Quantitative Structure-Activity Relationship (QSAR) modeling has been an integral part of computer-assisted drug discovery for over six decades . This approach is used to rationalize the experimental data on chemical bioactivity measurements and develop models that can assess the expected bioactivity of new chemicals in advance of experiments. In addition to their broad use in drug discovery and chemical toxicology , methods and approaches used for QSAR modeling have proliferated into many areas of research . These models are categorized based on the nature of the bioactivity data they process: continuous models, which use quantitative data like IC50 values, and classification models, which, in case of binary classification, categorize compounds as active or inactive, or more broadly, into positive or negative classes. Each type of QSAR model employs specific metrics for assessing accuracy. Continuous models typically rely on cross-validation R 2 , for accuracy assessment, while the accuracy of classification models is assessed using metrics derived from the confusion matrix, such as selectivity, specificity, and balanced accuracy (BA), also known as Correct Classification Rate (CCR) . Achieving the highest balanced accuracy, i.e., models that can equally well predict both the positive and negative classes, is the most common assessment of model performance . When dealing with imbalanced datasets, downsampling the predominant class is a common practice to enhance BA . Other common metrics, such as the area under the receiver operating characteristic curve (AUC), have also seen frequent use .