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. Manuscript profile

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. Manuscript profile

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. Manuscript profile

In this manuscript; Molecular dynamics simulation was tested on COVID -19 main protease (PDB: 6LU7) with the docking studies have been employed using autodock-vina-1.1.2-4 and autodock- mgltools-1.5.6 (flex) programs to evaluate the interactions. HIV-1 Protease is a prerequisite for viral replication. In this manuscript; Molecular dynamics simulation was tested on COVID -19 main protease (PDB: 6LU7) with the docking studies have been employed using autodock-vina-1.1.2-4 and autodock- mgltools-1.5.6 (flex) programs to evaluate the interactions. Regular and Flexible docking approaches were run. Molecular dynamics simulation was tested on COVID -19 main protease (PDB: 6LU7) with molecule 7. Drug-likeness descriptors of compounds such as logP (partition coefficient), H-Bond Acceptor (HBA), H-Bond Donor (HBD), number of Rotable Bond (nRB), and nHB calculated by DruLiTo. In the molecular docking study, the maximum binding affinity of -5.9 kcal/mol was obtained between each of COVID -19 main protease (PDB: 6LU7) enzyme systems and the geometric-optimized molecules, representing a strong interaction. The reference molecule PRD_002214 of Mpro forms four hydrogen bonds with Glu 166, Phe 140, Gln 189, His 163, and some hydrophobic bonds. In this study, molecules 7 (Amprenavir) and 15 were presented as the most stable ones that may be introduced for further investigations, including clinical experiments. Manuscript profile