Previous studies have primarily employed classic statistical methods or analogous approaches such as RR, SVM, MLP, RFR, GSE, multilinear regression (MLR), and artificial neural networks (ANN) for solubility prediction. A comparison of notable models and their performance adopted in the previous studies is presented in Table . While these methods have demonstrated predictive capabilities, their performance has often been constrained, even when achieving high predictive accuracy. This prompted us to introduce DNN for comparative analysis. A DNN model not only extracts and represents complex information from data but also creates a lower-dimensional latent space. This latent space can be utilized for purposes such as visualizing complex data, generating new data, and detecting anomalies. We evaluated the DNN model's performance using two different optimization approaches: one involving HPO alone, and the other leveraging the ANO, which optimizes features and hyperparameters. Although the ANO model is more complex, it emphasizes the critical selection of optimized descriptors. The chosen descriptors account for the inherent variance and errors in the data, leading to improved performance scores. Our approach involves a rigorous model optimization process, conducted in two rounds within the ANO framework, which has resulted in significant performance enhancements. Furthermore, our investigation revealed that using either the complete set of descriptors or a limited subset of top descriptors To optimize feature selection, we adopted an "exploration-based" approach, carefully investigating 49 distinct combinations derived from the available features. This exhaustive exploration covered a vast search space, with a potential 2 49 combinations. Our goal was to identify regions of high predictive accuracy while expanding the search space for feature optimization beyond the limitations of conventional methods. This approach prioritizes minimizing variance and bias during the optimization process, ultimately enhancing the overall performance of the DNN model. One notable limitation of this approach is the significant computational time and resources required to thoroughly explore the multitude of feature combinations. To mitigate this challenge, we implemented tailored optimization techniques, including vectorization and logarithmic transformations, for each of the 49 features. These optimizations helped minimize the impact of potential errors and allowed for performance evaluation using the R 2 metric. HPO was performed using BO with TPEs within the Optuna framework, a widely used AutoML platform. BO, a sequential model-based optimization algorithm, leverages the results from previous iterations to intelligently identify promising hyperparameter candidates, consistently seeking to achieve better scores than earlier iterations.