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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

PROVIDER: MODEL2404080002 | BioModels | 2024-04-22

REPOSITORIES: BioModels

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A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery.

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]

PMID: 36228001

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