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Filtros : "Journal of Chemical Information and Modeling" "Espanha" Limpar

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  • Artigo de periodico

    Predicting the Relative Binding Affinity for Reversible Covalent Inhibitors by Free Energy Perturbation Calculations (2021)

    Bonatto, Vinícius ; Shamim, Anwar ; Rocho, Fernanda dos Reis ; Leitão, Andrei ; Luque, F. Javier; Montanari, Carlos Alberto

    Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC

    Assuntos: MEDICAMENTO, ENZIMAS

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    • ABNT

      BONATTO, Vinícius et al. Predicting the Relative Binding Affinity for Reversible Covalent Inhibitors by Free Energy Perturbation Calculations. Journal of Chemical Information and Modeling, v. 61, p. 4733−4744, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.1c00515. Acesso em: 09 nov. 2025.

    • APA

      Bonatto, V., Shamim, A., Rocho, F. dos R., Leitão, A., Luque, F. J., & Montanari, C. A. (2021). Predicting the Relative Binding Affinity for Reversible Covalent Inhibitors by Free Energy Perturbation Calculations. Journal of Chemical Information and Modeling, 61, 4733−4744. doi:10.1021/acs.jcim.1c00515

    • NLM

      Bonatto V, Shamim A, Rocho F dos R, Leitão A, Luque FJ, Montanari CA. Predicting the Relative Binding Affinity for Reversible Covalent Inhibitors by Free Energy Perturbation Calculations [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 4733−4744.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.1c00515

    • Vancouver

      Bonatto V, Shamim A, Rocho F dos R, Leitão A, Luque FJ, Montanari CA. Predicting the Relative Binding Affinity for Reversible Covalent Inhibitors by Free Energy Perturbation Calculations [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 4733−4744.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.1c00515

  • Artigo de periodico

    Experimental–computational study of carbon nanotube effects on mitochondrial respiration: in silico nano-QSPR machine learning models based on new raman spectra transform with markov–shannon entropy invariants (2017)

    González-Durruthy, Michael; Alberici, Luciane C.; Curti, Carlos; Naal, Zeki; Sawazaki, David Tatsuo A; Vázquez-Naya, José M.; González-Díaz, Humberto; Munteanu, Cristian R.

    Fonte: Journal of Chemical Information and Modeling. Unidade: FCFRP

    Assuntos: ENTROPIA, REGRESSÃO LINEAR, MITOCÔNDRIAS, NANOTUBOS DE CARBONO, BIOMEDICINA, OXIGÊNIO

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    • ABNT

      GONZÁLEZ-DURRUTHY, Michael et al. Experimental–computational study of carbon nanotube effects on mitochondrial respiration: in silico nano-QSPR machine learning models based on new raman spectra transform with markov–shannon entropy invariants. Journal of Chemical Information and Modeling, v. 57, n. 5, p. 1029-1044, 2017Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.6b00458. Acesso em: 09 nov. 2025.

    • APA

      González-Durruthy, M., Alberici, L. C., Curti, C., Naal, Z., Sawazaki, D. T. A., Vázquez-Naya, J. M., et al. (2017). Experimental–computational study of carbon nanotube effects on mitochondrial respiration: in silico nano-QSPR machine learning models based on new raman spectra transform with markov–shannon entropy invariants. Journal of Chemical Information and Modeling, 57( 5), 1029-1044. doi:10.1021/acs.jcim.6b00458

    • NLM

      González-Durruthy M, Alberici LC, Curti C, Naal Z, Sawazaki DTA, Vázquez-Naya JM, González-Díaz H, Munteanu CR. Experimental–computational study of carbon nanotube effects on mitochondrial respiration: in silico nano-QSPR machine learning models based on new raman spectra transform with markov–shannon entropy invariants [Internet]. Journal of Chemical Information and Modeling. 2017 ; 57( 5): 1029-1044.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.6b00458

    • Vancouver

      González-Durruthy M, Alberici LC, Curti C, Naal Z, Sawazaki DTA, Vázquez-Naya JM, González-Díaz H, Munteanu CR. Experimental–computational study of carbon nanotube effects on mitochondrial respiration: in silico nano-QSPR machine learning models based on new raman spectra transform with markov–shannon entropy invariants [Internet]. Journal of Chemical Information and Modeling. 2017 ; 57( 5): 1029-1044.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.6b00458
  • 1

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