Dataset Information
Wong2024 - Discovery of a structural class of antibiotics with explainable deep learning
Ontology highlight
ABSTRACT: The authors use a large dataset (>30k) to train an explainable graph-based model to identify potential antibiotics with low cytotoxicity. The model uses a substructure-based approach to explore the chemical space. Using this method, they were able to screen 283 compounds and identify a candidate active against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci. Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos18ie
ORGANISM(S): Staphylococcus aureus
SUBMITTER: Zainab Ashimiyu-Abdusalam
PROVIDER: MODEL2405080002 | biostudies-other |
SECONDARY ACCESSION(S): 38123686
REPOSITORIES: biostudies-other
Publications
Discovery of a structural class of antibiotics with explainable deep learning.
Nature 20231220 7997
The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis<sup>1-9</sup>. Deep learning approaches have aided in exploring chemical spaces<sup>1,10-15</sup>; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We test ...[more]
