by Shelly Fan at Singularity Hub: Bacteria and antibiotics have been in a roughly century-long game of cat and mouse. Unfortunately, bacteria are gaining the upper hand.According to the World Health Organization, antibiotic resistance is a top public health risk that was responsible for 1.27 million deaths across the globe in 2019. When repeatedly exposed to antibiotics, bacteria rapidly learn to adapt their genes to counteract the drugs—and share the genetic tweaks with their peers—rendering the drugs ineffective.
Superpowered bacteria also torpedo medical procedures—surgery, chemotherapy, C-sections—adding risk to life-saving therapies. With antibiotic resistance on the rise, there are very few new drugs in development. While studies in petri dishes have zeroed in on potent candidates, some of these also harm the body’s cells, leading to severe side effects.
What if there’s a way to retain their bacteria-fighting ability, but with fewer side effects? This month, researchers used AI to reengineer a toxic antibiotic. They made thousands of variants and screened for the ones that maintained their bug-killing abilities without harming human cells.
The AI used in the study is a large language model similar to those behind famed chatbots from Google, OpenAI, and Anthropic. The algorithm sifted 5.7 million variants of the original antibiotic and found one that maintained its potency but with far less toxicity.
In lab tests, the new variant rapidly broke down bacteria “shields”—a fatty bubble that keeps the cells intact—but left host cells undamaged. Compared to the original antibiotic, the newer version was far less toxic to human kidney cells in petri dishes. It also rapidly eliminated deadly bacteria in infected mice with minimal side effects. The platform can also be readily adapted to screen other drugs in development, including those for various types of cancers.
“We have found that large language models are a major step forward for machine learning applications in protein and peptide engineering,” said Dr. Claus Wilke, a University of Austin biologist and data scientist and an author on the study, in a press release.
More here.
