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  • Source: Journal of Chemical Information and Modeling. Unidade: IQ

    Subjects: MECÂNICA QUÂNTICA, OXIDAÇÃO, REDUÇÃO

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      ARANTES, Guilherme Menegon. Redox activated proton transfer through a redundant network in the Qo site of Cytochrome bc1. Journal of Chemical Information and Modeling, v. 65, n. 5, p. 2660−2669, 2025Tradução . . Disponível em: https://dx.doi.org/10.1021/acs.jcim.4c02361. Acesso em: 08 out. 2025.
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      Arantes, G. M. (2025). Redox activated proton transfer through a redundant network in the Qo site of Cytochrome bc1. Journal of Chemical Information and Modeling, 65( 5), 2660−2669. doi:10.1021/acs.jcim.4c02361
    • NLM

      Arantes GM. Redox activated proton transfer through a redundant network in the Qo site of Cytochrome bc1 [Internet]. Journal of Chemical Information and Modeling. 2025 ; 65( 5): 2660−2669.[citado 2025 out. 08 ] Available from: https://dx.doi.org/10.1021/acs.jcim.4c02361
    • Vancouver

      Arantes GM. Redox activated proton transfer through a redundant network in the Qo site of Cytochrome bc1 [Internet]. Journal of Chemical Information and Modeling. 2025 ; 65( 5): 2660−2669.[citado 2025 out. 08 ] Available from: https://dx.doi.org/10.1021/acs.jcim.4c02361
  • Source: Journal of Chemical Information and Modeling. Unidade: IQ

    Subjects: BIOQUÍMICA INORGÂNICA, MOLÉCULA, PEPTÍDEOS, PROTEÍNAS

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      CAMILO, Sofia Rodrigues Guedes e ARANTES, Guilherme Menegon. Flexibility and hydration of the Qo site determine multiple pathways for proton transfer in cytochrome bc1. Journal of Chemical Information and Modeling, v. 65, n. 12, p. 6184-6197, 2025Tradução . . Disponível em: https://dx.doi.org/10.1021/acs.jcim.5c00655. Acesso em: 08 out. 2025.
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      Camilo, S. R. G., & Arantes, G. M. (2025). Flexibility and hydration of the Qo site determine multiple pathways for proton transfer in cytochrome bc1. Journal of Chemical Information and Modeling, 65( 12), 6184-6197. doi:10.1021/acs.jcim.5c00655
    • NLM

      Camilo SRG, Arantes GM. Flexibility and hydration of the Qo site determine multiple pathways for proton transfer in cytochrome bc1 [Internet]. Journal of Chemical Information and Modeling. 2025 ; 65( 12): 6184-6197.[citado 2025 out. 08 ] Available from: https://dx.doi.org/10.1021/acs.jcim.5c00655
    • Vancouver

      Camilo SRG, Arantes GM. Flexibility and hydration of the Qo site determine multiple pathways for proton transfer in cytochrome bc1 [Internet]. Journal of Chemical Information and Modeling. 2025 ; 65( 12): 6184-6197.[citado 2025 out. 08 ] Available from: https://dx.doi.org/10.1021/acs.jcim.5c00655
  • Source: Journal of Chemical Information and Modeling. Unidade: IFSC

    Subjects: INTELIGÊNCIA ARTIFICIAL, MODELAGEM MOLECULAR, MOLÉCULA

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      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.
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      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
  • Source: Journal of Chemical Information and Modeling. Unidade: IFSC

    Subjects: BACTÉRIAS, AGENTES ANTIMICROBIANOS, CRISTALOGRAFIA

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      FERNANDES, Philipe Oliveira et al. Machine learning-based virtual screening of antibacterial agents against methicillin-susceptible and resistant staphylococcus aureus. Journal of Chemical Information and Modeling, v. 64, n. 6, p. 1932-1944 + supporting information, 2024Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.4c00087. Acesso em: 08 out. 2025.
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      Fernandes, P. O., Dias, A. L. T., Santos Júnior, V. S. dos, Serafim, M. S. M., Sousa, Y. V., Monteiro, G. C., et al. (2024). Machine learning-based virtual screening of antibacterial agents against methicillin-susceptible and resistant staphylococcus aureus. Journal of Chemical Information and Modeling, 64( 6), 1932-1944 + supporting information. doi:10.1021/acs.jcim.4c00087
    • NLM

