Santos, Genisson R.; Chiari, Laise Pellegrini Alencar 
; Silva, Aldineia P. da; Lipinski, Célio Fernando ; Oliveira, Aline A.; Honório, Káthia Maria ; Sousa, Alexsandro Gama de; Silva, Alberico Borges Ferreira da
Fonte: Journal of Molecular Modeling: computational chemistry - life science - advanced materials - new methods. Unidades: EACH, IQSC
Assuntos: DEPRESSÃO, ANTIDEPRESSIVOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Genisson R. et al. A partial least squares and artifcial neural network study for a series of arylpiperazines as antidepressant agents. Journal of Molecular Modeling: computational chemistry - life science - advanced materials - new methods, p. 297, 2021Tradução . . Disponível em: https://doi.org/10.1007/s00894-021-04906-x. Acesso em: 19 abr. 2024.APA
Santos, G. R., Chiari, L. P. A., Silva, A. P. da, Lipinski, C. F., Oliveira, A. A., Honório, K. M., et al. (2021). A partial least squares and artifcial neural network study for a series of arylpiperazines as antidepressant agents. Journal of Molecular Modeling: computational chemistry - life science - advanced materials - new methods, 297. doi:10.1007/s00894-021-04906-xNLM
Santos GR, Chiari LPA, Silva AP da, Lipinski CF, Oliveira AA, Honório KM, Sousa AG de, Silva ABF da. A partial least squares and artifcial neural network study for a series of arylpiperazines as antidepressant agents [Internet]. Journal of Molecular Modeling: computational chemistry - life science - advanced materials - new methods. 2021 ;297.[citado 2024 abr. 19 ] Available from: https://doi.org/10.1007/s00894-021-04906-xVancouver
Santos GR, Chiari LPA, Silva AP da, Lipinski CF, Oliveira AA, Honório KM, Sousa AG de, Silva ABF da. A partial least squares and artifcial neural network study for a series of arylpiperazines as antidepressant agents [Internet]. Journal of Molecular Modeling: computational chemistry - life science - advanced materials - new methods. 2021 ;297.[citado 2024 abr. 19 ] Available from: https://doi.org/10.1007/s00894-021-04906-x
