Dataset Information

Rahman2022 - High throughput antibacterial screening with machine learning.

ABSTRACT: 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. Model Type: Predictive machine learning model. Model Relevance: Probability that a compound inhibits bacterial pathogens with a focus on ESKAPE. 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/eos5xng

SUBMITTER: Zainab Ashimiyu-Abdusalam