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Quantitative structure-activity relationship study of antitubercular fluoroquinolones.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Minovski, Nikola Vracko, Marjan Solmajer, Tom
Description	Country affiliation: Slovenia Author Affiliation: Minovski N ( National Institute of Chemistry, Hajdrihova 19, Ljubljana, 1001, Slovenia. nikola.minovski@ki.si)
Abstract	Quantitative structure-activity relationship study on three diverse sets of structurally similar fluoroquinolones was performed using a comprehensive set of molecular descriptors. Multiple linear regression technique was applied as a preprocessing tool to find the set of relevant descriptors (10) which are subsequently used in the artificial neural networks approach (non-linear procedure). The biological activity in the series (minimal inhibitory concentration (µg/mL) was treated as negative decade logarithm, pMIC). Using the non-linear technique counter propagation artificial neural networks, we obtained good predictive models. All models were validated using cross validation leave-one-out procedure. The results (the best models: Assay1, R = 0.8108; Assay2, R = 0.8454, and Assay3, R = 0.9212) obtained on external, previously excluded test datasets show the ability of these models in providing structure-activity relationship of fluoroquinolones. Thus, we demonstrated the advantage of non-linear approach in prediction of biological activity in these series. Furthermore, these validated models could be proficiently used for the design of novel structurally similar fluoroquinolone analogues with potentially higher activity.
File Format	HTM / HTML
ISSN	13811991
Issue Number	2
Volume Number	15
e-ISSN	1573501X
Journal	Molecular Diversity
Language	English
Publisher	Springer
Publisher Date	2011-05-01
Publisher Place	Netherlands
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Molecular Biology Antitubercular Agents Chemistry Metabolism Fluoroquinolones Quantitative Structure-activity Relationship Algorithms Drug Design Humans Models, Statistical Journal Article Research Support, Non-u.s. Gov't
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Medicine Drug Discovery Molecular Biology Physical and Theoretical Chemistry Information Systems Catalysis Inorganic Chemistry

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