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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
22/12/2015 |
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Data da última atualização: |
21/01/2020 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
BORRO, L. C.; SALIM, J. A.; MAZONI, I.; YANO, I.; JARDINE, J. G.; NESHICH, G. |
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Afiliação: |
IB/Unicamp; FEEC/Unicamp; IVAN MAZONI, CNPTIA; INACIO HENRIQUE YANO, CNPTIA; JOSÉ GILBERTO JARDINE, CNPTIA; GORAN NESHICH, CNPTIA. |
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Título: |
Improving binding affinity prediction by using a rule-based model with physical-chemical and structural descriptors of the nano-environment for protein-ligand interactions. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
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. |
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Páginas: |
p. 153. |
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Idioma: |
Inglês |
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Notas: |
C.047. |
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Conteúdo: |
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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Palavras-Chave: |
Aprendizado de máquina; Inteligência artificial; Interação proteína-ligante; Machine learning; Protein-ligand interaction; Scoring functions. |
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Thesaurus Nal: |
Artificial intelligence. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/136070/1/Improving-Borro.pdf
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Marc: |
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100 1 $aBORRO, L. C.
245 $aImproving binding affinity prediction by using a rule-based model with physical-chemical and structural descriptors of the nano-environment for protein-ligand interactions.$h[electronic resource]
260 $aIn: CONGRESS OF THE INTERNATIONAL UNION FOR BIOCHEMISTRY AND MOLECULAR BIOLOGY, 23.; ANNUAL MEETING OF THE BRAZILIAN SOCIETY FOR BIOCHEMISTRY AND MOLECULAR BIOLOGY, 44., 2015, Foz do Iguaçu. Biochemistry for a better world: abstracts book. [Foz do Iguaçu]: SBBq$c2015
300 $ap. 153.
500 $aC.047.
520 $aIn 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.
650 $aArtificial intelligence
653 $aAprendizado de máquina
653 $aInteligência artificial
653 $aInteração proteína-ligante
653 $aMachine learning
653 $aProtein-ligand interaction
653 $aScoring functions
700 1 $aSALIM, J. A.
700 1 $aMAZONI, I.
700 1 $aYANO, I.
700 1 $aJARDINE, J. G.
700 1 $aNESHICH, G.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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