Ontology highlight
ABSTRACT:
SUBMITTER: Zainab Ashimiyu-Abdusalam
PROVIDER: MODEL2404080002 | BioModels | 2024-04-22
REPOSITORIES: BioModels
Rahman A S M Zisanur ASMZ Liu Chengyou C Sturm Hunter H Hogan Andrew M AM Davis Rebecca R Hu Pingzhao P Cardona Silvia T ST
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]