Comparative analysis using machine learning methods for determination of pharmaceutical solubility in supercritical CO

Background: Reliable prediction of pharmaceutical solubility in supercritical carbon dioxide (SC-CO Methods: A leakage-aware machine-learning workflow was evaluated using molecular weight, melting point, temperature, and pressure as predictors of drug solubility in SC-CO Results: HGB provided the strongest overall performance. Under the random record-wise test split, it achieved R² = 0.99372, RMSE = 0.247192 g/L, and MAE = 0.113038 g/L. Under the more stringent LOCO evaluation, its performance decreased to R² = 0.93128, RMSE = 0.498217 g/L, and MAE = 0.271894 g/L. The difference between the two validation schemes demonstrates that random splitting yields an optimistic estimate when compound-specific observations are shared across subsets. Conclusion: HGB accurately correlated solubility within the investigated data distribution and retained useful predictive ability for compounds excluded from model training. Nevertheless, external validation and additional molecular descriptors are required before extrapolating the model to chemically distinct pharmaceuticals.