A series of N-carbonyl-functionalized ureas, carbamates and thiocarbamates derivatives (or N-Chloro sulfonyl isocyanate "N-CSI") were involved in linear and nonlinear physicochemical quantitative structure-activity relationship "QSAR" analysis to find out the structural keys to control the inhibition against Sterol O-Acyl-Transferase-1 "SOAT-1". The results indicate the important effects of geometrical and chemical descriptors on the inhibitory activity of SOAT-1. The molecules were also screened for three-dimensional molecular docking on the crystal structure of ACAT-1 (1WL5 for ACAT-1, PDB). A comparison between 2D-QSAR and 3D molecular docking studies shows that the latter confirm the first results and represent a good prediction of the chemical and physical nature of interactions between our drug molecules and enzyme SOAT-1.

Quantitative structure-activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using multiple linear regression (MLR) and artificial neural network (ANN) techniques. A model with four descriptors, including Hydrogen-bonding donors HBD(R 7 ), the partition coefficient between n-octanol and water logP and logP(R 1 ) and Molecular weight MW(R 7 ), showed good statistics both in the regression and artificial neural network with a configuration of (4-3-1) by using Bayesian and Levenberg-Marquardt Methods. Comparison of the descriptor's contribution obtained in MLR and ANN analysis shows that the contribution of some of the descriptors to activity may be non-linear.

Structure-activity relationships were studied for a series of 46 2.6-dimethyl-3.5-dicabomethoxy-4-phenyl-1.4-dihydropyridine derivatives by means of multiple linear regression (MLR) and artificial neural network (ANN) techniques. The values of log (1/EC50), which represents the 50% effective concentration for blocking the Ca2+ channel 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 0.99 (s = 0.23) for the training set (n = 46) was obtained for the ANN using the Levenberg-Marquardt algorithm with a 3-10-1 configuration. The results obtained from this study indicate that the activity of 2.6-dimethyl-3.5-dicabomethoxy-4-phenyl-1.4-dihydropyridine derivatives is strongly dependent on molar refractivity (MR), electronic factors (especially on the connectivity indices (IC0)) and hydrogen-bond donor's (HBD) of the molecule. Comparison of the descriptor's contribution obtained with MLR and ANN models indicates the presence of non-linearity in the data and the interaction effect between them since the efficiency of these descriptors was increased by the ANN model.On the other hand, we have used a new, robust structure-activity mapping technique, a Bayesian-regularized neural network, to develop a quantitative structure-activity relationship (QSAR) model, and the ability of the model was tested by using the cross validation technique. The results show that the method is robust and reliable and gives good results. Comparisons of Bayesian neural net models with those derived by classical MLR model analysis showed its superiority in generalization.

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