Solubility, defined as the process of dissolving a solute in a solvent to form a homogeneous system, is crucial for achieving desired drug concentrations in systemic circulation and ensuring pharmacological efficacy . Understanding the solubility of chemical compounds is vital for predicting their physicochemical behavior and facilitating the design and synthesis of next-generation drug-like compounds . As a key property, solubility influences interactions in various environments, making its prediction an essential research focus for decades, with substantial progress made through pivotal discoveries and advancements . The advent of computational methods has transformed solubility research by enabling in silico predictions, which are faster and more cost effective than experimental approaches. Several empirical modeling methods have been developed using nonexperimental structural parameters. A groundbreaking discovery in this field was the use of the octanol-water partition coefficient (logP), introduced by Hansch et al., to predict solubility . This discovery laid the foundation for further advancements, including the development of the general solubility equation (GSE) by the Yalkowsky group . The GSE provided a more comprehensive framework by integrating molecular structure descriptors, while group contribution methods established correlations between water solubility and functional groups by decomposing molecules into subunits and estimating their solubility contributions. However, the GSE's reliance on experimental data, such as the melting point (Tm), and the group contribution method's need for numerous parameters to achieve accurate predictions have led to the development of alternative approaches. These methods focus solely on structural features for estimating logP, as explored in subsequent research . Further advancements were made by Jorgensen and Duffy , who utilized Monte Carlo simulations and various molecular descriptors, including molecular weight (MW), volume, solvent accessible surface area (SASA), hydrogen bond (HB) counts, and physical descriptors like solute-water Coulomb (ESXC) and Lennard-Jones (ESXL) interactions, along with hydrophobic and hydrophilic components. These refinements have made predictive models more accurate and reliable, contributing significantly to the field of solubility prediction . Despite these advancements, traditional methodologies still face inherent limitations, particularly in highthroughput settings. These methods are often time-consuming and expensive, making them impractical for efficiently screening large libraries of compounds. In addition, their predictive accuracy can sometimes be suboptimal, limiting their applicability in real-world scenarios.