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Stokes2020 - Antibiotics discovery using deep learning approach.

ABSTRACT: Based on a simple E.coli growth inhibition assay, the authors trained a model capable of identifying antibiotic potential in compounds structurally divergent from conventional antibiotic drugs. One of the predicted active molecules, Halicin (SU3327), was experimentally validated in vitro and in vivo. Model Type: Predictive machine learning model. Model Relevance: Probability that a compound inhibits E.coli growth. Model Encoded by: Miquel Duran-Frigola(Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos4e40

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2404080001 | BioModels | 2024-04-22

REPOSITORIES: BioModels

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A Deep Learning Approach to Antibiotic Discovery.

Stokes Jonathan M JM Yang Kevin K Swanson Kyle K Jin Wengong W Cubillos-Ruiz Andres A Donghia Nina M NM MacNair Craig R CR French Shawn S Carfrae Lindsey A LA Bloom-Ackermann Zohar Z Tran Victoria M VM Chiappino-Pepe Anush A Badran Ahmed H AH Andrews Ian W IW Chory Emma J EJ Church George M GM Brown Eric D ED Jaakkola Tommi S TS Barzilay Regina R Collins James J JJ

Cell 20200201 4

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 ...[more]

PMID: 32084340

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