Comput Methods Programs Biomed - Drug/nondrug classification using Support Vector Machines with various feature selection strategies.

Tópicos

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Resumo

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.

Resumo Limpo

conjunct advanc comput technolog virtual screen small molecul start use drug discoveri sinc thousand compound earlyphas drug discoveri fast classif method can distinguish activ inact molecul can use screen larg compound collect studi use support vector machin svm type classif task svm power classif tool becom increas popular various machinelearn applic data set consist compound train set compound separ test set data preprocess step pearson correl coeffici use filter elimin redund featur applic correl filter singl svm appli reduc data set moreov investig perform svm differ featur select strategi includ svmrecurs featur elimin wrapper method subset select featur select method general repres better perform singl svm subset select outperform featur select method test svm classif tool reallif drug discoveri problem result reveal use method classif task earlyphas drug discoveri

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