Title: Lightweight Midas Touch: A Paired-learning Framework for Flexible Design of Antimicrobial Peptides
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Learning framework, Generative model, Antimicrobial peptides, Antimicrobial resistance

Abstract: Antimicrobial peptides (AMPs) are promising potential therapeutic agents against drug-resistant pathogens, owing to their broad-spectrum activity and minimal risk of resistance. However, prevailing AMP generative paradigms privilege only the dyad of model and data: small-scale AMP data and the inductive bias of bespoke models jointly circumscribe the generated candidates. This tenacious coupling between training data and model parameters imposes a constraint on the exploration of AMP diversity. To alleviate this, we introduce a paired-learning framework, lightweight Midas touch (LMT), which has four components: (1) a primer set, (2) a mapping table, (3) a target set, and (4) a converter. During training, a mapping table explicitly associates the primers with the targets, and a lightweight converter is trained to internalize this mapping correspondence. During inference, the candidate distribution is steered by re-specifying the primers, avoiding the need to re-design the converter. By liberating the primer set and mapping table from the data-model dyad, the framework dissolves their erstwhile tight coupling and affords flexible control over generation. Comprehensive evaluations demonstrate that the proposed framework yields AMP candidates of markedly enriched diversity while attaining accuracy. Consequently, by delivering diverse and novel AMP candidates, LMT constitutes a potent strength in the global campaign against antimicrobial resistance.