Filtros : "Journal of Chemical Information and Modeling" "Financiado pelo CNPq" Limpar

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

    Assuntos: QUÍMICA QUÂNTICA, MINERAÇÃO DE DADOS, FRAMEWORKS

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    • ABNT

      MUCELINI, Johnatan et al. Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters. Journal of Chemical Information and Modeling, v. 61, p. 1125-1135, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.0c01267. Acesso em: 09 nov. 2025.
    • APA

      Mucelini, J., Quiles, M. G., Prati, R. C., & Silva, J. L. F. da. (2021). Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters. Journal of Chemical Information and Modeling, 61, 1125-1135. doi:10.1021/acs.jcim.0c01267
    • NLM

      Mucelini J, Quiles MG, Prati RC, Silva JLF da. Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 1125-1135.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.0c01267
    • Vancouver

      Mucelini J, Quiles MG, Prati RC, Silva JLF da. Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 1125-1135.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.0c01267
  • Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC

    Assunto: QUÍMICA MÉDICA

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    • ABNT

      COSTA, Clauber H. S et al. Evaluating QM/MM Free Energy Surfaces for Ranking Cysteine Protease Covalent Inhibitors. Journal of Chemical Information and Modeling, p. 880-889, 2020Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.9b00847. Acesso em: 09 nov. 2025.
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      Costa, C. H. S., Bonatto, V., Santos, A. M. dos, Lameira, J., Leitão, A., & Montanari, C. A. (2020). Evaluating QM/MM Free Energy Surfaces for Ranking Cysteine Protease Covalent Inhibitors. Journal of Chemical Information and Modeling, 880-889. doi:10.1021/acs.jcim.9b00847
    • NLM

      Costa CHS, Bonatto V, Santos AM dos, Lameira J, Leitão A, Montanari CA. Evaluating QM/MM Free Energy Surfaces for Ranking Cysteine Protease Covalent Inhibitors [Internet]. Journal of Chemical Information and Modeling. 2020 ; 880-889.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.9b00847
    • Vancouver

      Costa CHS, Bonatto V, Santos AM dos, Lameira J, Leitão A, Montanari CA. Evaluating QM/MM Free Energy Surfaces for Ranking Cysteine Protease Covalent Inhibitors [Internet]. Journal of Chemical Information and Modeling. 2020 ; 880-889.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.9b00847
  • Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC

    Assunto: DOENÇA DE CHAGAS

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    • ABNT

      SILVA, JoséRogerio A. et al. Assessment of the Cruzain Cysteine Protease Reversible and Irreversible Covalent Inhibition Mechanism. Journal of Chemical Information and Modeling, v. 60, n. 3, p. 1666-1677, 2020Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.9b01138. Acesso em: 09 nov. 2025.
    • APA

      Silva, J. R. A., Cianni, L., Araujo, D., Batista, P. H. J., Vita, D. de, Rosini, F., et al. (2020). Assessment of the Cruzain Cysteine Protease Reversible and Irreversible Covalent Inhibition Mechanism. Journal of Chemical Information and Modeling, 60( 3), 1666-1677. doi:10.1021/acs.jcim.9b01138
    • NLM

      Silva JRA, Cianni L, Araujo D, Batista PHJ, Vita D de, Rosini F, Leitão A, Lameira J, Montanari CA. Assessment of the Cruzain Cysteine Protease Reversible and Irreversible Covalent Inhibition Mechanism [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 3): 1666-1677.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.9b01138
    • Vancouver

      Silva JRA, Cianni L, Araujo D, Batista PHJ, Vita D de, Rosini F, Leitão A, Lameira J, Montanari CA. Assessment of the Cruzain Cysteine Protease Reversible and Irreversible Covalent Inhibition Mechanism [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 3): 1666-1677.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.9b01138
  • Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC

    Assunto: CLUSTERS

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    • ABNT

      BATISTA, Krys Elly de Araújo et al. Ab Initio Investigation of CO2 Adsorption on 13-Atom 4d Clusters. Journal of Chemical Information and Modeling, v. 60, p. 537-545, 2020Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.9b00792. Acesso em: 09 nov. 2025.
    • APA

      Batista, K. E. de A., Restrepo, V. k, Soares, M. D., Quiles, M. G., Piotrowski, M. J., & Silva, J. L. F. da. (2020). Ab Initio Investigation of CO2 Adsorption on 13-Atom 4d Clusters. Journal of Chemical Information and Modeling, 60, 537-545. doi:10.1021/acs.jcim.9b00792
    • NLM

      Batista KE de A, Restrepo V k, Soares MD, Quiles MG, Piotrowski MJ, Silva JLF da. Ab Initio Investigation of CO2 Adsorption on 13-Atom 4d Clusters [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60 537-545.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.9b00792
    • Vancouver

      Batista KE de A, Restrepo V k, Soares MD, Quiles MG, Piotrowski MJ, Silva JLF da. Ab Initio Investigation of CO2 Adsorption on 13-Atom 4d Clusters [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60 537-545.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.9b00792
  • Fonte: Journal of Chemical Information and Modeling. Unidade: IFSC

