3D-QSAR models to predict anti-cancer activity on a series of protein P38 MAP kinase inhibitors. Issue 3 (1st May 2017)

• Record Type: Journal Article
• Authors: Hadaji, El Ghalia; Bourass, Mohamed; Ouammou, Abdelkarim; Bouachrine, Mohammed
• Is Part Of: Journal of Taibah University for science. Volume 11:Issue 3(2017)
• Page Start: 392
• Page End: 407
• Publication Date: 2017-05-01
• Subjects: QSAR -- Anti-cancer -- MLR -- PLS -- MNRL -- Neural Network (NN) -- Cross-validation (CV); Science -- Periodicals; Science; Periodicals; 505
• DOI: 10.1016/j.jtusci.2016.05.006
• Languages: English
• ISSNs: 1658-3655

Abstract

Abstract: 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.