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

ORGANISM(S): Burkholderia cenocepacia

SUBMITTER: Zainab Ashimiyu-Abdusalam 

PROVIDER: MODEL2404080002 | biostudies-other |

SECONDARY ACCESSION(S): 36228001

REPOSITORIES: biostudies-other