Application of machine learning approach to estimate the solubility of some solid drugs in supercritical CO2

Accurate estimation of the solubility of solid drugs (SDs) in the supercritical carbon dioxide (SC-CO₂) plays an essential role in the related technologies. In this study, artificial intelligence models (AIMs) by gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) methods were applied to estimate the solubility of SDs in SC-CO₂. Hence, a comprehensive database (1816 datasets) comprising operational conditions (T, P) in the wide ranges (308-348.2 K and 80-400 bar), SD's molecular weight (MW_SDs), and melting point (MP_SDs) were gathered. Investigation analysis of the models' strength showed that the model developed by ANFIS exhibited a more satisfactory approximation than the GEP model. According to the optimized ANFIS model, statistical parameters of R², RMSE, MAE, and AARD% were obtained, equivalent to 0.991, 0.260, 0.167, and 13.890% for training and 0.990, 0.256, 0.157, and 15.273% for validation, in that order. Sensitivity analysis showed that the highest effect of independent variables on calculating SDs solubility in SC-CO₂ belong to MW_SDs, P, MP_SDs, and T, respectively. Therefore, MW_SDs is a key factor for modeling the solubility of various SDs in SC-CO₂. Comparing the estimated results obtained from the optimized AIM with previous semi-empirical models showed that the AIMs could be more accurate in modeling the solubility of SDs in SC-CO₂.