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

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  • Artigo de periodico-carta/editorial

    Enhancing coarse-grained models through machine learning. [Editorial] (2024)

    Karmakar, Tarak; Soares, Thereza Amélia ; Merz Jr, Kenneth M

    Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP

    Subjects: APRENDIZADO COMPUTACIONAL, SIMULAÇÃO, MODELAGEM MOLECULAR, NANOPARTÍCULAS

    Versão PublicadaAcesso à fonteDOIHow to cite
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    • ABNT

      KARMAKAR, Tarak e SOARES, Thereza Amélia e MERZ JR, Kenneth M. Enhancing coarse-grained models through machine learning. [Editorial]. Journal of Chemical Information and Modeling. Washington: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://doi.org/10.1021/acs.jcim.4c00537. Acesso em: 09 nov. 2025. , 2024

    • APA

      Karmakar, T., Soares, T. A., & Merz Jr, K. M. (2024). Enhancing coarse-grained models through machine learning. [Editorial]. Journal of Chemical Information and Modeling. Washington: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. doi:10.1021/acs.jcim.4c00537

    • NLM

      Karmakar T, Soares TA, Merz Jr KM. Enhancing coarse-grained models through machine learning. [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2024 ; 64( 8): 2931-2932.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.4c00537

    • Vancouver

      Karmakar T, Soares TA, Merz Jr KM. Enhancing coarse-grained models through machine learning. [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2024 ; 64( 8): 2931-2932.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.4c00537

  • Artigo de periodico

    In silico infrared characterization of synthetic cannabinoids by quantum chemistry and chemometrics (2020)

    Castro, Jade Simões de; Rodrigues, Caio Henrique Pinke ; Bruni, Aline Thaís

    Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP

    Subjects: CANABINOIDES, SIMULAÇÃO, TRANSTORNOS RELACIONADOS AO USO DE SUBSTÂNCIAS

    PrivadoAcesso à fonteDOIHow to cite
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    • ABNT

      CASTRO, Jade Simões de e RODRIGUES, Caio Henrique Pinke e BRUNI, Aline Thaís. In silico infrared characterization of synthetic cannabinoids by quantum chemistry and chemometrics. Journal of Chemical Information and Modeling, v. 60, n. 4, p. 2100-2114, 2020Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.9b00871. Acesso em: 09 nov. 2025.

    • APA

      Castro, J. S. de, Rodrigues, C. H. P., & Bruni, A. T. (2020). In silico infrared characterization of synthetic cannabinoids by quantum chemistry and chemometrics. Journal of Chemical Information and Modeling, 60( 4), 2100-2114. doi:10.1021/acs.jcim.9b00871

    • NLM

      Castro JS de, Rodrigues CHP, Bruni AT. In silico infrared characterization of synthetic cannabinoids by quantum chemistry and chemometrics [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 4): 2100-2114.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.9b00871

    • Vancouver

      Castro JS de, Rodrigues CHP, Bruni AT. In silico infrared characterization of synthetic cannabinoids by quantum chemistry and chemometrics [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 4): 2100-2114.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.9b00871
  • 1

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