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QSAR Analysis (qsar + analysis)

Distribution by Scientific Domains

Chemistry	94%

Selected Abstracts

Insight into the Bioactivity and Metabolism of Human Glucagon Receptor Antagonists from 3D-QSAR Analyses

MOLECULAR INFORMATICS, Issue 8 2004
HaiFeng Chen
Abstract Descriptors, such as logP, the number of hydrogen bond donors, the number of hydrogen bond acceptors, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) combined with fields of CoMFA and CoMSIA to construct models for hyperglycemia decrease activity and metabolism of human glucagon receptor antagonists. The results reveal that including logP, HOMO and LUMO energies is meaningful for QSAR/QSMR model. The models were validated by using a test set of structural diverse compounds that had not been included in the CoMFA and CoMSIA models. Support Vector Machines (SVM) have been used to select the suitable additional descriptors to construct 3D-QSAR/QSMR models. A key factor to mention is that activity and metabolism models simultaneously. These in silico ADME models are helpful in making quantitative prediction of inhibitory activities and rates of metabolism before resorting in vitro and in vivo experimentation. [source]

QSAR Analysis of 2,3-Diaryl Benzopyrans/Pyrans as Selective COX-2 Inhibitors Based on Semiempirical AM1 Calculations

MOLECULAR INFORMATICS, Issue 8 2004
Sivaprakasam Prasanna
Abstract Quantitative structure-activity relationship (QSAR) analysis was performed on a combined series of 2,3 diaryl benzopyrans and pyrans for their cyclooxygenase-2 (COX-2) inhibition. QSAR investigations based on semiempirical, Austin Model-1 (AM1) calculations reveal that electronic and hydophobic interactions are primarily responsible for COX-2 enzyme-ligand interaction. The derived QSAR model aided by residual analysis demonstrated that the COX-2 inhibitory activity is highly correlated with the electronic descriptors, lowest unoccupied molecular orbital (ELUMO), Dipole-Z and hydrophobicity of the molecules. The conclusion can be drawn that more hydrophobic, electron-withdrawing substituents at 3rd aromatic ring of the lead structure improves activity. The lesser the Z component the ligand has, the more correct its orientation towards the COX-2 binding site. The derived QSAR model shows good internal (exemplified through leave one out-q2=0.786) and external (r=0.5737) predictive ability for a test set and can be used in designing better selective COX-2 inhibitors among these congeners in future. [source]

Combined 4D-fingerprint and clustering based membrane-interaction QSAR analyses for constructing consensus Caco-2 cell permeation virtual screens

JOURNAL OF PHARMACEUTICAL SCIENCES, Issue 1 2008
Osvaldo A. Santos-Filho
Abstract A set of 30 structurally diverse molecules, for which Caco-2 cell permeation coefficients were determined, formed the training set for construction of Caco-2 cell permeation models based upon membrane-interaction (MI) QSAR analysis and a new QSAR method called 4D-fingerprint QSAR analysis. The descriptor terms of the 4D-fingerprints equation are molecular similarity eigenvalues, and this set of descriptors is being evaluated as a potential "universal" QSAR descriptor set. The 4D-fingerprint model suggests that Caco-2 cell permeation is governed by the spatial distribution of hydrogen bonding and nonpolar groups over the molecular shape of a molecule. Moreover, a complementary resampling of the original Caco-2 cell permeation training set, followed by the construction of several "clustered" MI-QSAR models, led to a consensus model consistent in interpretation with the 4D-fingerprint model. © 2007 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97:566,583, 2008 [source]

