Browsing by Author "Freitas, Matheus P."
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Item 2D-Discrete Fourier Transform: Generalization of the MIA-QSAR strategy in molecular modeling(Chemometrics and Intelligent Laboratory Systems, 2015) Barigye, Stephen J.; Freitas, Matheus P.Adequate alignment of chemical structure images with respect to the basic scaffold in a series of chemical compounds constitutes an indispensable requirement for constructing multivariate images (MVIs) and subsequent molecular modeling using the Multivariate Image Analysis applied to Quantitative Structure–Activity Relationship (MIA-QSAR) approach. However, up to the moment, this alignment procedure has been manually performed, based on subjective ocular precision. The 2D-Discrete Fourier Transform (2D-DFT) is introduced as a strategy for creating a common base to construct MVIs for chemical structures using their magnitude spectra. The utility of magnitude spectra in QSAR studies has been evaluated through models for the antimalarial, anticancer and trichomonicidal activity of a series of 2, 5-diaminobenzophenone, 4-phenylpyrrolocarbazole and benzimidazole derivatives, respectively, yielding satisfactory results comparable to superior to those reported in the literature. It is anticipated that this strategy should enable the application of the MIA-QSAR approach to structurally diverse datasets other than a series of congeneric datasets.Item Aug-MIA-SPR/PLS-DA Classification of Carbonyl Herbicides according to Levels of Soil Sorption(Geoderma, 2016) Freitas, Mirlaine R.; Barigye, Stephen J.; Daré, Joyce K.; Freitas, Matheus P.A major challenge in the design of new herbicides lies in the development of highly active, environmentally friendly compounds. Soil sorption is an ecotoxicological parameter used to probe the prospective environmental fate of persistent organic pollutants, such as some herbicides. This parameter, described in terms of logKOC (the logarithm of the soil/water partition coefficient normalized to organic carbon), is usually estimated using the octanol/water partition coefficient (logP, easily calculated or determined experimentally). However, estimations obtained with the logP are not always accurate. Thus, this work reports the use of molecular descriptors derived from multivariate image analysis of carbonyl herbicides to achieve a predictive classification model based on the partial least squares-discriminant analysis (PLS-DA) method. This model yields 80% accuracy in calibration, 75% in leave-one-out cross-validation and 100% in external validation. In addition, the Y-randomization test reveals that the obtained model is stable from fortuitous correlation, since the accuracy in calibration after shuffling the classes block is only 0.5%. Chemical interpretation in terms of the structural features that affect soil sorption is performed, based on the weights of the selected variables in the classification model. Finally, novel herbicides are rationally designed, based on the inferences arrived at in the structural interpretation experiment and predictions of their qualitative and quantitative soil sorption profiles performed, using the built aug-MIA-SPR and Wang's models, respectively.Item Discrete Fourier Transform based Multivariate Image Analysis: Application to Modeling Aromatase Inhibitory Activity.(ACS Combinatorial Science, 2018) Barigye, Stephen J.; Freitas, Matheus P.; Ausina, Priscila; Castillo-Garit, Juan AlbertoWe recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure–activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.Item Exploring MIA-QSPR's for the modeling of biomagnification factors of aromatic organochlorine pollutants(Ecotoxicology and Environmental Safety, 2017) Mota, Estella G. da; Duarte, Mariene H.; Barigye, Stephen J.; Ramalho, Teodorico C.; Freitas, Matheus P.Biomagnification of organic pollutants in food webs has been usually associated to hydrophobicity and other molecular descriptors. However, direct information on atoms and substituent positions in a molecular scaffold that most affect this biological property is not straightforward using traditional QSPR techniques. This work reports the QSPR modeling of biomagnification factors (logBMF) of a series of aromatic organochlorine compounds using three MIA-QSPR (multivariate image analysis applied to QSPR) approaches. The MIA-QSPR model based on augmented molecular images (described with atoms represented as circles with sizes proportional to the respective van der Waals radii and having colors numerically proportional to the Pauling's electronegativity) encoded better the logBMF data. The average results for the main statistical parameters used to attest the model's predictability were r2=0.85, q2=0.72 and r2test=0.85. In addition, chemical insights on substituents and respective positions at the biphenyl rings A and B, and dibenzo-p-dioxin and dibenzofuran motifs are given to aid the design of more ecofriendly derivatives.Item Is Molecular Alignment an Indispensable Requirement in the MIA-QSAR Method?