Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	MODEL2404080002?filename=BioModelsMetadata%20-%20eos5xng.csv	Csv

Items per page:

1 - 1 of 1

Publications

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

Similar Datasets

Pereira2020 - Discovering growth Inhibitors of Neisseria gonorrhoeae with Machine Learning

Project description:The authors curated a dataset of 282 compounds from ChEMBL, of which 160 (56.7%) were labeled as active N. gonorrhoeae inhibitor compounds. They used this dataset to build a naïve Bayesian model and used it to screen a commercial library. With this method,they identified and validated two hits compound. Model Type: Predictive machine learning model. Model Relevance: The model predicts the probability of activity for the inhibition of the Antimicrobial pathogen N. gonorrhoeae. 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/eos5cl7

2024-05-08 | MODEL2405080003 | BioModels

Wong2024 - Cytotoxicity Prediction from Human Cell Lines

Project description:The authors use a large dataset (>30k) to train an explainable graph-based model to identify potential antibiotics with low cytotoxicity. The model uses a substructure-based approach to explore the chemical space. Using this method, they were able to screen 283 compounds and identify a candidate active against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci. Model Type: Predictive machine learning model. Model Relevance: Prediction of Human cytotoxicity endpoints. 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/eos42ez

2024-05-13 | MODEL2405130002 | BioModels

Wong2024 - Discovery of a structural class of antibiotics with explainable deep learning

Project description:The authors use a large dataset (>30k) to train an explainable graph-based model to identify potential antibiotics with low cytotoxicity. The model uses a substructure-based approach to explore the chemical space. Using this method, they were able to screen 283 compounds and identify a candidate active against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci. Model Type: Predictive machine learning model. Model Relevance: The model predicts the probability of growth inhibition. 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/eos18ie

2024-05-08 | MODEL2405080002 | BioModels

Li2021 - HDAC3i-Finder: A Machine Learning-based Computational Tool to Screen for HDAC3 Inhibitors

Project description:The model predicts the inhibitory potential of small molecules against Histone deacetylase 3 (HDAC3), a relevant human target for cancer, inflammation, neurodegenerative diseases and diabetes. The authors have used a dataset of 1098 compounds from ChEMBL and validated the model using the benchmark MUBD-HDAC3. Model Type: Predictive machine learning model. Model Relevance: Probability that the molecule is a HDAC3 inhibitor 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/eos1n4b

2024-08-06 | MODEL2406210001 | BioModels

Pemetrexed and gemcitabine as combination therapy for the treatment of Group3 medulloblastoma

Project description:We devised a high-throughput, cell-based assay to identify novel therapeutic compounds to treat Group3 medulloblastoma (G3 MB). Mouse G3 MBs, grown as neurospheres, were screened against a library of approximately 7,000 compounds including FDA-approved drugs. We identified two FDA-approved drugs, pemetrexed and gemcitabine that preferentially inhibited tumor proliferation in vitro compared to control neurospheres, and substantially inhibited tumor proliferation in vivo. When combined, the two drugs significantly increased survival P7 Trp53-/-, Cdkn2c-/- neurospheres and MYC mouse cells were compared treated and untreated with drugs pemetrexed+gemcitabine

2014-04-15 | E-GEOD-46406 | biostudies-arrayexpress

Pemetrexed and gemcitabine as combination therapy for the treatment of Group3 medulloblastoma

2014-04-15 | GSE46406 | GEO

van-Heerden2023 - Chemical Features Identification for Stage-Specific Antimalarial Compounds

Project description:Prediction of the antimalarial potential of small molecules using data from various chemical libraries that were screened against the asexual and sexual (gametocyte) stages of the parasite. Several compounds’ molecular fingerprints were used to train machine learning models to recognize stage-specific active and inactive compounds. Model Type: Predictive machine learning model. Model Relevance: Probability of inhibition of the malaria parasite growth. Model Encoded by: Gemma Turon (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos80ch

2024-03-28 | MODEL2403270002 | BioModels

Crizotinib, Tyrosine Kinase Inhibitors, Create an Antibacterial Active against Gram-positive Bacteria by Targeting the CTP Synthetase PyrG

Project description:Abstract The infection of drug resistant bacteria seriously threats to public health, which makes it more urgent to find novel antibacterial compounds. Compared with traditional development approaches, drug repurposing provides a faster and more effective strategy to find new antimicrobial agents. Here, we screened an FDA-approved small-molecule library upon S. aureus, and identified crizotinib as an antimicrobial agent. We confirmed the antibacterial activities of crizotinib in vitro and in vivo by MIC, growth curve, SEM and mice experiment. At the same time, crizotinib also showed low tendency to develop resistance. Mechanistically, quantitative proteomics, bioinformatics analyses, qRT-PCR and flow cytometry biochemical validation experience confirmed that crizotinib exerted its antibacterial effects by interfering pyrimidine metabolism and disrupting DNA synthesis eventually. Furthermore, our data from drug affinity responsive target stability, bio-layer interferometry and a series of functional assays demonstrated that crizotinib could target PyrG directly in pyrimidine metabolism pathway. Taken together, our results indicated that crizotinib could be a potential antimicrobial agent to treat Gram-positive bacterial infections in the future.

2022-03-10 | PXD029546 | Pride

Transcription profiling of human 786-O clear cell renal carcinoma cells reveals small molecule inhibitors of HIF-2a translation link its 5 UTR Iron-Responsive Element (IRE) to oxygen sensing

Project description:Cells transiently adapt to hypoxia by globally decreasing protein translation. However, specific proteins needed to respond to hypoxia evade this translational repression. The mechanisms of this phenomenon remain unclear. We screened for and identified small molecules that selectively decrease HIF-2a translation in an mTOR independent manner, by enhancing the binding of Iron Regulatory Protein 1 (IRP1) to a recently reported Iron-Responsive Element (IRE) within the 5â-untranslated region (UTR) of the HIF-2a message. Knocking down the expression of IRP1 by shRNA abolished the effect of the compounds. Hypoxia de-represses HIF-2a translation by disrupting the IRP1- HIF-2a IRE interaction. Thus, this chemical genetic analysis describes a molecular mechanism by which translation of the HIF-2a message is maintained during conditions of cellular hypoxia through inhibition of IRP-1 dependent repression. It also provides the chemical tools for studying this phenomenon. Experiment Overall Design: 3 replicate samples of 786-O human Clear Cell Renal Carcinoma cells untreated, mock treated with DMSO or treated with either of 4 HIF-2a inhibitor compounds identified by chemical genetic screening.

2009-01-14 | E-GEOD-13818 | biostudies-arrayexpress

Sun2017 - predictive and interpretable models for PAMPA permeability 7

Project description:Parallel Artificial Membrane Permeability is an in vitro surrogate to determine the permeability of drugs across cellular membranes. PAMPA at pH 7.4 was experimentally determined in a dataset of 5,473 unique compounds by the NIH-NCATS. 50% of the dataset was used to train a classifier (SVM) to predict the permeability of new compounds, and validated on the remaining 50% of the data, rendering an AUC = 0.88. The Peff was converted to logarithmic, log Peff value lower than 2.0 were considered to have low to moderate permeability, and those with a value higher than 2.5 were considered as high-permeability compounds. Model Type: Predictive machine learning model. Model Relevance: The model predicts a chemical compound as highly or low/moderate permeable. Model Encoded by: Pauline (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos9tyg

2024-04-23 | MODEL2404220001 | BioModels