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

BORRO, L.; YANO, I. H.; MAZONI, I.; NESHICH, G.

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Campo DC	Valor	Lengua/Idioma
dc.contributor.author	BORRO, L.	pt_BR
dc.contributor.author	YANO, I. H.	pt_BR
dc.contributor.author	MAZONI, I.	pt_BR
dc.contributor.author	NESHICH, G.	pt_BR
dc.date.accessioned	2017-01-17T11:11:11Z	pt_BR
dc.date.available	2017-01-17T11:11:11Z	pt_BR
dc.date.created	2017-01-17	pt_BR
dc.date.issued	2016	pt_BR
dc.identifier.citation	In: STRUCTURAL BIOINFORMATICS AND COMPUTATIONAL BIOPHYSICS, 2016, Orlando. [Proceedings...]. Orlando: [s.n.], 2016.	pt_BR
dc.identifier.uri	http://www.alice.cnptia.embrapa.br/alice/handle/doc/1060954	pt_BR
dc.description	We 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.	pt_BR
dc.format	1 pôster.	pt_BR
dc.language.iso	eng	eng
dc.rights	openAccess	eng
dc.subject	Interações entre proteína e ligantes	pt_BR
dc.subject	Modelagem	pt_BR
dc.subject	Modelos	pt_BR
dc.subject	Complexo proteína-ligante	pt_BR
dc.subject	Protein-ligand complex	pt_BR
dc.subject	Binding affinity prediction model	pt_BR
dc.subject	Empiric nonparametric predictive model	pt_BR
dc.subject	Plataforma Sting	pt_BR
dc.title	Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.	pt_BR
dc.type	Resumo em anais e proceedings	pt_BR
dc.date.updated	2020-01-21T11:11:11Z	pt_BR
dc.subject.nalthesaurus	Binding properties	pt_BR
dc.subject.nalthesaurus	Models	pt_BR
dc.description.notes	3Dsig 2016. Pôster #56.	pt_BR
dc.format.extent2	p. 116-117.	pt_BR
riaa.ainfo.id	1060954	pt_BR
riaa.ainfo.lastupdate	2020-01-21 -02:00:00	pt_BR
dc.contributor.institution	LUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; GORAN NESHICH, CNPTIA.	pt_BR
Aparece en las colecciones:	Resumo em anais de congresso (CNPTIA)

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