Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1060954
Research center of Embrapa/Collection: Embrapa Informática Agropecuária - Resumo em anais de congresso (ALICE)
Date Issued: 2016
Type of Material: Resumo em anais de congresso (ALICE)
Authors: BORRO, L.
YANO, I. H.
MAZONI, I.
NESHICH, G.
Additional Information: LUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; GORAN NESHICH, CNPTIA.
Title: Binding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.
Publisher: In: STRUCTURAL BIOINFORMATICS AND COMPUTATIONAL BIOPHYSICS, 2016, Orlando. [Proceedings...]. Orlando: [s. n.], 2016.
Pages: p. 116-117.
Language: en
Notes: 3Dsig 2016. Pôster #56.
Keywords: Interações entre proteína e ligantes
Modelagem
Modelos
Complexo proteína-ligante
Protein-ligand complex
Binding affinity prediction model
Empiric nonparametric predictive model
Plataforma Sting
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.
NAL Thesaurus: Binding properties
Models
Data Created: 2017-01-17
Appears in Collections:Resumo em anais de congresso (CNPTIA)

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