Our structural analysis of the PepVAE-like subset (Figure , bottom-left) predominantly showed three-four times more helical and coiled structures (1) than mostly coiled structures (6). The structural classes (2), ( ) and (4) were quasiinexistent. Removing cysteine-rich sequences to the original E. coli subset has drastically reduced PEP2D predictions across stranded and coiled structures (5) and mixed structures (7), i.e., <4% (Table ). These observations coincided with Dean and co-workers' observations, where their generated AMPs against E. coli were likely folding into α-helices (Group B, Figures and) . Likewise, the authors reported that the generated AMPs against S. aureus or P. aeruginosa would mostly be α-helical structures. We can therefore assume that removing cysteine-rich sequences from the relevant subsets would enrich their training sets with folds from structural classes (1), ( ) and ( ). Notably, their strain-specific predictive models towards the three bacterial strains were prone to fewer errors between predicted and experimental MICs with the α-helical subset (Group B) than the one with more diverse structures (Figure ). These results suggested a bias towards α-helical structures from model training and models that might not generalise well over other structural classes. It further highlights the importance of estimating the structures of medium-large training datasets before building predictive or generative ML models.