Three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis and molecular docking-based combined in silico rational approach to design potent and novel TRPV1 antagonists

3D-QSAR, molecular docking and molecular dynamics studies of a series of RORγt inhibitors

The discovery of clinically relevant inhibitors of retinoic acid receptor-related orphan receptor-gamma-t (RORγt) for autoimmune diseases therapy has proven to be a challenging task. In the present work, to find out the structural features required for the inhibitory activity, we show for the first time a three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations for a series of novel thiazole/thiophene ketone amides with inhibitory activity at the RORγt receptor. The optimum CoMFA and CoMSIA models, derived from ligand-based superimposition I, exhibit leave-one-out cross-validated correlation coefficient (R(2)cv) of .859 and .805, respectively. Furthermore, the external predictive abilities of the models were evaluated by a test set, producing the predicted correlation coefficient (R(2)pred) of .7317 and .7097, respectively. In addition, molecular docking analysis was applied to explore the binding modes between the inhibitors and the receptor. MD simulation and MM/PBSA method were also employed to study the stability and rationality of the derived conformations, and the binding free energies in detail. The QSAR models and the results of molecular docking, MD simulation, binding free energies corroborate well with each other and further provide insights regarding the development of novel RORγt inhibitors with better activity.

Molecular Modeling Studies of Substituted 2,4,5-Trisubstituted Triazolinones Aryl and Nonaryl Derivatives as Angiotensin II AT1Receptor Antagonists

The development of new therapies to treat hypertension and cardiovascular diseases. A series of 2,4,5-trisubstituted triazolinones aryl and nonaryl derivatives were subjected toGroup-based QSAR,k-nearest neighbourmolecular field analysis, and pharmacophore mapping. Multiple linear regression (MLR) methodology coupled with feature selection method namely simulated annealing, was applied to derive Group based QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The best physicochemical descriptors, namely, R1chiV1, R2T_N_O_3, R2chlorines count, R2T_C_N_4, and R2SssNHE index, contribute significantly to the biological activity. The statistically significant best Group-based QSAR model hasr2=0.8357andq2=0.7266with pred_r2=0.8138. The 3D-QSAR studies were performed using the simulated annealing selectionk-nearest neighbormolecular field analysis approach; a leave-one-out cross-validated correlation coefficientq2=0.7461and predicate activity pred_r2=0.7790were obtained. Contour maps using this approach showed that steric, electrostatic, and hydrophobic effects dominantly determine binding affinities. Pharmacophore hypotheses were generated by the mol sign module and found to contain common features like hydrogen bond donor acceptor, donor, positive, negative ionizable, and hydrophobic features. This model can be used for preliminary screening of large number of substituted 3H-1,-2,-4 triazolinone aryl and nonaryl derivatives. The information rendered by 3D-QSAR models may lead to a better understanding of structural requirements of triazolinone aryl and nonaryl derivatives and also aid in designing novel potent antihypertensive molecules.