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Artigo de periodico
Flexibility and hydration of the Qo site determine multiple pathways for proton transfer in cytochrome bc1 (2025)
Camilo, Sofia Rodrigues Guedes; Arantes, Guilherme Menegon
Fonte: Journal of Chemical Information and Modeling. Unidade: IQ
Assuntos: 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: 17 nov. 2025.
APA
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 nov. 17 ] 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 nov. 17 ] Available from: https://dx.doi.org/10.1021/acs.jcim.5c00655
Artigo de periodico
Redox activated proton transfer through a redundant network in the Qo site of Cytochrome bc1 (2025)
Arantes, Guilherme Menegon
Fonte: Journal of Chemical Information and Modeling. Unidade: IQ
Assuntos: MECÂNICA QUÂNTICA, OXIDAÇÃO, REDUÇÃO
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ABNT
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: 17 nov. 2025.
APA
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 nov. 17 ] 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 nov. 17 ] Available from: https://dx.doi.org/10.1021/acs.jcim.4c02361
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
Fonte: Journal of Chemical Information and Modeling. Unidade: IFSC
Assuntos: 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: 17 nov. 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 nov. 17 ] 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 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.4c01876
Artigo de periodico-carta/editorial
Enhancing coarse-grained models through machine learning. [Editorial] (2024)
Karmakar, Tarak; Soares, Thereza Amélia ; Merz Jr, Kenneth M
Fonte: Journal of Chemical Information and Modeling. Unidade: FFCLRP
Assuntos: APRENDIZADO COMPUTACIONAL, SIMULAÇÃO, MODELAGEM MOLECULAR, NANOPARTÍCULAS
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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: 17 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. 17 ] 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. 17 ] Available from: https://doi.org/10.1021/acs.jcim.4c00537
Artigo de periodico-carta/editorial
Editing DNA and RNA through Computations [Editorial] (2023)
Palermo, Giulia; Soares, Thereza A.
Fonte: Journal of Chemical Information and Modeling. Unidade: FFCLRP
Assuntos: QUÍMICA, REPLICAÇÃO DO DNA, GENÔMICA, ÁCIDOS NUCLEICOS
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ABNT
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: 17 nov. 2025. , 2023
APA
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 nov. 17 ] 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 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.3c01824
Artigo de periodico
Dissecting reaction mechanisms and catalytic contributions in Flavoprotein fumarate Reductases (2023)
Curtolo, Felipe ; Arantes, Guilherme Menegon
Fonte: Journal of Chemical Information and Modeling. Unidade: IQ
Assuntos: PROTEÍNAS, PEPTÍDEOS
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ABNT
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: 17 nov. 2025.
APA
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 nov. 17 ] 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 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.3c00292
Artigo de periodico
Molecular dynamics simulations of the human ecto-5′-nucleotidase (h-ecto-5′-NT, CD73): insights into protein flexibility and binding site dynamics (2023)
Viviani, Lucas Gasparello ; Kokh, Daria B ; Wade, Rebecca C ; Amaral, Antonia Tavares do
Fonte: Journal of Chemical Information and Modeling. Unidade: IQ
Assuntos: ESTRUTURA QUÍMICA, PROTEÍNAS
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ABNT
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: 17 nov. 2025.
APA
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 nov. 17 ] 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 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.3c01068
Artigo de periodico
Artificial intelligence agents for materials sciences (2023)
Oliveira Junior, Osvaldo Novais de ; Christino, Leonardo Milhomem Franco; Oliveira, Maria Cristina Ferreira de ; Paulovich, Fernando Vieira
Fonte: Journal of Chemical Information and Modeling. Unidades: IFSC, ICMC
Assuntos: ALGORITMOS, APRENDIZADO COMPUTACIONAL, GENÔMICA
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ABNT
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: 17 nov. 2025.
