A QSAR study was developed in order to model the antioxidant activity, specifically the radical scavenger activity (RSA), of 26 di(hetero)arylamines' derivatives of benzo[b]thiophenes. The QSAR model was constructed, using the partial least squares projection of latent structures (PLS) method, and its robustness and predictability were verified by internal and external cross-validation methods....

UNLABELLED BACKGROUND The discovery of clinically relevant inhibitors of HIV-RT for antiviral therapy has proven to be a challenging task. To identify novel and potent HIV-RT inhibitors, the quantitative structure-activity relationship (QSAR) approach became very useful and largely widespread technique forligand-based drug design. METHODS We perform the two- and three-dimensional (2D and 3...

In this study an attempt was made to understand the structural requirements for Topoisomerase I (Topo I) inhibition using a novel Group based QSAR (GQSAR) or fragment based QSAR technique. Here we combined the GQSAR technology with conventional 2D and 3D QSAR to derive GQSAR models for various reported naphthoquinone derivatives. Various regression models such as Multiple Regression (MRA), Part...

The study of type III RNases constitutes an important area in molecular biology. It is known that the pac1 gene encodes a particular RNase III that shares low amino acid similarity with other genes despite having a double-stranded ribonuclease activity. Bioinformatics methods based on sequence alignment may fail when there is a low amino acidic identity percentage between query sequence and oth...

BACKGROUND Apoptosis is known as programmed cell death that plays an important role in tumor biology. METHODS In this study, apoptosis-inducing activity is predicted by using a QSAR modeling approach for a series of 4-anilinoquinozaline derivatives. 2D-QSAR model for the prediction of apoptosis-inducing activity was obtained by applying multiple linear regression giving r2 = 0.8225 and q2 = 0...

Three different QSAR methods, Comparative Molecular Field Analysis (CoMFA), classical QSAR (utilizing the CODESSA program), and Hologram QSAR (HQSAR), are compared in terms of their potential for screening large data sets of chemicals as endocrine disrupting compounds (EDCs). While CoMFA and CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) have been commercially avail...