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.