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dc.contributor.authorPrado Prado, Francisco Javier
dc.contributor.authorGonzález Díaz, Humberto
dc.contributor.authorSantana Penín, María Lourdes
dc.contributor.authorUriarte Villares, Eugenio
dc.date.accessioned2021-08-18T08:03:37Z
dc.date.available2021-08-18T08:03:37Z
dc.date.issued2007
dc.identifier.citationPrado-Prado, F.J.; Gonzàlez-Dìaz, H.; Santana, L.; Uriarte, E. QSAR & Network-based multi-species activity models for antifungals, in Proceedings of the 11th International Electronic Conference on Synthetic Organic Chemistry, 1–30 November 2007, MDPI: Basel, Switzerland, doi:10.3390/ecsoc-11-01372
dc.identifier.isbn3-906980-19-7
dc.identifier.urihttp://hdl.handle.net/10347/26827
dc.descriptionThe 11th International Electronic Conference on Synthetic Organic Chemistry session Computational Chemistry
dc.description.abstractThere are many pathogen microbial species with very different antimicrobial drugs susceptibility. In this work, we selected pairs of antifungal drugs with similar/dissimilar species predicted-activity profile and represented it as a large network, which may be used to identify drugs with similar mechanism of action. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) susbtantialy increases the potentialities of this kind of networks avoiding time and resources consming experiments. Unfortunately, almost QSAR models are unspecific or predict activity against only one species. To solve this problem we developed here a multi-species QSAR classification model, which outputs were the inputs of the above-mentioned network. Overall model classification accuracy was 87.0% (161/185 compounds) in training, 83.4% (50/61) in validation, and 83.7% for 288 additional antifungal compounds used to extent model validation for network construction. The network predicted has 59 nodes (compounds), 648 edges (pairs of compounds with similar activity), low coverage density d = 37.8%, and distribution more close to normal than to exponential. These results are more characteristic of a not-overestimated random network, clustering different drug mechanisms of actions, than of a less useful power-law network with few mechanisms (network hubs)
dc.description.sponsorshipGonzalez-Díaz H. acknowledges contract/grant sponsorship from the Program Isidro Parga Pondal of the “Dirección Xeral de Investigación y Desenvolvemento” of “Xunta de Galicia”. This author also acknowledges two contracts as guest professor in the Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Spain in 2006. The authors thank the Xunta de Galicia (projects PXIB20304PR and BTF20302PR) and the Ministerio de Sanidad y Consumo (project PI061457) for partial financial support
dc.language.isoeng
dc.publisherMDPI
dc.relation.ispartofseriesElectronic Conference on Synthetic Organic Chemistry;11
dc.rights© 2007 The author(s). Published by MDPI, Basel, Switzerland. Open Access
dc.subjectMolecular descriptor
dc.subjectMarkov model
dc.subjectNetworks
dc.subjectQSAR
dc.subjectCo-expression network
dc.subjectProbability
dc.subjectAntimicrobials
dc.subjectAntifungals
dc.titleQSAR & Network-based multi-species activity models for antifungals
dc.typeinfo:eu-repo/semantics/bookPart
dc.identifier.DOI10.3390/ecsoc-11-01372
dc.relation.publisherversionhttps://doi.org/10.3390/ecsoc-11-01372
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Química Orgánica


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