      Fernandes PO, Dias ALT, Santos Júnior VS dos, Serafim MSM, Sousa YV, Monteiro GC, Coutinho ID, Valli M, Verzola MMSA, Ottoni FM, Pádua RM de, Oda FB, Santos AG dos, Andricopulo AD, Bolzani V da S, Mota BEF, Alves RJ, Oliveira RB de, Kronenberger T, Maltarollo VG. Machine learning-based virtual screening of antibacterial agents against methicillin-susceptible and resistant staphylococcus aureus [Internet]. Journal of Chemical Information and Modeling. 2024 ; 64( 6): 1932-1944 + supporting information.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.4c00087
    • Vancouver

      Fernandes PO, Dias ALT, Santos Júnior VS dos, Serafim MSM, Sousa YV, Monteiro GC, Coutinho ID, Valli M, Verzola MMSA, Ottoni FM, Pádua RM de, Oda FB, Santos AG dos, Andricopulo AD, Bolzani V da S, Mota BEF, Alves RJ, Oliveira RB de, Kronenberger T, Maltarollo VG. Machine learning-based virtual screening of antibacterial agents against methicillin-susceptible and resistant staphylococcus aureus [Internet]. Journal of Chemical Information and Modeling. 2024 ; 64( 6): 1932-1944 + supporting information.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.4c00087
  • Source: Journal of Chemical Information and Modeling. Unidade: IFSC

    Subjects: PRODUTOS NATURAIS, FÁRMACOS, CRISTALOGRAFIA

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      GARCIA, Alejandro Gomez et al. Latin american natural product database (LANaPDB): an update. Journal of Chemical Information and Modeling, v. No 2024, n. 22, p. 8495-8509, 2024Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.4c01560. Acesso em: 08 out. 2025.
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      Garcia, A. G., Jiménez, D. A. A., Zamora, W. J., Ccahuana, H. L. B., Fumagalli, M. A. C., Valli, M., et al. (2024). Latin american natural product database (LANaPDB): an update. Journal of Chemical Information and Modeling, No 2024( 22), 8495-8509. doi:10.1021/acs.jcim.4c01560
    • NLM

      Garcia AG, Jiménez DAA, Zamora WJ, Ccahuana HLB, Fumagalli MAC, Valli M, Andricopulo AD, Bolzani V da S, Olmedo DA, Solis PN, Nuñez MJ, Pérez JRR, Sánchez HAV, Hernández HFC, Martinez OMM, Franco JLM. Latin american natural product database (LANaPDB): an update [Internet]. Journal of Chemical Information and Modeling. 2024 ; No 2024( 22): 8495-8509.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.4c01560
    • Vancouver

      Garcia AG, Jiménez DAA, Zamora WJ, Ccahuana HLB, Fumagalli MAC, Valli M, Andricopulo AD, Bolzani V da S, Olmedo DA, Solis PN, Nuñez MJ, Pérez JRR, Sánchez HAV, Hernández HFC, Martinez OMM, Franco JLM. Latin american natural product database (LANaPDB): an update [Internet]. Journal of Chemical Information and Modeling. 2024 ; No 2024( 22): 8495-8509.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.4c01560
  • Source: Journal of Chemical Information and Modeling. Unidade: IQSC

    Subjects: ENERGIA, QUÍMICA TEÓRICA

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      GONZÁLEZ, José E. et al. Decoding Van der Waals Impact on Chirality Transfer in Perovskite Structures: Density Functional Theory Insights. Journal of Chemical Information and Modeling, v. 64, n. 4, p. 1306–1318, 2024Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.3c01895. Acesso em: 08 out. 2025.
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      González, J. E., Besse, R., Lima, M. P., & Silva, J. L. F. da. (2024). Decoding Van der Waals Impact on Chirality Transfer in Perovskite Structures: Density Functional Theory Insights. Journal of Chemical Information and Modeling, 64( 4), 1306–1318. doi:10.1021/acs.jcim.3c01895
    • NLM

