Recent advances in artificial intelligence (AI) have opened new avenues for improving QSPR models . Over the past decades, AI-based QSPR models have demonstrated promise in predicting the solubility of drug-like molecules . These models leverage molecular descriptors-quantitative representations of molecular properties-to describe and predict molecular behavior. By integrating such descriptors, AI models can construct highly accurate predictive frameworks. The descriptors serve as inputs to machine learning (ML) algorithms, which learn the relationships between molecular properties and solubility outcomes. A key advantage of AI-driven solubility prediction is its capacity to manage large datasets and capture complex relationships that may elude traditional methods. In addition, AI models can dynamically adjust to new data, enhancing their predictive accuracy over time. However, traditional ML models, such as ridge regression (RR), support vector machines (SVM), random forest regression (RFR) struggle with scalability and accuracy in high-throughput settings due to their reliance on manual feature selection and limited hyperparameter tuning capabilities.