فهرست مطالب robabeh sayyadi kordabadi

QSAR investigations of Caspofungin derivatives were conducted using Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Monte Carlo Methods. The obtained results were compared and GA-ANN and ICA-MLR combinations showed the best performance according to its correlation coefficient (R2) and Root Mean Sum Square Errors (RMSE). The most important physicochemical and structural descriptors were presented and discussed. Monte Carlo method revealed that the presence of a double bond with branching, a six-member cycle, the absence of halogens, the presence of sp2 carbon connected to branching, the presence of Nitrogen and Oxygen atoms, absence of Sulphur and Phosphorus are the most important molecular features. The best Caspofungin derivative was exposed to reaction with Cu, Zn, Fe using B3lyp/6-311g/lanl2dz to investigate the stability of the formed complexes, from which the Zn complex was perceived to be the most stable one. It was concluded that QSAR study and the Monte Carlo method can lead to a more comprehensive understanding of the relation between physicochemical, structural, or theoretical molecular descriptors of drugs to their biological activities and Lipophilicity.

This research presents quantitative structure-activity relationship (QSAR) of half maximal inhibitory concentration (IC50 ) values of 31 different Methotrxate derivatives by employing Multiple linear regression (MLR) and artificial neural networks (ANN), simulated annealing algorithm (SA) and genetic algorithm(GA). Furthermore, CORAL software was used for multiple probability simulation of the studied derivatives. The obtained results from MLR-MLR, MLR-SA, SA-ANN, MLR-GA and GA-ANN approaches were compared and GA-ANN combination showed the best performance according to its correlation coefficient (R2) and mean sum square errors (RMSE). From Monte Carlo simulations, it was found that the presence of double bond, the presence of nitrogen and oxygen, the absence of sulphur and phosphorus and connected sp2 carbon to the ring, are the most important molecular features that affect the biological activity of the drug. It was concluded that the simultaneous application of GA-ANN and Monte Carlo methods can provide a more comprehensive understanding of the relationship between a drug's physicochemical, structural, or theoretical molecular descriptors and its biological activity, leading to accelerate the development of new drugs.

Multiple linear regression (MLR) as modeling tool and Imperialist Competitive Algorithm (ICA) as optimization techniques employed to choose the best set of descriptors and The CORAL software has been used as a tool for linear prediction of -log( IC50) (empirical negative logarithm of half of maximal inhibitory concentration) for Bortezomib derivatives. A high predictive ability was observed for the MLR-ICA model with the best number of empires/ imperialists (nEmp) 90 with root-mean-sum-square errors (RMSE) of 0.0121 and correlation coefficient (R2predict) of 0.9896 in gas phase.
The 25 data sets were randomly splitted into the training set, the calibration set, the test set in the Monte Carlo method and the number of compounds in the each set (n), correlation coefficient (R2) , cross-validated correlation coefficient (Q2), standard error(s) were calculated 13, 0.9826, 0.9780, 0.161 in training set; and n=6, R2= 0.8463 , Q2=0.7377, s=0.715 in test set in the Threshold (T) of 2 and probe of 3, respectively.
From the MLR-ICA method, it was revealed that Espm15u, R5p+, B06 [O-O], F03[N-N], F07[C-O], MATs3m, RDF125v are the most important descriptors. From Monte Carlo simulations, it was found that the presence of double bond and ring, absence of halogens are the most important molecular features affecting the biological activity of the drug.
It was concluded that simultaneous utilization of MLR-ICA and Monte Carlo method can lead to a more comprehensive understanding of the relation between physico-chemical, structural or theoretical molecular descriptors of drugs to their biological activities and facilitate designing of new drugs.

