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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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Anais e Proceedings de eventos biblioteca |
| Language: | English eng |
| Published: |
2017-01-17
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| 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. |
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