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An unsupervised machine learning approach for the automatic construction of local chemical descriptors

dc.contributor.authorGallegos González, Miguel 
dc.contributor.authorIsamura, B. K.
dc.contributor.authorPopelier, P. L. A.
dc.contributor.authorMartín Pendás, Ángel 
dc.date.accessioned2024-10-09T06:18:52Z
dc.date.available2024-10-09T06:18:52Z
dc.date.issued2024
dc.identifier.citationJournal of Chemical Information and Modeling, 64(8), p. 3059-3079 (2024); doi:10.1021/acs.jcim.3c01906
dc.identifier.issn1549-9596
dc.identifier.urihttps://hdl.handle.net/10651/74944
dc.description.sponsorshipWe thank the Spanish MICIU, grant PID2021-122763NB-I00. M.G. specially thanks the Spanish MICIU for the predoctoral FPU grant, FPU19/02903, and the research stay grant, EST23/00004. B.K.I. acknowledges UK Research and Innovation (UKRI) for the award of a MADSIM Ph.D. studentship. He also expresses his gratitude to the BEBUC scholarship system for continued financial support. P.L.A.P. is grateful to the European Research Council (ERC) for the award of an Advanced Grant underwritten by the UKRIfunded Frontier Research grant EP/XO24393/1.
dc.format.extentp. 3059-3079
dc.language.isoeng
dc.relation.ispartofJournal of Chemical Information and Modeling
dc.rights© 2024 The Authors
dc.rightsCC Reconocimiento 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceScopus
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85188109061&doi=10.1021%2facs.jcim.3c01906&partnerID=40&md5=2af25074eef089d4f5e66523edbd7d64
dc.titleAn unsupervised machine learning approach for the automatic construction of local chemical descriptors
dc.typejournal article
dc.identifier.doi10.1021/acs.jcim.3c01906
dc.relation.projectIDPID2021-122763NB-I00 
dc.relation.projectIDFPU19/02903
dc.relation.projectIDEST23/00004
dc.relation.publisherversionhttp://dx.doi.org/10.1021/acs.jcim.3c01906
dc.rights.accessRightsopen access
dc.type.hasVersionVoR


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