Powered by deep learning, the study was a significant breakthrough. Few new antibiotics have come out since the 1960s, and this one in particular could be crucial in fighting tough-to-treat MRSA, which kills more than
The black box is generally thought of as impenetrable in complex machine learning models, and that poses a challenge in the drug discovery realm. Next, they used additional deep learning to narrow the field, ruling out compounds toxic to humans. Then, deploying their various models at once, they screened 12 million commercially available compounds. Five classes emerged as likely MRSA fighters. Further testing of 280 compounds from the five classes produced the final results: Two compounds from the same class. Both reduced MRSA infection in mouse models.
"The main idea is we can pinpoint which substructure of a chemical structure is causative instead of just correlated with high antibiotic activity," Wong said. "This is the first major study that I've seen seeking to incorporate explainability into deep learning models in the context of antibiotics," said, PhD, an assistant professor at the University of Pennsylvania, Philadelphia, Pennsylvania, whose lab has been engaged in AI for antibiotic discovery for the past 5 years.