A QSAR analysis of toxicity of Aconitum alkaloids

FUNDAMENTAL & CLINICAL PHARMACOLOGY, Issue 6 2004
Angélica M. Bello-Ramírez
Abstract Biological activity of Aconitum alkaloids may be related to their toxicity rather than to a specific pharmacological action. A Quantitative structure-activity relationships (QSAR) analysis was performed on the following two groups of alkaloids: compounds with an aroyl/aroyloxy group at R14 position (yunaconitine, bulleyaconitine, aconitine, beiwutine, nagarine, 3-acetyl aconitine, and penduline), and compounds with the aroyloxy group at R4 position (N -deacetyllappaconitine, lappaconitine, ranaconitine, N -deacetylfinaconitine, N -deacetylranaconitine). The LD50 (,mol/kg) of the 12 alkaloids were obtained from the literature. LD50 was significantly lower in group 1 than in group 2. The steric and core,core repulsion energies were significantly higher in group 1. The total energy and heat of formation and electronic energies were significantly lower in group 1. The reactivity index of N, C1,, C4, and C6, were similar between groups. The reactivity index of C2, was significantly higher and the reactivity index of C3, and C5, were significantly lower in group 1. Log P and pKa were similar between groups. Molecular weight was significantly higher in group 1. A significant linear relationship was observed between log LD50 and either analgesic log ED50 or local anesthetic log ED50. The LD50/analgesic ED50 obtained from average values was 5.9 for group 1 and 5.0 for group 2. However, the LD50/local anesthetic ED50 was 40.4 and 318, respectively. The study supports that the analgesic effects of these alkaloids are secondary to their toxic effects whereas alkaloids from group 2 are susceptible to be further studied as local anesthetic agents. [source]

Structure-hepatoprotective activity relationship study of sesquiterpene lactones: A QSAR analysis

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, Issue 1 2009
Yuliya Paukku
Abstract This study has been carried out using quantitative structure-activity relationship analysis (QSAR) for 22 sesquiterpene lactones to correlate and predict their hepatoprotective activity. Sesquiterpenoids, the largest class of terpenoids, are a widespread group of substances occurring in various plant organisms. QSAR analysis was carried out using methods such as genetic algorithm for variables selection among generated and calculated descriptors and multiple linear regression analysis. Quantum-chemical calculations have been performed by density functional theory at B3LYP/6-311G(d, p) level for evaluation of electronic properties using reference geometries optimized by semi-empirical AM1 approach. Three models describing hepatoprotective activity values for series of sesquiterpene lactones are proposed. The obtained models are useful for description of sesquiterpene lactones hepatoprotective activity and can be used to estimate the hepatoprotective activity of new substituted sesquiterpene lactones. The models obtained in our study show not only statistical significance, but also good predictive ability. The estimated predictive ability (r) of these models lies within 0.942,0.969. © 2008 Wiley Periodicals, Inc. Int J Quantum Chem, 2009 [source]

Development of a general quantum-chemical descriptor for steric effects: Density functional theory based QSAR study of herbicidal sulfonylurea analogues

JOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 13 2006
Zhen Xi
Abstract Quantitative structure-activity relationship (QSAR) analysis has become one of the most effective approaches for optimizing lead compounds and designing new drugs. Although large number of quantum-chemical descriptors were defined and applied successfully, it is still a big challenge to develop a general quantum-chemical descriptor describing the bulk effects more directly and effectively. In this article, we defined a general quantum-chemical descriptor by characterizing the volume of electron cloud for specific substituent using the method of density functional theory. The application of our defined steric descriptors in the QSAR analysis of sulfonylurea analogues resulted in four QSAR models with high quality (the best model: q2 = 0.881, r2 = 0.901, n = 35, s = 0.401, F = 68.44), which indicated that this descriptor may provide an effective way for solving the problem how to directly describe steric effect in quantum chemistry-based QSAR studies. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 1571,1576, 2006 [source]

Ionization-specific prediction of blood,brain permeability

JOURNAL OF PHARMACEUTICAL SCIENCES, Issue 1 2009
Kiril Lanevskij
Abstract This study presents a mechanistic QSAR analysis of passive blood,brain barrier permeability of drugs and drug-like compounds in rats and mice. The experimental data represented in vivo log,PS (permeability-surface area product) from in situ perfusion, brain uptake index, and intravenous administration studies. A data set of 280 log,PS values was compiled. A subset of 178 compounds was assumed to be driven by passive transport that is free of plasma protein binding and carrier-mediated effects. This subset was described in terms of nonlinear lipophilicity and ionization dependences, that account for multiple kinetic and thermodynamic effects: (i) drug's kinetic diffusion, (ii) ion-specific partitioning between plasma and brain capillary endothelial cell membranes, and (iii) hydrophobic entrapment in phospholipid bilayer. The obtained QSAR model provides both good statistical significance (RMSE,<,0.5) and simple physicochemical interpretations (log,P and pKa), thus providing a clear route towards property-based design of new CNS drugs. © 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:122,134, 2009 [source]

