Tiered screening protocol for the discovery of structurally diverse HIV Integrase inhibitors

CINF 80

Rajarshi Guha, rguha@indiana.edu1, Debojyoti Dutta, ddutta@usc.edu2, David J Wild, djwild@indiana.edu1, and Ting Chen2. (1) School of Informatics, Indiana University, 1130 Eigenmann Hall, 1900 E 10th Street, Bloomington, IN 47406, (2) Department of Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089
We report a virtual screening protocol for the identification of identifying diverse HIV inhibitors. We developed linear and non-linear classification models based on a 900 compound training set. The models were then used to predict the activity class of a large vendor library. The vendor compounds that were predicted active were then filtered based on similarity to the most active compound in the training set. The final hit list was prioritized according to similarity to the most outlying active compound in the training set. Our initial results did not lead to a significantly diverse set of hits. Furthermore, none of our hits were common to the set obtained previously using a pharmacophore model. However, relaxing the similarity constraints identified four compounds which were very similar to a known inhibitor. We discuss possible reasons why this was the case and describe docking results for the hits obtained using our tiered screening protocol.
 

Advanced Mining and Use of Life Science Information
8:25 AM-12:00 PM, Wednesday, March 28, 2007 McCormick Place North -- Room N134, Level 1, Oral

Division of Chemical Information

The 233rd ACS National Meeting, Chicago, IL, March 25-29, 2007