AI designs first viable bacteriophages from scratch using genome language models

Researchers have demonstrated the first successful generative design of viable bacteriophage genomes using frontier AI language models. The study leveraged two models called Evo 1 and Evo 2—genome language models trained to understand and generate DNA sequences. The team used the lytic phage ΦX174 (a well-studied virus that kills its bacterial host) as a design template, instructing the models to generate whole-genome sequences with two key properties: realistic genetic architectures (meaning the designs respect how actual genes are organized and regulated) and desirable host tropism (meaning the viruses would target specific bacterial hosts). When the AI-generated sequences were synthesized and tested experimentally in the laboratory, 16 viable phages emerged from the designs. Critically, these phages exhibited substantial evolutionary novelty—they were not slight variations on known sequences but genuinely new genomes that had never existed in nature. This achievement marks the first time genome language models have been validated as capable of designing functional, complete biological systems at whole-genome scale, rather than small fragments or optimized sections of existing genomes. The result suggests that AI trained on genetic sequence data can learn deep principles of biological design well enough to generate living systems, with potential applications in synthetic biology, phage therapy research, and rapid prototyping of novel viral systems.