Towards best practices in low-dimensional semi-supervised latent Bayesian optimization for the design of antimicrobial peptides

ArXi:2510.17569v3 Announce Type: replace Generative deep learning techniques have nstrated an impressive capacity for tackling biomolecular design problems in recent years. Despite their high performance, however, they still suffer from a lack of interpretability and rigorous quantification of associated search spaces, which are necessary to unlock their full potential for scientific inquiry beyond efficient design. An area in which they are of particular interest is in the design of antimicrobial peptides, which are a promising class of therapeutics to treat bacterial infections.