A new hybrid quantitative structure property relationships‐support vector regression (QSPR‐SVR) approach for predicting the solubility of drug compounds in supercritical carbon dioxide

Abstract

AbstractThe purpose of this work was to compare the performance of 7 meta‐heuristics algorithms namely: Dragonfly (DA), Ant Lion (ALO), Grey Wolf (GWO), Artificial Bee Colony (ABC), Particle Swarm (PSO), Whale (WAO), and a hybrid Particle Swarm with Grey Wolf (HPSOGWO) optimizers in terms of fine‐tuning hyper‐parameters of a hybrid quantitative structure property relationships (QSPR)‐support vector regression (SVR) for the prediction of molar fraction solubilities of drug compounds in supercritical carbon dioxide (SC‐CO2). A dataset of 168 drug compounds, 13 inputs, and 4490 experimental data points was used to achieve the goal. All 7 models were statistically and graphically approved while the HPSOGWO‐SVR was found to over‐perform with an average absolute relative deviation (AARD) of 0.706% and an AIC of −14,434,249. The model was subjected to an external test (validation) using 160 experimental data points that were not used in the training and the test set. The overall results proved that the obtained model has good predictivity ability and robustness.