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

Abstract

Human immunode¯ciency virus-1 (HIV-1) integrase appears to be a crucial target for developing new anti-HIV-1 therapeutic agents. Di®erent quantitative structure-activity relationships (QSARs) algorithms have been used in order to develop e±cient 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 di®erent approaches based on MLR method including; concrete-MLR, stepwise-MLR, concrete PCA-MLR and stepwise PCA-MLR were utilized for this aim. Twenty two di®erent sets of descriptors containing 1613 descriptors were constructed for each optimized molecule. Comparison between predictability of the \concrete" and \stepwise" procedure in two di®erent 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¯tting and prediction processes (R 2 test ¼ 0:78 and Q 2 ¼ 0:80). Therefore, using stepwise PCA approach is suitable to remove ine®ective descriptors, which results in remaining e±cient 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.

Mixed hemimicelle magnetic dispersive solid‐phase extraction using carbon‐coated magnetic nanoparticles for the determination of tramadol in urine samples

“…Several important variables were considered in the MHMDSPE study, including the kind and amount of adsorbent, pH of the sample solution, adsorption time and rate, type and volume of the eluting solvent, desorption time, ionic strength and sample volume. There are some computational approaches for modelling and optimization of independent variables .…”

High-Performance Liquid Chromatography Determination of Acrylamide after Its Extraction from Potato Chips

“…Optimization experiments were applied on blank chips sample which was spiked by 1.0 µg/mL acrylamide and parameter were optimized using one-parameter-at-a-time method. [16][17][18][19] Optimization efficiency was evaluated by calculating the recovery.…”

Dopamine‐Capped Silver Nanoparticles as a Colorimetric Probe for On‐Site Detection of Cyclosporine

“…Reaction conditions including the pH of reaction solution (6.0–11.0), volume of AgNO 3 (20‐60 μL), concentration of dopamine (38‐228 μM) and vortex time (1‐5 min) were tested in detail. There are some computational approaches for modeling and optimization of independent variables ,,,…”