Clinicians routinely administer antibiotics before surgeries to reduce the risk of infection. However, with the rise of antimicrobial resistance worldwide and the lack of new antibiotics, bacterial infections are becoming a growing challenge for the medical field. “We have a super lean antibiotic pipeline now that is being populated [mostly] by analogs of existing drug classes,” said Jon Stokes, a biochemist at McMaster University. Without the development of structurally and functionally novel antibacterial drugs, researchers predict that the mortality rate associated with antimicrobial-resistant bacteria will continue to rise reaching up to 10 million deaths annually by the year 2050.
In a recently published Nature Machine Intelligence paper, Stokes and his team enlisted the help of artificial intelligence (AI) to design structurally novel antibiotics that they could easily synthesize in the laboratory. This approach could help accelerate both antibiotic development and drug discovery.
Scientists are particularly concerned about six highly virulent and drug-resistant bacterial species, Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species, known as the ESKAPE pathogens. One of these pathogens, A. baumannii, is a desiccation- and disinfectant-resistant microbe that is responsible for life-threatening, hospital-acquired infections of the skin, lungs, urinary tract, brain, bloodstream, and soft tissues. Because of the gravity of its threat to global health, the World Health Organization ranked this pathogen as a critical priority for new treatment and diagnostic tool development.
To address this need, Stokes and his team decided to use AI to discover novel small molecules with antibacterial activity against A. baumannii. Traditionally used property prediction AI models forecast the characteristics of chemicals to allow researchers to find synthesizable compounds with the desired properties, but they screen only existing chemical libraries. On the other hand, new generative AI models allow scientists to produce novel chemical compounds with the required qualities, but this approach is not without its own pitfalls. “A lot of generative algorithms for de novo molecular design exist. The problem is they tend to build molecules, atom by atom, which means in a computer you can draw amazing, beautiful compounds, but you cannot bring them into the laboratory because you cannot make them. They are synthetically intractable. I always compare it to kids when they draw wild stuff, like a giraffe flying a spaceship. It is a really cool picture, but we cannot make that happen,” said Stokes.
By Charlene Lancaster, PhD
Article can be accessed on: The Scientist