A Multi-Channel Machine Learning Model for Predicting the Bioactivity Potential of Macrocyclic Peptides

Macrocyclic peptides have gained attention as promising drug candidates due to their unique therapeutic properties. Advances in artificial intelligence have demonstrated the potential to facilitate the discovery and optimization of macrocyclic peptides. However, accurately predicting their biological activities in advance remains a significant challenge. In this study, we developed a multichannel predictive model that integrates molecular fingerprints, graph structural data, physicochemical characteristics, and ADMET properties. With the assistance of this model, we successfully identified macrocyclic peptides exhibiting potent inhibitory activity against neutrophil Elastase and ADAM9. Validation was also performed on four independent peptide data sets. The results demonstrate a prediction accuracy of over 70% in unsupervised learning models and more than 90% with supervised learning models. This study provides a reliable multichannel machine learning model for predicting the bioactivity potential of macrocyclic peptides, demonstrating that the integration of a multichannel fusion strategy with machine learning can facilitate functional macrocyclic peptide screening.