Quantitative Structure Activity Relationship (QSAR) analysis techniques are tools largely utilized in many research fields, including drug discovery processes.
In this work electronic descriptors are calculated with the Gaussian 03W software using the DFT method with the BecKe 3-parameters exchange functional and Lee-Yang-Parr correlation functional, with Kohn and Sham orbitals (KS) developed on a Gaussian Basis of type 6-31G (d), in combination with five Lipinski parameters that have been calculated with ChemOffice software, in order to develop a statistically verified 2D-QSAR model able to predict the biological activity of new molecules belonging to the same range of coumarins rather than chemical synthesis and biological evaluations that require more time and resources. Two QSAR models against both MCF-7 and HepG-2 cell lines are obtained using the multiple linear regression method.
The predictive power of these models has been confirmed by internal and external validation. The Leverage method was used to determine the domain of applicability of the 2D-QSAR models developed. The results indicate that the best QSAR model is the one that links the 2D descriptors with the CDK inhibitory activity of the cell line (HepG-2) R
2
= 0.748, R
2
cv = 0.618, MSE = 0.03 for the learning series and R
2
= 0.73, MSE = 0.18 for the test series. This model implies that coumarin inhibitory activity is strongly related to dipole moment and the number of hydrogen bond donors. The results obtained suggest the importance of studying structure-activity relationships as a principal axis in drug design. The docking procedure using AutoDOCK Tools was also used to understand the mechanisms of molecular interactions and consequently, to develop new inhibitors.
Protein kinases are essential components of various signaling pathways and represent attractive targets for therapeutic interventions. Kinase inhibitors are currently used to treat malignant tumors, as well as autoimmune diseases, due to their involvement in immune cell signaling. In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Multiple Non-Linear Regression (MNLR), Artificial Neural Network (ANN) and cross-validation analyses, were performed on a set of P38 MAP kinases as anti-cancer agents. This method, which is based on molecular modeling (molecular mechanics, Hartree-Fock (HF)), was used to determine the structural parameters, electronic properties, and energy associated with the molecules we examined. MLR, PLS, and MNLR analyses were performed on 46 protein P38 MAP kinase analogs to determine the relationships between molecular descriptors and the anti-cancer properties of the P38 MAP kinase analogs. The MLR model was validated by the external validation and standardization approach. The ANN, given the descriptors obtained from the MLR, exhibited a correlation coefficient close to 0.94. The predicted model was confirmed by two methods, leave-one-out (LOO) cross-validation and scrambling (or Y-randomization). We observed a high correlation between predicted and experimental activity, thereby both validating and demonstrating the high quality of the QSAR model that we described.
In this study, the anticancer activity of a series of 32 molecules based on anthra[1,9-cd]pyrazol-6(2H)-one was studied by threedimensional quantitative structure-activity relationship (QSAR) analyses: multiple linear regression (MLR), partial least squares (PLS), multiple nonlinear regression (MNLR), cross-validation analyses, and Y-randomization. A theoretical study of series was firstly studied using density functional theory (DFT) calculations at B3LYP/6-31 level of theory for employing to determine the structural parameters and electronic properties. Then the topological descriptors were computed using ACD/ChemSketch and ChemDraw 8.0 programs. The RNLM, given the descriptors obtained from the MLR and PLS, exhibited a correlation coefficient close to 0.91. The prediction models collected were confirmed by two methods of cross-validation and scrambling (or Yrandomization). The strong correlation between experimental and predicted activity values was observed, indicating the validation and good quality of the derived QSAR model.
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