Prediction of parallel artificial membrane permeability assay of some drugs from their theoretically calculated molecular descriptors

Abstract

Parallel artificial membrane permeation assays (PAMPA) have been extensively utilized to determine the drug permeation potentials. In the present work, the permeation of miscellaneous drugs measured as flux by PAMPA (logF) of 94 drugs, are predicted by quantitative structure property relationships modeling based on a variety of calculated theoretical descriptors, which screened and selected by genetic algorithm (GA) variable subset selection procedure. These descriptors were used as inputs for generated artificial neural networks. After generation, optimization and training of artificial neural network (5:3:1), it was used for the prediction of logF for the training, test and validation sets. The standard error for the GA-ANN calculated logF for training, test and validation sets are 0.17, 0.028 and 0.15 respectively, which are smaller than those obtained by GA-MLR model (0.26, 0.051 and 0.22, respectively). Results obtained reveal the reliability and good predictably of neural network model in the prediction of membrane permeability of drugs.