Development of Novel Repellents Using Structure−Activity Modeling of Compounds in the USDA Archival Database

摘要: Abstract : The United States Department of Agriculture (USDA) has developed repellents and insecticides for the U.S. military since 1942. Repellency toxicity data over 30,000 compounds are contained within USDA archive. from subsets similarly structured were used to develop artificial neural network (ANN) models predict new testing. Compounds then synthesized evaluated their repellency against Aedes aegypti mosquitoes. Rellency data, i.e., complete protection time (CPT) Quantitative Structure Activity Relationship (QSAR) repellency. Successful prediction novel acylpiperidine structures by ANN resulted in discovery that provided more than three times longer DEET. QSAR employed 4 descriptors describe relationship between structure repellent duration. model carboxamides did not compound with exceptional CPTs as accurately; however, several carboxamide candidates perform good or better