Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/94559
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dc.creatorAliuska M Helguera
dc.creatorRodriguez Borges, JE
dc.creatorOlga Caamano
dc.creatorXerardo Garcia Mera
dc.creatorMaykel P Perez Gonzalez
dc.creatorNatalia N D S Cordeiro
dc.date.accessioned2019-04-11T23:07:00Z-
dc.date.available2019-04-11T23:07:00Z-
dc.date.issued2010
dc.identifier.issn1868-1743
dc.identifier.othersigarra:48926
dc.identifier.urihttps://hdl.handle.net/10216/94559-
dc.description.abstractCancer is the leading cause of death among men and women under age 85. Every year, millions of individuals are diagnosed with cancer. But finding new drugs is a complex, expensive, and very time-consuming task. Over the past decade, the cancer research community has begun to address the in silico modeling approaches, such as Quantitative Structure-Activity Relationships (QSAR), as an important alternative tool for targeting potential anticancer drugs. With the compilation of a large dataset of nucleosides synthesized in our laboratories, or elsewhere, and tested in a single cytotoxic assay under the same experimental conditions, we recognized a unique opportunity to attempt to build predictive QSAR models. Early efforts with 2D classification models built from part of this dataset were very encouraging. Here we report a further detailed evaluation of classification models to flag potential anticancer activities derived from a variety of 3D molecular representations. A quantitative 3D-model model that discriminates anticancer compounds from the inactive ones was attained, which allowed the correct classification of 82% of compounds in such a large and diverse dataset, with only 5% of false inactives and 11% of false actives. The model developed here was then used to select and design a new series of nucleosides, by classifying beforehand them as active/inactive anticancer compounds. From the compounds so designed, 22 were synthesized and evaluated for their inhibitory effects on the proliferation of murine leukemia cells (L1210/0), of which 86% were well-classified as active or inactive, and only two were false actives, corroborating the good predictive ability of the present discriminant model. The results of this study thus provide a valuable tool for the design of novel potent anticancer nucleoside analogues.
dc.language.isoeng
dc.rightsrestrictedAccess
dc.subjectMatemática
dc.subjectMathematics
dc.titleDesign, Synthesis, and Evaluation of Antineoplastic Activity of Novel Carbocyclic Nucleosides
dc.typeArtigo em Revista Científica Internacional
dc.contributor.uportoFaculdade de Ciências
dc.identifier.doi10.1002/minf.200900033
dc.identifier.authenticusP-003-8XG
dc.subject.fosCiências exactas e naturais::Matemática
dc.subject.fosNatural sciences::Mathematics
Appears in Collections:FCUP - Artigo em Revista Científica Internacional

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