Docking-based 3D-QSAR Studies of Phosphodiesterase 9A Inhibitors

Objective: We aim to provide new insight and scientific evidence for rational design and discovery of phosphodiesterase-9A (PDE9A) inhibitors with remarkable potency and weak side effect to treat CNS diseases such as Alzheimer's disease. Methods: Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of PDE9A inhibitors. Moreover, two different alignment methods, docking-based structural alignment (DCBA) and local lowest energy structure based alignment (LESBA), were employed to scrutinize their effects on the robustness and predictive capability of 3D-QSAR models. Results: The models generated by CoMFA had a cross-validated coefficient (q2) of 0.771 and a regression coefficient (r2) of 0.983. The CoMSIA models had a (q2) of 0.776 and (r2) of 0.960. The external predictive capability of the built models was evaluated by using the test set of nine compounds. From obtained results, the CoMSIA models were found to have highly predictive capability in comparison with CoMFA models. Contour maps of CoMSIA models provided many helpful structural insights, including N1-bulkier hydrophobic group such as cyclopentyl group better filling the metal binding pocket in the PDE9A to show stronger inhibitory activity. Conclusions: Docking-based 3D-QSAR studies is helpful to improve the design of pyrazolopyrimidinone derivatives as PDE9A inhibitors to develop new chemical entities with higher selectivity.