Quantitative Structure Activity Relationship (quantitative + structure_activity_relationship)

Distribution by Scientific Domains

Selected Abstracts

An Electrophilicity Based Analysis of Toxicity of Aromatic Compounds Towards Tetrahymena Pyriformis

R. Roy
Abstract Electrophilicity index is one of the important quantum chemical descriptors in describing toxicity or biological activities of the diverse classes of chemicals to bio-systems in the context of development of Quantitative Structure Activity Relationship (QSAR). In this study a large number of selected 174 aromatic compounds containing phenols, nitrobenzenes and benzonitriles are chosen as the training set to verify their toxic potency to Tetrahymena pyriformis in the light of electrophilicity. A systematic analysis has been made to find out the electron donation/acceptance nature of these model compounds by comparing their electronegativity values with those of the NA bases/DNA base pairs. The training sets are classified into two groups, viz., the electron donor group comprising 97 phenol derivatives and the electron acceptor group consisting of 77 nitrobenzenes and benzonitriles grouped together. Regression analysis in terms of correlation coefficient (), variance adjusted to degrees of freedom () and variance of leave-one-out cross-validation () has been made for both the electron donor and acceptor aromatic groups to predict the toxicity values of these model compounds to Tetrahymena pyriformis. It is heartening to note that the global and local electrophilicity indices along with the total Hartree-Fock energy can explain more than 80% of cross-validation variance of data of those aromatic molecules. [source]

Theoretical studies of some sulphonamides as corrosion inhibitors for mild steel in acidic medium

Eno E. Ebenso
Abstract Density functional theory (DFT) at the B3LYP/6-31G (d,p) and BP86/CEP-31G* basis set levels and ab initio calculations using the RHF/6-31G (d,p) methods were performed on four sulfonamides (namely sulfaacetamide (SAM), sulfapyridine (SPY), sulfamerazine (SMR), and sulfathiazole (STI)) used as corrosion inhibitors for mild steel in acidic medium to determine the relationship between molecular structure and their inhibition efficiencies (%IE). The order of inhibition efficiency obtained was SMR > SPY > STI > SAM which corresponded with the order of most of the calculated quantum chemical parameters namely EHOMO (highest occupied molecular orbital energy), ELUMO (lowest unoccupied molecular orbital energy), the energy gap (,E), the Mulliken charges on the C, O, N, S atoms, hardness (,), softness (S), polarizability (,), dipole moment (,), total energy change (,ET), electrophilicity (,), electron affinity (A), ionization potential (I), the absolute electronegativity (,), and the fraction of electrons transferred (,N). Quantitative structure activity relationship (QSAR) approach has been used and a correlation of the composite index of some of the quantum chemical parameters was performed to characterize the inhibition performance of the sulfonamides studied. The results showed that the %IE of the sulfonamides was closely related to some of the quantum chemical parameters but with varying degrees/order. The calculated %IE of the sulfonamides studied was found to be close to their experimental corrosion inhibition efficiencies. The experimental data obtained fits the Langmuir adsorption isotherm. The negative sign of the EHOMO values and other thermodynamic parameters obtained indicates that the data obtained supports physical adsorption mechanism. 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2010 [source]

Comparative QSAR Studies on Toxicity of Phenol Derivatives Using Quantum Topological Molecular Similarity Indices

Bahram Hemmateenejad
Quantitative structure activity relationship (QSAR) analyses using a novel type of electronic descriptors called quantum topological molecular similarity (QTMS) indices were operated to describe and compare the mechanisms of toxicity of phenols toward five different strains (i.e., Tetrahymena pyriformis, L1210 Leukemia, Pseudomonas putida, Raja japonica and Cucumis sativus). The appropriate QSAR models for the toxicity data were obtained separately employing partial least squares (PLS) regression combined with genetic algorithms (GA), as a variable selection method. The resulting QSAR models were used to identify molecular fragments of phenol derivatives whose electronic properties contribute significantly to the observed toxicities. Using this information, it was feasible to discriminate between the mechanisms of action of phenol toxicity to the studied strains. It was found that toxicities of phenols to all strains, except with L1210 Leukemia, are significantly affected by electronic features of the phenolic hydroxyl group (C-O-H). Meanwhile, the resulting models can describe the inductive and resonance effects of substituents on various toxicities. [source]

Quantitative structure,activity relationship study on the inhibitors of fatty acid amide hydrolase

Peng Lu
Abstract A quantitative structure activity relationship (QSAR) analysis was performed on the values of a series of fatty acid amide hydrolase (FAAH) inhibitors. Six molecular descriptors selected by CODESSA software were used as inputs to perform heuristic method (HM) and support vector machine (SVM). The results obtained by SVM were compared with those obtained by the HM. The root mean square errors (RMSEs) for the training set given by HM and SVM were 0.555 and 0.404, respectively, which shows that the performance of the SVM model is better than that of the HM model. This paper provides a new and effective method for predicting the activity of FAAH inhibitors. Copyright 2010 John Wiley & Sons, Ltd. [source]

