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Filtros : "Financiamento FAPESP" "Journal of Chemical Information and Modeling" "INTELIGÊNCIA ARTIFICIAL" Limpar

Filtros

Artigo de periodico
Fuzz testing molecular representation using deep variational anomaly generation (2025)
Nogueira, Victor Henrique Rabesquine ; Sharma, Rishabh; Guido, Rafael Victorio Carvalho ; Keiser, Michael James
Source: Journal of Chemical Information and Modeling. Unidade: IFSC
Subjects: INTELIGÊNCIA ARTIFICIAL, MODELAGEM MOLECULAR, MOLÉCULA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
NOGUEIRA, Victor Henrique Rabesquine et al. Fuzz testing molecular representation using deep variational anomaly generation. Journal of Chemical Information and Modeling, v. 65, n. 4, p. 1911-1927 + supporting information, 2025Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.4c01876. Acesso em: 08 out. 2025.
APA
Nogueira, V. H. R., Sharma, R., Guido, R. V. C., & Keiser, M. J. (2025). Fuzz testing molecular representation using deep variational anomaly generation. Journal of Chemical Information and Modeling, 65( 4), 1911-1927 + supporting information. doi:10.1021/acs.jcim.4c01876
NLM
Nogueira VHR, Sharma R, Guido RVC, Keiser MJ. Fuzz testing molecular representation using deep variational anomaly generation [Internet]. Journal of Chemical Information and Modeling. 2025 ; 65( 4): 1911-1927 + supporting information.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.4c01876
Vancouver
Nogueira VHR, Sharma R, Guido RVC, Keiser MJ. Fuzz testing molecular representation using deep variational anomaly generation [Internet]. Journal of Chemical Information and Modeling. 2025 ; 65( 4): 1911-1927 + supporting information.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.4c01876
Artigo de periodico
Quantum machine learning algorithms for drug discovery applications (2021)
Batra, Kushal; Zorn, Kimberley M.; Foil, Daniel H.; Minerali, Eni; Gawriljuk, Victor Oliveira; Lane, Thomas R.; Ekins, Sean
Source: Journal of Chemical Information and Modeling. Unidade: IFSC
Subjects: PLANEJAMENTO DE FÁRMACOS, COMPUTAÇÃO APLICADA, INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BATRA, Kushal et al. Quantum machine learning algorithms for drug discovery applications. Journal of Chemical Information and Modeling, v. 61, n. 6, p. 2641-2647, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.1c00166. Acesso em: 08 out. 2025.
APA
Batra, K., Zorn, K. M., Foil, D. H., Minerali, E., Gawriljuk, V. O., Lane, T. R., & Ekins, S. (2021). Quantum machine learning algorithms for drug discovery applications. Journal of Chemical Information and Modeling, 61( 6), 2641-2647. doi:10.1021/acs.jcim.1c00166
NLM
Batra K, Zorn KM, Foil DH, Minerali E, Gawriljuk VO, Lane TR, Ekins S. Quantum machine learning algorithms for drug discovery applications [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 6): 2641-2647.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c00166
Vancouver
Batra K, Zorn KM, Foil DH, Minerali E, Gawriljuk VO, Lane TR, Ekins S. Quantum machine learning algorithms for drug discovery applications [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 6): 2641-2647.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c00166

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