AI‑driven phage research confronts antimicrobial resistance rooted in antibiotic overuse and profit‑driven drug pipelines
Original framing: “Nature has spent billions of years fighting bacteria. AI could help us learn its secrets” — Phys.org
AI methods such as deep‑learning protein structure prediction and genome‑wide association studies accelerate the identification of receptor‑binding proteins, reducing the experimental bottleneck that has limited phage therapy. Robust validation pipelines, however, remain essential to avoid overfitting and ensure clinical safety.
The convergence of AI, bacteriophage biology, and historical lessons from early 20th‑century therapy reveals a systemic opportunity to rebalance the antimicrobial landscape away from profit‑driven antibiotics toward community‑owned biologics.
Indigenous and cross‑cultural practices provide a reservoir of ecological insight that, when coupled with open‑access AI platforms, can democratise discovery and deployment. Robust future modelling underscores the life‑saving potential while warning of ecological side‑effects, demanding a precautionary regulatory regime. By foregrounding marginalised patient voices and embracing the trickster inversion of pathogen‑enemy narratives, policymakers can craft equitable, resilient health systems that harness nature’s own weapons against resistance.