The study employs density functional theory (DFT) to examine the drug-loading efficiency of graphyne (GYN) as a vehicle for the Tioguanine (TG) drug. The researchers analyzed the interaction energy, electrical properties of pure GYN, TG molecules, and TG@GYN complex to determine their effectiveness as a carrier. Configuration a, which utilized nitrogen and sulfur atoms in interactions, was deemed the most suitable among the three considered TG sites. Gas-phase interaction between TG drug and GYN resulted in an energy of adsorption about -1.64 eV. The study utilized non-covalent interaction (NCI) analysis to assess the interaction between GYN and TG drug, indicating weak forces of interaction in the TG@GYN complex. The HOMO-LUMO and charge-decomposition analysis described the transfer of charge from TG molecules to pure GYN during formation of TG@GYN. The results suggest that GYN could function as a promising candidate for carrying and delivering TG drug, leading to further research into similar 2D nanomaterials for drug transport applications.
The accurate prediction of solubility and solvent properties in supercritical CO₂ systems remains a critical challenge in pharmaceutical process design due to the nonlinear and coupled effects of temperature and pressure. This study proposes a novel artificial intelligence-based modeling framework for predicting solvent density and paracetamol mole fraction under supercritical conditions using temperature and pressure as input variables. Three regression models-Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Tweedie Regression (TDR)-were developed and systematically optimized using the Artificial Rabbits Optimization (ARO) algorithm. Unlike conventional single-model studies, this study provides a comparative and optimization-driven evaluation of both nonlinear machine learning models and statistically grounded regression methods under identical conditions. The results demonstrate that the ARO-optimized MLP model achieves superior predictive performance for both solvent density (R² = 0.99898) and mole fraction (R² = 0.96555), outperforming SVR and TDR models across all evaluation metrics. The study further reveals clear nonlinear dependencies of solubility and density on pressure and temperature, which are effectively captured through data-driven modeling and visualized via contour-based response surfaces. The main innovation of this work lies in the integration of a metaheuristic optimization strategy (ARO) with multiple regression paradigms to establish a unified and systematic framework for supercritical solubility prediction. This approach provides both high predictive accuracy and interpretable process insights, supporting early-stage optimization of pharmaceutical manufacturing in supercritical CO₂ environments.