The study concludes that dimensionally-reduced latent spaces improve interpretability and can aid optimization for antimicrobial peptide design, with context-dependent advantages from using less-relevant versus more-relevant properties for space organization.
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Towards best practices in low-dimensional semi-supervised latent Bayesian optimization for the design of antimicrobial peptides
The study concludes that dimensionally-reduced latent spaces improve interpretability and can aid optimization for antimicrobial peptide design, with context-dependent advantages from using less-relevant versus more-relevant properties for space organization.