Solubility prediction of drugs in supercritical carbon dioxide using artificial neural network

Abstract

The descriptors computed by HyperChem (R) software were employed to represent the solubility of 40 drug molecules in supercritical carbon dioxide using an artificial neural network with the architecture of 15-4-1. The accuracy of the proposed method was evaluated by computing average of absolute error (AE) of calculated and experimental logarithm of solubilities. The AE (+/- SD) of data sets was 0.4 (+/- 0.3) when all data points were used as training set and the solubilities were back-calculated. The AE for predicted solubilities using a trained network employing 1/3 of data points from each set was 0.4 (+/- 0.3) and this finding reveals that the network is well trained using a limited number of experimental data. To provide a full predictive method, data sets were divided into two sets and the network was trained using 20 data sets and the next 20 sets were used as prediction sets. The produced average AEs (+/- SD) were 1.7 (+/- 1.1) and 1.6 (+/- 1.5), for two sets of analyses. In these analyses, only the computational descriptors, temperature and pressure of SC-CO(2) were used and no experimental solubility data is employed.