New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists (2017)
- Authors:
- USP affiliated authors: HONORIO, KÁTHIA MARIA - EACH ; OLIVEIRA, PATRÍCIA RUFINO - EACH ; ROMERO, ROSELI APARECIDA FRANCELIN - ICMC ; SILVA, ALBÉRICO BORGES FERREIRA DA - IQSC
- Unidades: EACH; ICMC; IQSC
- DOI: 10.1007/s00894-017-3444-3
- Subjects: FARMACOLOGIA MOLECULAR; RECEPTORES; MODELOS (ANÁLISE MULTIVARIADA); REDES NEURAIS
- Keywords: Sigma-1 receptor; 1-arylpyrazole; QSAR; PLS; MLP-ANN; Consensus modeling
- Language: Inglês
- Imprenta:
- Source:
- Título: Journal of Molecular Modeling
- ISSN: 1610-2940
- Volume/Número/Paginação/Ano: v. 23, p. 1-15, Oct. 2017
- Status:
- Artigo possui versão em acesso aberto em repositório (Green Open Access)
- Versão do Documento:
- Versão submetida (Pré-print)
- Acessar versão aberta:
-
ABNT
OLIVEIRA, Aline A et al. New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists. Journal of Molecular Modeling, v. 23, p. 1-15, 2017Tradução . . Disponível em: https://doi.org/10.1007/s00894-017-3444-3. Acesso em: 01 abr. 2026. -
APA
Oliveira, A. A., Lipinski, C. F., Pereira, E. B., Honorio, K. M., Oliveira, P. R., Weber, K. C., et al. (2017). New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists. Journal of Molecular Modeling, 23, 1-15. doi:10.1007/s00894-017-3444-3 -
NLM
Oliveira AA, Lipinski CF, Pereira EB, Honorio KM, Oliveira PR, Weber KC, Romero RAF, Sousa AG de, Silva ABF da. New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists [Internet]. Journal of Molecular Modeling. 2017 ; 23 1-15.[citado 2026 abr. 01 ] Available from: https://doi.org/10.1007/s00894-017-3444-3 -
Vancouver
Oliveira AA, Lipinski CF, Pereira EB, Honorio KM, Oliveira PR, Weber KC, Romero RAF, Sousa AG de, Silva ABF da. New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists [Internet]. Journal of Molecular Modeling. 2017 ; 23 1-15.[citado 2026 abr. 01 ] Available from: https://doi.org/10.1007/s00894-017-3444-3 - Machine learning techniques and drug design
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