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Data related to model that predicts molecules that are structurally unrelated to known antibiotics

Chemists typically develop new antibiotics by testing close chemical relatives of tried-and-true compounds like penicillin. That approach becomes less effective, though, as dangerous bacteria evolve resistance to those very chemical structures. Instead, researchers enlisted neural networks.

What’s new: Jonathan Stokes and colleagues at MIT, Harvard, and McMaster University built an ensemble model that predicts molecules that are structurally unrelated to known antibiotics, harmless to humans, and deadly to E. coli, a common bacterium that served as a proxy microorganism. The model spotted a previously unrecognized antibiotic that proved effective at killing a variety of germs.

Key insight: Neural networks can stand in for petri dishes to zero in on promising molecules. An initial simulation reduced an enormous number of molecules to a few thousand solid possibilities, of which the model selected a couple dozen for testing in a wet lab.

How it works: The researchers used an ensemble of 20 graph neural networks (GNNs) to evaluate molecules’ ability to inhibit E. coli, and another ensemble of five GNNs to evaluate toxicity. They used a standard measure to evaluate chemical structure. Then they tested the most promising compounds on mice.

  • Each GNN examines molecules atom by atom. The Chemprop architecture learns a vector for each atom based on its atomic number, mass, other properties along with vectors of the atoms it’s bound to.
  • The GNN graphs collapse into vectors that describe the molecule as a whole.
  • Fully connected layers predict either E. coli inhibition based on labels from an FDA library or toxicity based on a dataset of qualitative evaluations of approved drugs.

Results: The researchers examined more than 107 million compounds to produce a ranked list. Empirical tests on the top-ranked 3,260 chemicals yielded 51 that were effective. Of those, 23 had low predicted toxicity and structures distinct from known antibiotics. In mouse experiments, Halicin, a known diabetes treatment, proved effective as a broad-spectrum antibiotic.

Why it matters: Alexander Fleming’s discovery of penicillin in 1928 revolutionized medicine. Now that transformation is at risk as bugs evolve resistance to that drug and its successors. Discovery of new antibiotics has been hampered by lack of a way to narrow the list of possibilities for lab tests. This method offers a way to vet candidates quickly and efficiently.

We’re thinking: Antibiotic-resistant bugs are responsible for 2.8 million infections and 35,000 deaths annually in the U.S. alone. Crank up those GNNs!


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