(20b) A Genetic Algorithm for Virtual Design of Chemical Penteration Enhancers

Traditional drug design is a laborious and expensive process that often challenges the pharmaceutical industries. As a result, researchers in the pharmaceutical industry have turned to computational methods for computer assisted molecular design. Recently, genetic and evolutionary algorithms have emerged as efficient methods in solving combinatorial problems for computer aided molecular design. Further, combining genetic algorithms (GAs) with quantitative structure-property relationship (QSPR) analyses has proved effective in drug design.

In this work, we have (a) developed a GA program to generate novel molecules from a set of candidate molecules through crossover and mutation, and (b) integrated non-linear, QSPR models and genetic algorithms to develop a reliable virtual screening algorithm for generation of potential chemical penetration enhancers (CPEs).

The GA-QSPR algorithm has been implemented successfully to identify potential CPEs for transdermal drug delivery. Validation of the newly-identified CPE molecular structures is conducted through carefully designed experiments, which elucidate the cytotoxicity and permeability of the CPEs.