Artificial Intelligence Driven Prediction of Aqueous Solubility of Drug Molecules Using Molecular Descriptors and Optimized ANN Architectures

Accurate prediction of aqueous solubility (logS) is a cornerstone of drug development, influencing bioavailability, pharmacokinetics, and therapeutic efficacy. Traditional models, such as ESOL, often exhibit limited accuracy across diverse chemical datasets, whereas Artificial Neural Networks (ANNs) offer a robust alternative by capturing complex non-linear relationships in molecular descriptors derived from SMILES representations. This study evaluates the performance of various ANN architectures against baseline models, including Random Forest (RF) and Linear Regression (LR), demonstrating the superior accuracy of ANNs, which achieved the lowest mean error and most consistent error distribution. Nonetheless, model performance was influenced by the logS range and architectural complexity, with deeper networks prone to overfitting and simpler architectures susceptible to underfitting. These findings position ANNs as powerful tools for solubility prediction, underscoring the importance of balanced model design and expanded datasets to enhance generalization. AI-driven approaches offer transformative potential to accelerate drug discovery, reduce costs, and optimize therapeutic outcomes.