The application of scCO 2 spans a wide range of fields, including the extraction of compounds, 2,3 biomass conversion, the production of micro-and nanoparticles, control of polymorphic forms, 8-10 impregnation, and polymer synthesis, among others. The supercritical fluid extraction method is thus comprehensively applied across various industries, including pharmaceuticals, food, cosmetics, and beverages. Understanding solubility is crucial for optimizing extraction parameters (temperature, pressure, and time) to maximize the yield of target compounds. Furthermore, knowledge of solubility helps to predict the behavior of substances during extraction, which is particularly important for developing cost-effective and environmentally sustainable technologies. Another critical area where solubility in scCO 2 plays a pivotal role is in the modification of pharmaceutical compounds, which is essential for enhancing their bioavailability, dosing efficiency, and therapeutic applicability. Nanonization technologies leveraging scCO 2 have emerged as a promising approach for producing drug nanoparticles, offering improved solubility, enhanced permeability of hydrophobic drugs, and greater stability. In additionproviding microparticles (usually with the size range of 1-5 µm) of the inhalation drugs to be used in treatment of respiratory and/or systemic diseases as inhaled pharmaceutical formulations is another important industrial application for scCO 2 technologies. However, the experimental investigation of solute solubility in scCO 2 presents significant challenges, including substantial time requirements, high equipment costs, and variability in results. Consequently, the development of computational modeling methods for predicting solubility has become a critical task, as it can reduce the need for extensive laboratory experiments and accelerate research and development processes. Currently, there are no universal methods for predicting the solubility of substances in scCO 2 . A variety of computational approaches have been developed for predicting solubility in scCO 2 . These methods encompass density-based correlation models, equation-ofstate (EoS) approaches, quantitative structure-property relationship models, classical density functional theory (cDFT), molecular dynamics (MD) simulations and others. Among these, density-based correlations and EoS methods are the most widely employed due to their relative simplicity and ease of use. However, these approaches rely on experimental solubility data to calibrate computational parameters through fitting procedures. In contrast, more complex methodologies, such as cDFT or full-atomic MD simulation, focus primarily on solvation contributions and require experimental sublimation data for the compounds under investigation.