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Machine learning to predict the specific optical rotations of chiral fluorinated molecules

2019-12-05Artigo científico Acesso aberto
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dc.contributor.authorChen, Mengyao
dc.contributor.authorWu, Ting
dc.contributor.authorXiao, Kaixia
dc.contributor.authorZhao, Tanfeng
dc.contributor.authorZhou, Yanmei
dc.contributor.authorZhang, Qingyou
dc.contributor.authorAires-de-Sousa, Joao
dc.contributor.institutionLAQV@REQUIMTE
dc.contributor.institutionDQ - Departamento de Química
dc.contributor.pblElsevier Science Publisher B.V.
dc.date.accessioned2026-01-16T14:37:42Z
dc.date.available2026-01-16T14:37:42Z
dc.date.embargoedUntil2021-06-27
dc.date.issued2019-12-05
dc.descriptionPOCI-01-0145-FEDER – 007265 National Natural Science Foundation of China (No. 21576071 ; 21776061 ) Foundation of International Science and Technology Cooperation of Henan Province (No. 162102410012 ) Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (No. 20091001 ) Science & Technology Innovation Team in Universities of Henan Province ( 19IRTSTHN029 )
dc.description.abstractA chemoinformatics method was applied to the assignment of absolute configurations and to the quantitative prediction of specific optical rotations using a data set of 88 chiral fluorinated molecules (44 pairs of enantiomers). Counterpropagation neural networks were explored for the classification of enantiomers as dextrorotatory or levorotatory. Regression models were trained using multilayer perceptrons (MLP), random forests (RF) or multilinear regressions (MLR), on the basis of physicochemical atomic stereo (PAS) descriptors. New descriptors were also derived considering the common structural features of the data set (cPAS descriptors), which enabled RF models to predict the whole data set with R = 0.964, mean absolute error (MAE) of 9.8° and root mean square error (RMSE) of 12.5° in leave-one-pair-out cross-validation experiments. The predictions for the 30 compounds measured in chloroform were obtained with R = 0.971, MAE = 9.1° and RMSE = 12.5°, which compares favorably with quantum chemistry calculations reported in the literature.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent383877
dc.identifier.doi10.1016/j.saa.2019.117289
dc.identifier.issn1386-1425
dc.identifier.otherPURE: 13971674
dc.identifier.otherPURE UUID: 67b8f024-38df-4114-bdc7-83d491da1fdc
dc.identifier.otherScopus: 85067948025
dc.identifier.otherPubMed: 31255865
dc.identifier.urihttp://hdl.handle.net/10362/199320
dc.identifier.urlhttps://www.scopus.com/pages/publications/85067948025
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FQUI%2F50006%2F2019/PT
dc.subjectChiral fluorinated molecules
dc.subjectChirality
dc.subjectMachine learning
dc.subjectMolecular descriptors
dc.subjectSpecific optical rotation
dc.subjectAnalytical Chemistry
dc.subjectAtomic and Molecular Physics, and Optics
dc.subjectInstrumentation
dc.subjectSpectroscopy
dc.titleMachine learning to predict the specific optical rotations of chiral fluorinated moleculesen
dc.typejournal article
degois.publication.titleSpectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
degois.publication.volume223
dspace.entity.typePublication
rcaap.rightsopenAccess

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