Machine learning models primarily predict or generate novel peptides and proteins from sequential representation, lacking structural information. To minimise the impact that structural factors might have upon biological prediction or sequence generation, researchers have voluntarily selected sequences based on structural or evolutionary constraints, so they presumably adopt the same tridimensional structure(s). In recent years, computational peptide designers have capitalised on neural network architectures to predict or generate novel α-helical AMPs , α-helical non-hemolytic AMPs or α-helical non-hemolytic anticancer peptides . Likewise, Batra and co-workers trained their models on peptides susceptible to form β-sheets to develop self-assembling materials . In machine learning-guided directed evolution , researchers have used directed evolution to assemble a set of homologous sequences, then devised robust ML strategies to engineer the next batch of proteins (e.g., channelrhodopsins , fluorescent proteins ) or enzymes (e.g., glycosyltransferase superfamily 1 ). Alternatively, several upcoming "structure-first" ML strategies tackle the sequence-structure-function problem upside down; by designing sequences that would fold into a pre-determined backbone structure derived from native topologies or generated de novo . These approaches are often regrouped under the terms inverse protein folding, structure-based protein design or fixed-backbone protein design.