Antibiotic-resistant bacteria are spreading faster than new treatments can be discovered, creating a growing global health crisis. While AI has already been used to identify potential antibiotics from existing data, this work marks a shift: using generative AI to create new drug candidates entirely from scratch.
The model behind this work, AMP-Diffusion, builds on a type of generative AI known as a diffusion model—technology more commonly used to generate realistic images. These models start with random noise and gradually refine it into meaningful output. Applied to biology, that means converting random amino acid sequences into coherent, functional peptides.
This approach doesn’t just search existing biological databases; it enables the design of antibiotic candidates that go beyond what evolution has produced.
How AMP-Diffusion Works
AMP-Diffusion is a latent diffusion model fine-tuned specifically for antimicrobial peptide (AMP) design. Unlike traditional models that attempt to build biological sequences from scratch, AMP-Diffusion incorporates ESM-2, a protein language model developed by Meta and trained on millions of natural sequences. This gives the AI a foundational understanding of how proteins work, improving its chances of generating viable, functional peptides.
The model begins with a static, random sequence of amino acids and uses a “denoising” process to gradually refine it into a novel peptide. During this process, ESM-2 acts as a guide, offering biological context to keep the model’s output grounded in real-world rules.
This innovation allows researchers to focus on generating diverse and potent candidates, rather than spending time teaching the model basic biology—speeding up development and improving the quality of the output.
From In Silico to In Vivo
To test its capabilities, AMP-Diffusion was used to generate 50,000 candidate peptide sequences. Researchers then turned to another AI tool, APEX 1.1, to rank the candidates based on predicted potency, uniqueness, and molecular diversity. From there, the top 46 peptides were synthesized in the lab.
In vitro testing showed that 76 % of these peptides effectively killed bacteria while remaining non-toxic to human cells. The best performers were tested further in a preclinical mouse model of skin infection.
Two candidates stood out, significantly reducing bacterial loads at levels comparable to established antibiotics like polymyxin B and levofloxacin—with no detectable side effects in the animals.
Why it Matters
What makes this work especially exciting is that it shows how generative AI can go beyond analyzing existing data—it can actually create entirely new therapeutic molecules.
By building on pre-trained biological knowledge, AMP-Diffusion doesn’t need to learn everything from scratch. Instead, it can focus on designing peptides that are not only original but also more likely to work in the real world. That’s a big deal when time and accuracy are critical.
But what really stands out is the fact that some of these AI-designed peptides didn’t just look good on paper; they worked in real, living organisms, and without harmful side effects. That takes this from an impressive technical feat to something with real medical potential. In a world where antibiotic resistance is rising fast and drug discovery is painfully slow, having a tool like this could make a real difference.
Journal Reference
Torres, M. D. T., Chen, L. T., Wan, F., Chatterjee, P., & de la Fuente-Nunez, C. (2025). Generative latent diffusion language modeling yields anti-infective synthetic peptides. Cell Biomaterials, 100183. DOI:10.1016/j.celbio.2025.100183. https://www.cell.com/cell-biomaterials/fulltext/S3050-5623(25)00174-6
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