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QSAR modeling of antitubercular activity of diverse organic compounds

2011-05Journal article Open access
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dc.contributor.authorKovalishyn, Vasyl
dc.contributor.authorAires-de-Sousa, Joao
dc.contributor.authorVentura, Cristina
dc.contributor.authorElvas Leitao, Ruben
dc.contributor.authorMartins, Filomena
dc.date.accessioned2013-02-16T17:50:57Z
dc.date.available2013-02-16T17:50:57Z
dc.date.issued2011-05
dc.description.abstractTuberculosis (TB) is a worldwide infectious disease that has shown over time extremely high mortality levels. The urgent need to develop new antitubercular drugs is due to the increasing rate of appearance of multi-drug resistant strains to the commonly used drugs, and the longer durations of therapy and recovery, particularly in immuno-compromised patients. The major goal of the present study is the exploration of data from different families of compounds through the use of a variety of machine learning techniques so that robust QSAR-based models can be developed to further guide in the quest for new potent anti-TB compounds. Eight QSAR models were built using various types of descriptors (from ADRIANA.Code and Dragon software) with two publicly available structurally diverse data sets, including recent data deposited in PubChem. QSAR methodologies used Random Forests and Associative Neural Networks. Predictions for the external evaluation sets obtained accuracies in the range of 0.76-0.88 (for active/inactive classifications) and Q(2)=0.66-0.89 for regressions. Models developed in this study can be used to estimate the anti-TB activity of drug candidates at early stages of drug development (C) 2011 Elsevier B.V. All rights reserved.por
dc.identifier.citationKOVALISHYN, Vasyl; AIRES-DE-SOUSA, João; VENTURA, Cristina; LEITÃO, Ruben Elvas; MARTINS, Filomena - QSAR modeling of antitubercular activity of diverse organic compounds. Chemometrics and Intelligent Laboratory Systems. ISSN 0169-7439. Vol. 107. n.º 1 (2011) p. 69-74.por
dc.identifier.issn0169-7439
dc.identifier.urihttp://hdl.handle.net/10400.21/2232
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherElsevier Science BVpor
dc.subjectQSARpor
dc.subjectNeural Networkspor
dc.subjectRandom Forestspor
dc.subjectAntitubercularpor
dc.subjectDrug Designpor
dc.subjectNeural-Networkpor
dc.subjectAntimycobacterial Activitypor
dc.subjectBenzimidazole Derivativespor
dc.subjectVariable Selectionpor
dc.subjectIn-Vitropor
dc.subjectMycobacterium-Tuberculosispor
dc.subjectIsoniazid Derivativespor
dc.subjectAgentspor
dc.subjectInhibitorpor
dc.subjectDesignpor
dc.titleQSAR modeling of antitubercular activity of diverse organic compoundspor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceAmsterdampor
oaire.citation.endPage74por
oaire.citation.issue1por
oaire.citation.startPage69por
oaire.citation.titleChemometrics and Intelligent Laboratory Systemspor
oaire.citation.volume107por
person.familyNameKovalishyn
person.familyNameAires-de-Sousa
person.familyNameElvas Leitao
person.givenNameVasyl
person.givenNameJoao
person.givenNameRuben
person.identifier.ciencia-id171B-1434-6FC1
person.identifier.ciencia-idF41B-8A26-88D4
person.identifier.orcid0000-0002-9352-7332
person.identifier.orcid0000-0002-5887-2966
person.identifier.orcid0000-0002-2196-412X
person.identifier.ridI-6823-2018
person.identifier.ridC-7826-2013
person.identifier.ridD-2452-2009
person.identifier.scopus-author-id56160092400
person.identifier.scopus-author-id6603089025
person.identifier.scopus-author-id55667178200
rcaap.rightsrestrictedAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication9df4c185-2270-47af-8813-c18428b5498d
relation.isAuthorOfPublication6662c1f3-0ccf-4f2c-9b34-81229d7b09db
relation.isAuthorOfPublication440b7129-d4d9-4225-a936-ca694c0984b6
relation.isAuthorOfPublication.latestForDiscovery9df4c185-2270-47af-8813-c18428b5498d

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