Modeling based on subspace orthogonal projections for QSAR and QSPR research

Yizeng Liang
Abstract A novel projection modeling method for quantitative structure activity relationship (QSAR) and quantitative structure property relationship (QSPR) is developed in this paper. Orthogonalization of block variables is introduced to deal with the problem of variable selection. Projections based on least squares are used to construct the modeling space in order to search for the best regression directions for chemical modeling. A suitable prediction space for such a model is further defined to confine the usage range of the model. Three real data sets were analyzed to check the performance of the proposed modeling method. The results obtained from Monte-Carlo cross-validation (MCCV) showed that the proposed modeling method might provide better results for QSAR and QSPR modeling than PCR and PLS with respect to both fitting and prediction abilities. Copyright 2007 John Wiley & Sons, Ltd. [source]

Impartial graphical comparison of multivariate calibration methods and the harmony/parsimony tradeoff

Forrest Stout
Abstract For multivariate calibration with the relationship y,=,Xb, it is often necessary to determine the degrees of freedom for parsimony consideration and for the error measure root mean square error of calibration (RMSEC). This paper shows that degrees of freedom can be estimated by an effective rank (ER) measure to estimate the model fitting degrees of freedom and the more parsimonious model has the smallest ER. This paper also shows that when such a measure is used on the X-axis, simultaneous graphing of model errors and other regression diagnostics is possible for ridge regression (RR), partial least squares (PLS) and principal component regression (PCR) and thus, a fair comparison between all potential models can be accomplished. The ER approach is general and applicable to other multivariate calibration methods. It is often noted that by selecting variables, more parsimonious models are obtained; typically by multiple linear regression (MLR). By using the ER, the more parsimonious model is graphically shown to not always be the MLR model. Additionally, a harmony measure is proposed that expresses the bias/variance tradeoff for a particular model. By plotting this new measure against the ER, the proper harmony/parsimony tradeoff can be graphically assessed for RR, PCR and PLS. Essentially, pluralistic criteria for fairly valuating and characterizing models are better than a dualistic or a single criterion approach which is the usual tactic. Results are presented using spectral, industrial and quantitative structure activity relationship (QSAR) data. Copyright 2007 John Wiley & Sons, Ltd. [source]

Exhaustive Structure Generation for Inverse-QSPR/QSAR

Tomoyuki Miyao
Abstract Chemical structure generation based on quantitative structure property relationship (QSPR) or quantitative structure activity relationship (QSAR) models is one of the central themes in the field of computer-aided molecular design. The objective of structure generation is to find promising molecules, which according to statistical models, are considered to have desired properties. In this paper, a new method is proposed for the exhaustive generation of chemical structures based on inverse-QSPR/QSAR. In this method, QSPR/QSAR models are constructed by multiple linear regression method, and then the conditional distribution of explanatory variables given the desired properties is estimated by inverse analysis of the models using the framework of a linear Gaussian model. Finally, chemical structures are exhaustively generated by a sophisticated algorithm that is based on a canonical construction path method. The usefulness of the proposed method is demonstrated using a dataset of the boiling points of acyclic hydrocarbons containing up to 12 carbon atoms. The QSPR model was constructed with 600 hydrocarbons and their boiling points. Using the proposed method, chemical structures which had boiling points of 100, 150, or 200,C were exhaustively generated. [source]

Estimation of Aqueous-Phase Reaction Rate Constants of Hydroxyl Radical with Phenols, Alkanes and Alcohols

Ya-nan Wang
Abstract A quantitative structure activity relationship (QSAR) model was developed for the aqueous-phase hydroxyl radical reaction rate constants (kOH) employing quantum chemical descriptors and multiple linear regressions (MLR). The QSAR development followed the OECD guidelines, with special attention to validation, applicability domain (AD) and mechanistic interpretation. The established model yielded satisfactory performance: the correlation coefficient square (R2) was 0.905, the root mean squared error (RMSE) was 0.139, the leave-many-out cross-validated QLMO2 was 0.806, and the external validated QEXT2 was 0.922 log units. The AD of the model covering compounds of phenols, alkanes and alcohols, was analyzed by Williams plot. The main molecular structural factors governing kOH are the energy of the highest occupied molecular orbital (EHOMO), average net atomic charges on hydrogen atoms (), molecular surface area (MSA) and dipole moment (,). It was concluded that kOH increased with increasing EHOMO and MSA, while decreased with increasing and ,. [source]