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Structure–Activity Relationships on Purine and 2,3‐Dihydropurine Derivatives as Antitubercular Agents: a Data Mining Approach
Author(s) -
Pietra Daniele,
Imbriani Marcello,
Borghini Alice,
Giorgi Irene,
Settimo Federico Da,
Breschi Maria Cristina,
Campa Mario,
Batoni Giovanna,
Brancatisano Franca Lisa,
Bianucci Anna Maria
Publication year - 2011
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/j.1747-0285.2011.01181.x
Subject(s) - mycobacterium tuberculosis , quantitative structure–activity relationship , isoniazid , purine , purine analogue , tuberculosis , computational biology , cheminformatics , computer science , chemistry , biology , stereochemistry , medicine , biochemistry , computational chemistry , pathology , enzyme
Nowadays, many people still fall victim to tuberculosis, the disease that has a worldwide spreading. Moreover, the problem of resistance to isoniazid and rifampin, the two most effective antitubercular drugs, is assuming an ever‐growing importance. The need for new drugs active against Mycobacterium tuberculosis represents nowadays a quite relevant problem in medicinal chemistry. Several purine and 2,3‐dihydropurine derivatives have recently emerged, showing considerable antitubercular properties. In this work, a quantitative structure–activity relationship (QSAR) model was developed, which is able to predict whether new purine and 2,3‐dihydropurine derivatives belong to an ‘Active’ or ‘Inactive’ class against the above micro‐organism. The obtained prediction model is based on a classification tree; it was built with a small number of descriptors, which allowed us to outline structural features important to predict antitubercular activity of such classes of compounds.