Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.21/2232
Título: QSAR modeling of antitubercular activity of diverse organic compounds
Autor: Kovalishyn, Vasyl
Aires-de-Sousa, João
Ventura, Cristina
Leitão, Rúben Elvas
Martins, Filomena
Palavras-chave: QSAR
Neural Networks
Random Forests
Drug Design
Antimycobacterial Activity
Benzimidazole Derivatives
Variable Selection
Isoniazid Derivatives
Data: Mai-2011
Editora: Elsevier Science BV
Citação: KOVALISHYN, 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.
Resumo: Tuberculosis (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.
Peer review: yes
URI: http://hdl.handle.net/10400.21/2232
ISSN: 0169-7439
Aparece nas colecções:ISEL - Eng. Quim. Biol. - Artigos

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