      González JE, Besse R, Lima MP, Silva JLF da. Decoding Van der Waals Impact on Chirality Transfer in Perovskite Structures: Density Functional Theory Insights [Internet]. Journal of Chemical Information and Modeling. 2024 ;64( 4): 1306–1318.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.3c01895
    • Vancouver

      González JE, Besse R, Lima MP, Silva JLF da. Decoding Van der Waals Impact on Chirality Transfer in Perovskite Structures: Density Functional Theory Insights [Internet]. Journal of Chemical Information and Modeling. 2024 ;64( 4): 1306–1318.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.3c01895
  • Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP

    Subjects: APRENDIZADO COMPUTACIONAL, SIMULAÇÃO, MODELAGEM MOLECULAR, NANOPARTÍCULAS

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      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: 08 out. 2025. , 2024
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      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 out. 08 ] 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 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.4c00537
  • Source: Journal of Chemical Information and Modeling. Unidades: IQSC, FFCLRP

    Subjects: BIOENGENHARIA, BIOTECNOLOGIA, BIOLOGIA, MATERIAIS

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      PRATI, Ronaldo C. et al. The Impact of Interdisciplinary, Gender and Geographic Distributions on the Citation Patterns of the Journal of Chemical Information and Modeling. Journal of Chemical Information and Modeling, v. 64, n. 4, p. 1107–1111, 2024Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.3c02014. Acesso em: 08 out. 2025.
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      Prati, R. C., Rodrigues, B. S. M., Aragão, I., Silva, T. A. S. da, Quiles, M. G., & Silva, J. L. F. da. (2024). The Impact of Interdisciplinary, Gender and Geographic Distributions on the Citation Patterns of the Journal of Chemical Information and Modeling. Journal of Chemical Information and Modeling, 64( 4), 1107–1111. doi:10.1021/acs.jcim.3c02014
    • NLM

      Prati RC, Rodrigues BSM, Aragão I, Silva TAS da, Quiles MG, Silva JLF da. The Impact of Interdisciplinary, Gender and Geographic Distributions on the Citation Patterns of the Journal of Chemical Information and Modeling [Internet]. Journal of Chemical Information and Modeling. 2024 ;64( 4): 1107–1111.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.3c02014
    • Vancouver

      Prati RC, Rodrigues BSM, Aragão I, Silva TAS da, Quiles MG, Silva JLF da. The Impact of Interdisciplinary, Gender and Geographic Distributions on the Citation Patterns of the Journal of Chemical Information and Modeling [Internet]. Journal of Chemical Information and Modeling. 2024 ;64( 4): 1107–1111.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.3c02014
  • Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP

    Subjects: QUÍMICA, REPLICAÇÃO DO DNA, GENÔMICA, ÁCIDOS NUCLEICOS

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      PALERMO, Giulia e SOARES, Thereza A. Editing DNA and RNA through Computations [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.3c01824. Acesso em: 08 out. 2025. , 2023
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      Palermo, G., & Soares, T. A. (2023). Editing DNA and RNA through Computations [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.3c01824
    • NLM

      Palermo G, Soares TA. Editing DNA and RNA through Computations [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2023 ; 63( 24): 7603-7604.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.3c01824
    • Vancouver

      Palermo G, Soares TA. Editing DNA and RNA through Computations [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2023 ; 63( 24): 7603-7604.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.3c01824
  • Source: Journal of Chemical Information and Modeling. Unidade: IQ

    Subjects: PROTEÍNAS, PEPTÍDEOS

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      CURTOLO, Felipe e ARANTES, Guilherme Menegon. Dissecting reaction mechanisms and catalytic contributions in Flavoprotein fumarate Reductases. Journal of Chemical Information and Modeling, v. 63, p. 3510−3520, 2023Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.3c00292. Acesso em: 08 out. 2025.
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      Curtolo, F., & Arantes, G. M. (2023). Dissecting reaction mechanisms and catalytic contributions in Flavoprotein fumarate Reductases. Journal of Chemical Information and Modeling, 63, 3510−3520. doi:10.1021/acs.jcim.3c00292
    • NLM

      Curtolo F, Arantes GM. Dissecting reaction mechanisms and catalytic contributions in Flavoprotein fumarate Reductases [Internet]. Journal of Chemical Information and Modeling. 2023 ; 63 3510−3520.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.3c00292
    • Vancouver

