Deep Learning Discovers Antibiotics Researchers used neural networks to find a new class of antibiotics.

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Deep Learning Discovers Antibiotics: Researchers used neural networks to find a new class of antibiotics.

Biologists used neural networks to find a new class of antibiotics.

What’s new: Researchers at MIT and Harvard trained models to screen chemical compounds for those that kill methicillin-resistant Staphylococcus aureus (MRSA), the deadliest among bacteria that have evolved to be invulnerable to common antibiotics, and aren’t toxic to humans. 

How it works: The authors built a training set of 39,312 compounds including most known antibiotics and a diverse selection of other molecules. In a lab, they tested each compound for its ability to inhibit growth of MRSA and its toxicity to human liver, skeletal muscle, and lung cells. Using the resulting data, they trained four ensembles of 20 graph neural networks each to classify compounds for (i) antibiotic properties, (ii) toxicity to the liver, (iii) toxicity to skeletal muscles, and (iv) toxicity to the lungs. 

  • They ran their four ensembles on 12 million compounds from the Mcule database and a Broad Institute database. They filtered out compounds with the lowest probability of being antibiotics and the highest probability of being toxic to humans, leaving 3,646 antibiotic, low-toxicity compounds. 
  • Within these compounds, they found the minimal chemical structure responsible for the antibiotic properties. To do this, they removed atoms or rings of atoms from a molecule’s edges, predicted the probability that the modified molecule was an active antibiotic, and repeated these steps until the probability fell below a threshold. Compounds that share a chemical structure are likely to work in similar ways within the body, giving scientists a pathway to discover further compounds with similar benefits.

Results: Of the compounds predicted to be likely antibiotics and nontoxic, the authors lab-tested 241 that were not known to work against MRSA. Of those, 8.7 percent inhibited the bacterium’s growth. This exceeds the percentage of antibiotics in the training set (1.3 percent), suggesting that the authors’ approach could be a useful first step in finding new antibiotics. The authors also tested 30 compounds predicted not to be antibiotics. None of them (0 percent) inhibited the bacterium’s growth — further evidence that their approach could be a useful first step. Two of the compounds that inhibited MRSA share a similar and novel mechanism of action against bacteria and also inhibited other antibiotic-resistant infections in lab tests. One of them proved effective against MRSA infections in mice.

Behind the news: Most antibiotics currently in use were discovered in the mid-20th century, a golden age of antibiotics, which brought many formerly deadly pathogens under control. Modern techniques, including genomics and synthetic antibiotics, extended discoveries through the end of the century by identifying variants on existing drugs. However, in the 21st century, new antibiotics have either been redundant or haven’t been clinically successful, a report by the National Institutes of Health noted. At the same time, widespread use of antibiotics has pushed many dangerous bacteria to evolve resistance. Pathogens chiefly responsible for a variety of ailments are generally resistant even to antibiotics reserved for use as a last resort.

Why it matters: Antibiotic-resistant infections are among the top global public health threats directly responsible for 1.27 million deaths in 2019, according to the World Health Organization. New options, as well as efforts to fight the emergence of resistant strains, are needed.

We’re thinking: If neural networks can identify new classes of medicines, AI could bring a golden age of medical discovery. That hope helps to explain why pharmaceutical companies are hiring machine learning engineers at unprecedented rates.


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