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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Invasive Insect Biocontrol & Behavior Laboratory » Research » Publications at this Location » Publication #299503

Title: In silico models for development of insect repellents

item Chauhan, Kamlesh
item Bernier, Ulrich

Submitted to: Insect Repellents Handbook. 2nd edition
Publication Type: Book / Chapter
Publication Acceptance Date: 9/1/2014
Publication Date: 9/30/2014
Citation: Chauhan, K.R., Bernier, U.R. 2014. In silico models for development of insect repellents. In: Strickman, D., Francis, editors. Insect Repellents Handbook. 2nd edition. Boca Raton, FL: CRC Press. p. 53-72.

Interpretive Summary: The design and development of biologically active molecules is sometimes difficult when performed in a synthetic chemistry laboratory. Models that relate structure of chemical to its predicted biological activity must be developed. In this book chapter we focus on new, next-generation computer techniques for molecular modeling for the development of novel insect repelling chemicals and present a novel model for function of insect repellents. This new information, combined with our review of recent trends in repellent design, will benefit development of novel insect repellents for industry and public health research groups.

Technical Abstract: In silico modeling a common term to describe computer-assisted molecular modeling has been used to make remarkable advances in mechanistic drug design and in the discovery of new potential bioactive chemical entities in recent years. The goal of this chapter will be to focus on new, next-generation computer techniques of molecular modeling to illustrate to researchers in the field of arthropod repellents how information on the three dimensional structure of small molecules can facilitate the identification, design, and synthesis of repellents through structural activity relationship. The emphasis is primarily on discussing three recent research approaches of in-silico modeling, namely 1) Molecular overlay, 2) Artificial Neural Network Modeling, and 3) pharmacophore development* with specific sets of arthropod repellents in focus.