Biomedical subjects

O Ivanciuc

Publications and source records attributed to O Ivanciuc.

9 recordsLinked to original sources

PubMed2001

Quantitative structure-retention relationships for gas chromatographic retention indices of alkylbenzenes with molecular graph descriptors.

Quantitative structure-retention relationships (QSRR) represent statistical models that quantify the connection between the molecular structure and the chromatographic retention indices of organic compounds, allowing the prediction of retention indices of novel, not yet synthesized compounds, solely from their structural descriptors. Using multiple linear regression, QSRR models for the gas chromatographic Kováts retention indices of 129 alkylbenzenes are generated using molecular graph descriptors. The correlational ability of structural descriptors computed from 10 molecular matrices is investigated, showing that the novel reciprocal matrices give numerical indices with improved correlational ability. A QSRR equation with 5 graph descriptors gives the best calibration and prediction results, demonstrating the usefulness of the molecular graph descriptors in modeling chromatographic retention parameters. The sequential orthogonalization of descriptors suggests simpler QSRR models by eliminating redundant structural information.

Alkylation↗

PubMed2001

Quantitative structure-property relationships generated with optimizable even/odd Wiener polynomial descriptors.

Chemical structures of organic compounds are characterized numerically by a variety of structural descriptors, one of the earliest and most widely used being the Wiener index W, derived from the interatomic distances in a molecular graph. Extensive use of distance-based structural descriptors or topological indices has been made in QSPR and QSAR models, drug design, toxicology, virtual screening of combinatorial libraries, similarity and diversity assessment. Novel topological indices are introduced representing a partitioning of the Wiener polynomial based on counts of even and odd molecular graph distances. During the QSAR/QSPR modeling process the variables of the even and odd power functions are optimized in order to offer an improved mapping of the investigated property. These novel topological indices are tested in QSPR models for the boiling temperature, molar heat capacity, standard Gibbs energy of formation, vaporization enthalpy, refractive index, and density of alkanes. In many cases, the even/odd Wiener polynomial indices proposed here give notably improved correlations or suggest simpler QSPR models.

Chemical Phenomena↗

PubMed2001

Comparative receptor surface analysis (CoRSA) model for calcium channel antagonists.

Three-dimensional quantitative structure-activity relationships (3D QSAR) are widely used for the prediction of in vitro or in vivo interactions between chemical compounds and their biological targets (transporters, receptors, ion channels, enzymes). Comparative receptor surface analysis (CoRSA) is a new 3D QSAR algorithm that can be applied to study ligand-receptor interactions whenever the structure of the biological target is not known. The steric and electrostatic features of the most active compounds from a QSAR set are used by CoRSA to generate a virtual receptor model, represented as points on a surface complementary to the van der Waals surface of the aligned compounds. The CoRSA structural descriptors, represented by the total interaction energies between each surface point of the virtual receptor and all atoms in a molecule, are used in a partial least squares data analysis to generate a structure-activity model. In this paper the calcium channel antagonist activity of 35 dihydropyridine derivatives is modeled with CoRSA, giving a 3D QSAR with r2 = 0.928 for calibration and r2cv = 0.921 for the leave-one-out cross-validation.

Calcium Channel Blockers↗

PubMed2000

Quasi-orthogonal basis sets of molecular graph descriptors as a chemical diversity measure

In the pharmaceutical industry, the virtual screening of combinatorial libraries is used to rationally select compounds for biological testing from databases of hundreds of thousands of compounds. In addition to structural descriptors, such as fingerprints and pharmacophores, the application of relatively simple structural descriptors traditionally used in quantitative structure-activity studies offers speed and efficiency for rapidly measuring the molecular diversity of such collections. We explore new topological indices computed from the molecular graph as potential structural descriptors for the characterization of molecular diversity. A database of 2000 compounds randomly selected from the National Cancer Institute AIDS database was used to measure the intercorrelation of the descriptors. The initial collection of 240 structural descriptors was reduced to several quasi-orthogonal sets of up to 9 descriptors, using different thresholds for the maximum intercorrelation coefficient.

Journal Article↗

PubMed2000

Evaluation in quantitative structure--property relationship models of structural descriptors derived from information-theory operators

During the search for new structural descriptors we have defined the information-theory operators U(M), V(M), X(M), and Y(M), that are computed from atomic invariants and measure the information content of the elements of molecular matrices. Structural descriptors computed with these four information-theory operators are used to develop structure-property models for the boiling temperature, molar heat capacity, standard Gibbs energy of formation, vaporization enthalpy, refractive index, and density of alkanes. The information-theory operators were applied to six molecular matrices, namely, the distance D, the reciprocal distance RD, the distance-path Dp, the reciprocal distance-path RDp, the path Szeged Sz(p), and the reciprocal path Szeged RSz(p) matrices. In combination with other topological indices, the information-theory indices offer good structure-property models for all six alkane properties investigated in this study.

