The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Matheus P Freitas - One of the best experts on this subject based on the ideXlab platform.
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improvement of multivariate image analysis applied to Quantitative structure activity Relationship qsar analysis by using wavelet principal component analysis ranking variable selection and least squares support vector machine regression qsar study o
Chemical Biology & Drug Design, 2009Co-Authors: Rodrigo A Cormanich, Mohammad Goodarzi, Matheus P FreitasAbstract:Inhibition of tyrosine kinase enzyme WEE1 is an important step for the treatment of cancer. The bioactivities of a series of WEE1 inhibitors have been previously modeled through comparative molecular field analyses (CoMFA and CoMSIA), but a two-dimensional image-based Quantitative Structure-Activity Relationship approach has shown to be highly predictive for other compound classes. This method, called multivariate image analysis applied to Quantitative Structure-Activity Relationship, was applied here to derive Quantitative Structure-Activity Relationship models. Whilst the well-known bilinear and multilinear partial least squares regressions (PLS and N-PLS, respectively) correlated multivariate image analysis descriptors with the corresponding dependent variables only reasonably well, the use of wavelet and principal component ranking as variable selection methods, together with least-squares support vector machine, improved significantly the prediction statistics. These recently implemented mathematical tools, particularly novel in Quantitative Structure-Activity Relationship studies, represent an important advance for the development of more predictive Quantitative Structure-Activity Relationship models and, consequently, new drugs.
Yufeng Tseng - One of the best experts on this subject based on the ideXlab platform.
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Characterization of a ligand-receptor binding event using receptor-dependent four-dimensional Quantitative Structure-Activity Relationship analysis.
Journal of Medicinal Chemistry, 2004Co-Authors: Craig L. Senese, And Anton J. Hopfinger, Yufeng TsengAbstract:Receptor-dependent four-dimensional Quantitative structure−activity Relationship (RD-4D-QSAR) analysis is used to map the ligand−receptor binding event characteristic of a set of 47 glucose analogue inhibitors of glycogen phosphorylase (GPb). Specifically, the geometric and energetic binding profiles are constructed, conformational changes are determined, and conformational couplings among structural units are identified for the composite set of ligand−receptor complexes. A pruned ligand−receptor model is used to estimate ligand−receptor thermodynamics. Rather than explicitly handling the large amount of structural data generated from each of the pruned ligand−receptor models, these complexes were divided into three subregions. The subregions consist of a “functional” region, the smallest region providing definitive information about inhibitor binding, and two “allosteric” regions that surround the “functional” region and are based on distances from the center of the catalytic site. Maximum information on...
Qianshu Li - One of the best experts on this subject based on the ideXlab platform.
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Quantitative structure activity Relationship model for amino acids as corrosion inhibitors based on the support vector machine and molecular design
Corrosion Science, 2014Co-Authors: Hongxia Zhao, Xiuhui Zhang, Lin Ji, Haixiang Hu, Qianshu LiAbstract:Abstract The inhibition performance of nineteen amino acids was studied by theoretical methods. The affection of acidic solution and protonation of inhibitor were considered in molecular dynamics simulation and the results indicated that the protonated amino-group was not adsorbed on Fe (1 1 0) surface. Additionally, a nonlinear Quantitative structure–activity Relationship (QSAR) model was built by the support vector machine. The correlation coefficient was 0.97 and the root mean square error, the differences between predicted and experimental inhibition efficiencies (%), was 1.48. Furthermore, five new amino acids were theoretically designed and their inhibition efficiencies were predicted by the built QSAR model.
Anitha Paleti - One of the best experts on this subject based on the ideXlab platform.
