Development of Novel Repellents Using Structure - Activity Modeling of Compounds in the USDA Archival Database
Abstract
The United States Department of Agriculture (USDA) has developed repellents and insecticides for the U.S. military since 1942. Repellency and toxicity data for over 30,000 compounds are contained within the USDA archive. Repellency data from subsets of similarly structured compounds were used to develop artificial neural network (ANN) models to predict new compounds for testing. Compounds were then synthesized and evaluated for their repellency against Aedes aegypti mosquitoes. Rellency data, i.e., complete protection time (CPT) were 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 more than 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 as good as or better than DEET.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2011
- Accession Number
- ADA558685
Entities
People
- Maia Tsikolia
- Ulrich R. Bernier
Organizations
- Agricultural Research Service