Abstract
Aqueous solubility, an essential physical property of compounds, has significant applications across various fields. However, verifying the solubility of compounds through experimental methods often requires substantial human and material resources. To address this issue, this study introduces the StackBoost model for predicting the solubility of organic compounds and systematically compares it with five well-known ensemble learning algorithms: Adaptive Boosting (AdaBoost), Gradient Boosted Regression Trees (GBRT), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). The prediction results indicate that the StackBoost model excels in predicting aqueous solubility, achieving a coefficient of determination ([Formula: see text]) of 0.90, a root mean square error (RMSE) of 0.29, and a mean absolute error (MAE) of 0.22, significantly outperforming the other comparative models. Furthermore, this study further conducted high-throughput screening on large-scale datasets and successfully identified compounds with high potential for water solubility. Additionally, the model's generalization ability is verified through transfer learning. Although the performance of the StackBoost model decreases when applied to different datasets, it still shows considerable transferability, making it a more generalizable prediction model for aqueous solubility.