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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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ABNT
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: 08 out. 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 out. 08 ] 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 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.4c01876
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: 08 out. 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 out. 08 ] 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 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.4c00087
Artigo de periodico
Latin american natural product database (LANaPDB): an update (2024)
Garcia, Alejandro Gomez ; Jiménez, Daniel A. Acuña; Zamora, William J. ; Ccahuana, Haruna L. Barazorda; Fumagalli, Miguel Angel Chávez ; Valli, Marilia ; Andricopulo, Adriano Defini ; Bolzani, Vanderlan da Silva ; Olmedo, Dionisio Antonio ; Solis, Pablo N. ; Nuñez, Marvin J. ; Pérez, Johny Roberto Rodríguez ; Sánchez, Hoover A. Valencia; Hernández, Héctor Fabio Cortés ; Martinez, Oscar Marino Mosquera; Franco, José L. Medina
Fonte: Journal of Chemical Information and Modeling. Unidade: IFSC
Assuntos: PRODUTOS NATURAIS, FÁRMACOS, CRISTALOGRAFIA
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ABNT
GARCIA, Alejandro Gomez et al. Latin american natural product database (LANaPDB): an update. Journal of Chemical Information and Modeling, v. No 2024, n. 22, p. 8495-8509, 2024Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.4c01560. Acesso em: 08 out. 2025.
APA
Garcia, A. G., Jiménez, D. A. A., Zamora, W. J., Ccahuana, H. L. B., Fumagalli, M. A. C., Valli, M., et al. (2024). Latin american natural product database (LANaPDB): an update. Journal of Chemical Information and Modeling, No 2024( 22), 8495-8509. doi:10.1021/acs.jcim.4c01560
NLM
Garcia AG, Jiménez DAA, Zamora WJ, Ccahuana HLB, Fumagalli MAC, Valli M, Andricopulo AD, Bolzani V da S, Olmedo DA, Solis PN, Nuñez MJ, Pérez JRR, Sánchez HAV, Hernández HFC, Martinez OMM, Franco JLM. Latin american natural product database (LANaPDB): an update [Internet]. Journal of Chemical Information and Modeling. 2024 ; No 2024( 22): 8495-8509.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.4c01560
Vancouver
Garcia AG, Jiménez DAA, Zamora WJ, Ccahuana HLB, Fumagalli MAC, Valli M, Andricopulo AD, Bolzani V da S, Olmedo DA, Solis PN, Nuñez MJ, Pérez JRR, Sánchez HAV, Hernández HFC, Martinez OMM, Franco JLM. Latin american natural product database (LANaPDB): an update [Internet]. Journal of Chemical Information and Modeling. 2024 ; No 2024( 22): 8495-8509.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.4c01560
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: 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
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
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: 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
Discovery of new Zika protease and polymerase inhibitors through the open science collaboration Project OpenZika (2022)
Mottin, Melina ; Sousa, Bruna Katiele de Paula ; Mesquita, Nathalya Cristina de Moraes Roso; Oliveira, Ketllyn Irene Zagato de; Noske, Gabriela Dias; Sartori, Geraldo Rodrigues ; Albuquerque, Aline de Oliveira; Urbina, Fabio; Puhl, Ana C. ; Moreira Filho, José Teófilo; Souza, Guilherme Eduardo de ; Guido, Rafael Victorio Carvalho ; Muratov, Eugene ; Neves, Bruno Junior ; Silva, João Hermínio Martins da ; Clark, Alex E.; Siqueira Neto, Jair L. ; Perryman, Alexander L.; Oliva, Glaucius ; Ekins, Sean ; Andrade, Carolina Horta
Fonte: Journal of Chemical Information and Modeling. Unidade: IFSC
Assuntos: ZIKA VÍRUS, PLANEJAMENTO DE FÁRMACOS, ANTIVIRAIS
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ABNT
MOTTIN, Melina et al. Discovery of new Zika protease and polymerase inhibitors through the open science collaboration Project OpenZika. Journal of Chemical Information and Modeling, v. 62, n. 24, p. 6825-6843, 2022Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.2c00596. Acesso em: 08 out. 2025.
