Abstract
The 3D-QSAR models were developed using CoMFA and CoMSIA techniques to investigate essential molecular fields, optimization strategies, and structure-activity relationships for utrophin-modulating compounds. The data set (71 molecules) was divided into two training and test sets using the hierarchical clustering approach. The training set was aligned based on the most active compound. The built and optimized models based on the PLS approach provided acceptable results. The results were q (2) = 0.528 and r (2) = 0.776 for CoMFA and q (2) = 0.600 and r (2) = 0.811 for CoMSIA models. According to the statistical results, it was found that both the CoMFA models with and without regional focusing and also the CoMSIA model have good estimation ability. Molecular docking was also performed with high-activity compounds (as ligands) and target receptors (protein), and its results, together with the results of 3D-QSAR, give new insights for the design of compounds with higher biological activity. Finally, based on the overall results, the design of new compounds with higher utrophin modulation activity was carried out.