“…Therefore, the performance of the supervised models was assessed in terms of metrics, demonstrating that supervised learning models such as notably logistic regression, decision tree, random forest, k-nearest neighbors, naïve Bayes, support vector machine, and neural network could effectively predict peptide binding properties. The approach developed in this study has the potential for future applications in targeting diseases such as GBM by enabling precise peptide selection and design …”