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

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.

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Bibliographic Details
Main Authors: BORRO, L., YANO, I. H., MAZONI, I., NESHICH, G.
Other Authors: LUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; GORAN NESHICH, CNPTIA.
Format: Anais e Proceedings de eventos biblioteca
Language:English
eng
Published: 2017-01-17
Subjects:Interações entre proteína e ligantes, Modelagem, Modelos, Complexo proteína-ligante, Protein-ligand complex, Binding affinity prediction model, Empiric nonparametric predictive model, Plataforma Sting, Binding properties, Models,
Online Access:http://www.alice.cnptia.embrapa.br/alice/handle/doc/1060954
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Summary: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.