Abstract:
: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 37 µM. The IN inhibitor database is available for download from http://cactus.nci.nih.gov/download/iidb/.
journal_name
Curr Top Med Chemjournal_title
Current topics in medicinal chemistryauthors
Guasch L,Zakharov AV,Tarasova OA,Poroikov VV,Liao C,Nicklaus MCdoi
10.2174/1568026615666150813150433subject
Has Abstractpub_date
2016-01-01 00:00:00pages
441-8issue
4eissn
1568-0266issn
1873-4294pii
CTMC-EPUB-69524journal_volume
16pub_type
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