A novel non-stochastic quadratic fingerprints-based approach for the “in silico” discovery of new antitrypanosomal compounds

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dc.contributor.authorMontero Torres, Alina
dc.contributor.authorVega, María Celeste
dc.contributor.authorMarrero Ponce, Yovani
dc.contributor.authorRolóm, Miriam
dc.contributor.authorGómez Barrio, Alicia
dc.contributor.authorEscario, José Antonio
dc.contributor.authorArán, Vicente J.
dc.contributor.authorMartinez Fernández, Antonio R.
dc.contributor.authorMeneses Marcel, Alfredo
dc.contributor.departmentUniversidad Central "Marta Abreu" de Las Villas. Centro de Bioactivos Químicosen_US
dc.contributor.departmentUniversidad Complutense de Madrid. Facultad de Farmaciaen_US
dc.contributor.departmentUniversidad Central "Marta Abreu" de Las Villas. Facultad de Química y Farmacia. Departamento de Farmaciaen_US
dc.contributor.departmentInstituto de Química Médica, CSIC. Madriden_US
dc.coverage.spatialSanta Claraen_US
dc.date.accessioned2018-03-09T00:27:43Z
dc.date.available2018-03-09T00:27:43Z
dc.date.issued2005
dc.description.abstractA Non-Stochastic Quadratic Fingerprints-based approach is introduced to classify and design, in a rational way, new antitrypanosomal compounds. A data set of 153 organic-chemicals; 62 with antitrypanosomal activity and 91 having other clinical uses, was processed by a k-means cluster analysis in order to design training and predicting data sets. Afterwards, a linear classification function was derived allowing the discrimination between active and inactive compounds. The model classifies correctly more than 93% of chemicals in both training and external prediction groups. The predictability of this discriminant function was also assessed by a leave-group-out experiment, in which 10% of the compounds were removed at random at each time and their activity a posteriori predicted. Also a comparison with models generated using four well-known families of 2D molecular descriptors was carried out. As an experiment of virtual lead generation, the present TOMOCOMD approach was finally satisfactorily applied on the virtual evaluation of ten already synthesized compounds. The in vitro antitrypanosomal activity of this series against epimastigotes forms of T. cruzi was assayed. The model was able to predict correctly the behaviour of these compounds in 90% of the cases.en_US
dc.identifier.doihttps://doi.org/10.1016/j.bmc.2005.06.049en_US
dc.identifier.issn09680896en_US
dc.identifier.urihttps://dspace.uclv.edu.cu/handle/123456789/8864
dc.language.isoen_USen_US
dc.relation.journalBioorganic & Medicinal Chemistryen_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.holderElsevier Ltd.en_US
dc.subjectAntitrypanosomal compoundsen_US
dc.subjectChagas’ diseaseen_US
dc.subjectEnfermedad de Chagasen_US
dc.subjectNon-stochastic quadratic indicesen_US
dc.subjectDiseño de fármacosen_US
dc.titleA novel non-stochastic quadratic fingerprints-based approach for the “in silico” discovery of new antitrypanosomal compoundsen_US
dc.typeArticleen_US
dc.type.article1en_US

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