    Assuntos: TRIPANOSSOMICIDAS, PLANEJAMENTO DE FÁRMACOS, INIBIDORES QUÍMICOS

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      SOUZA, Mariana Laureano de et al. Discovery of potent, reversible, and competitive cruzain inhibitors with trypanocidal activity: a structure-based drug design approach. Journal of Chemical Information and Modeling, v. 60, n. 2, p. 1028-1041, 2020Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.9b00802. Acesso em: 09 nov. 2025.
    • APA

      Souza, M. L. de, Rezende Junior, C. de O., Ferreira, R. S., Chávez, R. M. E., Ferreira, L. L. G., Slafer, B. W., et al. (2020). Discovery of potent, reversible, and competitive cruzain inhibitors with trypanocidal activity: a structure-based drug design approach. Journal of Chemical Information and Modeling, 60( 2), 1028-1041. doi:10.1021/acs.jcim.9b00802
    • NLM

      Souza ML de, Rezende Junior C de O, Ferreira RS, Chávez RME, Ferreira LLG, Slafer BW, Magalhães LG, Andricopulo RK, Oliva G, Cruz FC, Dias LC, Andricopulo AD. Discovery of potent, reversible, and competitive cruzain inhibitors with trypanocidal activity: a structure-based drug design approach [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 2): 1028-1041.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.9b00802
    • Vancouver

      Souza ML de, Rezende Junior C de O, Ferreira RS, Chávez RME, Ferreira LLG, Slafer BW, Magalhães LG, Andricopulo RK, Oliva G, Cruz FC, Dias LC, Andricopulo AD. Discovery of potent, reversible, and competitive cruzain inhibitors with trypanocidal activity: a structure-based drug design approach [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 2): 1028-1041.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.9b00802
  • Fonte: Journal of Chemical Information and Modeling. Unidade: IFSC

    Assuntos: ANTINEOPLÁSICOS, PRODUTOS NATURAIS

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    • ABNT

      SALDÍVAR-GONZÁLEZ, Fernanda I. et al. Chemical space and diversity of the NuBBE database: a chemoinformatic characterization. Journal of Chemical Information and Modeling, v. 59, n. Ja 2019, p. 74-85, 2019Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.8b00619. Acesso em: 09 nov. 2025.
    • APA

      Saldívar-González, F. I., Valli, M., Andricopulo, A. D., Bolzani, V. S., & Medina-Franco, J. L. (2019). Chemical space and diversity of the NuBBE database: a chemoinformatic characterization. Journal of Chemical Information and Modeling, 59( Ja 2019), 74-85. doi:10.1021/acs.jcim.8b00619
    • NLM

      Saldívar-González FI, Valli M, Andricopulo AD, Bolzani VS, Medina-Franco JL. Chemical space and diversity of the NuBBE database: a chemoinformatic characterization [Internet]. Journal of Chemical Information and Modeling. 2019 ; 59( Ja 2019): 74-85.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.8b00619
    • Vancouver

      Saldívar-González FI, Valli M, Andricopulo AD, Bolzani VS, Medina-Franco JL. Chemical space and diversity of the NuBBE database: a chemoinformatic characterization [Internet]. Journal of Chemical Information and Modeling. 2019 ; 59( Ja 2019): 74-85.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.8b00619
  • Fonte: Journal of Chemical Information and Modeling. Unidade: IF

    Assuntos: FLUÍDOS COMPLEXOS, ENDOCITOSE, MEMBRANAS (BIOLOGIA), BIOTECNOLOGIA

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    • ABNT

      SANTOS, Denys E. S. et al. SuAVE: A Tool for Analyzing Curvature-Dependent Properties in Chemical Interfaces. Journal of Chemical Information and Modeling, 2019Tradução . . Disponível em: https://doi-org.ez67.periodicos.capes.gov.br/10.1021/acs.jcim.9b00569. Acesso em: 09 nov. 2025.
    • APA

      Santos, D. E. S., Pontes, F. J. S., Lins, R. D., Coutinho, K. R., & Soares, T. A. (2019). SuAVE: A Tool for Analyzing Curvature-Dependent Properties in Chemical Interfaces. Journal of Chemical Information and Modeling. doi:10.1021/acs.jcim.9b00569
    • NLM

      Santos DES, Pontes FJS, Lins RD, Coutinho KR, Soares TA. SuAVE: A Tool for Analyzing Curvature-Dependent Properties in Chemical Interfaces [Internet]. Journal of Chemical Information and Modeling. 2019 ;[citado 2025 nov. 09 ] Available from: https://doi-org.ez67.periodicos.capes.gov.br/10.1021/acs.jcim.9b00569
    • Vancouver

      Santos DES, Pontes FJS, Lins RD, Coutinho KR, Soares TA. SuAVE: A Tool for Analyzing Curvature-Dependent Properties in Chemical Interfaces [Internet]. Journal of Chemical Information and Modeling. 2019 ;[citado 2025 nov. 09 ] Available from: https://doi-org.ez67.periodicos.capes.gov.br/10.1021/acs.jcim.9b00569
  • Fonte: Journal of Chemical Information and Modeling. Unidade: FCFRP

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

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      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

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