Traditional drug design is a laborious and expensive process that often challenges the pharmaceutical industries. As a result, researchers have turned to computational methods for computer-assisted molecular design. Recently, genetic and evolutionary algorithms have emerged as efficient methods in solving combinatorial problems associated with computer-aided molecular design. Further, combining genetic algorithms with quantitative structure-property relationship analyses has proved effective in drug design. In this work, we have integrated a new genetic algorithm and nonlinear quantitative structure-property relationship models to develop a reliable virtual screening algorithm for the generation of potential chemical penetration enhancers. The genetic algorithms-quantitative structure-property relationship algorithm has been implemented successfully to identify potential chemical penetration enhancers for transdermal drug delivery of insulin. Validation of the newly identified chemical penetration enhancer molecular structures was conducted through carefully designed experiments, which elucidated the cytotoxicity and permeability of the chemical penetration enhancers.
© 2011 John Wiley & Sons A/S.