Dataset Information
Stokes2020 - Antibiotics discovery using deep learning approach (Antiviral implementation)
Ontology highlight
ABSTRACT: This model was developed to support the early efforts in the identification of novel drugs against SARS-CoV2. It predicts the probability that a small molecule inhibits SARS-3CLpro-mediated peptide cleavage. It was developed using a high-throughput screening against the 3CL protease of SARS-CoV1, as no data was yet available for the new virus (SARS-CoV2) causing the COVID-19 pandemic. It uses the ChemProp model. Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos9f6t
ORGANISM(S): Homo sapiens
SUBMITTER: Zainab Ashimiyu-Abdusalam
PROVIDER: MODEL2405080006 | 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]