The activity of the 25 different Carfilzomib derivatives was estimated using multiple linear regression (MLR), artificial neural network (ANN), and genetic algorithm(GA) and simulated annealing algorithm (SA) and Imperialist Competitive Algorithm (ICA) as optimization methods. The obtained results from MLR-MLR, MLR-GA, SA-ANN and GA-ANN techniques were compared and for combinations of modelling-optimization methods observed root mean sum square errors (RMSE) of 0.290, 0.0482, 0.0294, 0.0098 in gas phase, respectively (N=25). A high predictive ability was observed for the MLR-ICA model with the best number of empires/ imperialists (nEmp=50 ) and nEmp=100 with root-mean-sum-squared error (RMSE) of 0.00996 in gas phase. From the MLR-ICA method, it was revealed that RDF 075m, MATS1m, F04[N-O], O-059, F09[C-O] and Mor21p are the most important descriptors. From Monte Carlo simulations, it was found that the presence of double, absence of halogens, oxygen connected to double bond, sp2 carbon connected to double bond, double bond with ring, branching, nitrogen are the most important molecular features affecting the biological activity of the drug. It was concluded that simultaneous utilization of MLR-ICA, GA-ANN and Monte Carlo method can lead to a more comprehensive understanding of the relation between physico-chemical, structural or theoretical molecular descriptors of drugs to their biological activities and facilitate designing of new drugs.

QSAR investigations of lipophilicity (XLOGP3) and biological activity (IC50) values of some Doxazolidine derivatives were conducted using combinations of multiple linear regression (MLR) and artificial neural network (ANN) modeling methods and three different optimization techniques including simulated annealing (SA), genetic algorithm (GA) and Imperialist Competitive algorithm (ICA). In addition CORAL software was used to correlate the lipophilicity and biological activity to the structural parameters of the drugs. The obtained results were compared and GA-ANN and ICA-MLR combinations showed the best performance with regard to the correlation coefficient (R2) and root-mean-square error (RMSE). The most effective descriptors extracted from lipophilicity and biological activity studies were presented and discussed. From GA-ANN method, the most important physico-chemical descriptors were found to be minimum value in atomic Sanderson electronegativities and maximum value in Squared Moriguchi Octanol-Water partition coeff.(log P ˆ2) descriptors.ICA-MLR method suggests the maximum value in polarizibility, electrotopological state and atom van der Walls volume as the most important physicochemical descriptors.It was concluded that QSAR study and Monte Carlo method can lead to a more comprehensive understanding of the relation between physico-chemical, structural or theoretical molecular descriptors of drugs to their biological activities and Lipophilicity.

Monte Carlo and Multiple Linear Regression (MLR) and Imperialist Competitive Algorithm (ICA) were used to select the most appropriate descriptors. Examining the quality of the model by comparing the mean squared error (MSE) and correlation coefficient (R2), indicated that 140 is the most appropriate number of empires for the gas phase . In the Monte Carlo method, CORAL software was used and the data were randomly divided into training, calibration, and test subsets in three splits. The correlation coefficient (R2), cross-validated correlation coefficient (Q2) and standard error of the model were calculated to be respectively 0.9301, 0.7377, and 0.595 for the test set with an optimum threshold of 4. It was concluded that simultaneous utilization of MLR-ICA and Monte Carlo method can lead to a more comprehensive understanding of the relation between physico-chemical, structural or theoretical molecular descriptors of drugs to their biological activities and facilitate designing of new drugs.

QSAR investigations of some platinum (IV) derivatives were conducted using multiple linear regression (MLR) and artificial neural network (ANN) as modelling tools, along with simulated annealing (SA) and genetic algorithm (GA) optimization algorithms. In addition, CORAL software was used to correlate the biological activity to the structural parameters of the drugs. The obtained results from different approaches were compared and GA-ANN combination showed the best performance according to its correlation coefficient (R2) and mean sum square errors (RMSE). From the GA-ANN method, it was revealed that MTAS8e, ESpm05d, BElv3, MWC09, ESpm14u, BEHe2, RDF125e, and S3K are the most important descriptors. From Monte Carlo simulations, it was found that the presence of double bond, present of Platinum, number of chlorine connected to Pt, branching in molecular skeleton and presence of N and O atoms are the most important molecular features affecting the biological activity of the drug. It was concluded that simultaneous utilization of QSAR and Monte Carlo method can lead to a more comprehensive understanding of the relation between physico-chemical, structural or theoretical molecular descriptors of drugs to their biological activities.