Efficiently solving the curse of feature-space dimensionality for improved peptide classification

Machine learning is becoming an important tool for predicting peptide function that holds promise for accelerating their discovery. In this paper, we explore feature selection techniques to improve data mining of antimicrobial and catalytic peptides, boost predictive performance and model explainability. SMILES is a widely employed software-readable format for the chemical structures of peptides, and it allows for extraction of numerous molecular descriptors. To reduce the high number of features therein, we conduct a systematic data preprocessing procedure including the widespread wrapper techniques and a computationally better solution provided by the filter technique to build a classification model and make the search for relevant numerical descriptors more efficient without reducing its effectiveness. Comparison of the outcomes of four model implementations in terms of execution time and classification performance together with Shapley-based model explainability method provide valuable insight into the impact of feature selection and suitability of the models with SMILE-derived molecular descriptors. The best results were achieved using the filter method with a ROC-AUC score of 0.954 for catalytic and 0.977 for antimicrobial peptides, with the execution time of feature selection lower by 2 or 3 orders of magnitude. The proposed models were also validated by comparison with established models used for the prediction of antimicrobial and catalytic functions.