Antimicrobial peptides (AMPs) play a significant role in
guiding drug design, advancing targeted therapies, and can-
cer treatment research. The function of peptides is highly
associated with their three-dimensional structure. AMPs par-
ticularly favor alpha-helical structures, or alpha-folds, due
to their ability to disrupt the protective layers that surround
cells effectively and their structural stability. Existing classi-
fiers mainly identify AMPs but overlook their structural fold
which can provide valuable insights into their function. To
address this limitation, we introduce an innovative multitask
classifier that recognizes AMPs and predicts their alpha-
helical folds simultaneously. Our approach employs k-mers
and Transformer networks for efficient, accurate multitask
classification.Results on the datasets indicate comparable single-task
performance compared method in half the time complexity.
