Multiple linear regression and principal component analysis-based prediction of the anti-tuberculosis activity of some 2-aryl-1,3,4-thiadiazole derivatives

QSAR and Combinatorial Science

Volumn 25, Issue 1, 2006, Pages 56-66

  Hemmateenejad, B ^a,b   Miri, R ^b   Jafarpour, M ^b   Tabarzad, M ^b   Foroumadi, A ^c  

^a SHIRAZ UNIVERSITY   (Iran)

^b SHIRAZ UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^c KERMAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

Author keywords

Anti tuberculosis; Classification; MLR; PCA; QSAR; Thiadiazole

Indexed keywords

COMPUTATIONAL CHEMISTRY; FORECASTING; MOLECULES; ORGANIC COMPOUNDS; PRINCIPAL COMPONENT ANALYSIS; QUANTUM CHEMISTRY; TUBES (COMPONENTS);

ANTI-TUBERCULOSIS; ATYPICALS; CLASSIFIEDS; DESCRIPTORS; MULTIPLE LINEAR REGRESSIONS; MYCOBACTERIUM TUBERCULOSIS; PRINCIPAL-COMPONENT ANALYSIS; QSAR; QSAR ANALYSIS; THIADIAZOLES;

MULTIPLE LINEAR REGRESSION;

1,3,4 THIADIAZOLE DERIVATIVE; TUBERCULOSTATIC AGENT;

ANTIBACTERIAL ACTIVITY; ARTICLE; DRUG STRUCTURE; MINIMUM INHIBITORY CONCENTRATION; MULTIPLE LINEAR REGRESSION ANALYSIS; MYCOBACTERIUM TUBERCULOSIS; NONHUMAN; PARTITION COEFFICIENT; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTUM CHEMISTRY; SCORING SYSTEM;

ATYPICAL MYCOBACTERIUM; MYCOBACTERIUM TUBERCULOSIS;

EID: 31944438172     PISSN: 1611020X     EISSN: 16110218     Source Type: Journal    
DOI: 10.1002/qsar.200530006     Document Type: Article

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