Discovery of potent low-toxicity antimicrobial peptides through diffusion modeling

Abstract The rapid emergence of multidrug-resistant bacteria has created an urgent need for improved antimicrobial discovery and screening platforms. Here, we present ARCADIAMP, a generative and virtual screening platform that couples an iterative-learning discrete denoising diffusion probabilistic model with a two-stage Evolutionary Scale Modeling 2 (ESM2)-based antibacterial activity classifier to generate, classify, and prioritize potent AMPs with high activity, low toxicity, and favorable serum stability. Eight of the ten experimentally screened peptide candidates showed antimicrobial activity (MIC ≤ 32 μg/mL), while one generated candidate, Arcinin, demonstrated strong activity against ESKAPE pathogens (MIC 8–32 μg/mL), low hemolytic activity (LC 50 > 512 μg/mL for human red blood cells), and strong serum-retained activity (MIC 32 μg/mL in 50% bovine serum for four ESKAPE species). Electron microscopy, membrane depolarization assays, time-kill kinetics, and molecular dynamics simulations showed that Arcinin acts through sub-microsecond insertion and penetration consistent with the behavior of other well-known AMPs. In a bacteria-infected wound murine model, Arcinin achieved a 4-log reduction in bacterial burden, which facilitated subsequent re-epithelialization and wound recovery. By framing antimicrobial discovery as an AI-assisted iterative optimization problem, ARCADIAMP links activity, toxicity, and efficacy and provides a scalable template for discovering therapeutically promising biologics.