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V A Palyulin

Publications and source records attributed to V A Palyulin.

At least 19 recordsLinked to original sources
PubMed2005

Role of two chloride-binding sites in functioning of testicular angiotensin-converting enzyme.

Modeling the structure of the C-domain of bovine angiotensin-converting enzyme revealed two putative chloride-binding sites. The kinetic parameters, K(m) and k(cat), of hydrolysis of the substrate Cbz-Phe-His-Leu catalyzed by the testicular (C-domain) enzyme were determined over a wide range of chloride concentrations. Chloride anions were found to be enzyme activators at relatively low concentrations, but they inhibit enzymatic activity at high concentrations. A general scheme for the effect of chloride anions on activity of the C-domain of bovine angiotensin-converting enzyme accounting for binding the "activating" and "inhibiting" anions is suggested.

Amino Acid Sequence↗
PubMed2005

Highly diverse, massive organic data as explored by a composite QSPR strategy: an advanced study of boiling point.

An improved strategy of quantitative structure-property relationship (QSPR) studies of diverse and inhomogeneous organic datasets has been proposed. A molecular connectivity term was successively corrected for different structural features encoded in fragmental descriptors. The so-called solvation index 1chis (a weighted Randic index) was used as a "leading" variable and standardized molecular fragments were employed as "corrective" class-specific variables. Performance of the new approach was illustrated by modelling a dataset of experimental normal boiling points of 833 organic compounds belonging to 20 structural classes. Firstly, separate QSPR models were derived for each class and for eight groups of structurally similar classes. Finally, a general model formed by combining all the classes together was derived (r2=0.957, s=12.9degreesC). The strategy outlined can find application in QSPR analyses of massive, highly diverse databases of organic compounds.

Linear Models↗
PubMed2002

An approach to the interpretation of backpropagation neural network models in QSAR studies.

An approach to the interpretation of backpropagation neural network models for quantitative structure-activity and structure-property relationships (QSAR/QSPR) studies is proposed. The method is based on analyzing the first and second moments of distribution of the values of the first and the second partial derivatives of neural network outputs with respect to inputs calculated at data points. The use of such statistics makes it possible not only to obtain actually the same characteristics as for the case of traditional "interpretable" statistical methods, such as the linear regression analysis, but also to reveal important additional information regarding the non-linear character of QSAR/QSPR relationships. The approach is illustrated by an example of interpreting a backpropagation neural network model for predicting position of the long-wave absorption band of cyane dyes.

Adsorption↗
PubMed2002

A new scheme for electronegativity equalization as a source of electronic descriptors: application to chemical reactivity.

Recently, we have proposed a new unconventional scheme of electronegativity equalization. We assume that atomic electronegativities are equalized in a molecule in the same manner as electrical potentials in the nodes of a closed electrical network, with the molecule being represented by a molecular graph (MG). Computational procedure includes several operations over the matrices associated with MG. The only parameter used is the atomic electronegativity (EN). The method is very fast and easy-to-implement. With the help of the method one can generate a family of electronic descriptors (equalized or "effective" ENs and atomic charges) which are applicable to the treatment of some types of chemical reactivity. Quite good correlations of the descriptors obtained are observed for such thermodynamic and kinetic data as proton affinity and Taft's inductive sigma* constants. The EN equalization scheme suggested can be used as an independent tool for molecular electrostatics modeling, or can be implemented into an integrated QSAR software.

Electricity↗
PubMed2001

The learned symmetry concept in revealing quantitative structure-activity relationships with artificial neural networks.

A method to build QSAR models based on substituent constants for congeneric sets of compounds having several topologically equivalent substituent positions was proposed. The approach is based on the application of artificial neural networks (learning to construct nonlinear structure-activity relationships taking into account necessary symmetry properties of training set structures) to a training set expanded by adding the copies of compounds with the same activity values but with permuted assignment of equivalent substituent positions. The better predictive power of these constructed models, as compared with the performances of neural network models for non-expanded sets was demonstrated for the calcium channel blockers of 1,4-dihydropyridine type and for hallucinogenic phenylalkylamines.

Calcium Channel Blockers↗
PubMedDate not supplied

QSAR for boiling points of "small" sulfides. Are the "high-quality structure-property-activity regressions" the real high quality QSAR models?

Our investigation was motivated by a recent paper concerning "high-quality structure-property-activity regressions" (J. Chem. Inf. Comput. Sci. 2000, 40, 899-905) for boiling points of small sulfides using variable connectivity indices. We performed QSAR study of the same data set using a logically preselected solvation index and obtained a very good one-parameter regression. The structures of the whole possible set of small sulfides (C2-C6) were generated and the statistics were proven by real prediction using an external test set of sulfides. The variants of extended prediction with extrapolated data and QSAR using an expanded training set were also performed, and all these data also revealed the preference of the solvation index. A general problem of descriptive vs predictive QSAR is discussed.

Journal Article↗