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Artigo de periodico
Andrographolide: a diterpenoid from cymbopogon schoenanthus identified as a new hit compound against trypanosoma cruzi using machine learning and experimental approaches (2024)
Barbosa, Henrique ; Espinoza, Gabriel Zarzana; Amaral, Maiara; Levatti, Erica Valadares de Castro ; Abiuzi, Mariana Babberg ; Veríssimo, Gabriel Corrêa ; Fernandes, Philipe Oliveira ; Maltarollo, Vinícius Gonçalves ; Tempone, Andre Gustavo ; Honorio, Kathia Maria ; Lago, João Henrique Ghilardi
Fonte: Journal of Chemical Information and Modeling. Unidade: EACH
Assunto: DOENÇA DE CHAGAS
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ABNT
BARBOSA, Henrique et al. Andrographolide: a diterpenoid from cymbopogon schoenanthus identified as a new hit compound against trypanosoma cruzi using machine learning and experimental approaches. Journal of Chemical Information and Modeling, v. 64, p. 2565−2576, 2024Tradução . . Disponível em: http://dx.doi.org/10.1021/acs.jcim.3c01410. Acesso em: 23 nov. 2025.
APA
Barbosa, H., Espinoza, G. Z., Amaral, M., Levatti, E. V. de C., Abiuzi, M. B., Veríssimo, G. C., et al. (2024). Andrographolide: a diterpenoid from cymbopogon schoenanthus identified as a new hit compound against trypanosoma cruzi using machine learning and experimental approaches. Journal of Chemical Information and Modeling, 64, 2565−2576. doi:10.1021/acs.jcim.3c01410
NLM
Barbosa H, Espinoza GZ, Amaral M, Levatti EV de C, Abiuzi MB, Veríssimo GC, Fernandes PO, Maltarollo VG, Tempone AG, Honorio KM, Lago JHG. Andrographolide: a diterpenoid from cymbopogon schoenanthus identified as a new hit compound against trypanosoma cruzi using machine learning and experimental approaches [Internet]. Journal of Chemical Information and Modeling. 2024 ; 64 2565−2576.[citado 2025 nov. 23 ] Available from: http://dx.doi.org/10.1021/acs.jcim.3c01410
Vancouver
Barbosa H, Espinoza GZ, Amaral M, Levatti EV de C, Abiuzi MB, Veríssimo GC, Fernandes PO, Maltarollo VG, Tempone AG, Honorio KM, Lago JHG. Andrographolide: a diterpenoid from cymbopogon schoenanthus identified as a new hit compound against trypanosoma cruzi using machine learning and experimental approaches [Internet]. Journal of Chemical Information and Modeling. 2024 ; 64 2565−2576.[citado 2025 nov. 23 ] Available from: http://dx.doi.org/10.1021/acs.jcim.3c01410
Artigo de periodico
Machine learning-based virtual screening of antibacterial agents against methicillin-susceptible and resistant staphylococcus aureus (2024)
Fernandes, Philipe Oliveira ; Dias, Anna Letícia Teotonio; Santos Júnior, Valtair Severino dos ; Serafim, Mateus Sá Magalhães ; Sousa, Yamara Viana; Monteiro, Gustavo Claro ; Coutinho, Isabel Duarte ; Valli, Marilia ; Verzola, Marina Mol Sena Andrade; Ottoni, Flaviano Melo; Pádua, Rodrigo Maia de; Oda, Fernando Bombarda ; Santos, André Gonzaga dos ; Andricopulo, Adriano Defini ; Bolzani, Vanderlan da Silva ; Mota, Bruno Eduardo Fernandes; Alves, Ricardo José; Oliveira, Renata Barbosa de ; Kronenberger, Thales ; Maltarollo, Vinícius Gonçalves
Fonte: Journal of Chemical Information and Modeling. Unidade: IFSC
Assuntos: BACTÉRIAS, AGENTES ANTIMICROBIANOS, CRISTALOGRAFIA
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ABNT
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: 23 nov. 2025.
APA
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 nov. 23 ] 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 nov. 23 ] Available from: https://doi.org/10.1021/acs.jcim.4c00087
Artigo de periodico-carta/editorial
Machine Learning in Materials Science. [Editorial] (2024)
Merz, Kenneth M; Choong, Yee Siew; Cournia, Zoe; Isayev, Olexandr; Soares, Thereza Amélia ; Wei, Guo-Wei; Zhu, Feng
Fonte: Journal of Chemical Information and Modeling. Unidade: FFCLRP
Assuntos: ALGORITMOS, APRENDIZADO COMPUTACIONAL, BIOMATERIAIS
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ABNT
MERZ, Kenneth M et al. Machine Learning in Materials Science. [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.4c00727. Acesso em: 23 nov. 2025. , 2024
APA
Merz, K. M., Choong, Y. S., Cournia, Z., Isayev, O., Soares, T. A., Wei, G. -W., & Zhu, F. (2024). Machine Learning in Materials Science. [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.4c00727
NLM
Merz KM, Choong YS, Cournia Z, Isayev O, Soares TA, Wei G-W, Zhu F. Machine Learning in Materials Science. [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2024 ; 64( 10): 3959-3960.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1021/acs.jcim.4c00727
Vancouver
Merz KM, Choong YS, Cournia Z, Isayev O, Soares TA, Wei G-W, Zhu F. Machine Learning in Materials Science. [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2024 ; 64( 10): 3959-3960.[citado 2025 nov. 23 ] Available from: https://doi.org/10.1021/acs.jcim.4c00727
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
Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC
Assuntos: 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: 23 nov. 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 nov. 23 ] 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 nov. 23 ] 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
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: 23 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. 23 ] 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. 23 ] 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: 23 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. 23 ] 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. 23 ] Available from: https://doi.org/10.1021/acs.jcim.3c02014

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