A Computer-Based Approach to the rational discovery of new trichomonacidal drugs by atom-type linear indices

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dc.contributor.authorMarrero Ponce, Yovani
dc.contributor.authorMachado Tugores, Yanetsy
dc.contributor.authorPereira, David M.
dc.contributor.authorBarrio, Alicia G.
dc.contributor.authorNogal Ruiz, Juan J.
dc.contributor.authorMontero Torres, Alina
dc.contributor.authorMeneses Marcel, Alfredo
dc.contributor.authorTorrens, Francisco
dc.contributor.authorMartinez Fernández, Antonio R.
dc.contributor.authorGarcia Sánchez, Rory
dc.contributor.authorEscario, José A.
dc.contributor.authorAran, Vicente J.
dc.contributor.authorOchoa, Carmen
dc.contributor.departmentUniversidad Central "Marta Abreu" de Las Villas. Facultad de Química y Farmacia. Departamento de Farmaciaen_US
dc.contributor.departmentUniversidad Central "Marta Abreu" de Las Villas. Centro de Bioactivos Químicosen_US
dc.coverage.spatialSanta Claraen_US
dc.date.accessioned2018-03-08T22:38:53Z
dc.date.available2018-03-08T22:38:53Z
dc.date.issued2005
dc.description.abstractComputational approaches are developed to design or rationally select, from structural databases, new lead trichomonacidal compounds. First, a data set of 111 compounds was split (design) into training and predicting series using hierarchical and partitional cluster analyses. Later, two discriminant functions were derived with the use of non-stochastic and stochastic atom-type linear indices. The obtained LDA (linear discrimination analysis)-based QSAR (quantitative structure-activity relationship) models, using non-stochastic and stochastic descriptors were able to classify correctly 95.56% (90.48%) and 91.11% (85.71%) of the compounds in training (test) sets, respectively. The result of predictions on the 10% full-out cross-validation test also evidenced the quality (robustness, stability and predictive power) of the obtained models. These models were orthogonalized using the Randic´ orthogonalization procedure. Afterwards, a simulation experiment of virtual screening was conducted to test the possibilities of the classification models developed here in detecting antitrichomonal chemicals of diverse chemical structures. In this sense, the 100.00% and 77.77% of the screened compounds were detected by the LDA-based QSAR models (Eq. 13 and Eq. 14, correspondingly) as trichomonacidal. Finally, new lead trichomonacidals were discovered by prediction of their antirichomonal activity with obtained models. The most of tested chemicals exhibit the predicted antitrichomonal effect in the performed ligand-based virtual screening, yielding an accuracy of the 90.48% (19/21). These results support a role for TOMOCOMD-CARDD descriptors in the biosilico discovery of new compounds.en_US
dc.identifier.doihttps://doi.org/10.2174/157016305775202955en_US
dc.identifier.issn1570-1638en_US
dc.identifier.urihttps://dspace.uclv.edu.cu/handle/123456789/8862
dc.language.isoen_USen_US
dc.relation.journalCurrent Drug Discovery Technologiesen_US
dc.rightsEste documento es propiedad patrimonial de la editorial Bentham Science Publishers que se reserva todos los derechos. La UCLV reproduce el mismo con fines exclusivamente educativos y cientificos, restringiendo su empleo a la comunidad universitaria de nuestro centro. Los usarios están obligados a emplear este documento sin amimos de lucro y sin realizar otras reproducciones, asi como a citar el material y sus autores cada vez que sea utilizado.en_US
dc.rights.holderBentham Science Publishersen_US
dc.subjectAtom based linear indexen_US
dc.subjectCytocidal activityen_US
dc.subjectHeterocyclesen_US
dc.subjectLead antitrichomonal compounden_US
dc.subjectLDA based QSAR modelen_US
dc.subjectTrichomonacidal activityen_US
dc.subjectTrichomonasen_US
dc.subjectDiseño de fármacosen_US
dc.titleA Computer-Based Approach to the rational discovery of new trichomonacidal drugs by atom-type linear indicesen_US
dc.typeArticleen_US
dc.type.article1en_US

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