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Artigo de periodico
Redox activated proton transfer through a redundant network in the Qo site of Cytochrome bc1 (2025)
Arantes, Guilherme Menegon
Source: Journal of Chemical Information and Modeling. Unidade: IQ
Subjects: 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: 08 out. 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 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
Artigo de periodico
Decoding Van der Waals Impact on Chirality Transfer in Perovskite Structures: Density Functional Theory Insights (2024)
González, José E.; Besse, Rafael; Lima, Matheus P.; Silva, Juarez Lopes Ferreira da
Source: Journal of Chemical Information and Modeling. Unidade: IQSC
Subjects: ENERGIA, QUÍMICA TEÓRICA
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ABNT
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.
APA
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
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
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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: 08 out. 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 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
Artigo de periodico
The Impact of Interdisciplinary, Gender and Geographic Distributions on the Citation Patterns of the Journal of Chemical Information and Modeling (2024)
Prati, Ronaldo C.; Rodrigues, Bruno S. M.; Aragão, Iberis; Silva, Thereza Amélia Soares da; Quiles, Marcos Gonçalves ; Silva, Juarez Lopes Ferreira da
Source: Journal of Chemical Information and Modeling. Unidades: IQSC, FFCLRP
Subjects: BIOENGENHARIA, BIOTECNOLOGIA, BIOLOGIA, MATERIAIS
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ABNT
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.
APA
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
Artigo de periodico-carta/editorial
Editing DNA and RNA through Computations [Editorial] (2023)
Palermo, Giulia; Soares, Thereza A.
Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP
Subjects: 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: 08 out. 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 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
Artigo de periodico
Dissecting reaction mechanisms and catalytic contributions in Flavoprotein fumarate Reductases (2023)
Curtolo, Felipe ; Arantes, Guilherme Menegon
Source: Journal of Chemical Information and Modeling. Unidade: IQ
Subjects: 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: 08 out. 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 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
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
Source: Journal of Chemical Information and Modeling. Unidade: IQ
Subjects: 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: 08 out. 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 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
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
Source: Journal of Chemical Information and Modeling. Unidades: IFSC, ICMC
Subjects: 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: 08 out. 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 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
Artigo de periodico
Bias amplification in gender, gender identity, and geographical affiliation (2022)
Cascella, Michele; Silva, Thereza Amélia Soares da
Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP
Subjects: 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: 08 out. 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 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
Artigo de periodico
Surface Assessment via Grid Evaluation (SuAVE) for Every Surface Curvature and Cavity Shape (2022)
Santos, Denys ; Coutinho, Kaline Rabelo ; Silva, Thereza Amélia Soares da
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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ABNT
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.
APA
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
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
Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP
Subjects: 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: 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
Artigo de periodico
SMICLR: Contrastive Learning on Multiple Molecular Representations for Semisupervised and Unsupervised Representation Learning (2022)
Pinheiro, Gabriel A.; Silva, Juarez Lopes Ferreira da ; Quiles, Marcos Gonçalves
Source: Journal of Chemical Information and Modeling. Unidade: IQSC
Subjects: MODELAGEM MOLECULAR, MOLÉCULA, QUÍMICA TEÓRICA
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ABNT
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
Artigo de periodico
Theoretical Framework Based on Molecular Dynamics and Data Mining Analyses for the Study of Potential Energy Surfaces of FiniteSize Particles (2022)
Mendonça, João Paulo A. de ; Calderan, Felipe V.; Lourenço, Tuanan da Costa ; Quiles, Marcos G.; Silva, Juarez Lopes Ferreira da
Source: Journal of Chemical Information and Modeling. Unidade: IQSC
Subjects: COMBUSTÍVEIS, COBRE, NANOCOMPOSITOS
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ABNT
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
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
Source: Journal of Chemical Information and Modeling. Unidades: IQ, IFSC
Subjects: 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: 08 out. 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 out. 08 ] 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 out. 08 ] 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
Source: Journal of Chemical Information and Modeling. Unidade: IFSC
Subjects: 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: 08 out. 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 out. 08 ] 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 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c00166
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
Source: 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: 08 out. 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 out. 08 ] 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 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c00253
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
Source: Journal of Chemical Information and Modeling. Unidade: IFSC
Subjects: FEBRE AMARELA, APRENDIZADO COMPUTACIONAL, PLANEJAMENTO DE FÁRMACOS, ANTIVIRAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 08 out. 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 out. 08 ] 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 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c00460
Artigo de periodico
Machine learning models identify inhibitors of SARS-CoV2 (2021)
Oliveira, Victor Gawriljuk Ferraro; Zin, Phyo Phyo Kyaw; Puhl, Ana C. ; Zorn, Kimberley M.; Foil, Daniel H.; Lane, Thomas R.; Hurst, Brett; Tavella, Tatyana Almeida; Costa, Fabio Trindade Maranhão; Lakshmanane, Premkumar; Bernatchez, Jean; Godoy, Andre Schutzer de ; Oliva, Glaucius ; Siqueira-Neto, Jair L.; Madrid, Peter B.; Ekins, Sean
Source: Journal of Chemical Information and Modeling. Unidade: IFSC
Subjects: CORONAVIRUS, COVID-19, APRENDIZADO COMPUTACIONAL, PLANEJAMENTO DE FÁRMACOS, ANTIVIRAIS
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ABNT
OLIVEIRA, Victor Gawriljuk Ferraro et al. Machine learning models identify inhibitors of SARS-CoV2. Journal of Chemical Information and Modeling, v. 61, n. 9, p. 4224-4235, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.1c00683. Acesso em: 08 out. 2025.
