Wong2024 - Cytotoxicity Prediction from Human Cell Lines

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: Prediction of Human cytotoxicity endpoints. 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/eos42ez

SUBMITTER: Zainab Ashimiyu-Abdusalam  

PROVIDER: MODEL2405130002 | BioModels | 2024-05-13

REPOSITORIES: BioModels