Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/147672
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Campo DCValorIdioma
dc.contributor.authorCarrera, Gonçalo Valente da Silva Marino-
dc.date.accessioned2023-01-16T22:14:42Z-
dc.date.available2023-12-01T01:31:41Z-
dc.date.issued2022-11-
dc.identifier.issn2513-0390-
dc.identifier.otherPURE: 47181119-
dc.identifier.otherPURE UUID: 4f4a4c43-bef5-41f9-ad97-41122c7fbcf3-
dc.identifier.otherScopus: 85139858922-
dc.identifier.otherWOS: 000868840300001-
dc.identifier.urihttp://hdl.handle.net/10362/147672-
dc.description-
dc.description.abstractThe combination of the generical molecular maps of atom-level properties (MOLMAPs) encoding approach and the Random Forest algorithm (RF) is applied in order to model, predict, and interpret the structural motifs responsible for a certain organic molecule's melting point (mp) profile. A high-quality database is used for model build-up and evaluation of predictive ability. The obtained results for the complete independent test set (R2 = 0.811, MAE = 31.99 K, RMS = 43.98 K) are comparable or better than reference works. The form of codification represents implicitly the structure of a given molecule and highlights the interactions responsible for a certain melting point profile. This generical encoding approach groups different structural motifs based on its calculated atomic-based properties leading to good predictive ability for structurally different chemical systems not contained in the training set.en
dc.language.isoeng-
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50006%2F2020/PT-
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50006%2F2020/PT-
dc.rightsopenAccesspt_PT
dc.subjectchemoinformatics-
dc.subjectcodification-
dc.subjectkohonen neural-networks-
dc.subjectmelting points-
dc.subjectorganic molecules-
dc.subjectqspr-
dc.subjectrandom forests-
dc.titleThe Melting Point Profile of Organic Molecules-
dc.typearticle-
degois.publication.issue11-
degois.publication.titleAdvanced Theory and Simulations-
degois.publication.volume5-
dc.peerreviewedyes-
dc.identifier.doihttps://doi.org/10.1002/adts.202200503-
dc.description.versionauthorsversion-
dc.description.versionpublished-
dc.title.subtitleA Chemoinformatic Approach-
dc.contributor.institutionDQ - Departamento de Química-
dc.contributor.institutionLAQV@REQUIMTE-
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