Bayesian Flow-Based Peptide Design for Antimicrobial Resistance Mitigation and Therapeutic Discovery

Abstract

Antimicrobial resistance has become one of the most consequential socio-technical failures of contemporary medicine, linking clinical overuse, agricultural practices, weak diagnostics, uneven regulatory capacity, and global inequities in access to care. Antimicrobial peptides offer a promising therapeutic class because they can disrupt microbial membranes, modulate immunity, and evade some conventional resistance pathways, yet their discovery remains limited by sequence space, toxicity, instability, and costly wet-lab validation. This paper examines Bayesian flow-based peptide design as a system-level strategy for accelerating antimicrobial resistance mitigation and therapeutic discovery. Rather than treating generative modeling as a purely computational exercise, the analysis positions Bayesian flow within a larger architecture that includes multi-modal molecular representation, all-atom rationalization, closed-loop experimental validation, manufacturing infrastructure, clinical translation, biosecurity governance, and equitable access. The discussion emphasizes structural trade-offs among generative fidelity, interpretability, computational cost, data provenance, and regulatory accountability. It also considers robustness under distribution shift, fairness in global health deployment, dual-use risk, environmental sustainability, and policy mechanisms for responsible innovation. The central argument is that Bayesian flow-based peptide design can meaningfully contribute to antimicrobial resistance mitigation only if it is embedded in durable socio-technical systems that connect model development to laboratory verification, production capacity, surveillance, and public governance. The paper concludes with forward-looking priorities for interdisciplinary research and policy.