APA
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 nov. 17 ] 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 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.3c01778
Artigo de periodico
Bias amplification in gender, gender identity, and geographical affiliation (2022)
Cascella, Michele; Silva, Thereza Amélia Soares da
Fonte: Journal of Chemical Information and Modeling. Unidade: FFCLRP
Assuntos: PRECONCEITO, PESQUISA CIENTÍFICA
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ABNT
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: 17 nov. 2025.
APA
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
NLM
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 nov. 17 ] 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 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.2c00533
Artigo de periodico-carta/editorial
The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [Editorial] (2022)
Soares, Thereza A. ; Alves, Ariane Ferreira Nunes; Mazzolari, Angelica; Ruggiu, Fiorella; Wei, Guo-Wei; Merz, Kenneth
Fonte: Journal of Chemical Information and Modeling. Unidade: FFCLRP
Assuntos: APRENDIZADO COMPUTACIONAL, MODELOS MATEMÁTICOS, ESTRUTURA MOLECULAR (QUÍMICA TEÓRICA)
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ABNT
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: 17 nov. 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 nov. 17 ] 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 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.2c01422
Artigo de periodico
How the strain origin of Zika Virus NS1 protein impacts its dynamics and implications to their differential virulence (2021)
Poveda Cuevas, Sergio Alejandro; Silva, Fernando Luís Barroso da ; Etchebest, Catherine
Fonte: Journal of Chemical Information and Modeling. Unidades: FCFRP, Interunidades em Bioinformática
Assuntos: ZIKA VÍRUS, VIRULÊNCIA, FLAVIVIRUS
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ABNT
POVEDA CUEVAS, Sergio Alejandro e SILVA, Fernando Luís Barroso da e ETCHEBEST, Catherine. How the strain origin of Zika Virus NS1 protein impacts its dynamics and implications to their differential virulence. Journal of Chemical Information and Modeling, v. 61, n. 3, p. 1516-1530, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.0c01377. Acesso em: 17 nov. 2025.
APA
Poveda Cuevas, S. A., Silva, F. L. B. da, & Etchebest, C. (2021). How the strain origin of Zika Virus NS1 protein impacts its dynamics and implications to their differential virulence. Journal of Chemical Information and Modeling, 61( 3), 1516-1530. doi:10.1021/acs.jcim.0c01377
NLM
Poveda Cuevas SA, Silva FLB da, Etchebest C. How the strain origin of Zika Virus NS1 protein impacts its dynamics and implications to their differential virulence [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 3): 1516-1530.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.0c01377
Vancouver
Poveda Cuevas SA, Silva FLB da, Etchebest C. How the strain origin of Zika Virus NS1 protein impacts its dynamics and implications to their differential virulence [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 3): 1516-1530.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.0c01377
Artigo de periodico
Differences in gluco and galacto substrate-binding interactions in a dual 6Pβ-Glucosidase/6Pβ-Galactosidase glycoside hydrolase 1 enzyme from Bacillus licheniformis (2021)
Veldman, Wayde ; Liberato, Marcelo Vizoná; Souza, Valquiria P; Almeida, Vitor M; Marana, Sandro Roberto ; Bishop, Ozlem Tastan ; Polikarpov, Igor
Fonte: Journal of Chemical Information and Modeling. Unidades: IQ, IFSC
Assuntos: CRISTALOGRAFIA, PEPTÍDEOS, PROTEÍNAS, LIGANTES
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ABNT
VELDMAN, Wayde et al. Differences in gluco and galacto substrate-binding interactions in a dual 6Pβ-Glucosidase/6Pβ-Galactosidase glycoside hydrolase 1 enzyme from Bacillus licheniformis. Journal of Chemical Information and Modeling, v. 61, n. 9, p. 4554-4570, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.1c00413. Acesso em: 17 nov. 2025.