      Curtolo F, Arantes GM. Dissecting reaction mechanisms and catalytic contributions in Flavoprotein fumarate Reductases [Internet]. Journal of Chemical Information and Modeling. 2023 ; 63 3510−3520.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.3c00292
  • Source: Journal of Chemical Information and Modeling. Unidade: IQ

    Subjects: ESTRUTURA QUÍMICA, PROTEÍNAS

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      VIVIANI, Lucas Gasparello et al. Molecular dynamics simulations of the human ecto-5′-nucleotidase (h-ecto-5′-NT, CD73): insights into protein flexibility and binding site dynamics. Journal of Chemical Information and Modeling, v. 63, n. 15, p. 4691-4707, 2023Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.3c01068. Acesso em: 08 out. 2025.
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      Viviani, L. G., Kokh, D. B., Wade, R. C., & Amaral, A. T. do. (2023). Molecular dynamics simulations of the human ecto-5′-nucleotidase (h-ecto-5′-NT, CD73): insights into protein flexibility and binding site dynamics. Journal of Chemical Information and Modeling, 63( 15), 4691-4707. doi:10.1021/acs.jcim.3c01068
    • NLM

      Viviani LG, Kokh DB, Wade RC, Amaral AT do. Molecular dynamics simulations of the human ecto-5′-nucleotidase (h-ecto-5′-NT, CD73): insights into protein flexibility and binding site dynamics [Internet]. Journal of Chemical Information and Modeling. 2023 ; 63( 15): 4691-4707.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.3c01068
    • Vancouver

      Viviani LG, Kokh DB, Wade RC, Amaral AT do. Molecular dynamics simulations of the human ecto-5′-nucleotidase (h-ecto-5′-NT, CD73): insights into protein flexibility and binding site dynamics [Internet]. Journal of Chemical Information and Modeling. 2023 ; 63( 15): 4691-4707.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.3c01068
  • Source: Journal of Chemical Information and Modeling. Unidades: IFSC, ICMC

    Subjects: ALGORITMOS, APRENDIZADO COMPUTACIONAL, GENÔMICA

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      OLIVEIRA JUNIOR, Osvaldo Novais de et al. Artificial intelligence agents for materials sciences. Journal of Chemical Information and Modeling, v. 63, n. 24, p. 7605-7609, 2023Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.3c01778. Acesso em: 08 out. 2025.
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      Oliveira Junior, O. N. de, Christino, L. M. F., Oliveira, M. C. F. de, & Paulovich, F. V. (2023). Artificial intelligence agents for materials sciences. Journal of Chemical Information and Modeling, 63( 24), 7605-7609. doi:10.1021/acs.jcim.3c01778
    • NLM

      Oliveira Junior ON de, Christino LMF, Oliveira MCF de, Paulovich FV. Artificial intelligence agents for materials sciences [Internet]. Journal of Chemical Information and Modeling. 2023 ; 63( 24): 7605-7609.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.3c01778
    • Vancouver

      Oliveira Junior ON de, Christino LMF, Oliveira MCF de, Paulovich FV. Artificial intelligence agents for materials sciences [Internet]. Journal of Chemical Information and Modeling. 2023 ; 63( 24): 7605-7609.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.3c01778
  • Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP

    Subjects: PRECONCEITO, PESQUISA CIENTÍFICA

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      CASCELLA, Michele e SILVA, Thereza Amélia Soares da. Bias amplification in gender, gender identity, and geographical affiliation. Journal of Chemical Information and Modeling, v. 62, n. 24, p. 6297-6301, 2022Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.2c00533. Acesso em: 08 out. 2025.
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      Cascella, M., & Silva, T. A. S. da. (2022). Bias amplification in gender, gender identity, and geographical affiliation. Journal of Chemical Information and Modeling, 62( 24), 6297-6301. doi:10.1021/acs.jcim.2c00533
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      Cascella M, Silva TAS da. Bias amplification in gender, gender identity, and geographical affiliation [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 24): 6297-6301.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00533
    • Vancouver

      Cascella M, Silva TAS da. Bias amplification in gender, gender identity, and geographical affiliation [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 24): 6297-6301.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00533
  • Source: Journal of Chemical Information and Modeling. Unidades: IF, FFCLRP

