Biomedical subjects

Driss Zakarya

Publications and source records attributed to Driss Zakarya.

6 recordsLinked to original sources

PubMed2006

Structure-olfactive threshold relationships for pyrazine derivatives.

Structure-olfactive threshold relationships for 40 pyrazine derivatives have been studied by multivariate statistical analysis. Variable descriptors used to describe the molecules studied were obtained using molecular-mechanics software. A correlation coefficient of 0.82 was obtained when all the molecules were included in the model. When the model was established for closely related subsets of molecules, the correlation coefficients obtained were higher and the established models were bilinear. Such models allow the identification of optimal structures corresponding to low olfactive thresholds for the subsets studied. Surprisingly, we find that the optimum structures are included in the set of 40 molecules. The efficiency of the models was supported by the cross-validation technique, where the correlation coefficients were found to be good with respect to the precision of the values of the olfactive thresholds.

Pyrazines↗

PubMed2004

Determination of fuzzy logic membership functions using genetic algorithms: application to structure-odor modeling.

Fuzzy logic has been used as a tool in structure-camphoraceous odor relationships. The data base studied included 99 molecules. The rules used to discriminate between camphor and non camphor molecules lead to 77% correct discrimination. Such rules account for the shape and the size of the molecule. Their adjustment by means of genetic algorithms led to 84% correct discrimination between camphor and non-camphor molecules. [figure: see text]. Membership function for the chosen variables.

Algorithms↗

PubMed2003

Prediction of solubility of aliphatic alcohols using the restricted components of autocorrelation method (RCAM).

Structure-water solubility modeling of aliphatic alcohols was performed using the multifunctional autocorrelation method. The molecule is represented by using a set of parameters describing global molecules, and others that take the structural environment of the edge O-C into account. Multiple linear regression (MLR) and multilayer feed-forward artificial neural network architectures are utilized to construct linear and nonlinear QSPR models, respectively. The optimal QSPR model was developed based on a 4-4-1 neural network architecture. The efficiency of the approach is demonstrated through the predictive ability of the ANN and MLR models by the leave-20%-out (L20%O) cross-validation method, demonstrating that the neural model is more reliable than that obtained using MLR. The root mean square errors in the solubility prediction (ln SOL) for the calibration and predictive models were 0.13 and 0.18 respectively. On the other hand, we tested four activation functions: the hyperbolic tangent, sigmoid function or Gaussian functions for the hidden layer and a linear, sigmoid, hyperbolic tangent or Gaussian function for the output layer. The influence and the contribution of each type of descriptor in the model is examined. After omission of a set of descriptors, we calculate the error for the solubility and classify them into discrete categories. The standard error and the percentage of the prediction in the precision interval considered have been estimated. The results imply that the solubility of aliphatic alcohols is dominated by the shape and branching of the molecule. The hydrogen-bonding interactions caused by the C-OH group seem to be a less important factor influencing the solubility. The model was compared with other models; especially that using weighted path numbers, which is considered to be the most accurate QSPR model for predicting the water solubility of aliphatic alcohols.

Alcohols↗

PubMed2003

Quantitative structure-diastereoselectivity relationships for arylsulfoxide derivatives in radical chemistry.

Quantitative structure-diastereoselectivity relationships were studied for the intermolecular radical addition of deuterium and allyltributyltin to chiral arylsulfoxides by means of multiple linear regression and artificial neural networks (ANN). The values of diastereoselectivity (% syn) of the compounds studied were well correlated with the descriptors encoding the chemical structure. Using the pertinent descriptors revealed by the regression analysis, a square correlation coefficient of 0.9577 ( s=5.3825) for the training set was obtained for the ANN model in a 2-4-1 configuration. The results obtained from this study indicate that the diastereoselectivity of arylsulfoxide derivatives is strongly dependent on the shape of the R and X groups. FIGURE General structure of alpha-sulfinyl radicals

Chemistry↗

PubMed2002

Structure-cytotoxicity relationships for a series of HEPT derivatives.

Structure-cytotoxicity relationships were studied for a series of 90 HEPT derivatives by means of multiple linear regression (MLR) and artificial neural network (ANN) techniques. The values of log(1/CC50) (CC50=cytotoxic dose of compound required to reduce the proliferation of normal uninfected MT-4 cells by 50%) of the studied compounds were correlated with the descriptors encoding the chemical structures. Using the pertinent descriptors revealed by the regression analysis, a correlation coefficient of 0.935 ( s=0.149) for the training set ( n=81) was obtained for the ANN model with a 5-6-1 configuration. The results obtained from this study indicate that the cytotoxicity of HEPT derivatives is strongly dependent on hydrophobic factors, mainly log P(R1), and dependent on the steric factors, especially SigmaMW(R3+R4). Comparison of the descriptors' contribution obtained in MLR and ANN analysis shows that the contribution of some of the descriptors to cytotoxicity may be non-linear.

Animals↗

PubMed2002

Structure-toxicity relationships study of a series of organophosphorus insecticides.

Structure-toxicity relationships were studied for a set of 47 insecticides by means of multiple linear regression (MLR) and artificial neural network (ANN). A model with three descriptors, including shape surface [S(R2)], hydrogen-bonding acceptors [HBA(R2)] and molar refraction [MR(R1)], showed good statistics both in the regression (r = 0.875, s = 0.417 and q2 = 0.675) and artificial neural network model with a configuration of [3-5-1] (r = 0.966, s = 0.200 and q2 = 0.647). The statistics for the prediction on toxicity [log LD50 (lethal dose 50, oral, rat)] in the test set of 20 organophosphorus insecticides derivatives is (r = 0.849, s = 0.435) and (r = 0.748, s = 0.576) for MLR and ANN respectively. The model descriptors indicate the importance of molar refraction and shape contributions toward toxicity of organophosphorus insecticides derivatives used in this study. This information is pertinent to the further design of new insecticides.

Animals↗