Dataset Information
Stokes2020 - Antibiotics discovery using deep learning approach.
Ontology highlight
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. Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos4e40
ORGANISM(S): Homo sapiens
SUBMITTER: Zainab Ashimiyu-Abdusalam
PROVIDER: MODEL2404080001 | biostudies-other |
SECONDARY ACCESSION(S): 32084340
REPOSITORIES: biostudies-other
Publications
A Deep Learning Approach to Antibiotic Discovery.
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]