APA
Veldman, W., Liberato, M. V., Souza, V. P., Almeida, V. M., Marana, S. R., Bishop, O. T., & Polikarpov, I. (2021). Differences in gluco and galacto substrate-binding interactions in a dual 6Pβ-Glucosidase/6Pβ-Galactosidase glycoside hydrolase 1 enzyme from Bacillus licheniformis. Journal of Chemical Information and Modeling, 61( 9), 4554-4570. doi:10.1021/acs.jcim.1c00413
NLM
Veldman W, Liberato MV, Souza VP, Almeida VM, Marana SR, Bishop OT, Polikarpov I. Differences in gluco and galacto substrate-binding interactions in a dual 6Pβ-Glucosidase/6Pβ-Galactosidase glycoside hydrolase 1 enzyme from Bacillus licheniformis [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 9): 4554-4570.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.1c00413
Vancouver
Veldman W, Liberato MV, Souza VP, Almeida VM, Marana SR, Bishop OT, Polikarpov I. Differences in gluco and galacto substrate-binding interactions in a dual 6Pβ-Glucosidase/6Pβ-Galactosidase glycoside hydrolase 1 enzyme from Bacillus licheniformis [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 9): 4554-4570.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.1c00413
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
Fonte: Journal of Chemical Information and Modeling. Unidade: IFSC
Assuntos: PLANEJAMENTO DE FÁRMACOS, COMPUTAÇÃO APLICADA, INTELIGÊNCIA ARTIFICIAL
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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: 17 nov. 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 nov. 17 ] 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 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.1c00166
Artigo de periodico
Development of machine learning models and the discovery of a new antiviral compound against yellow fever virus (2021)
Oliveira, Victor Gawriljuk Ferraro; Foil, Daniel H.; Puhl, Ana C. ; Zorn, Kimberley M.; Lane, Thomas R.; Riabova, Olga; Makarov, Vadim; Godoy, Andre Schutzer de ; Oliva, Glaucius ; Ekins, Sean
Fonte: Journal of Chemical Information and Modeling. Unidade: IFSC
Assuntos: FEBRE AMARELA, APRENDIZADO COMPUTACIONAL, PLANEJAMENTO DE FÁRMACOS, ANTIVIRAIS
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ABNT
OLIVEIRA, Victor Gawriljuk Ferraro et al. Development of machine learning models and the discovery of a new antiviral compound against yellow fever virus. Journal of Chemical Information and Modeling, v. 61, n. 8, p. 3804-3813, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.1c00460. Acesso em: 17 nov. 2025.
APA
Oliveira, V. G. F., Foil, D. H., Puhl, A. C., Zorn, K. M., Lane, T. R., Riabova, O., et al. (2021). Development of machine learning models and the discovery of a new antiviral compound against yellow fever virus. Journal of Chemical Information and Modeling, 61( 8), 3804-3813. doi:10.1021/acs.jcim.1c00460
NLM
Oliveira VGF, Foil DH, Puhl AC, Zorn KM, Lane TR, Riabova O, Makarov V, Godoy AS de, Oliva G, Ekins S. Development of machine learning models and the discovery of a new antiviral compound against yellow fever virus [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 8): 3804-3813.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.1c00460
Vancouver
Oliveira VGF, Foil DH, Puhl AC, Zorn KM, Lane TR, Riabova O, Makarov V, Godoy AS de, Oliva G, Ekins S. Development of machine learning models and the discovery of a new antiviral compound against yellow fever virus [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 8): 3804-3813.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.1c00460
Artigo de periodico
Investigation of the stability mechanisms of eight-atom binary metal clusters using DFT calculations and k‑means clustering algorithm (2021)
Morais, Felipe Orlando; Andriani, Karla Furtado ; Silva, Juarez Lopes Ferreira da
Fonte: Journal of Chemical Information and Modeling. Unidades: IQSC, IFSC
Assunto: METAIS
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ABNT
MORAIS, Felipe Orlando e ANDRIANI, Karla Furtado e SILVA, Juarez Lopes Ferreira da. Investigation of the stability mechanisms of eight-atom binary metal clusters using DFT calculations and k‑means clustering algorithm. Journal of Chemical Information and Modeling, v. 61, n. 7, p. 3411-3420, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.1c00253. Acesso em: 17 nov. 2025.
