Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1032260
Research center of Embrapa/Collection: Embrapa Informática Agropecuária - Resumo em anais de congresso (ALICE)
Date Issued: 2015
Type of Material: Resumo em anais de congresso (ALICE)
Authors: BORRO, L. C.
SALIM, J. A.
MAZONI, I.
YANO, I.
JARDINE, J. G.
NESHICH, G.
Additional Information: IB/Unicamp; FEEC/Unicamp; IVAN MAZONI, CNPTIA; INACIO HENRIQUE YANO, CNPTIA; JOSÉ GILBERTO JARDINE, CNPTIA; GORAN NESHICH, CNPTIA.
Title: Improving binding affinity prediction by using a rule-based model with physical-chemical and structural descriptors of the nano-environment for protein-ligand interactions.
Publisher: In: CONGRESS OF THE INTERNATIONAL UNION FOR BIOCHEMISTRY AND MOLECULAR BIOLOGY, 23.; ANNUAL MEETING OF THE BRAZILIAN SOCIETY FOR BIOCHEMISTRY AND MOLECULAR BIOLOGY, 44., 2015, Foz do Iguaçu. Biochemistry for a better world: abstracts book. [Foz do Iguaçu]: SBBq, 2015.
Pages: p. 153.
Language: en
Notes: C.047.
Keywords: Interação proteína-ligante
Aprendizado de máquina
Inteligência artificial
Protein-ligand interaction
Scoring functions
Machine learning
Description: 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.
NAL Thesaurus: Artificial intelligence
Data Created: 2015-12-22
Appears in Collections:Resumo em anais de congresso (CNPTIA)

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