Literature DB >> 21943870

Informatics resources for tuberculosis--towards drug discovery.

Jagadish Chandrabose Sundaramurthi1, S Brindha, T B K Reddy, Luke Elizabeth Hanna.   

Abstract

Integration of biological data on gene sequence, genome annotation, gene expression, metabolic pathways, protein structure, drug target prioritization and selection, has resulted in several online bioinformatics databases and tools for Mycobacterium tuberculosis. Alongside there has been a growth in the list of cheminformatics databases for small molecules and tools to facilitate drug discovery. In spite of these efforts there is a noticeable lag in the drug discovery process which is an urgent need in the case of emerging and re-emerging infectious diseases. For example, more than 25 online databases are available freely for tuberculosis and yet these resources have not been exploited optimally. Informatics-centered drug discovery based on the integration and analysis of both bioinformatics and cheminformatics data could fill in the gap and help to accelerate the process of drug discovery. This article aims to review the current standing of developments in tuberculosis-bioinformatics and highlight areas where integration of existing resources could lead to acceleration of drug discovery against tuberculosis. Such an approach could be adapted for other diseases as well.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 21943870     DOI: 10.1016/j.tube.2011.08.006

Source DB:  PubMed          Journal:  Tuberculosis (Edinb)        ISSN: 1472-9792            Impact factor:   3.131


  6 in total

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Authors:  Elizabeth L Ponder; Joel S Freundlich; Malabika Sarker; Sean Ekins
Journal:  Pharm Res       Date:  2013-08-30       Impact factor: 4.200

Review 2.  21st century natural product research and drug development and traditional medicines.

Authors:  Linh T Ngo; Joseph I Okogun; William R Folk
Journal:  Nat Prod Rep       Date:  2013-04       Impact factor: 13.423

3.  Some Nigerian anti-tuberculosis ethnomedicines: a preliminary efficacy assessment.

Authors:  Nneka N Ibekwe; John B Nvau; Peters O Oladosu; Auwal M Usman; Kolo Ibrahim; Helena I Boshoff; Cynthia S Dowd; Abayomi T Orisadipe; Olapeju Aiyelaagbe; Akinbo A Adesomoju; Clifton E Barry; Joseph I Okogun
Journal:  J Ethnopharmacol       Date:  2014-06-06       Impact factor: 4.360

4.  Bigger data, collaborative tools and the future of predictive drug discovery.

Authors:  Sean Ekins; Alex M Clark; S Joshua Swamidass; Nadia Litterman; Antony J Williams
Journal:  J Comput Aided Mol Des       Date:  2014-06-19       Impact factor: 3.686

5.  GCAC: galaxy workflow system for predictive model building for virtual screening.

Authors:  Deepak R Bharti; Anmol J Hemrom; Andrew M Lynn
Journal:  BMC Bioinformatics       Date:  2019-02-04       Impact factor: 3.169

6.  New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0.

Authors:  Alex M Clark; Malabika Sarker; Sean Ekins
Journal:  J Cheminform       Date:  2014-08-04       Impact factor: 5.514

  6 in total

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