Quantum Annealing Designs Nonhemolytic Antimicrobial Peptides in a Discrete Latent Space.

ABSTRACT: Increasing the variety of antimicrobial peptides is crucial in meeting the global challenge of multi-drug-resistant bacterial pathogens. While several deep-learning-based peptide design pipelines are reported, they may not be optimal in data efficiency. High efficiency requires a well-compressed latent space, where optimization is likely to fail due to numerous local minima. We present a multi-objective peptide design pipeline based on a discrete latent space and D-Wave quantum annealer with the aim of solving the local minima problem. To achieve multi-objective optimization, multiple peptide properties are encoded into a score using non-dominated sorting. Our pipeline is applied to design therapeutic peptides that are antimicrobial and non-hemolytic at the same time. From 200 000 peptides

SUBMITTER: Tucs A 

PROVIDER: S-EPMC10184305 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature