Neural Net Discovers Antibiotic

A deep neural net algorithm discovered a new antibiotic that displays bactericidal activity against a wide phylogenetic spectrum of pathogens.


(Machine Learning in Antibiotic Discovery)

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity.

We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub—halicin—that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models.

Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.

As far as I know, the earliest mention of a computer able to generate new science is the Total Environmental and Mental Simulator from John M. Faucette's 1968 novel Crown of Infinity.

...A computer that could simulate or duplicate the mental processes of any actual or artificial being, and through simulated total environmental stimuli create a situation of maximum stress upon that entity... No matter how much in intelligence potential Master or ally had, the TEMS always came up with a simulated being that was superior...

... instantly the TEMS was creating and destroying entire systems of logic and mathematics, creating and discarding scores of new sciences.

Via A Deep Learning Approach to Antibiotic Discovery by Jonathan M. Stokes, Kevin Yang, Kyle Swanson, Tommi S. Jaakkola, Regina Barzilay and James J. Collins .

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