Transdermal delivery of certain drugs is challenging because of skin barrier resistance. This study focuses on the implementation of feature-selection algorithms to design chemical penetration enhancers. A database, consisting of 145 polar and nonpolar chemicals, was chosen for the investigation. Replacement, enhanced replacement and stepwise algorithms were applied to identify relevant structural properties of these compounds. The descriptors were calculated using Molecular Modeling Pro™ Plus. Based on the coefficient of determination, the replacement methods outperformed the stepwise approach in selecting the features that best correlated with the flux enhancement ratio. An artificial neural network model was built to map a subset of descriptors from sixty-one nonpolar enhancers onto the output vector. The R2 value improved from 0.68, for a linear model, to 0.74, which shows that the improved framework might be effective in the design of compounds with user-defined properties.