Comparative QSAR and pharmacophore modeling of substituted 2-[2′-(dimethylamino) ethyl]-1, 2-dihydro-3H-dibenz[de,h]isoquinoline-1,3-diones derivatives as anti-tumor activity

Abstract

To elucidate the structural properties required for anti-tumor activity, four different molecular modeling techniques; two-dimensional (2D-QSAR), group-based QSAR (G-QSAR), 3D-QSAR, and pharmacophore identification studies have been carried out on a series of substituted 2-[2′-(dimethylamino) ethyl]-1, 2-dihydro-3H-dibenz[de,h]isoquinoline-1,3-diones derivatives. The partial least square (PLS) regression method and k-nearest neighbor -MFA methodology coupled with feature selection methods viz. genetic algorithm and simulated annealing were applied to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. Further analysis using two-dimensional, group-based QSAR and k-nearest neighbor QSAR technique identifies a suitable model obtained by genetic algorithm and simulated annealing coupled with partial least square method leading to anti-tumor activity prediction. The statistically significant best 2D-QSAR model having r2 = 0.9041 and q2 = 0.8357 with pred_r2 = 0.8633 and best group-based QSAR model having r2 = 0.7315 and q2 = 0.6743 with pred_r2 = 0.7950 was developed by Genetic algorithm (melanoma)-PLS method. Further analysis using three-dimensional QSAR technique identifies a suitable model obtained by GA-partial least square method leading to anti-tumor activity prediction. The conformational alignment provided by the above-best pharmacophore was found to be highly robust as proved by the additional kNN-MFA approach studies. The best Genetic algorithm (melanoma)-PLS model with good external and internal predictivity for the training and test set has shown cross validation (q2) and external validation (pred_r2) values of 0.78 and 0.85, respectively. The influences of steric, electrostatic, and hydrophobic field effects generated by the contribution plot are analyzed and discussed. Continuing with the series of substituted 2-[2′-(dimethylamino) ethyl] -1, 2-dihydro-3H-dibenz[de,h]isoquinoline-1,3-diones derivatives chemical feature-based pharmacophore models with lowest RMSD value (0.0861 A°), consists of one AroC (aromatic), two HAc (hydrogen-bond acceptor), and one negative ionizable features were developed. In addition to the 3D QSAR models, impact of two-dimensional and group-based (2D; thermodynamic, structural, and topological including E-state parameters) descriptors toward the inhibitory activity was also studied. The information rendered by 2D-QSAR, group-based QSAR, k-nearest neighbor and Pharmacophore identification models may lead to a better understanding of structural requirements of anti-tumor and can help in the design of novel potent molecules.