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.

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

Autores

Descripción

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.
3Dsig 2016. Pôster #56.

Palabras clave

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