Ordered Predictors Selection Research Articles

LQTA-QSAR: A New 4D-QSAR Methodology

A novel 4D-QSAR approach which makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package is presented in this study. This new methodology, named LQTA-QSAR (LQTA, Laboratório de Quimiometria Teórica e Aplicada), has a module (LQTAgrid) that calculates intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. The comparison of the proposed methodology to other 4D-QSAR and CoMFA formalisms was performed using a set of forty-seven glycogen phosphorylase b inhibitors (data set 1) and a set of forty-four MAP p38 kinase inhibitors (data set 2). The QSAR models for both data sets were built using the ordered predictor selection (OPS) algorithm for variable selection. Model validation was carried out applying y-randomization and leave-N-out cross-validation in addition to the external validation. PLS models for data set 1 and 2 provided the following statistics: q(2) = 0.72, r(2) = 0.81 for 12 variables selected and 2 latent variables and q(2) = 0.82, r(2) = 0.90 for 10 variables selected and 5 latent variables, respectively. Visualization of the descriptors in 3D space was successfully interpreted from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Multivariate QSAR study of 4,5-dihydroxypyrimidine carboxamides as HIV-1 integrase inhibitors

A multivariate QSAR study of thirty-three 4,5-dihydroxypyrimidine carboxamides as HIV-1 integrase (HIV-1 IN) inhibitors was performed employing Ordered Predictors Selection (OPS) algorithm and PLS regression for variable selection and model construction, respectively. Four descriptors were chosen and a reasonable model ( n = 30; R 2 = 0.68; SEC = 0.57; PRESS cal = 8.72; F (2,27) = 28.97; Q 2 LOO = 0.58; SEV = 0.62; PRESS val = 11.62; R 2 pred = 0.87; SEP = 0.29; ARE pred = 4.37%; k = 0.99; k′ = 1.01; | r 2 0 − r 2 0′| = −0.18) was built with two latent variables (59.54% of the information). Leave- N-out (LNO) and Y-randomization methods confirmed the model robustness. The descriptors indicated that the HIV-1 IN inhibition depends on the electronic distribution of the investigated compounds. The interpretation of the model is related to the most accepted mechanism of action.

Prediction of sensory properties of Brazilian Arabica roasted coffees by headspace solid phase microextraction-gas chromatography and partial least squares

Volatile compounds in fifty-eight Arabica roasted coffee samples from Brazil were analyzed by SPME-GC-FID and SPME-GC-MS, and the results were compared with those from sensory evaluation. The main purpose was to investigate the relationships between the volatile compounds from roasted coffees and certain sensory attributes, including body, flavor, cleanliness and overall quality. Calibration models for each sensory attribute based on chromatographic profiles were developed by using partial least squares (PLS) regression. Discrimination of samples with different overall qualities was done by using partial least squares-discriminant analysis (PLS-DA). The alignment of chromatograms was performed by the correlation optimized warping (COW) algorithm. Selection of peaks for each regression model was performed by applying the ordered predictors selection (OPS) algorithm in order to take into account only significant compounds. The results provided by the calibration models are promising and demonstrate the feasibility of using this methodology in on-line or routine applications to predict the sensory quality of unknown Brazilian Arabica coffee samples. According to the PLS-DA on chromatographic profiles of different quality samples, compounds 3-methypropanal, 2-methylfuran, furfural, furfuryl formate, 5-methyl-2-furancarboxyaldehyde, 4-ethylguaiacol, 3-methylthiophene, 2-furanmethanol acetate, 2-ethyl-3,6-dimethylpyrazine, 1-(2-furanyl)-2-butanone and three others not identified compounds can be considered as possible markers for the coffee beverage overall quality.