Abstract:
Acquired immunodeficiency syndrome (AIDS) is a life-threatening disease which is a collection of symptoms and infections
caused by a retrovirus, human immunodeficiency virus (HIV). There is currently no curative treatment and therapy is
reliant on the use of existing anti-retroviral drugs. Pharmacoinformatics approaches have already proven their pivotal role
in the pharmaceutical industry for lead identification and optimization. In the current study, we analysed the binding
preferences and inhibitory activity of HIV-integrase inhibitors using pharmacoinformatics. A set of 30 compounds were
selected as the training set of a total 540 molecules for pharmacophore model generation. The final model was validated
by statistical parameters and further used for virtual screening. The best mapped model (R = 0.940, rmsd = 2.847, Q2 =
0.912, se = 0.498, R2
pred = 0.847 and r2
m (test) = 0.636) explained that two hydrogen bond acceptor and one aromatic ring
features were crucial for the inhibition of HIV-integrase. From virtual screening, initial hits were sorted using a number of
parameters and finally two compounds were proposed as promising HIV-integrase inhibitors. Drug-likeness properties of
the final screened compounds were compared to FDA approved HIV-integrase inhibitors. HIV-integrase structure in
complex with the most active and final screened compounds were subjected to 50ns molecular dynamics (MD) simulation
studies to check comparative stability of the complexes. The study suggested that the screened compounds might be
promising HIV-integrase inhibitors. The new chemical entities obtained from the NCI database will be subjected to
experimental studies to confirm potential inhibition of HIV integrase.
