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dc.contributor.authorNocedo Mena, Deyani
dc.contributor.authorCornelio, Carlos
dc.contributor.authorCamacho Corona, María del Rayo
dc.contributor.authorGarza González, Elvira
dc.contributor.authorWaksman de Torres, Noemi
dc.contributor.authorArrasate Gil, Sonia
dc.contributor.authorSotomayor Anduiza, María Nuria
dc.contributor.authorLete Expósito, María Esther
dc.contributor.authorGonzález Díaz, Humberto
dc.date.accessioned2023-11-24T12:56:58Z
dc.date.available2023-11-24T12:56:58Z
dc.date.issued2019-02-25
dc.identifier.citationJournal of Chemical Information and Modeling 59(3) : 1109-1120(2019)es_ES
dc.identifier.issn1549-9596
dc.identifier.issn1549-960X
dc.identifier.urihttp://hdl.handle.net/10810/63147
dc.description.abstractPredicting the activity of new chemical compounds over pathogenic microorganisms with different metabolic reaction networks (MRNs) is an important goal due to the different susceptibility to antibiotics. The ChEMBL database contains >160 000 outcomes of preclinical assays of antimicrobial activity for 55 931 compounds with >365 parameters of activity (MIC, IC50, etc.) and >90 bacteria strains of >25 bacterial species. In addition, the Leong and Barabàsi data set includes >40 MRNs of microorganisms. However, there are no models able to predict antibacterial activity for multiple assays considering both drug and MRN structures at the same time. In this work, we combined perturbation theory, machine learning, and information fusion techniques to develop the first PTMLIF model. The best linear model found presented values of specificity = 90.31/90.40 and sensitivity = 88.14/88.07 in training/validation series. We carried out a comparison to nonlinear artificial neural network (ANN) techniques and previous models from the literature. Next, we illustrated the practical use of the model with an experimental case of study. We reported for the first time the isolation and characterization of terpenes from the plant Cissus incisa. The antibacterial activity of the terpenes was experimentally determined. The more active compounds were phytol and α-amyrin, with MIC = 100 μg/mL for Vancomycin-resistant Enterococcus faecium and Acinetobacter baumannii resistant to carbapenems. These compounds are already known from other sources. However, they have been isolated and evaluated for the first time here against several strains of multidrug-resistant bacteria including World Health Organization (WHO) priority pathogens. Last, we used the model to predict the activity of these compounds versus other microorganisms with different MRNs in order to find other potential targets.es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad (CTQ2016-74881-P) // Gobierno Vasco (IT1045-16)es_ES
dc.language.isoenges_ES
dc.publisherAmerican Chemical Societyes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/CTQ2016-74881-Pes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectcheminformaticses_ES
dc.subjectmachine learninges_ES
dc.subjectantibacterial activityes_ES
dc.subjectterpeneses_ES
dc.subjectdrug discoveryes_ES
dc.titleModeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2019, American Chemical Societyes_ES
dc.relation.publisherversionhttps://pubs.acs.org/doi/10.1021/acs.jcim.9b00034es_ES
dc.relation.publisherversionhttps://doi.org/10.1021/acs.jcim.9b00034es_ES
dc.identifier.doi10.1021/acs.jcim.9b00034
dc.departamentoesQuímica orgánica Ies_ES
dc.departamentoeuKimika organikoa Ies_ES


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