Halicin, an antibiotic found by a neural network screening a compound library
A graph neural network trained on 2,335 molecules picked halicin out of a repurposing library; it killed multidrug-resistant bacteria including Acinetobacter baumannii and Mycobacterium tuberculosis in vitro and cleared infections in mice.
- Model
- Directed message-passing graph neural network (Chemprop)
- Field
- Medicine
- Date
- 2020-02-20
- Human collaborators
- Jonathan Stokes, Regina Barzilay, James Collins
Sources
Original work
Announcement
What was found
The model was trained to predict growth inhibition of E. coli from structure alone, then applied to the Drug Repurposing Hub. Halicin, an abandoned diabetes candidate, scored highly despite being structurally unlike known antibiotics. It kills by dissipating the proton-motive force across the bacterial membrane, and the team could not evolve resistant E. coli over 30 days of serial passage. A follow-up screen of about 107 million molecules from ZINC15 produced eight further candidates. Published in Cell.
Novelty check
Virtual screening long predates deep learning. What was new was a model surfacing a hit structurally distant from its training set, with a mechanism distinct from existing antibiotic classes, and the follow-through to efficacy in animals. Halicin itself was a known molecule; the discovery is of its antibacterial activity, not of the compound.
Caveats and known objections
Halicin had not entered clinical trials six years on, which is the usual fate of antibiotic leads and a reminder that discovery is not the bottleneck in this field. The model ranked compounds; the mechanism work, the animal studies and the interpretation are human. Membrane-disrupting antibacterials carry a known selectivity risk in mammals.
Independent checks
Booq et al., King Abdulaziz City for Science and Technology (Antibiotics, 2021): Reproduced broad-spectrum in vitro activity against every Gram-positive and Gram-negative strain tested, including a multidrug-resistant A. baumannii isolate, though at MICs of 128 to 256 micrograms per mL for A. baumannii, and found halicin loses potency after about a week of storage at 4 C. · link ↗
Higashihira et al., Indiana University School of Medicine (Clinical Orthopaedics and Related Research, 2022): Found halicin effective against S. aureus biofilms in vitro, a setting the original work did not test. · link ↗
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History of this entry
- CheckedRecorded two unaffiliated laboratories that reproduced halicin's in vitro antibacterial activity, one with higher MICs for A. baumannii and a storage-stability concern. No grade moved. source ↗
- AddedEntered the registry graded Peer reviewed and Search scaffold.
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Cite this entry
whataifound.org. (2020). Halicin, an antibiotic found by a neural network screening a compound library. whataifound.org: A Registry of AI Scientific and Mathematical Discoveries. https://whataifound.org/finding/2020-02-20-halicinBibTeX
@misc{whataifound-mit-2020-halicin,
title = {Halicin, an antibiotic found by a neural network screening a compound library},
author = {{whataifound.org}},
year = {2020},
howpublished = {whataifound.org: A Registry of AI Scientific and Mathematical Discoveries},
note = {Result by MIT / Broad Institute. Verification: Peer reviewed. Autonomy: Search scaffold.},
url = {https://whataifound.org/finding/2020-02-20-halicin}
}