Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Pereira2020 - Discovering growth Inhibitors of Neisseria gonorrhoeae with Machine Learning

ABSTRACT: 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

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2405080003 | BioModels | 2024-05-08

REPOSITORIES: BioModels

ACCESS DATA

Json Xml

Publications

Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.

Pereira Janaina Cruz JC Daher Samer S SS Zorn Kimberley M KM Sherwood Matthew M Russo Riccardo R Perryman Alexander L AL Wang Xin X Freundlich Madeleine J MJ Ekins Sean S Freundlich Joel S JS

Pharmaceutical research 20200713 7

<h4>Purpose</h4>To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology.<h4>Methods</h4>Inspection and curation of data from the publicly available ChEMBL web site for small molecule growth inhibition data of the bacterium Neisseria gonorrhoeae resulted in a training set for the construction of machine learning models. A naïve Bayesian model for b ...[more]

PMID: 32661900

Similar Datasets

Rahman2022 - High throughput antibacterial screening with machine learning.

Project description: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

2024-04-22 | MODEL2404080002 | 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

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

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

Williams2022 - Permeable compound selection using in vitro ADME data (PAMPA pH 5).

Project description:Parallel Artificial Membrane Permeability is an in vitro surrogate to determine the permeability of drugs across cellular membranes. PAMPA at pH 5 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: Predicting compound permeability across cellular membranes. Model Encoded by: Pauline Banye (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos81ew

2024-04-22 | MODEL2404220004 | BioModels

Shah2020 - Predicting liver cytosol stability of small molecules.

Project description:The human liver cytosol stability model is used for predicting the stability of a drug in the cytosol of human liver cells, which is beneficial for identifying potential drug candidates early during the drug discovery process. If a drug compound is quickly absorbed, it may not reach the intended target in the body or become toxic. On the other hand, if a drug compound is too stable, it could accumulate and cause detrimental effects. The authors use an NCATS dataset of 1450 compounds screened in vitro in mouse and human cytosol fractions. Compounds were classified as stable (half-life > 30min) or unstable (half-life ≤ 30 min). Note that authors report the dataset was biased towards stable compounds. Model Type: Machine learning model. Model Relevance: Predicts probability of a compound stability due to liver cells metabolism. 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/eos9yy1

2024-04-22 | MODEL2404220005 | BioModels

Shah2020 - Predicting Human Liver Microsomal Stability of small molecules

Project description:The Human Liver Microsomal assay takes into account the liver-mediated drug metabolism to assess the stability of a compound in the human body. The NIH-NCATS group took a proprietary dataset of 4300 compounds with its associated HLM (in vitro half-life; unstable ≤ 30 min, stable >30 min) and used it to train a classifier. Model Type: Machine learning model. Model Relevance: Probability of a compound being unstable in a HLM assay. Model Encoded by: Pauline Banye (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos31ve

2024-05-13 | MODEL2405130005 | BioModels

Khan2022 - QcrB Inhibition Prediction with Machine Learning Protocol

Project description:The cytochrome bcc complex (QcrB) is a subunit of the mycobacterial cyt-bcc-aa3 oxidoreductase, and it has been suggested as a good M.tb target due to the bacteria's dependence on oxidative phosphorylation for its growth. The authors use a dataset of 352 molecules, of which 277 are classified as active (QIM QIM 20). Model Type: Predictive machine learning model. Model Relevance: The model predicts a compound for QcrB Inhibition. 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/eos24jm

2024-05-08 | MODEL2405080004 | BioModels

Siramshetty2020 - Assessment of rat liver microsomal stability with QSAR models.

Project description:Hepatic metabolic stability is key to ensure the drug attains the desired concentration in the body. The Rat Liver Microsomal (RLM) stability is a good approximation of a compound’s stability in the human body, and NCATS has collected a proprietary dataset of 20216 compounds with its associated RLM (in vitro half-life; unstable ≤30 min, stable >30 min) and used it to train a classifier based on an ensemble of several ML approaches (random forest, deep neural networks, graph convolutional neural networks and recurrent neural networks. Model Type: Predictive machine learning model. Model Relevance: Predicting the stability of a compound in RLM assay. 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/eos5505

2024-04-22 | MODEL2404220002 | BioModels

Employing fingerprinting of medicinal plants by means of LC-MS and machine learning for species identification task

Project description:<p>A dataset of liquid chromatography-mass spectrometry measurements of medicinal plant extracts from 76 species was generated and used for training and validating plant species identification algorithms. Various strategies for data handling and feature space selection were tested. Constrained Tucker decomposition, large-scale (more than 1500 variables) discrete Bayesian Networks and autoencoder based dimensionality reduction coupled with continuous Bayes classifier and logistic regression were optimized to achieve the best accuracy. Classification algorithms based on Tucker decomposition of original data and logistic regression on representation learned with autoencoder showed identification accuracy of up to 96%, outperforming various implementations of Bayesian Networks. Benefits and drawbacks of used approaches were discussed. Tolerance to changes in data created by using different extraction methods and equipment was tentatively tested.</p><p><br></p><p><strong>Main study</strong> is reported in the current study <strong>MTBLS688</strong></p><p><strong><em>Helianthus tuberosu</em>s assay</strong> is reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS759/' rel='noopener noreferrer' target='_blank'><strong>MTBLS759</strong></a></p>

2022-04-15 | MTBLS688 | MetaboLights

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data