New D2R partial agonist candidates:: an in silico approach from statistical models, molecular docking, and ADME/Tox propertie (2021)
- Authors:
- USP affiliated authors: HONORIO, KÁTHIA MARIA - EACH ; SILVA, ALBÉRICO BORGES FERREIRA DA - IQSC ; SILVA, ALDINEIA PEREIRA DA - IQSC ; CHIARI, LAISE PELLEGRINI ALENCAR - IQSC ; GUIMARÃES, AMANDA RIBEIRO - IQSC
- Unidades: EACH; IQSC
- DOI: 10.1007/s11224-021-01742-w
- Subjects: ESQUIZOFRENIA; QUÍMICA
- Keywords: Molecular docking; Drug design; ADME/Tox
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Structural Chemistry
- ISSN: 1572-9001
- Volume/Número/Paginação/Ano: v. 32, p. 2019–2033, 2021
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
SILVA, Aldineia Pereira da et al. New D2R partial agonist candidates:: an in silico approach from statistical models, molecular docking, and ADME/Tox propertie. Structural Chemistry, v. 32, p. 2019–2033, 2021Tradução . . Disponível em: https://doi.org/10.1007/s11224-021-01742-w. Acesso em: 02 abr. 2026. -
APA
Silva, A. P. da, Chiari, L. P. A., Guimaraes, A. R., Honorio, K. M., & Silva, A. B. F. da. (2021). New D2R partial agonist candidates:: an in silico approach from statistical models, molecular docking, and ADME/Tox propertie. Structural Chemistry, 32, 2019–2033. doi:10.1007/s11224-021-01742-w -
NLM
Silva AP da, Chiari LPA, Guimaraes AR, Honorio KM, Silva ABF da. New D2R partial agonist candidates:: an in silico approach from statistical models, molecular docking, and ADME/Tox propertie [Internet]. Structural Chemistry. 2021 ; 32 2019–2033.[citado 2026 abr. 02 ] Available from: https://doi.org/10.1007/s11224-021-01742-w -
Vancouver
Silva AP da, Chiari LPA, Guimaraes AR, Honorio KM, Silva ABF da. New D2R partial agonist candidates:: an in silico approach from statistical models, molecular docking, and ADME/Tox propertie [Internet]. Structural Chemistry. 2021 ; 32 2019–2033.[citado 2026 abr. 02 ] Available from: https://doi.org/10.1007/s11224-021-01742-w - Drug design of new 5-HT6R antagonists aided by artificial neural networks
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