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) abd 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.
- EOS model ID:
eos5xng - Slug:
chemprop-burkholderia
- Input:
Compound - Input Shape:
Single - Task:
Classification - Output:
Score - Output Type:
Float - Output Shape:
Single - Interpretation: Probability that a compound inhibits the drug resistant bacteria Burkholderia cenocepacia. Scores range from 0 to 1. With 1 indicating the highest probability for growth inhibitory activity.
- Publication
- Source Code
- Ersilia contributor: Richioo
If you use this model, please cite the original authors of the model and the Ersilia Model Hub.
This package is licensed under a GPL-3.0 license. The model contained within this package is licensed under a GPL-3.0 license.
Notice: Ersilia grants access to these models 'as is' provided by the original authors, please refer to the original code repository and/or publication if you use the model in your research.
The Ersilia Open Source Initiative is a Non Profit Organization (1192266) with the mission is to equip labs, universities and clinics in LMIC with AI/ML tools for infectious disease research.
Help us achieve our mission!