(Journal of Computational Chemistry, 2015) Barigye, Stephen J.; Freitas, Matheus P.For a decade, the multivariate image analysis applied to quantitative structure–activity relationship (MIA-QSAR) approach has been successfully used in the modeling of several chemical and biological properties of chemical compounds. However, the key pitfall of this method has been its exclusive applicability to congeneric datasets due to the prerequisite of aligning the chemical images with respect to the basic molecular scaffold. The present report aims to explore the use of the 2D-discrete Fourier transform (2D-DFT) as a means of opening way to the modeling, for the first time, of structurally diverse noncongruent chemical images. The usability of the 2D-DFT in QSAR modeling of noncongruent chemical compounds is assessed using a structurally diverse dataset of 100 compounds, with reported inhibitory activity against MCF-7 human breast cancer cell line. An analysis of the statistical parameters of the built regression models validates their robustness and high predictive power. Additionally, a comparison of the results obtained with the 2D-DFT MIA-QSAR approach with those of the DRAGON molecular descriptors is performed, revealing superior performance for the former. This result represents a milestone in the MIA-QSAR context, as it opens way for the possibility of screening for new molecular entities with the desired chemical or therapeutic utility.Item MIA-plot: a Graphical Tool for Viewing Descriptor Contributions in MIA-QSAR(RSC advances, 2016) Barigye, Stephen J.; Duarte, Mariene H.; Nunes, Cleiton A.; Freitas, Matheus P.A major challenge faced with the MIA-QSAR (Multivariate Image Analysis applied to Quantitative Structure–Activity Relationships) technique in molecular modeling is to obtain chemically intuitive information from the molecular descriptors that correlate to bioactivity values. This work reports a graphical tool that uses PLS regression coefficients (b) and the variable importance in projection (VIP) scores to give insight on the structural motifs responsible for increased or attenuated biological activities in a congeneric series of compounds. Three sets of compounds useful in food chemistry, namely fatty acids and derivatives (antimicrobial), anilides (antimicrobial) and disaccharides (sweeteners), were used to assess the practical contribution of these parameters in yielding more interpretable models. The MIA-QSAR models demonstrated satisfactory prediction performance and, most importantly, the relevance of single MIA predictors to the response variable could be assessed, thus allowing for the structural, electronic and physicochemical interpretation of the MIA-QSAR models. While VIP-based graphs were especially useful in providing insight on the molecular regions/substituents most affecting the property values, the b-based analysis indicated whether the particular regions/substituents negatively or positively influenced the dependent variables. It is therefore recommended that these parameters be employed as complementary tools for a more robust and lucid model interpretation.Item Quantitative modeling of bioconcentration factors of carbonyl herbicides using multivariate image analysis(Chemosphere, 2016) Freitas, Mirlaine R.; Barigye, Stephen J.; Dare, Joyce K.; Freitas, Matheus P.The bioconcentration factor (BCF) is an important parameter used to estimate the propensity of chemicals to accumulate in aquatic organisms from the ambient environment. While simple regressions for estimating the BCF of chemical compounds from water solubility or the n-octanol/water partition coefficient have been proposed in the literature, these models do not always yield good correlations and more descriptive variables are required for better modeling of BCF data for a given series of organic pollutants, such as some herbicides. Thus, the logBCF values for a set of carbonyl herbicides comprising amide, urea, carbamate and thiocarbamate groups were quantitatively modeled using multivariate image analysis (MIA) descriptors, derived from colored image representations for chemical structures. The logBCF model was calibrated and vigorously validated (r2 = 0.79, q2 = 0.70 and rtest2 = 0.81), providing a comprehensive three-parameter linear equation after variable selection (logBCF = 5.682 − 0.00233 × X9774 − 0.00070 × X813 − 0.00273 × X5144); the variables represent pixel coordinates in the multivariate image. Finally, chemical interpretation of the obtained models in terms of the structural characteristics responsible for the enhanced or reduced logBCF values was performed, providing key leads in the prospective development of more eco-friendly synthetic herbicides.Item Towards Molecular Design using 2D-Molecular Contour Maps Obtained from PLS Regression Coefficients(Molecular Physics, 2017) Borges, Cleber N.; Barigye, Stephen J.; Freitas, Matheus P.The multivariate image analysis descriptors used in quantitative structure-activity relationships are direct representations of chemical structures as they are simply numerical decodifications of pixels forming the 2D chemical images. These MDs have found great utility in the modeling of diverse properties of organic molecules. Given the multicollinearity and high dimensionality of the data matrices generated with the MIA-QSAR approach, modeling techniques that involve the projection of the data space onto orthogonal components e.g. Partial Least Squares (PLS) have been generally used. However, the chemical interpretation of the PLS-based MIA-QSAR models, in terms of the structural moieties affecting the modeled bioactivity has not been straightforward. This work describes the 2D-contour maps based on the PLS regression coefficients, as a means of assessing the relevance of single MIA predictors to the response variable, and thus allowing for the structural, electronic and physicochemical interpretation of the MIA-QSAR models. A sample study to demonstrate the utility of the 2D-contour maps to design novel drug-like molecules is performed using a dataset of some anti-HIV-1 2-amino-6-arylsulfonylbenzonitriles and derivatives, and the inferences obtained are consistent with other reports in the literature. In addition, the different schemes for encoding atomic properties in molecules are discussed and evaluated.