HIV-1 integrase (IN) plays an important role in the life cycle of HIV and is responsible for
integration of the virus into the human genome. We present computational approaches used to design
novel HIV-1 IN inhibitors. We created an IN inhibitor database by collecting experimental data from
the literature. We developed quantitative structure-activity relationship (QSAR) models of HIV-1 IN
strand transfer (ST) inhibitors using this database. The prediction accuracy of these models was estimated
by external 5-fold cross-validation as well as with an additional validation set of 308 structurally distinct compounds
from the publicly accessible BindingDB database. The validated models were used to screen a small combinatorial
library of potential synthetic candidates to identify hits, with a subsequent docking approach applied to further filter out
compounds to arrive at a small set of potential HIV-1 IN inhibitors. As result, 236 compounds with good druglikeness
properties and with correct docking poses were identified as potential candidates for synthesis. One of the six compounds
finally chosen for synthesis was experimentally confirmed to inhibit the ST reaction with an IC50(ST) of 37µM. The IN
inhibitor database is available for download from http://cactus.nci.nih.gov/download/iidb/.
Keywords: AIDS, CADD, HIV-1 integrase, QSAR, Strand transfer inhibitor, Virtual screening.
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