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Drug-resistant infections kill more than a million people each year. But big pharmaceutical companies have largely abandoned the quest for new antibiotics, because the research and development needed to discover them is time-consuming and expensive. Autumn Arnold, a PhD student in the Stokes Lab at McMaster University in Canada, came up with a fix: an AI model that predicts which chemical compounds might kill dangerous bacteria. The model, called ESKAPE, is free to use: Researchers upload a list of compounds and get a prediction of their antibacterial activity. “All you need is an internet connection,” said Jonathan Stokes, a microbiologist who runs the lab. Researchers retain ownership of their discoveries, and don’t have to share the compounds they submit. “We have this perfect storm with rising resistance, a really lean antibiotic pipeline, and enormous economic headwinds,” Stokes said. “It’s going to cost you $1 billion to make a new drug, and you’re never going to make your money back.” So far, researchers have screened more than 12 million molecules using the ESKAPE model. In total, the model can screen more than 40,000 compounds a day, which has the potential to dramatically speed up drug discovery. Another approach, developed by researchers at MIT and Sweden’s Karolinska Institutet, is using “generative deep learning” to create entirely new molecules to target antibiotic resistance. “This is a global problem,” Stokes says. “We need everybody helping out.”
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