01495nam a2200289 a 450000100080000000500110000800800410001910000140006024502030007426001190027730000280039650000290042452004410045365000230089465000110091765300380092865300310096665300430099765300440104065300140108465300120109865300210111065300270113170000160115870000150117470000160118920609542020-01-21       2016    bl uuuu     u00u1 u    #d1 aBORRO, L.  aBinding affinity prediction using a nonparametric regression model based on physicochemical and structural descriptors of the nano-environment for protein-ligand interactions.h[electronic resource]  aIn: STRUCTURAL BIOINFORMATICS AND COMPUTATIONAL BIOPHYSICS, 2016, Orlando. [Proceedings...]. Orlando: [s.n.]c2016  ap. 116-117.c1 pôster.  a3Dsig 2016. Pôster #56.  aWe 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.  aBinding properties  aModels  aBinding affinity prediction model  aComplexo proteína-ligante  aEmpiric nonparametric predictive model  aInterações entre proteína e ligantes  aModelagem  aModelos  aPlataforma Sting  aProtein-ligand complex1 aYANO, I. H.1 aMAZONI, I.1 aNESHICH, G.