ReP USP - Resultado da busca

Artigo de periodico
Interfacial structurization between triolein and water from pH and buffer ions (2024)
Frigerio, Matteo; Freire, Rafael V. M; Soares, Thereza Amélia ; Amenitsch, Heinz; Leser, Martin E; Salentinig, Stefan
Fonte: Journal of Colloid and Interface Science. Unidade: FFCLRP
Assuntos: ADSORÇÃO, TENSÃO INTERFACIAL, INTERAÇÃO ENTRE ALIMENTOS E MEDICAMENTOS
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ABNT
FRIGERIO, Matteo et al. Interfacial structurization between triolein and water from pH and buffer ions. Journal of Colloid and Interface Science, v. 665, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.jcis.2024.03.089. Acesso em: 08 out. 2025.
APA
Frigerio, M., Freire, R. V. M., Soares, T. A., Amenitsch, H., Leser, M. E., & Salentinig, S. (2024). Interfacial structurization between triolein and water from pH and buffer ions. Journal of Colloid and Interface Science, 665. doi:10.1016/j.jcis.2024.03.089
NLM
Frigerio M, Freire RVM, Soares TA, Amenitsch H, Leser ME, Salentinig S. Interfacial structurization between triolein and water from pH and buffer ions [Internet]. Journal of Colloid and Interface Science. 2024 ; 665[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.jcis.2024.03.089
Vancouver
Frigerio M, Freire RVM, Soares TA, Amenitsch H, Leser ME, Salentinig S. Interfacial structurization between triolein and water from pH and buffer ions [Internet]. Journal of Colloid and Interface Science. 2024 ; 665[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.jcis.2024.03.089
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: 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
Fonte: Journal of Chemical Information and Modeling. Unidades: IQSC, FFCLRP
Assuntos: 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
Inhibition of 3-Hydroxykynurenine transaminase from Aedes aegypti and Anopheles gambiae: a mosquito-specific target to combat the transmission of arboviruses (2023)
Maciel, Larissa Gonçalves ; Ferraz, Matheus Vitor Ferreira ; Oliveira, Andrew Albert de ; Lins, Roberto Dias ; Anjos, Janaína Versiani; Guido, Rafael Victorio Carvalho ; Soares, Thereza Amélia
Fonte: ACS Bio and Med Chem Au. Unidades: IFSC, FFCLRP, FMRP
Assuntos: AEDES, LARVICIDAS, ARBOVÍRUS, EXPRESSÃO GÊNICA
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ABNT
MACIEL, Larissa Gonçalves et al. Inhibition of 3-Hydroxykynurenine transaminase from Aedes aegypti and Anopheles gambiae: a mosquito-specific target to combat the transmission of arboviruses. ACS Bio and Med Chem Au, v. 3, n. 2, p. 211-222 + supporting information, 2023Tradução . . Disponível em: https://doi.org/10.1021/acsbiomedchemau.2c00080. Acesso em: 08 out. 2025.
APA
Maciel, L. G., Ferraz, M. V. F., Oliveira, A. A. de, Lins, R. D., Anjos, J. V., Guido, R. V. C., & Soares, T. A. (2023). Inhibition of 3-Hydroxykynurenine transaminase from Aedes aegypti and Anopheles gambiae: a mosquito-specific target to combat the transmission of arboviruses. ACS Bio and Med Chem Au, 3( 2), 211-222 + supporting information. doi:10.1021/acsbiomedchemau.2c00080
NLM
Maciel LG, Ferraz MVF, Oliveira AA de, Lins RD, Anjos JV, Guido RVC, Soares TA. Inhibition of 3-Hydroxykynurenine transaminase from Aedes aegypti and Anopheles gambiae: a mosquito-specific target to combat the transmission of arboviruses [Internet]. ACS Bio and Med Chem Au. 2023 ; 3( 2): 211-222 + supporting information.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acsbiomedchemau.2c00080
Vancouver
Maciel LG, Ferraz MVF, Oliveira AA de, Lins RD, Anjos JV, Guido RVC, Soares TA. Inhibition of 3-Hydroxykynurenine transaminase from Aedes aegypti and Anopheles gambiae: a mosquito-specific target to combat the transmission of arboviruses [Internet]. ACS Bio and Med Chem Au. 2023 ; 3( 2): 211-222 + supporting information.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acsbiomedchemau.2c00080
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: 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
Fonte: Journal of Chemical Information and Modeling. Unidades: IF, FFCLRP
Assuntos: 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
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: 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

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