Application of machine learning approach to estimate the solubility of some solid drugs in supercritical CO.
Journal:
Scientific reports
PMID:
39939386
Abstract
Accurate estimation of the solubility of solid drugs (SDs) in the supercritical carbon dioxide (SC-CO) 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-CO. 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), and melting point (MP) 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 R, 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-CO belong to MW, P, MP, and T, respectively. Therefore, MW is a key factor for modeling the solubility of various SDs in SC-CO. 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-CO.