ReP USP - Resultado da busca

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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
KARMAKAR, Tarak e SOARES, Thereza Amélia e MERZ JR, Kenneth M. Enhancing coarse-grained models through machine learning. [Editorial]. Journal of Chemical Information and Modeling. Washington: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://doi.org/10.1021/acs.jcim.4c00537. Acesso em: 09 nov. 2025. , 2024
APA
Karmakar, T., Soares, T. A., & Merz Jr, K. M. (2024). Enhancing coarse-grained models through machine learning. [Editorial]. Journal of Chemical Information and Modeling. Washington: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. doi:10.1021/acs.jcim.4c00537
NLM
Karmakar T, Soares TA, Merz Jr KM. Enhancing coarse-grained models through machine learning. [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2024 ; 64( 8): 2931-2932.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.4c00537
Vancouver
Karmakar T, Soares TA, Merz Jr KM. Enhancing coarse-grained models through machine learning. [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2024 ; 64( 8): 2931-2932.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.4c00537
Artigo de periodico
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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 09 nov. 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 nov. 09 ] 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 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.3c02014
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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 09 nov. 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 nov. 09 ] 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 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.2c00673
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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 09 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. 09 ] 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. 09 ] 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
Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP
Subjects: APRENDIZADO COMPUTACIONAL, MODELOS MATEMÁTICOS, ESTRUTURA MOLECULAR (QUÍMICA TEÓRICA)
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 09 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. 09 ] 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. 09 ] Available from: https://doi.org/10.1021/acs.jcim.2c01422

USP Schools

ReP

Filtros

Authors

Subjects

Funding Agencies

Indexado em

Non normalized affiliation

Countries of institutions of collaboration