Advances in computer sciences, accessible high-performing machines and the proliferation of large public databases have accelerated the development of computational models for peptide design and protein engineering. The advent of artificial intelligence (AI) in the biological sciences has led to the creation of machine learning (ML) models capable of predicting and generating new peptides and proteins with the desired characteristics. Predictive models are trained on small-to-large datasets to learn the relationships between the biological sequences and their respective functional measurements (e.g., thermostability, bacterial growth inhibition, protein binding affinity). Generative models can learn meaningful representations (e.g., a conserved cysteine framework, a catalytic site) to create new peptide/protein sequences that resemble the native counterparts. The interplay between predictive and generative AI models provide an informed and efficient sequence design by predicting the outcomes of different peptide/protein sequences. Many comprehensive reviews have referenced the successful applications of ML-guided sequence design to antimicrobial peptides (AMPs) , protein binders 5 , antigen-specific monoclonal antibodies , protein families and enzymes . The field of ML-guided peptide/protein sequence design is snowballing; the reader is encouraged to consult these two GitHub repositories to stay informed.