Título:	Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.
Autoria:	BORRO, L. YANO, I. H. MAZONI, I. NESHICH, G.
Afiliação:	LUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; GORAN NESHICH, CNPTIA.
Ano de publicação:	2016
Referência:	In: STRUCTURAL BIOINFORMATICS AND COMPUTATIONAL BIOPHYSICS, 2016, Orlando. [Proceedings...]. Orlando: [s.n.], 2016.
Páginas:	p. 116-117.
Conteúdo:	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.
NAL Thesaurus:	Binding properties Models
Palavras-chave:	Interações entre proteína e ligantes Modelagem Modelos Complexo proteína-ligante Protein-ligand complex Binding affinity prediction model Empiric nonparametric predictive model Plataforma Sting
Notas:	3Dsig 2016. Pôster #56.
Tipo do material:	Resumo em anais e proceedings
Acesso:	openAccess
Aparece nas coleções:	Resumo em anais de congresso (CNPTIA)