    Subjects: FÍSICO-QUÍMICA, FÍSICA MOLECULAR, SOFTWARE ESTATÍSTICO PARA MICROCOMPUTADORES, LIPÍDEOS DA MEMBRANA

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      SANTOS, Denys e COUTINHO, Kaline Rabelo e SILVA, Thereza Amélia Soares da. Surface Assessment via Grid Evaluation (SuAVE) for Every Surface Curvature and Cavity Shape. Journal of Chemical Information and Modeling, v. 62, n. 19, p. 4690-4701, 2022Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.2c00673. Acesso em: 08 out. 2025.
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      Santos, D., Coutinho, K. R., & Silva, T. A. S. da. (2022). Surface Assessment via Grid Evaluation (SuAVE) for Every Surface Curvature and Cavity Shape. Journal of Chemical Information and Modeling, 62( 19), 4690-4701. doi:10.1021/acs.jcim.2c00673
    • NLM

      Santos D, Coutinho KR, Silva TAS da. Surface Assessment via Grid Evaluation (SuAVE) for Every Surface Curvature and Cavity Shape [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 19): 4690-4701.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00673
    • Vancouver

      Santos D, Coutinho KR, Silva TAS da. Surface Assessment via Grid Evaluation (SuAVE) for Every Surface Curvature and Cavity Shape [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 19): 4690-4701.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00673
  • Source: Journal of Chemical Information and Modeling. Unidade: IQSC

    Subjects: AMINOÁCIDOS, MECÂNICA QUÂNTICA, ENERGIA

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      SANTOS, Alberto M. Dos et al. Assessment of Reversibility for Covalent Cysteine Protease Inhibitors Using Quantum Mechanics/Molecular Mechanics Free Energy Surfaces. Journal of Chemical Information and Modeling, v. 62, p. 4083-4094, 2022Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.2c00466. Acesso em: 08 out. 2025.
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      Santos, A. M. D., Oliveira, A. R. S., Costa, C. H. S. da, Kenny, P. W., Montanari, C. A., Varela Júnior, J. de J. G., & Lameira, J. (2022). Assessment of Reversibility for Covalent Cysteine Protease Inhibitors Using Quantum Mechanics/Molecular Mechanics Free Energy Surfaces. Journal of Chemical Information and Modeling, 62, 4083-4094. doi:10.1021/acs.jcim.2c00466
    • NLM

      Santos AMD, Oliveira ARS, Costa CHS da, Kenny PW, Montanari CA, Varela Júnior J de JG, Lameira J. Assessment of Reversibility for Covalent Cysteine Protease Inhibitors Using Quantum Mechanics/Molecular Mechanics Free Energy Surfaces [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62 4083-4094.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00466
    • Vancouver

      Santos AMD, Oliveira ARS, Costa CHS da, Kenny PW, Montanari CA, Varela Júnior J de JG, Lameira J. Assessment of Reversibility for Covalent Cysteine Protease Inhibitors Using Quantum Mechanics/Molecular Mechanics Free Energy Surfaces [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62 4083-4094.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00466
  • Source: Journal of Chemical Information and Modeling. Unidade: IQSC

    Subjects: ENERGIA, MOLÉCULA

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      OLIVEIRA, Andre F e SILVA, Juarez Lopes Ferreira da e QUILES, Marcos Gonçalves. Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder. Journal of Chemical Information and Modeling, v. 62, p. 817−828, 2022Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.1c01573. Acesso em: 08 out. 2025.
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      Oliveira, A. F., Silva, J. L. F. da, & Quiles, M. G. (2022). Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder. Journal of Chemical Information and Modeling, 62, 817−828. doi:10.1021/acs.jcim.1c01573
    • NLM

      Oliveira AF, Silva JLF da, Quiles MG. Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62 817−828.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c01573
    • Vancouver

      Oliveira AF, Silva JLF da, Quiles MG. Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62 817−828.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c01573
  • Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP

    Subjects: APRENDIZADO COMPUTACIONAL, MODELOS MATEMÁTICOS, ESTRUTURA MOLECULAR (QUÍMICA TEÓRICA)

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      SOARES, Thereza A. et al. The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [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.2c01422. Acesso em: 08 out. 2025. , 2022
    • APA

