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
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: green
- Licença: other-oa
-
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: 27 dez. 2025. -
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 2025 dez. 27 ] 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 2025 dez. 27 ] Available from: https://doi.org/10.1007/s00894-017-3444-3 - Machine learning techniques and drug design
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Informações sobre o DOI: 10.1007/s00894-017-3444-3 (Fonte: oaDOI API)
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