Tipo
Artigos em Revista
Tipo de Documento
Artigo Completo
Título
QSAR Modeling of Antitubercular Activity of Diverse Organic Compounds
Participantes na publicação
V. Kovalishyn (Author)
J. Aires-de-Sousa (Author)
C. Ventura (Author)
R. E. Leitão (Author)
F. Martins (Author)
Dep. Química e Bioquímica
CQB
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 Q2 = 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.
Data de Submissão/Pedido
2010-11-26
Data de Aceitação
2011-01-25
Data de Publicação
2011-02
Instituição
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA
Suporte
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Identificadores da Publicação
ISSN - 0169-7439
Editora
Elsevier
Número de Páginas
6
Página Inicial
69
Página Final
74
Identificadores do Documento
DOI -
https://doi.org/10.1016/j.chemolab.2011.01.011
URL -
https://doi.org/10.1016/j.chemolab.2011.01.011
Identificadores de Qualidade
SCIMAGO Q1 (2011) - 0.77 - Computer Science Appplications
Web Of Science Q2 (2011) - 1.920 - Computer Science, Artificial Intelligence
Web Of Science Q1 (2011) - 1.920 - Mathematics, Interdisciplinary Applications
Keywords
QSAR
Neural Networks
Random Forests
Antitubercular
Drug design