      Soares, T. A., Alves, A. F. N., Mazzolari, A., Ruggiu, F., Wei, G. -W., & Merz, K. (2022). The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [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.2c01422
    • NLM

      Soares TA, Alves AFN, Mazzolari A, Ruggiu F, Wei G-W, Merz K. The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 22): 5317-5320.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c01422
    • Vancouver

      Soares TA, Alves AFN, Mazzolari A, Ruggiu F, Wei G-W, Merz K. The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 22): 5317-5320.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c01422
  • Source: Journal of Chemical Information and Modeling. Unidade: IQSC

    Subjects: MODELAGEM MOLECULAR, MOLÉCULA, QUÍMICA TEÓRICA

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      PINHEIRO, Gabriel A. e SILVA, Juarez Lopes Ferreira da e QUILES, Marcos Gonçalves. SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning. Journal of Chemical Information and Modeling, v. 62, n. 17, p. 3948–3960, 2022Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.2c00521. Acesso em: 08 out. 2025.
    • APA

      Pinheiro, G. A., Silva, J. L. F. da, & Quiles, M. G. (2022). SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning. Journal of Chemical Information and Modeling, 62( 17), 3948–3960. doi:10.1021/acs.jcim.2c00521
    • NLM

      Pinheiro GA, Silva JLF da, Quiles MG. SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 17): 3948–3960.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00521
    • Vancouver

      Pinheiro GA, Silva JLF da, Quiles MG. SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 17): 3948–3960.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00521
  • Source: Journal of Chemical Information and Modeling. Unidades: FFCLRP, IQSC

    Subjects: ÍONS ELETRÔNICOS, ESTRUTURA ATÔMICA (QUÍMICA TEÓRICA), ENERGIA

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      MORAES, Alex S. et al. Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining. Journal of Chemical Information and Modeling, v. 62, n. 19, p. 4702–4712, 2022Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.2c00748. Acesso em: 08 out. 2025.
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      Moraes, A. S., Pinheiro, G. A., Lourenço, T. da C., Lopes, M. C., Quiles, M. G., Dias, L. G., & Silva, J. L. F. da. (2022). Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining. Journal of Chemical Information and Modeling, 62( 19), 4702–4712. doi:10.1021/acs.jcim.2c00748
    • NLM

      Moraes AS, Pinheiro GA, Lourenço T da C, Lopes MC, Quiles MG, Dias LG, Silva JLF da. Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 19): 4702–4712.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00748
    • Vancouver

      Moraes AS, Pinheiro GA, Lourenço T da C, Lopes MC, Quiles MG, Dias LG, Silva JLF da. Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 19): 4702–4712.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00748
  • Source: Journal of Chemical Information and Modeling. Unidade: IQSC

    Subjects: COMBUSTÍVEIS, COBRE, NANOCOMPOSITOS

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      MENDONÇA, João Paulo A. de et al. Theoretical Framework Based on Molecular Dynamics and Data Mining Analyses for the Study of Potential Energy Surfaces of FiniteSize Particles. Journal of Chemical Information and Modeling, v. 27, p. 5503-5512, 2022Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.2c00957. Acesso em: 08 out. 2025.
    • APA

      Mendonça, J. P. A. de, Calderan, F. V., Lourenço, T. da C., Quiles, M. G., & Silva, J. L. F. da. (2022). Theoretical Framework Based on Molecular Dynamics and Data Mining Analyses for the Study of Potential Energy Surfaces of FiniteSize Particles. Journal of Chemical Information and Modeling, 27, 5503-5512. doi:10.1021/acs.jcim.2c00957
    • NLM

      Mendonça JPA de, Calderan FV, Lourenço T da C, Quiles MG, Silva JLF da. Theoretical Framework Based on Molecular Dynamics and Data Mining Analyses for the Study of Potential Energy Surfaces of FiniteSize Particles [Internet]. Journal of Chemical Information and Modeling. 2022 ; 27 5503-5512.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00957
    • Vancouver

      Mendonça JPA de, Calderan FV, Lourenço T da C, Quiles MG, Silva JLF da. Theoretical Framework Based on Molecular Dynamics and Data Mining Analyses for the Study of Potential Energy Surfaces of FiniteSize Particles [Internet]. Journal of Chemical Information and Modeling. 2022 ; 27 5503-5512.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00957

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