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dc.contributor.authorCarracedo Reboredo, Paula
dc.contributor.authorCorona, Ramiro
dc.contributor.authorMartínez Nunes, Mikel ORCID
dc.contributor.authorFernández Lozano, Carlos
dc.contributor.authorTsiliki, Georgia
dc.contributor.authorSarimveis, Haralambos
dc.contributor.authorAranzamendi Uruburu, Eider
dc.contributor.authorArrasate Gil, Sonia
dc.contributor.authorSotomayor Anduiza, María Nuria
dc.contributor.authorLete Expósito, María Esther
dc.contributor.authorMunteanu, Cristian R.
dc.contributor.authorGonzález Díaz, Humberto
dc.date.accessioned2024-05-09T13:49:54Z
dc.date.available2024-05-09T13:49:54Z
dc.date.issued2020
dc.identifier.citationCurrent Topics in Medicinal Chemistry 20(4) : 305-317 (2020)es_ES
dc.identifier.issn1873-4294
dc.identifier.issn1568-0266
dc.identifier.urihttp://hdl.handle.net/10810/67787
dc.description.abstractCheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). Specifically, Cheminformatics prediction of complex catalytic enantiose- lective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used be- fore. In addition, the calculation of MCDs is done very often using specific software not always avail- able for general users and there is not an R library public available for the calculation of MCDs. This fact limits the availability of MCMD-based Cheminformatics procedures. In this work, we developed the first library in R for the calculation of MCDs. We also report here the first public web server for the calculation of MCDs online. In addition, we also compiled a desktop version of the software for offline use. These tools called MCDCalc include the calculation of a new class of MCDs called Markov Singu- lar values SVmax. We also report the first Cheminformatics study of a set of enantioselective organic reactions using the new class of indices. Not only enantioselectivity but a study of biological activity has also been investigated. Firstly, we studied the enantiomeric excess ee(%)[Rcat] for 324 α- amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activ- ity of 5644 compounds against colorectal cancer was studied. We developed a very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series. The work shows the potential of the new tool for computational studies in organic and medicinal chemistryes_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad: CTQ2016-74881-P, CTQ2013-41229-P // Gobierno Vasco: IT1045-16es_ES
dc.language.isoenges_ES
dc.publisherBentham Sciencees_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/CTQ2016-74881-Pes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/CTQ2013-41229-Pes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectmolecular descriptorses_ES
dc.subjectMarkov chainses_ES
dc.subjectsingular valueses_ES
dc.subjectonline tooles_ES
dc.subjectR-scriptes_ES
dc.subjectChiral catalystes_ES
dc.subjectenantioselectivityes_ES
dc.subjectα-amidoalkylation reactionses_ES
dc.subjectbiological activityes_ES
dc.subjectcolorectal canceres_ES
dc.titleMCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistryes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2020, Bentham Science Publishers under Creative Commons License CC BY-NC-ND 4.0 - Attribution-NonCommercial-NoDerivatives 4.0 International.es_ES
dc.relation.publisherversionhttps://www.eurekaselect.com/article/103216es_ES
dc.identifier.doi10.2174/1568026620666191226092431
dc.departamentoesQuímica orgánica IIes_ES
dc.departamentoeuKimika organikoa IIes_ES


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© 2020, Bentham Science Publishers under  Creative Commons License CC BY-NC-ND 4.0 - Attribution-NonCommercial-NoDerivatives 4.0 International.
Except where otherwise noted, this item's license is described as © 2020, Bentham Science Publishers under Creative Commons License CC BY-NC-ND 4.0 - Attribution-NonCommercial-NoDerivatives 4.0 International.