Dataset Information
Rahman2022 - High throughput antibacterial screening with machine learning.
Ontology highlight
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. Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos5xng
ORGANISM(S): Burkholderia cenocepacia
SUBMITTER: Zainab Ashimiyu-Abdusalam
PROVIDER: MODEL2404080002 | biostudies-other |
SECONDARY ACCESSION(S): 36228001
REPOSITORIES: biostudies-other
Publications
A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery.
PLoS computational biology 20221013 10
Screening for novel antibacterial compounds in small molecule libraries has a low success rate. We applied machine learning (ML)-based virtual screening for antibacterial activity and evaluated its predictive power by experimental validation. We first binarized 29,537 compounds according to their growth inhibitory activity (hit rate 0.87%) against the antibiotic-resistant bacterium Burkholderia cenocepacia and described their molecular features with a directed-message passing neural network (D-M ...[more]
