Location: Mosquito and Fly ResearchTitle: Novel chemicals for vector surveillance and control) Author
Submitted to: Meeting Abstract
Publication Type: Abstract only
Publication Acceptance Date: 9/27/2012
Publication Date: N/A
Citation: Interpretive Summary:
Technical Abstract: Since 1942, the United States Department of Agriculture (USDA) has developed repellents and insecticides for the U.S. military. Within the archives, there exist similarly structured compounds that function as repellents against mosquitoes. We examined subsets of these compounds by artificial neural network (ANN) models to generate structures of new compounds that have potential as repellents. Compounds were synthesized and evaluated for their repellency against Aedes aegypti mosquitoes. The complete protection time (CPT) of compounds was used to develop Quantitative Structure Activity Relationship (QSAR) models to predict repellency. Successful prediction of novel acylpiperidine structures by ANN models resulted in the discovery of compounds that provided protection over three times longer than DEET. The acylpiperidine QSAR models employed 4 descriptors to describe the relationship between structure and repellent duration. The ANN model of the carboxamides did not predict compound structures with exceptional CPTs as accurately; however, several carboxamide candidates did perform equal to or better than DEET.