Cyclic peptides are less explored by machine learning models, and most studies are based on experimental medicinal chemistry and SAR analyses. Subtle chemical modifications often lead to significant activity changes , complicating activity prediction. While one machine learning model successfully classified NGC-causing cyclic peptides and showed that the sequence design of linear and cyclic peptides are close cognates , this finding contrasts with the observation that linearized polymyxins lose activity [109], highlighting the unique properties of cyclic peptides . Dedicated models for cyclic peptides are urgently needed to better harness their distinct features.