PepMSND: Integrating Multi-level Feature Engineering and Comprehensive Databases to Enhance in vitro/in vivo Peptide Blood Stability Prediction

Abstract

Deep learning has emerged as a transformative tool for peptide drug discovery, yet predicting peptide blood stability—a critical determinant of bioavailability and therapeutic efficacy—remains a major challenge. While such a task can be accomplished by experiments, it requires much time and cost. Here, to address this challenge, we collect extensive experimental data on peptide stability in blood from public databases and literature, and construct a database of peptide blood stability that includes 635 samples. Based on this database, we develop a novel model called PepMSND, integrating KAN, Transformer, GAT, and SE(3)-Transformer to make multi-level feature engineering for making peptide blood stability prediction. Our model can achieve the ACC of 0.867 and the AUC of 0.912 on average and outperforms the baseline models. We also develop a user-friendly web for the PepMSND model, which is freely available at http://model.highslab.com/pepmsnd. This research is crucial for the development of novel peptides with strong blood stability, as the stability of peptide drugs directly determines their effectiveness and reliability in clinical applications.