APA
Morais, F. O., Andriani, K. F., & Silva, J. L. F. da. (2021). Investigation of the stability mechanisms of eight-atom binary metal clusters using DFT calculations and k‑means clustering algorithm. Journal of Chemical Information and Modeling, 61( 7), 3411-3420. doi:10.1021/acs.jcim.1c00253
NLM
Morais FO, Andriani KF, Silva JLF da. Investigation of the stability mechanisms of eight-atom binary metal clusters using DFT calculations and k‑means clustering algorithm [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 7): 3411-3420.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.1c00253
Vancouver
Morais FO, Andriani KF, Silva JLF da. Investigation of the stability mechanisms of eight-atom binary metal clusters using DFT calculations and k‑means clustering algorithm [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 7): 3411-3420.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.1c00253
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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 17 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. 17 ] 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. 17 ] Available from: https://doi.org/10.1021/acs.jcim.1c00515
Artigo de periodico
Making soup: preparing and validating models of the bacterial cytoplasm for molecular simulation (2020)
Bortot, Leandro Oliveira; Bashardanesh, Zahedeh; van der Spoel, David
Fonte: Journal of Chemical Information and Modeling. Unidade: FCFRP
Assuntos: CITOPLASMA, RNA, ESCHERICHIA COLI
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BORTOT, Leandro Oliveira e BASHARDANESH, Zahedeh e VAN DER SPOEL, David. Making soup: preparing and validating models of the bacterial cytoplasm for molecular simulation. Journal of Chemical Information and Modeling, v. 60, n. 1, p. 322-331, 2020Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.9b00971. Acesso em: 17 nov. 2025.
APA
Bortot, L. O., Bashardanesh, Z., & van der Spoel, D. (2020). Making soup: preparing and validating models of the bacterial cytoplasm for molecular simulation. Journal of Chemical Information and Modeling, 60( 1), 322-331. doi:10.1021/acs.jcim.9b00971
NLM
Bortot LO, Bashardanesh Z, van der Spoel D. Making soup: preparing and validating models of the bacterial cytoplasm for molecular simulation [Internet]. Journal of Chemical Information and Modeling. 2020 ;60( 1): 322-331.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.9b00971
Vancouver
Bortot LO, Bashardanesh Z, van der Spoel D. Making soup: preparing and validating models of the bacterial cytoplasm for molecular simulation [Internet]. Journal of Chemical Information and Modeling. 2020 ;60( 1): 322-331.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.9b00971
Artigo de periodico
Combined experimental and molecular simulation study of insulin–chitosan complexation driven by electrostatic interactions (2020)
Prudkin-Silva, Cecilia; Pérez, Oscar E.; Martínez, Karina D.; Silva, Fernando Luís Barroso da
Fonte: Journal of Chemical Information and Modeling. Unidade: FCFRP
Assuntos: BIOMATERIAIS, MÉTODO DE MONTE CARLO, INSULINA, QUITOSANA, ELETROSTÁTICA, MACROMOLÉCULA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PRUDKIN-SILVA, Cecilia et al. Combined experimental and molecular simulation study of insulin–chitosan complexation driven by electrostatic interactions. Journal of Chemical Information and Modeling, v. 60, n. 2, p. 854-865, 2020Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.9b00814. Acesso em: 17 nov. 2025.
