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 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 R2
pred = 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 prioritized for 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.