Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets.
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Open source drug discovery in practice: a case studyLooking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosisChecking the STEP-Associated Trafficking and Internalization of Glutamate Receptors for Reduced Cognitive Deficits: A Machine Learning Approach-Based Cheminformatics Study and Its Application for Drug RepurposingComputational Analysis and Predictive Cheminformatics Modeling of Small Molecule Inhibitors of Epigenetic ModifiersComputational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets.Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation.Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosisData-mining of potential antitubercular activities from molecular ingredients of traditional Chinese medicinesCheminformatic models based on machine learning for pyruvate kinase inhibitors of Leishmania mexicana.Predictive modeling of anti-malarial molecules inhibiting apicoplast formationBayesian models leveraging bioactivity and cytotoxicity information for drug discovery.Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery.Cheminformatics models based on machine learning approaches for design of USP1/UAF1 abrogators as anticancer agents.Machine Learning Approaches Toward Building Predictive Models for Small Molecule Modulators of miRNA and Its Utility in Virtual Screening of Molecular Databases.Computational strategies to explore antimalarial thiazine alkaloid lead compounds based on an Australian marine sponge Plakortis Lita.Designing of inhibitors against drug tolerant Mycobacterium tuberculosis (H37Rv).Computational analysis and predictive modeling of small molecule modulators of microRNAExploration of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one derivatives as JAK inhibitors using various in silico techniques.Computational models for neglected diseases: gaps and opportunities.A Systematic Machine Learning Based Approach for the Diagnosis of Non-Alcoholic Fatty Liver Disease Risk and Progression.Time for the zebrafish ENCODE.Cheminformatics models for inhibitors of Schistosoma mansoni thioredoxin glutathione reductase.Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach
P2860
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P2860
Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets.
description
2011 nî lūn-bûn
@nan
2011 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Predictive models for anti-tub ...... biological screening datasets.
@ast
Predictive models for anti-tub ...... biological screening datasets.
@en
type
label
Predictive models for anti-tub ...... biological screening datasets.
@ast
Predictive models for anti-tub ...... biological screening datasets.
@en
prefLabel
Predictive models for anti-tub ...... biological screening datasets.
@ast
Predictive models for anti-tub ...... biological screening datasets.
@en
P2093
P2860
P356
P1433
P1476
Predictive models for anti-tub ...... biological screening datasets.
@en
P2093
Abdul Uc Jaleel
Jinuraj K Rajappan
Open Source Drug Discovery Consortium
P2860
P2888
P356
10.1186/1756-0500-4-504
P577
2011-11-18T00:00:00Z
P5875
P6179
1042459618