Journal
JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY
Volume 16, Issue 5, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0219633617500389
Keywords
HIV-1 integrase; AIDS; QSAR; PCA; MLR; regression
Categories
Funding
- Research Institute for Fundamental Sciences (RIFS)-University of Tabriz
Ask authors/readers for more resources
Human immunodeficiency virus-1 (HIV-1) integrase appears to be a crucial target for developing new anti-HIV-1 therapeutic agents. Different quantitative structure-activity relationships (QSARs) algorithms have been used in order to develop effcient model(s) to predict the activity of new pyridinone derivatives against HIV-1 integrase. Multiple linear regression (MLR) and combined principal component analysis (PCA) with MLR have been applied to build QSAR models for a set of new pyridinone derivatives as potent anti-HIV-1 therapeutic agents. Four different approaches based on MLR method including; concrete-MLR, stepwise-MLR, concrete PCA-MLR and stepwise PCA-MLR were utilized for this aim. Twenty two different sets of descriptors containing 1613 descriptors were constructed for each optimized molecule. Comparison between predictability of the concrete and stepwise procedure in two different algorithms of MLR and PCA models indicated the advantage of the stepwise procedure over that of the simple concrete method. Although the PCA was employed for dimension reduction, using stepwise PCA-MLR model showed that the method has higher ability to predict the compounds' activity. The stepwise PCA-MLR model showed highly validated statistical results both in fitting and prediction processes (R-test(2) = 0.78 and Q(2) = 0.80). Therefore, using stepwise PCA approach is suitable to remove ineffective descriptors, which results in remaining effcient descriptors for building good predictability stepwise PCA-MLR. The stepwise hybrid approach of PCA-MLR may be useful in derivation of highly predictive and interpretable QSAR models.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available