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Wong2024 - Discovery of a structural class of antibiotics with explainable deep learning


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. Model Type: Predictive machine learning model. Model Relevance: The model predicts the probability of growth inhibition. Model Encoded by: Sarima Chiorlu (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos18ie

SUBMITTER: Zainab Ashimiyu-Abdusalam  

PROVIDER: MODEL2405080002 | BioModels | 2024-05-08

REPOSITORIES: BioModels

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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]

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