Background: Glutaminyl cyclase (QC) is a novel target in the battle against Alzheimer’s disease, a highly prevalent neurodegenerative disorder. QC inhibitors have the potential to be developed as therapeutically useful anti-Alzheimer’s disease agents.
Methods: Linear and non-linear 2D-quantitative structure–activity relationship (QSAR) models were developed using stepwise-multiple linear regression (S-MLR) and neural networks. Partial least squares (PLS) method was used to develop a 3D-QSAR model. Also, the developed models were used in a virtual screening of the ZINC database to identify potential QC inhibitors.
Results: The 2D neural network model showed superior predictive ability, as demonstrated by the validation parameters R2 = 0.933, Q2 = 0.886 and R2pred = 0.911. The 3D-QSAR model’s steric and electrostatic fields’ isocontour maps were visualized and revealed important structural requirements associated with good activity. The virtual screening identified six compounds as potentially active QC inhibitors with improved pharmacokinetic profiles.
Conclusion: The developed QSAR models can be used to predict the activity of compounds not yet synthesized and prioritize their synthesis and biological evaluation. Also, potentially active QC inhibitors have been identified with attractive lead-like properties that can be used to develop anti-Alzheimer’s disease agents.