Location: Mosquito and Fly ResearchTitle: Development of acylpiperidine and carboxamide repellents using structure-activity models) Author
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/23/2012
Publication Date: N/A
Citation: N/A Interpretive Summary:
Technical Abstract: The United States Department of Agriculture (USDA) has developed repellents and insecticides for the U.S. military since 1942. Data for over 30,000 compounds are contained within the USDA archive. Repellency data from similarly structured compounds were used to develop artificial neural network (ANN) models to predict new compounds for testing. 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.