Prediction of intrinsic solubility for drug-like organic compounds using automated network optimizer (ANO) for physicochemical feature and hyperparameter optimization

= 0.991. This result outperforms conventional hyperparameter optimization methods such as grid search and random search in predicting the intrinsic solubility of 3,745 compounds across four external experimental datasets. Based on feature importance analysis, we identified key molecular features and structures that significantly influence solubility. Additionally, combining three molecular fingerprints (Morgan, MACCS key, and Avalon) with molecular descriptors enhances model performance, providing a deeper understanding of the relationship between molecular structure and solubility within the physicochemical feature optimization process. These findings underscore the potential of machine learning models to improve predictive modeling of physical properties, apply automated modeling and feature selection to new chemical datasets, and offer explainable insights into the principles driving solubility predictions.Scientific contributionsThis article focuses on recent advancements in the prediction of molecular properties through the application of a Quantitative Structure-Property Relationship (QSPR)-based deep neural network (DNN) model. This model employs a dual optimization approach that integrates both molecular feature selection and hyperparameter tuning. A review of publicly available datasets for drug-sized molecules is presented, highlighting the contributions of automated modeling and feature selection in enhancing the predictive accuracy of physical properties. Additionally, the article addresses the efficacy of these machine learning models in optimizing features, an essential consideration for practical applications in the field.