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dc.contributor.authorBORRO, L. C.pt_BR
dc.contributor.authorSALIM, J. A.pt_BR
dc.contributor.authorMAZONI, I.pt_BR
dc.contributor.authorYANO, I.pt_BR
dc.contributor.authorJARDINE, J. G.pt_BR
dc.contributor.authorNESHICH, G.pt_BR
dc.date.accessioned2015-12-22T11:11:11Zpt_BR
dc.date.available2015-12-22T11:11:11Zpt_BR
dc.date.created2015-12-22pt_BR
dc.date.issued2015pt_BR
dc.identifier.citationIn: CONGRESS OF THE INTERNATIONAL UNION FOR BIOCHEMISTRY AND MOLECULAR BIOLOGY, 23.; ANNUAL MEETING OF THE BRAZILIAN SOCIETY FOR BIOCHEMISTRY AND MOLECULAR BIOLOGY, 44., 2015, Foz do Iguaçu. Biochemistry for a better world: abstracts book. [Foz do Iguaçu]: SBBq, 2015.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1032260pt_BR
dc.descriptionIn order to improve binding affinity prediction, we developed a new scoring function, named STINGSF, derived from physical-chemical and structural features that describe the protein-ligand interaction nano-environment of experimentally determined structures.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectInteração proteína-ligantept_BR
dc.subjectAprendizado de máquinapt_BR
dc.subjectInteligência artificialpt_BR
dc.subjectProtein-ligand interactionpt_BR
dc.subjectScoring functionspt_BR
dc.subjectMachine learningpt_BR
dc.titleImproving binding affinity prediction by using a rule-based model with physical-chemical and structural descriptors of the nano-environment for protein-ligand interactions.pt_BR
dc.typeResumo em anais e proceedingspt_BR
dc.date.updated2020-01-21T11:11:11Zpt_BR
dc.subject.nalthesaurusArtificial intelligencept_BR
dc.description.notesC.047.pt_BR
dc.format.extent2p. 153.pt_BR
riaa.ainfo.id1032260pt_BR
riaa.ainfo.lastupdate2020-01-21 -02:00:00pt_BR
dc.contributor.institutionIB/Unicamp; FEEC/Unicamp; IVAN MAZONI, CNPTIA; INACIO HENRIQUE YANO, CNPTIA; JOSÉ GILBERTO JARDINE, CNPTIA; GORAN NESHICH, CNPTIA.pt_BR
Aparece en las colecciones:Resumo em anais de congresso (CNPTIA)

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