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Stokes2020 - Antibiotics discovery using deep learning approach (Antiviral implementation)


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. Model Type: Predictive machine learning model. Model Relevance: Probability of 3CL protease inhibition. 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/eos9f6t

SUBMITTER: Zainab Ashimiyu-Abdusalam  

PROVIDER: MODEL2405080006 | BioModels | 2024-05-08

REPOSITORIES: BioModels

Dataset's files

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Publications


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]

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