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dc.contributor.authorDeeb, Omar
dc.contributor.authorGoodarzi, Mohammad
dc.date.accessioned2018-09-02T19:54:53Z
dc.date.available2018-09-02T19:54:53Z
dc.date.issued2010-03-24
dc.identifier.issn1747-0285
dc.identifier.urihttps://dspace.alquds.edu/handle/20.500.12213/797
dc.description.abstractThe support vector machine (SVM) and partial least square (PLS) methods were used to develop quantitative structure activity relationship (QSAR) models to predict the inhibitory activity of non-peptide HIV-1 protease inhibitors. Genetic algorithm (GA) was employed to select variables that lead to the best-fitted models. A comparison between the obtained results using SVM with those of PLS revealed that the SVM model is much better than that of PLS. The root mean square errors of the training set and the test set for SVM model were calculated to be 0.2027, 0.2751, and the coefficients of determination (R(2)) are 0.9800, 0.9355 respectively. Furthermore, the obtained statistical parameter of leave-one-out cross-validation test (Q(2)) on SVM model was 0.9672, which proves the reliability of this model. The results suggest that TE2, Ui, GATS5e, Mor13e, ATS7m, Ss, Mor27e, and RDF035e are the main independent factors contributing to the inhibitory activities of the studied compounds.en_US
dc.description.sponsorshipThe authors would like to acknowledge the computational chemistry laboratory at Al-Quds University for providing Matlab software and for the time dedicated for performing the calculations of the study.en_US
dc.language.isoen_USen_US
dc.publisherJohn Wiley and Sonsen_US
dc.subjectinhibitory activityen_US
dc.subjectHIV-1 protease inhibitorsen_US
dc.subjectquantitative structure activity relationshipen_US
dc.subjectsupport vector machineen_US
dc.subjectpartial least squareen_US
dc.subjectgenetic algorithmsen_US
dc.titleExploring QSARs for Inhibitory Activity of Non-peptide HIV-1 Protease Inhibitors by GA-PLS and GA-SVMen_US
dc.typeArticleen_US


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