Advances in artificial intelligence (AI), machine learning (ML), and bioinformatics have transformed AMP discovery by leveraging patterns from diverse methods or datasets to generate novel peptide sequences. These methods integrate data from molecular dynamics (MD), fluorescence imaging, and experimental assays, creating efficient discovery pipelines. demonstrated the power of such an approach, employing deep learning and unlabeled data alongside known AMPs to teach a model the "peptide grammar." This method generated novel sequences, with the best candidates screened through coarse-grained MD (CG-MD) simulations using the Martini force field and insane . The top 20 peptides from this shortlist were tested in vitro, showing the CG-MD model predicted antimicrobial activity with an 88% sensitivity and 63% specificity. While CG-MD of simplified model systems excels in high-throughput screening, all-atom MD simulations are essential for detailed mechanism-of-action studies.