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Quantitative structure activity Relationship studies on some nonbenzodiazepine series of compounds acting at the benzodiazepine receptor
Bioorganic & Medicinal Chemistry, 1998Co-Authors: S. P. Gupta, Anitha PaletiAbstract:Quantitative structure–activity Relationship (QSAR) studies have been made on a few non-benzodiazepine series of compounds such as 3-substituted imidazo[1,2-b]pyridazines, 2-phenylimidazo[1,2-α]pyridines, 2-(alkoxycarbonyl)imidazo[2,1-b]benzothiazoles, and 2-arylquinolines. For the first series of compounds a Fujita–Ban approach has been followed, which revealed the highest activity contribution for 3,4-OCH2O group of 2-phenyl moiety and for a methoxy group at 6-position. For the rest of the series, a Hansch approach has been adopted. The hydrophobic and electronic properties of the various substituents have been found to play major roles in the binding of these compounds with the receptor. Based on these studies, a hypothetical model for the drug–receptor interaction has been proposed.
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Quantitative Structure–Activity Relationship Studies on Some Nonbenzodiazepines Binding to Benzodiazepine Receptor
Quantitative Structure-Activity Relationships, 1997Co-Authors: Anitha Paleti, S. P. GuptaAbstract:Quantitative structure–activity Relationship (QSAR) studies are made on some nonbenzodiazepine ligands of benzodiazepine receptor (BZR), namely a series of 6-arylpyrrolo[2,1-d][1,5]benzo-thiazepines, a series of pyrido[1,2-a]benzimidazoles, and a series of some fused imidazopyridines. A Fujita-Ban approach adopted for the first series led to the suggestion that the most advantageous substituents in the series were only 4-Cl and 7-OCON(CH3)2. All other substituents were found to have negative contributions to the binding of the compounds with the BZR. In the case of pyrido[1,2-a]benzimidazoles, the 8-CONHR group was found to play an important role in the binding. The activity was found to be significantly correlated with the hydrophobic property of the R moiety and the electron-withdrawing ability of the ortho substituents in it. In the case of imidazopyridines, the Fujita-Ban approach revealed only the negative effects of the substituents that really mattered. By Hansch analysis these negative effects were accounted for by steric parameters.
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Quantitative structure activity Relationship studies on benzodiazepine receptor binding of some nonbenzodiazepine series of ligands
Quantitative Structure-activity Relationships, 1996Co-Authors: S. P. Gupta, Anitha PaletiAbstract:Quantitative structure - activity Relationship (QSAR) studies have been performed on some non-benzodiazepine series of benzodiazepine receptor (BZR) ligands, namely a series of thienylpyrazologuinolines and a series of imidazoquinoxalines. Studies reveal the merits and demerits of the substituents and their physicochemical properties in each case, in addition to the essential requirements for the binding for each type of ligand.
Rodrigo A Cormanich - One of the best experts on this subject based on the ideXlab platform.
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improvement of multivariate image analysis applied to Quantitative structure activity Relationship qsar analysis by using wavelet principal component analysis ranking variable selection and least squares support vector machine regression qsar study o
Chemical Biology & Drug Design, 2009Co-Authors: Rodrigo A Cormanich, Mohammad Goodarzi, Matheus P FreitasAbstract:Inhibition of tyrosine kinase enzyme WEE1 is an important step for the treatment of cancer. The bioactivities of a series of WEE1 inhibitors have been previously modeled through comparative molecular field analyses (CoMFA and CoMSIA), but a two-dimensional image-based Quantitative Structure-Activity Relationship approach has shown to be highly predictive for other compound classes. This method, called multivariate image analysis applied to Quantitative Structure-Activity Relationship, was applied here to derive Quantitative Structure-Activity Relationship models. Whilst the well-known bilinear and multilinear partial least squares regressions (PLS and N-PLS, respectively) correlated multivariate image analysis descriptors with the corresponding dependent variables only reasonably well, the use of wavelet and principal component ranking as variable selection methods, together with least-squares support vector machine, improved significantly the prediction statistics. These recently implemented mathematical tools, particularly novel in Quantitative Structure-Activity Relationship studies, represent an important advance for the development of more predictive Quantitative Structure-Activity Relationship models and, consequently, new drugs.
