Background: Inhibition of HIV-I protease enzyme is a strategic step for providing better
treatment in retrovirus infections, which avoids resistance and possesses less toxicity.
Objectives: In the course of our research to discover new and potent protease inhibitors, 3D-QSAR
(CoMFA and CoMSIA) models were generated using 3 different alignment techniques, including
multifit alignment, docking based and Distill based alignment for 63 compounds. Novel molecules
were designed from the output of this study.
Methods: A total of 3 alignment methods were used to generate CoMFA and CoMSIA models. A
Distill based alignment method was considered a better method according to different validation parameters.
A 3D-QSAR model was generated and contour maps were discussed. The biological activity
of designed molecules was predicted using the generated QSAR model to validate QSAR.
The newly designed molecules were docked to predict binding affinity.
Results: In CoMFA, leave one out cross-validated coefficient (q2), conventional coefficient (r2) and
predicted correlation coefficient (r2Predicted) values were found to be 0.721, 0.991 and 0.780, respectively.
The best obtained CoMSIA model also showed significant cross-validated coefficient (q2),
conventional coefficient (r2) and predicted correlation coefficient (r2Predicted) values of 0.714, 0.987
and 0.721, respectively. Steric and electrostatic contour maps generated from CoMFA and hydrophobic
and hydrogen bond donor and hydrogen bond acceptor contour maps from CoMSIA models
were used to design new and bioactive protease inhibitors by incorporating bioisosterism and
knowledge-based structure-activity relationship.
Conclusion: The results from both these approaches, ligand-based drug design and structure-based
drug design, are adequate and promising to discover protease inhibitors.