Generative Models for Synthetic Phage Therapeutics

About

This project tackles antibiotic resistance by using AI to design engineered bacteriophages – viruses infecting only specific bacterial hosts – as therapeutic agents. The research team has developed advanced machine learning systems that can generate complete viral genomes, not just individual components, representing a major advancement in synthetic biology.

Working with the largest curated dataset of phage DNA sequences to date, the researchers implemented strict safety protocols to ensure generated sequences don't contain harmful elements. They tested multiple AI architectures, ultimately selecting one that efficiently handles long genetic sequences. The system already produces synthetic phage genomes with realistic biological features, soon to be tested in the lab against E. coli.

Beyond creation, the team's models can predict which genetic features make phages effective at killing bacteria - crucial knowledge for developing therapies.

The work demonstrates how AI can accelerate the development of precision antimicrobials while maintaining rigorous safety standards. As antibiotic resistance grows increasingly urgent, this approach offers a sustainable alternative that could be applied in both healthcare and agriculture. Future work will expand the system to target more dangerous pathogens while validating results through laboratory experiments.