Understanding PPAR-δ affinity and selectivity using hologram quantitative structure–activity modeling, molecular docking and GRID calculations (2016)
- Authors:
- USP affiliated authors: HONORIO, KÁTHIA MARIA - EACH ; TROSSINI, GUSTAVO HENRIQUE GOULART - FCF
- Unidades: EACH; FCF
- DOI: 10.4155/fmc-2016-0061
- Subjects: DIABETES MELLITUS; RELAÇÕES QUANTITATIVAS ENTRE ESTRUTURA QUÍMICA E ATIVIDADE BIOLÓGICA; MODELAGEM MOLECULAR
- Language: Inglês
- Imprenta:
- Source:
- Título: Future Medicinal Chemistry
- ISSN: 1756-8919
- Volume/Número/Paginação/Ano: v. 8, n. 16, p. 1913-1926, oct. 2016
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
MALTAROLLO, Vinícius Gonçalves et al. Understanding PPAR-δ affinity and selectivity using hologram quantitative structure–activity modeling, molecular docking and GRID calculations. Future Medicinal Chemistry, v. 8, n. 16, p. 1913-1926, 2016Tradução . . Disponível em: https://doi.org/10.4155/fmc-2016-0061. Acesso em: 16 fev. 2026. -
APA
Maltarollo, V. G., Araujo, S. C., Trossini, G. H. G., & Honorio, K. M. (2016). Understanding PPAR-δ affinity and selectivity using hologram quantitative structure–activity modeling, molecular docking and GRID calculations. Future Medicinal Chemistry, 8( 16), 1913-1926. doi:10.4155/fmc-2016-0061 -
NLM
Maltarollo VG, Araujo SC, Trossini GHG, Honorio KM. Understanding PPAR-δ affinity and selectivity using hologram quantitative structure–activity modeling, molecular docking and GRID calculations [Internet]. Future Medicinal Chemistry. 2016 ; 8( 16): 1913-1926.[citado 2026 fev. 16 ] Available from: https://doi.org/10.4155/fmc-2016-0061 -
Vancouver
Maltarollo VG, Araujo SC, Trossini GHG, Honorio KM. Understanding PPAR-δ affinity and selectivity using hologram quantitative structure–activity modeling, molecular docking and GRID calculations [Internet]. Future Medicinal Chemistry. 2016 ; 8( 16): 1913-1926.[citado 2026 fev. 16 ] Available from: https://doi.org/10.4155/fmc-2016-0061 - Use of machine learning approaches for novel drug discovery
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Informações sobre o DOI: 10.4155/fmc-2016-0061 (Fonte: oaDOI API)
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