Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.

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dc.contributorLUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; GORAN NESHICH, CNPTIA.
dc.creatorBORRO, L.
dc.creatorYANO, I. H.
dc.creatorMAZONI, I.
dc.creatorNESHICH, G.
dc.date2017-01-17T11:11:11Z
dc.date2017-01-17T11:11:11Z
dc.date2017-01-17
dc.date2016
dc.date2020-01-21T11:11:11Z
dc.date.accessioned2026-07-07T04:14:49Z
dc.descriptionWe propose a new empirical scoring function for binding affinity prediction modeled based on physicochemical and structural descriptors that characterize the nano-environment that encompass both ligand and binding pocket residues. Our hypothesis is that a more detailed characterization of protein-ligand complexes in terms of describing nano-environment as precisely as possible can lead to improvements in binding affinity prediction.
dc.description3Dsig 2016. Pôster #56.
dc.format1 pôster.
dc.formatp. 116-117.
dc.identifierIn: STRUCTURAL BIOINFORMATICS AND COMPUTATIONAL BIOPHYSICS, 2016, Orlando. [Proceedings...]. Orlando: [s.n.], 2016.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1060954
dc.identifier.urihttp://hdl.handle.net/123456789/455368
dc.languageeng
dc.rightsopenAccess
dc.subjectInterações entre proteína e ligantes
dc.subjectModelagem
dc.subjectModelos
dc.subjectComplexo proteína-ligante
dc.subjectProtein-ligand complex
dc.subjectBinding affinity prediction model
dc.subjectEmpiric nonparametric predictive model
dc.subjectPlataforma Sting
dc.subjectBinding properties
dc.subjectModels
dc.titleBinding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.
dc.typeResumo em anais e proceedings

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