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Publication details

Document type
Journal articles

Document subtype
Full paper

Title
QSAR Modeling of Antitubercular Activity of Diverse Organic Compounds

Participants in the publication
V. Kovalishyn (Author)
J. Aires-de-Sousa (Author)
C. Ventura (Author)
R. E. Leitão (Author)
F. Martins (Author)
Dep. Química e Bioquímica
 CQB
Summary
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.

Date of Submisson/Request
2010-11-26
Date of Acceptance
2011-01-25
Date of Publication
2011-02

Institution
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA

Where published
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Publication Identifiers
ISSN - 0169-7439

Publisher
Elsevier

Volume
107
Number
1

Number of pages
6
Starting page
69
Last page
74

Document Identifiers
DOI - https://doi.org/10.1016/j.chemolab.2011.01.011
URL - https://doi.org/10.1016/j.chemolab.2011.01.011

Rankings
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


Export

APA
V. Kovalishyn, J. Aires-de-Sousa, C. Ventura, R. E. Leitão, F. Martins, (2011). QSAR Modeling of Antitubercular Activity of Diverse Organic Compounds. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 107, 69-74. ISSN 0169-7439. eISSN . https://doi.org/10.1016/j.chemolab.2011.01.011

IEEE
V. Kovalishyn, J. Aires-de-Sousa, C. Ventura, R. E. Leitão, F. Martins, "QSAR Modeling of Antitubercular Activity of Diverse Organic Compounds" in CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, vol. 107, pp. 69-74, 2011. 10.1016/j.chemolab.2011.01.011

BIBTEX
@article{25037, author = {V. Kovalishyn and J. Aires-de-Sousa and C. Ventura and R. E. Leitão and F. Martins}, title = {QSAR Modeling of Antitubercular Activity of Diverse Organic Compounds}, journal = {CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS}, year = 2011, pages = {69-74}, volume = 107 }