Literature DB >> 17305592

Cheminformatics in anti-infective agents discovery.

S Sardari1, M Dezfulian.   

Abstract

The existing chemical data such as those created by high throughput screening (HTS), structure-activity relationship (SAR) studies are converted into information as a result of storage and registration. Accessibility, manipulation, and data mining of such information make up the knowledge for drug development. Cheminformatics, exploiting the combination of chemical structural knowledge, biological screening, and data mining approaches is used to guide drug discovery and development and would assist by integrating complex series of rational selection of designed compounds with drug-like properties, building smarter focused libraries. This paper presents cheminformatics approaches and tools for designing and data mining of chemical databases and information. Many examples of success in lead identification and optimization in the area of anti-infective therapy have been discussed.

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Year:  2007        PMID: 17305592     DOI: 10.2174/138955707779802633

Source DB:  PubMed          Journal:  Mini Rev Med Chem        ISSN: 1389-5575            Impact factor:   3.862


  3 in total

Review 1.  In Silico Strategies in Tuberculosis Drug Discovery.

Authors:  Stephani Joy Y Macalino; Junie B Billones; Voltaire G Organo; Maria Constancia O Carrillo
Journal:  Molecules       Date:  2020-02-04       Impact factor: 4.411

2.  Forward Modeling of the Coumarin Antifungals; SPR/SAR Based Perspective.

Authors:  Saeed Soltani; Shima Dianat; Soroush Sardari
Journal:  Avicenna J Med Biotechnol       Date:  2009-07

Review 3.  Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach.

Authors:  Murtala A Ejalonibu; Segun A Ogundare; Ahmed A Elrashedy; Morufat A Ejalonibu; Monsurat M Lawal; Ndumiso N Mhlongo; Hezekiel M Kumalo
Journal:  Int J Mol Sci       Date:  2021-12-09       Impact factor: 5.923

  3 in total

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