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D Zakarya

Publications and source records attributed to D Zakarya.

7 recordsLinked to original sources
PubMed2002

QSAR for anti-HIV activity of HEPT derivatives.

QSARs were derived for 103 analogues of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT), a potent inhibitor of the HIV-1 reverse transcriptase (RT). The activity of these compounds was investigated by means of multiple linear regression (MLR) and artificial neural network (ANN) techniques. Considering the relevant descriptors obtained from the MLR, a correlation coefficient of 0.92 (n = 95) was obtained with a 4-5-1 ANN model. The contribution of each descriptor to the structure-activity relationships was evaluated. The results showed that the anti-HIV activity of HEPT derivatives was strongly dependent on hydrophobic character and also steric factors of substituents.

Anti-HIV Agents↗
PubMed1997

QSARs for toxicity of DDT-type analogs using neural network.

Structure-toxicity relationships for 120 insecticidal DDT-type molecules including diaryl nitropropanes (Prolan analogs), diaryl trichloroethane and other DDT isosters, collected from different literature sources, to Musca domestica. were analysed by regression analysis (RA) and a neural network model (NN). The steric factors are extremely important to the toxicity of all the DDT-type analogs. The lipophilicity is also important for the groups, because it facilitates delivery of these neurotoxicants to the site of action in the nerve. On the basis of training results, the NNs proved to give better results than a regression analysis technique and the most accurate predictions. To describe the role of each of the descriptors we suggested a new method based upon the estimation of the connection weights, the identification and the rationalisation of the residuals.

DDT↗
PubMed1996

Analysis of structure-toxicity relationships for a series of amide herbicides using statistical methods and neural network.

Structure-toxicity relationships were studied for a set of 44 herbicides by means of principal component analysis (PCA), multiple regression analysis (MRA), and neural network (NN). The values of log LD50 (lethal dose 50, acute, oral, rat) of the studied compounds were well correlated with the descriptors encoding the chemical structures. Considering the pertinent descriptors, a correlation coefficient of 0.90 (n = 41) was obtained for the NN model with a configuration of 4-3-1 (and 0.92 (n = 41) with a configuration of 4-5-1). To evaluate the contribution of each descriptor on the activity, log LD50 was calculated by removing each descriptor a part. This approach provides the tendency of a descriptor to be favourable (or not) to the activity.

Amides↗
PubMed1993

Quantitative structure-biodegradability relationships (QSBRs) using modified autocorrelation method (MAM).

Quantitative structure-biodegradability relationships (QSBRs) were established for a set of various organic compounds using autocorrelation components as molecular descriptors. The molecules were described by their size (van der Waals volume), electronegativity, hydrogen bonding donor and acceptor ability and lipophilicity (log P). In addition to the established models for alcohols, ketones, and aromatics, we have elaborated a model for both alcohols and ketones (5-day BOD = 0.06 V0 + 1.067 log P - 0.356 (log P)2; n = 29, r = 0.958, s = 0.44, F = 145.6) and another for all the compounds (5-day BOD = 0.065 V0 + 0.748 log P - 0.316 (log P)2; n = 43, r = 0.906, s = 0.575, F = 91.2).

Alcohols↗
PubMed1989

The stochastic regression analysis as a tool in ecotoxicological QSAR studies.

Correspondence factor analysis (CFA) was used in conjunction with linear regression analysis to examine the structure-activity relationships of 50 benzene derivatives tested on Pimephales promelas. From nine molecular descriptions (numbers of C, H, O, N, Br, Cl, NO2, OH, and NH2 included in the molecules), CFA made it possible to define five new independent variables which were introduced in a stepwise regression analysis procedure to describe the acute toxicity (96-h LC50) of the aromatic compounds. The model log 1/C = -0.727F1 + 1.248F3 + 4.052 (r = 0.918; s = 0.270) is more relevant to describe the ecotoxicological behavior of the studied compounds on the fathead minnow than that obtained with principal components (log 1/C = 0.151 PC1 -0.271 PC2 + 4.124; r = 0.737; s = 0.460). The heuristic potency of this particular statistical analysis, which is called stochastic regression analysis, is discussed in detail.

Animals↗
PubMedDate not supplied

Autocorrelation method adapted to generate new atomic environments: application for the prediction of 13-C chemical shifts of alkanes.

The concept of the multifunctional autocorrelation method governing global description of molecules was changed in order to take into account the structural environment of each atom. New atomic environments are generated as possible descriptors in QSARs and can be useful for database characterization. The principles of this approach are widely explained through a case study dealing with the design of a model allowing the simulation of the carbon-13 nuclear magnetic spectra for alkanes. Carbon atoms in alkanes are described by using as structural descriptors a vector corresponding to only four components vectors of the multifunctional autocorrelation method. The statistical method used for deriving the model was a classical three-layer feedforward neural network trained by the back-propagation algorithm and multilinear regression (MLR). The predictive ability of the ANN model was tested by -10%-out(L10%O) cross-validation method, demonstrating the superior quality of the neural model. The established model allows us the prediction of the 13-C chemical shifts with success because since all types of carbons are taken into account without distinction of connectivity. The neural network possessed a 4:7:1 architecture with a sigmoid shape as a activation function. The model produced a cross-validation standard coefficient r between delta(exp) and delta(calc) about 0.99, while the cross-validation standard s and the mean error are equal to 0.87 and 0.60 ppm, respectively.

Journal Article↗