Currently, in drug discovery, the combination of chemoinformatics methods and quantitative structureactivity relationship (QSAR) modeling presents itself as a highly favorable duo, allowing researchers to incorporate a new player into the equation: the use of ML techniques for predictive molecular design and analysis . Chemoinformatics is understood as the interface between chemistry and informatics, where inductive learning is employed to predict chemical phenomena . With increasing accessibility to chemical data, the application of ML in chemoinformatics emerges as a significant tool for exploring, analyzing, and predicting the properties and activities of molecules . ML models undertake prediction tasks based on training data provided in the form of mathematical equations or numerical representations , with many of these data available in chemical datasets or databases.