Dataset Information

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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			Action	DRS
	MODEL2405080002?filename=BioModelsMetadata%20-%20eos18ie.csv	Csv

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Discovery of a structural class of antibiotics with explainable deep learning.

Wong Felix F Zheng Erica J EJ Valeri Jacqueline A JA Donghia Nina M NM Anahtar Melis N MN Omori Satotaka S Li Alicia A Cubillos-Ruiz Andres A Krishnan Aarti A Jin Wengong W Manson Abigail L AL Friedrichs Jens J Helbig Ralf R Hajian Behnoush B Fiejtek Dawid K DK Wagner Florence F FF Soutter Holly H HH Earl Ashlee M AM Stokes Jonathan M JM Renner Lars D LD Collins James J JJ

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]

PMID: 38123686

Dataset Information

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

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Discovery of a structural class of antibiotics with explainable deep learning.

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