APA
Oliveira, V. G. F., Zin, P. P. K., Puhl, A. C., Zorn, K. M., Foil, D. H., Lane, T. R., et al. (2021). Machine learning models identify inhibitors of SARS-CoV2. Journal of Chemical Information and Modeling, 61( 9), 4224-4235. doi:10.1021/acs.jcim.1c00683
NLM
Oliveira VGF, Zin PPK, Puhl AC, Zorn KM, Foil DH, Lane TR, Hurst B, Tavella TA, Costa FTM, Lakshmanane P, Bernatchez J, Godoy AS de, Oliva G, Siqueira-Neto JL, Madrid PB, Ekins S. Machine learning models identify inhibitors of SARS-CoV2 [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 9): 4224-4235.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c00683
Vancouver
Oliveira VGF, Zin PPK, Puhl AC, Zorn KM, Foil DH, Lane TR, Hurst B, Tavella TA, Costa FTM, Lakshmanane P, Bernatchez J, Godoy AS de, Oliva G, Siqueira-Neto JL, Madrid PB, Ekins S. Machine learning models identify inhibitors of SARS-CoV2 [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 9): 4224-4235.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c00683
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
Source: Journal of Chemical Information and Modeling. Unidade: FCFRP
Subjects: CITOPLASMA, RNA, ESCHERICHIA COLI
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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: 08 out. 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 out. 08 ] 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 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.9b00971
Artigo de periodico
Molecular dynamics simulations of polymer–ionic liquid (1-ethyl-3-methylimidazolium tetracyanoborate) ternary electrolyte for sodium and potassium ion batteries (2020)
Souza, Rafael Maglia de ; Siqueira, Leonardo José Amaral de; Karttunen, Mikko; Dias, Luis Gustavo
Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP
Subjects: BATERIAS ELÉTRICAS, ENERGIA ELÉTRICA, SÓDIO, POTÁSSIO, ELETROQUÍMICA, SOLUÇÕES ELETROLÍTICAS
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ABNT
SOUZA, Rafael Maglia de et al. Molecular dynamics simulations of polymer–ionic liquid (1-ethyl-3-methylimidazolium tetracyanoborate) ternary electrolyte for sodium and potassium ion batteries. Journal of Chemical Information and Modeling, v. 60, n. 2, p. 485-499, 2020Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.9b00750. Acesso em: 08 out. 2025.
APA
Souza, R. M. de, Siqueira, L. J. A. de, Karttunen, M., & Dias, L. G. (2020). Molecular dynamics simulations of polymer–ionic liquid (1-ethyl-3-methylimidazolium tetracyanoborate) ternary electrolyte for sodium and potassium ion batteries. Journal of Chemical Information and Modeling, 60( 2), 485-499. doi:10.1021/acs.jcim.9b00750
NLM
Souza RM de, Siqueira LJA de, Karttunen M, Dias LG. Molecular dynamics simulations of polymer–ionic liquid (1-ethyl-3-methylimidazolium tetracyanoborate) ternary electrolyte for sodium and potassium ion batteries [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 2): 485-499.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.9b00750
Vancouver
Souza RM de, Siqueira LJA de, Karttunen M, Dias LG. Molecular dynamics simulations of polymer–ionic liquid (1-ethyl-3-methylimidazolium tetracyanoborate) ternary electrolyte for sodium and potassium ion batteries [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 2): 485-499.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.9b00750

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