APA
Prudkin-Silva, C., Pérez, O. E., Martínez, K. D., & Silva, F. L. B. da. (2020). Combined experimental and molecular simulation study of insulin–chitosan complexation driven by electrostatic interactions. Journal of Chemical Information and Modeling, 60( 2), 854-865. doi:10.1021/acs.jcim.9b00814
NLM
Prudkin-Silva C, Pérez OE, Martínez KD, Silva FLB da. Combined experimental and molecular simulation study of insulin–chitosan complexation driven by electrostatic interactions [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 2): 854-865.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.9b00814
Vancouver
Prudkin-Silva C, Pérez OE, Martínez KD, Silva FLB da. Combined experimental and molecular simulation study of insulin–chitosan complexation driven by electrostatic interactions [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 2): 854-865.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.9b00814
Artigo de periodico
Self-assembly of phosphocholine derivatives using the ELBA coarse-grained model: micelles, bicelles, and reverse micelles (2020)
Souza, Rafael Maglia de ; Ratochinski, Rafael Henrique; Karttunen, Mikko; Dias, Luis Gustavo
Fonte: Journal of Chemical Information and Modeling. Unidade: FFCLRP
Assuntos: LIPÍDEOS, ELETROSTÁTICA, MOLÉCULA, QUÍMICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUZA, Rafael Maglia de et al. Self-assembly of phosphocholine derivatives using the ELBA coarse-grained model: micelles, bicelles, and reverse micelles. Journal of Chemical Information and Modeling, v. 60, n. 2, p. 522-536, 2020Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.9b00790. Acesso em: 17 nov. 2025.
APA
Souza, R. M. de, Ratochinski, R. H., Karttunen, M., & Dias, L. G. (2020). Self-assembly of phosphocholine derivatives using the ELBA coarse-grained model: micelles, bicelles, and reverse micelles. Journal of Chemical Information and Modeling, 60( 2), 522-536. doi:10.1021/acs.jcim.9b00790
NLM
Souza RM de, Ratochinski RH, Karttunen M, Dias LG. Self-assembly of phosphocholine derivatives using the ELBA coarse-grained model: micelles, bicelles, and reverse micelles [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 2): 522-536.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.9b00790
Vancouver
Souza RM de, Ratochinski RH, Karttunen M, Dias LG. Self-assembly of phosphocholine derivatives using the ELBA coarse-grained model: micelles, bicelles, and reverse micelles [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 2): 522-536.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.9b00790
Artigo de periodico
Identification of electrostatic epitopes in flavivirus by computer simulations: the PROCEEDpKa method (2019)
Poveda Cuevas, Sergio Alejandro; Etchebest, Catherine; Silva, Fernando Luís Barroso da
Fonte: Journal of Chemical Information and Modeling. Unidades: Interunidades em Bioinformática, FCFRP
Assuntos: ANTÍGENOS, IMUNOLOGIA, FLAVIVIRUS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
POVEDA CUEVAS, Sergio Alejandro e ETCHEBEST, Catherine e SILVA, Fernando Luís Barroso da. Identification of electrostatic epitopes in flavivirus by computer simulations: the PROCEEDpKa method. Journal of Chemical Information and Modeling, v. 60, n. 2, p. 944-963, 2019Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.9b00895. Acesso em: 17 nov. 2025.
APA
Poveda Cuevas, S. A., Etchebest, C., & Silva, F. L. B. da. (2019). Identification of electrostatic epitopes in flavivirus by computer simulations: the PROCEEDpKa method. Journal of Chemical Information and Modeling, 60( 2), 944-963. doi:10.1021/acs.jcim.9b00895
NLM
Poveda Cuevas SA, Etchebest C, Silva FLB da. Identification of electrostatic epitopes in flavivirus by computer simulations: the PROCEEDpKa method [Internet]. Journal of Chemical Information and Modeling. 2019 ; 60( 2): 944-963.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.9b00895
Vancouver
Poveda Cuevas SA, Etchebest C, Silva FLB da. Identification of electrostatic epitopes in flavivirus by computer simulations: the PROCEEDpKa method [Internet]. Journal of Chemical Information and Modeling. 2019 ; 60( 2): 944-963.[citado 2025 nov. 17 ] Available from: https://doi.org/10.1021/acs.jcim.9b00895

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