In this study, we employed the SMILES (Simplified Molecular Input Line Entry System) notation to represent chemical structures for the input dataset. While SMILES provides a concise, text-based representation of molecular structures, its use of special characters (e.g., parentheses, numbers) and complex combinations (e.g., molecules, ring structures) poses challenges for direct processing by ML models. To address these limitations, we utilized molecular fingerprints (FPs) from the RDKit library . We incorporated two types of features to build our ML model: molecular fingerprints and physicochemical molecular descriptors. Specifically, 2,727 bits of fingerprints, including Morgan (2048 bits) [44], MACCS keys (167 bits) , and Avalon (512 bits), as well as 49 molecular descriptors, were utilized for all compounds. First, the Morgan fingerprint, also known as extended-connectivity fingerprints (ECFPs) , is developed using a circular environment around each atom, encompassing the connectivity of atoms (elements, neighbors, hydrogen counts, charges, isotopes, ring counts) and chemical features such as donor, acceptor, aromatics, halogens, basic, and acidic groups. Second, the MACCS keys provide a powerful representation of structural patterns or fragment commonly observed in organic molecules, encoded in 166-bit strings.