support Vector Machine
chemical_list: D009363:Neoplasm Proteins; D004364:Pharmaceutical Preparations
keywords: Drug discovery; Feature selection; Machine learning; Molecular descriptors; Support Vector Machines