Improving binding affinity prediction by using a rule-based model with physical-chemical and structural descriptors of the nano-environment for protein-ligand interactions.

Improving binding affinity prediction by using a rule-based model with physical-chemical and structural descriptors of the nano-environment for protein-ligand interactions.

In order to improve binding affinity prediction, we developed a new scoring function, named STINGSF, derived from physical-chemical and structural features that describe the protein-ligand interaction nano-environment of experimentally determined structures.

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Bibliographic Details
Main Authors: BORRO, L. C., SALIM, J. A., MAZONI, I., YANO, I., JARDINE, J. G., NESHICH, G.
Other Authors: IB/Unicamp; FEEC/Unicamp; IVAN MAZONI, CNPTIA; INACIO HENRIQUE YANO, CNPTIA; JOSÉ GILBERTO JARDINE, CNPTIA; GORAN NESHICH, CNPTIA.
Format: Anais e Proceedings de eventos biblioteca
Language:English
eng
Published: 2015-12-22
Subjects:Interação proteína-ligante, Aprendizado de máquina, Inteligência artificial, Protein-ligand interaction, Scoring functions, Machine learning, Artificial intelligence,
Online Access:http://www.alice.cnptia.embrapa.br/alice/handle/doc/1032260
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Summary:In order to improve binding affinity prediction, we developed a new scoring function, named STINGSF, derived from physical-chemical and structural features that describe the protein-ligand interaction nano-environment of experimentally determined structures.

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