Combined 4D-fingerprint and clustering based membrane-interaction QSAR analyses for constructing consensus Caco-2 cell permeation virtual screens

QSAR analysis of interstudy variable skin permeability based on the "latent membrane permeability" concept

JOURNAL OF PHARMACEUTICAL SCIENCES, Issue 10 2003
Shin-Ichi Fujiwara
Abstract A number of QSAR models for skin permeability have been proposed, but these models lack consistency due to interspecies and interlaboratory differences. This study was initiated to extract an essential QSAR from the multiplicity of data sets of skin permeability by using a novel statistical approach. Ten data sets were collected from the literature, which include a total of 111 permeability coefficients in human, hairless mouse, or hairless rat skin for 94 structurally diverse compounds. Following a Potts and Guy's approach, the octanol/water partition coefficient and molecular weight were chosen as molecular descriptors. All of the data sets were analyzed simultaneously, assuming that all of the sets share a latent, common factor as far as the structure/permeability relationship is concerned. Despite the fact that the degree-of-freedom for the present analysis was limited compared with that for individual regression analyses, the determination coefficients (R2) were high enough for all the 10 data sets, with an average R2 of 0.815 (average R2,=,0.825 for individual analyses). Thus, skin permeability of compounds can be well explained from the log P and M.W., where the ratio of the contribution to skin permeability was approximately 1:1. © 2003 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 92:1939,1946, 2003 [source]

Molecular Modeling and Receptor-Dependent (RD) 3D-QSAR Approach to a Set of Antituberculosis Derivatives

MOLECULAR INFORMATICS, Issue 11-12 2009
Fernanda, Kerly, Mesquita Pasqualoto
Abstract In this study, receptor-dependent (RD) 3D-QSAR models were built for a set of thirty-seven isoniazid derivatives bound to the enoyl-acp reductase from M. tuberculosis, called InhA (PDB entry code 1zid). Ligand-receptor (L-R) molecular dynamics (MD) simulations [500,000 steps; the step size was 0.001,ps (1,fs)] were carried out at 310,K (biological assay temperature). The hypothesized active conformations resulting from a previously reported receptor-independent (IR) 4D-QSAR analysis were used as the molecular geometries of each ligand in this structure-based L-R binding research. The dependent variable is the reported MIC values against M. tuberculosis var. bovis. The independent variables (descriptors) are energy terms of a modified first-generation AMBER force field combined with a hydration shell aqueous solvation model. Genetic function approximation (GFA) formalism and partial least squares (PLS) regression were employed as the fitting functions to develop 3D-QSAR models. The bound ligand solvation energy, the sum of electrostatic and hydrogen bonding energies of the unbound ligand, the bending energy of the unbound ligand, the electrostatic intermolecular L-R energy, and the change in hydrogen bonding energy upon binding were found as important energy contributions to the binding process. The 3D-QSAR model at 310,K has good internal and external predictability and may be regarded as representative of the binding process of ligands to InhA. [source]

The QSAR Modeling of Cytotoxicity on Anthraquinones

MOLECULAR INFORMATICS, Issue 8 2009
Kalev Takkis
Abstract A QSAR analysis was carried out on a dataset of 126 anthraquinone-based cytotoxic compounds. A PCA of the molecular descriptors was used to cluster the dataset into smaller subsets according to their structural features and QSAR models were derived for the selected sets. During the modeling, protonated states of molecules and nonlinear transformations of the descriptors were considered. The developed models have been interpreted in the context of cytotoxicity and validated with leave-one-out, and leave-many-out cross-validation. The descriptors in the resulting models describe the size and charge distribution of molecules although in different clusters their proportions vary. [source]