Journal Article↗

PubMed2000

Comparison of weighting schemes for molecular graph descriptors: application in quantitative structure-retention relationship models for alkylphenols in gas-liquid chromatography

Organic compounds containing heteroatoms or multiple bonds can be conveniently represented as vertex- and edge-weighted molecular graphs. These atom and bond parameters can be computed for any organic compound with two parameter sets that we have recently defined, namely, the relative electronegativity X and the relative covalent radius Y weighting schemes. Structural descriptors computed with these two weighting schemes and the previously defined atomic number Z parameter set are used to develop quantitative structure-retention relationship (QSRR) models for alkylphenols in gas-liquid chromatography. The QSRR models are generated with structural descriptors computed with several newly introduced graph operators, namely, the Wiener, hyper-Wiener, minimum eigenvalue, maximum eigenvalue, Ivanciuc-Balaban, and information on distance operators. These molecular graph operators were applied to the distance D and the reciprocal distance RD matrixes.

Journal Article↗

PubMedDate not supplied

QSAR comparative study of Wiener descriptors for weighted molecular graphs.

Quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) studies use statistical models to compute physical, chemical, or biological properties of a chemical substance from its molecular structure, encoded in a numerical form with the aid of various descriptors. Structural indices derived from molecular graph matrices represent an important group of descriptors used in QSPR and QSAR models; recently, their utilization was extended to molecular similarity and diversity, in database mining and virtual screening of combinatorial libraries. Initially defined from the distance matrix, the Wiener index W was the source of novel graph descriptors derived from recently proposed molecular matrices and of the Wiener graph operator. In this work we present a comparative study of several Wiener-type descriptors computed for vertex- and edge-weighted molecular graphs, corresponding to organic compounds with heteroatoms and multiple bonds. The acute toxicities toward Tetrahymena pyriformis of 47 nitrobenzenes are modeled with multilinear regression equations, using as structural descriptors the hydrophobicity (corrected for ionization) and various Wiener-type indices, with better results than a comparative molecular field analysis model.

Journal Article↗

PubMedDate not supplied

Wiener index extension by counting even/odd graph distances.

Chemical structures of organic compounds are characterized numerically by a variety of structural descriptors, one of the earliest and most widely used being the Wiener index W, derived from the interatomic distances in a molecular graph. Extensive use of such structural descriptors or topological indices has been made in drug design, screening of chemical databases, and similarity and diversity assessment. A new set of topological indices is introduced representing a partitioning of the Wiener index based on counts of even and odd molecular graph distances. These new indices are further generalized by weighting exponents which can be optimized during the quantitative structure-activity/-property relationship (QSAR/QSPR) modeling process. These novel topological indices are tested in QSPR models for the boiling temperature, molar heat capacity, standard Gibbs energy of formation, vaporization enthalpy, refractive index, and density of alkanes. In many cases, the even/odd distance indices proposed here give notably improved correlations.

Journal Article↗

PubMedDate not supplied

Identification of groupings of graph theoretical molecular descriptors using a hybrid cluster analysis approach.

There is an abundance of structural molecular descriptors of various forms that have been proposed and tested over the years. Very often different descriptors represent, more or less, the same aspects of molecular structures and, thus, they have diminished discriminating power for the identification of different structural features that might contribute to the molecular property, or activity of interest. Therefore, it is essential that noncorrelated descriptors be employed to ensure the wider and the less inflated possible coverage of the chemical space. The most usual approach for reducing the number of descriptors and employing noncorrelated (or orthogonal) descriptors involves principal component analysis (PCA) or other factor analytical techniques. In this work we present an approach for determining relationships (groupings) among 240 graph-theoretical descriptors, as a means for selecting nonredundant ones, based on the application of cluster analysis (CA). To remove inherent biases and particularities of different CA algorithms, several clustering solutions, using these algorithms, were "hybridized" to obtain a reliable and confident overall solution concerning how the interrelationships within the data are structured. The calculated correlation coefficients between descriptors were used as a reference for a discussion on the different CA methods employed, and the resulted clusters of descriptors were statistically analyzed for deriving the intercorrelations between the different operators, weighting schemes and matrices used for the computation of these descriptors.

Cluster Analysis↗