Efforts to overcome these limitations through structural fine-tuning have shown significant success. Rational modifications supported by structural models, such as MD simulations, docking studies, conformational sampling, X-ray crystallography, and NMR, have led to improved AMPs . However, machine-learning (ML) models have struggled to deliver substantial novelty in AMP discovery, often generating peptides with the same limitations as their natural counterparts. Currently, rational optimization of existing compounds remains a more reliable approach for developing effective peptide drugs than ML-driven from-scratch designs. To overcome the inherent PK limitations of natural AMPs it is often necessary to specifically modify the AMPs, and cyclization [424], stereochemical inversion or the use of peptide-like constructs [426] appear as three feasible roads to address the issue.