Quantitative Structure,Activity Relationship Studies for the Binding Affinities of Imidazobenzodiazepines for the ,6 Benzodiazepine Receptor Isoform Utilizing Optimized Blockwise Variable Combination by Particle Swarm Optimization for Partial Least Squares Modeling

MOLECULAR INFORMATICS, Issue 1 2007
Leqian Hu
Abstract Binding affinities of a series of substituted imidazobenzodiazepines for the ,6 Benzodiazepine Receptor (BzR) isoform are investigated by the Optimized Blockwise Variable Combination (OBVC) by Particle Swarm Optimization (PSO) based on Partial Least Squares (PLS) modeling. The QSAR analysis result showed that MolRef, AlogP, MRCM**-3, Rotatable bonds (Rotlbonds), Hydrogen Bond Acceptors (Hbond acceptor), five Jurs descriptors, two Shadow indices descriptors and principal moment of inertia are the most important descriptors among all the investigated descriptors. One can change the molar refractivity, the polar interactions between molecules, the shape of the molecules, the principal moments of inertia about the principal axes of a molecule, the hydrophobic character of the molecule, the number of Rotlbonds and Hbond acceptors of the compounds to adjust the binding affinities of imidazobenzodiazepine for the ,6 BzR isoform. The Quantitative Structure,Activity Relationship (QSAR) analysis result was also compared with MLR, PLS, and hierarchical PLS algorithms. It has been demonstrated that OBVC by PSO for PLS modeling shows satisfactory performance in the QSAR analysis. [source]

A Quantum-Mechanical QSAR Model to Predict the Refractive Index of Polymer Matrices

MOLECULAR INFORMATICS, Issue 10 2006
Andrew
Abstract Refractive index (RI) is an important optical property for polymer matrices, especially when the color or tint of the cured material is of interest. This is certainly the case for dental restorative applications. In this work, results are presented for a quantitative,structure activity relationship derived from relevant semiempirical quantum mechanical information. This model predicts the RI for a wide variety of polymer matrices using representative structures of polymers, including resin components of several currently used dental restorative materials. The AM1 semiempirical method was used for calculations due to its speed and general reliability. Several structural subunits of the polymer chains were used for the QSAR analysis, but dimer moieties produced the best results for some 60 polymers. The final QSAR model was composed of a multilinear equation that featured the highest occupied molecular orbital , the lowest unoccupied molecular orbital gap and a polarizability index as the two descriptors best able to account for the variation in the data. The final model had R2=0.963, R2cv=0.959, F=740, and s2=0.0002. Other quality indicators for the correlation and the individual descriptors were within acceptable limits. The presence of electronically related descriptors is encouraging, as these are conceptually tied to the phenomenon of RI. The difference between a theoretically predicted value for poly(propylene oxide) and its monomer was 0.04, as compared to 0.09 from experimental data. [source]

An Information-Theoretic Approach to Descriptor Selection for Database Profiling and QSAR Modeling

MOLECULAR INFORMATICS, Issue 5 2003
Jeffrey
Abstract In order to rationalize the selection of molecular descriptors for QSAR and other applications, we have adapted the Shannon entropy concept that was originally developed in digital communication theory. The approach has been extended to facilitate the large-scale analysis of molecular descriptors and their information content in diverse compound databases. This has enabled us to identify descriptors with consistently high information content. Furthermore, it has been possible to select descriptors that are sensitive to systematic property differences in diverse compound collections (synthetic compounds, natural products, drug-like molecules, or drugs) and, in addition, to quantify such database-specific differences. Selection of descriptors based on information content has been proven useful for binary QSAR analysis. In this review, we describe the principles of entropy-based descriptor selection and discuss different applications. [source]

Searching Inhibitors of Adenosine Kinase by Simulation Methods

CHINESE JOURNAL OF CHEMISTRY, Issue 11 2006
Rui-Xin Zhu
Abstract Searching new inhibitors of adenosine kinase (AK) is still drawing attention of experimental scientists. A better and solid model is here proposed by means of simulation methods from different ways, the direct analysis of receptor itself, the conventional 3D-QSAR methods and the integration of docking method and the conventional QSAR analysis. [source]