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D Zakarya

Publications and source records attributed to D Zakarya.

5 recordsLinked to original sources
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↗