Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis - Wikidata - awgldk - TriplyDB

Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis

Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis

http://www.wikidata.org/entity/Q28538272

about

Learning from the past for TB drug discovery in the future Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets Bigger data, collaborative tools and the future of predictive drug discovery Computational prediction and validation of an expert's evaluation of chemical probes Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosis Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015).A High-throughput Compatible Assay to Evaluate Drug Efficacy against Macrophage Passaged Mycobacterium tuberculosis.Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).A Macrophage Infection Model to Predict Drug Efficacy Against Mycobacterium Tuberculosis.Novel Pyrimidines as Antitubercular Agents.Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.Mobile Apps for Green Chemistry

P2860

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P2860

Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis

http://www.wikidata.org/entity/Q28538272

description

2014 nî lūn-bûn

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2014 թուականի Ապրիլին հրատարակուած գիտական յօդուած

@hyw

2014 թվականի ապրիլին հրատարակված գիտական հոդված

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2014年の論文

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2014年論文

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2014年論文

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2014年論文

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2014年論文

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2014年論文

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2014年论文

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name

Looking back to the future: pr ...... sus Mycobacterium tuberculosis

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Looking back to the future: pr ...... sus Mycobacterium tuberculosis

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Looking back to the future: pr ...... sus Mycobacterium tuberculosis

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type

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Looking back to the future: pr ...... sus Mycobacterium tuberculosis

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Looking back to the future: pr ...... sus Mycobacterium tuberculosis

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Looking back to the future: pr ...... sus Mycobacterium tuberculosis

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Looking back to the future: pr ...... sus Mycobacterium tuberculosis

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Looking back to the future: pr ...... sus Mycobacterium tuberculosis

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Looking back to the future: pr ...... sus Mycobacterium tuberculosis

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Journal of Chemical Information and Modeling

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Looking back to the future: pr ...... sus Mycobacterium tuberculosis

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P2093

Joel S Freundlich

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Richard Pottorf

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Robert C Reynolds

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P2860

A medicinal chemists' guide to the unique difficulties of lead optimization for tuberculosis

Rising to the challenge: new therapies for tuberculosis

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

Genes required for mycobacterial growth defined by high density mutagenesis

Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure

Tetrahydropyrazolo[1,5-a]pyrimidine-3-carboxamide and N-benzyl-6',7'-dihydrospiro[piperidine-4,4'-thieno[3,2-c]pyran] analogues with bactericidal efficacy against Mycobacterium tuberculosis targeting MmpL3

Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

TB Mobile: a mobile app for anti-tuberculosis molecules with known targets

Combining cheminformatics methods and pathway analysis to identify molecules with whole-cell activity against Mycobacterium tuberculosis

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1070-82

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10.1021/CI500077V

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4

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54

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Antony John Williams

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261101354

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24665947

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P921

Mycobacterium tuberculosis

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4004261

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