biostudies-otherUnknown18Zainab Ashimiyu-AbdusalamPLoS computational biologye1010613Burkholderia cenocepaciahttps://www.ebi.ac.uk/biostudies/studies/MODEL2404080002biostudies-other36228001Zainab Ashimiyu-AbdusalamfalseRahman2022 - High throughput antibacterial screening with machine learning.<p>Prediction of antimicrobial potential using a dataset of 29537 compounds screened against the antibiotic resistant pathogen Burkholderia cenocepacia. The model uses the Chemprop Direct Message Passing Neural Network (D-MPNN) and has an AUC score of 0.823 for the test set. It has been used to virtually screen the FDA approved drugs as well as a collection of natural product list (>200k compounds) with hit rates of 26% and 12% respectively.</p><p><normal>Model Type:</normal> Predictive machine learning model.<br><normal>Model Relevance:</normal> Probability that a compound inhibits bacterial pathogens with a focus on ESKAPE.<br><normal>Model Encoded by:</normal> Sarima Chiorlu (Ersilia)<br><normal>Metadata Submitted in BioModels by:</normal> Zainab Ashimiyu-Abdusalam</p><p>Implementation of this model code by <a href="https://ersilia.io/">Ersilia</a> is available here: <br><a href="https://github.com/ersilia-os/eos5xng">https://github.com/ersilia-os/eos5xng</a></p><img src="https://www.ebi.ac.uk/biomodels/static-assets/images/ersilia-logo.png" alt="Ersilia Logo" width="150">2024-04-08T00:00:00Z2025-07-14T17:03:06.872Z2025-03-31T13:25:14.382ZMODEL2404080002MCRO:0000009MCRO:0000026STATO:0000274STATO:0000415STATO:0000524STATO:00000310000094000230536228001NCIT:C52588C14187NCIT:C16309NCIT:C176258NCIT:C19146C154407C45329topic_3336topic_0154topic_3474CHEMINF:000800CHEMINF:000018186922795486eos5xngPrediction-of-ATB-ActivitychempropRaw%20data%20used%20in%20ML0005741