Structure−Activity Relationships of 2‘-Fluoro-2‘,3‘-unsaturatedd-Nucleosides as Anti-HIV-1 Agents

2002 ◽  
Vol 45 (6) ◽  
pp. 1313-1320 ◽  
Author(s):  
Kyeong Lee ◽  
Yongseok Choi ◽  
Giuseppe Gumina ◽  
Wen Zhou ◽  
Raymond F. Schinazi ◽  
...  
Keyword(s):  
Structure Activity Relationships ◽  
Structure Activity ◽  
Anti Hiv ◽  
Hiv 1

Quantitative structure–activity relationships study of potent pyridinone scaffold derivatives as HIV-1 integrase inhibitors with therapeutic applications

2017 ◽  
Vol 16 (05) ◽  
pp. 1750038 ◽  
Author(s):  
Abolfazl Barzegar ◽  
Hossein Hamidi
Keyword(s):  
Therapeutic Agents ◽  
Quantitative Structure ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Qsar Models ◽  
Stepwise Procedure ◽  
Quantitative Structure Activity Relationships ◽  
Anti Hiv ◽  
Hiv 1 ◽  
Mlr Model

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 efficient 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 ([Formula: see text] and [Formula: see text]). Therefore, using stepwise PCA approach is suitable to remove ineffective descriptors, which results in remaining efficient 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.

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