APA
Mottin, M., Sousa, B. K. de P., Mesquita, N. C. de M. R., Oliveira, K. I. Z. de, Noske, G. D., Sartori, G. R., et al. (2022). Discovery of new Zika protease and polymerase inhibitors through the open science collaboration Project OpenZika. Journal of Chemical Information and Modeling, 62( 24), 6825-6843. doi:10.1021/acs.jcim.2c00596
NLM
Mottin M, Sousa BK de P, Mesquita NC de MR, Oliveira KIZ de, Noske GD, Sartori GR, Albuquerque A de O, Urbina F, Puhl AC, Moreira Filho JT, Souza GE de, Guido RVC, Muratov E, Neves BJ, Silva JHM da, Clark AE, Siqueira Neto JL, Perryman AL, Oliva G, Ekins S, Andrade CH. Discovery of new Zika protease and polymerase inhibitors through the open science collaboration Project OpenZika [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 24): 6825-6843.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00596
Vancouver
Mottin M, Sousa BK de P, Mesquita NC de MR, Oliveira KIZ de, Noske GD, Sartori GR, Albuquerque A de O, Urbina F, Puhl AC, Moreira Filho JT, Souza GE de, Guido RVC, Muratov E, Neves BJ, Silva JHM da, Clark AE, Siqueira Neto JL, Perryman AL, Oliva G, Ekins S, Andrade CH. Discovery of new Zika protease and polymerase inhibitors through the open science collaboration Project OpenZika [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 24): 6825-6843.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00596
Artigo de periodico
Assessment of Reversibility for Covalent Cysteine Protease Inhibitors Using Quantum Mechanics/Molecular Mechanics Free Energy Surfaces (2022)
Santos, Alberto M. Dos; Oliveira, Amanda Ruslana Santana; Costa, Clauber H. S. da; Kenny, Peter W.; Montanari, Carlos Alberto ; Varela Júnior, Jaldyr de Jesus G.; Lameira, Jerônimo
Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC
Assuntos: AMINOÁCIDOS, MECÂNICA QUÂNTICA, ENERGIA
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ABNT
SANTOS, Alberto M. Dos et al. Assessment of Reversibility for Covalent Cysteine Protease Inhibitors Using Quantum Mechanics/Molecular Mechanics Free Energy Surfaces. Journal of Chemical Information and Modeling, v. 62, p. 4083-4094, 2022Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.2c00466. Acesso em: 08 out. 2025.
APA
Santos, A. M. D., Oliveira, A. R. S., Costa, C. H. S. da, Kenny, P. W., Montanari, C. A., Varela Júnior, J. de J. G., & Lameira, J. (2022). Assessment of Reversibility for Covalent Cysteine Protease Inhibitors Using Quantum Mechanics/Molecular Mechanics Free Energy Surfaces. Journal of Chemical Information and Modeling, 62, 4083-4094. doi:10.1021/acs.jcim.2c00466
NLM
Santos AMD, Oliveira ARS, Costa CHS da, Kenny PW, Montanari CA, Varela Júnior J de JG, Lameira J. Assessment of Reversibility for Covalent Cysteine Protease Inhibitors Using Quantum Mechanics/Molecular Mechanics Free Energy Surfaces [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62 4083-4094.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00466
Vancouver
Santos AMD, Oliveira ARS, Costa CHS da, Kenny PW, Montanari CA, Varela Júnior J de JG, Lameira J. Assessment of Reversibility for Covalent Cysteine Protease Inhibitors Using Quantum Mechanics/Molecular Mechanics Free Energy Surfaces [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62 4083-4094.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00466
Artigo de periodico
Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder (2022)
Oliveira, Andre F ; Silva, Juarez Lopes Ferreira da ; Quiles, Marcos Gonçalves
Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC
Assuntos: ENERGIA, MOLÉCULA
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ABNT
OLIVEIRA, Andre F e SILVA, Juarez Lopes Ferreira da e QUILES, Marcos Gonçalves. Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder. Journal of Chemical Information and Modeling, v. 62, p. 817−828, 2022Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.1c01573. Acesso em: 08 out. 2025.
APA
Oliveira, A. F., Silva, J. L. F. da, & Quiles, M. G. (2022). Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder. Journal of Chemical Information and Modeling, 62, 817−828. doi:10.1021/acs.jcim.1c01573
NLM
Oliveira AF, Silva JLF da, Quiles MG. Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62 817−828.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c01573
Vancouver
Oliveira AF, Silva JLF da, Quiles MG. Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62 817−828.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c01573
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
Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC
Assuntos: 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
Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining (2022)
Moraes, Alex S.; Pinheiro, Gabriel A.; Lourenço, Tuanan da Costa ; Lopes, Mauro C.; Quiles, Marcos G.; Dias, Luis G. ; Silva, Juarez Lopes Ferreira da
Fonte: Journal of Chemical Information and Modeling. Unidades: FFCLRP, IQSC
Assuntos: ÍONS ELETRÔNICOS, ESTRUTURA ATÔMICA (QUÍMICA TEÓRICA), ENERGIA
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ABNT
MORAES, Alex S. et al. Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining. Journal of Chemical Information and Modeling, v. 62, n. 19, p. 4702–4712, 2022Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.2c00748. Acesso em: 08 out. 2025.
APA
Moraes, A. S., Pinheiro, G. A., Lourenço, T. da C., Lopes, M. C., Quiles, M. G., Dias, L. G., & Silva, J. L. F. da. (2022). Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining. Journal of Chemical Information and Modeling, 62( 19), 4702–4712. doi:10.1021/acs.jcim.2c00748
NLM
Moraes AS, Pinheiro GA, Lourenço T da C, Lopes MC, Quiles MG, Dias LG, Silva JLF da. Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 19): 4702–4712.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00748
Vancouver
Moraes AS, Pinheiro GA, Lourenço T da C, Lopes MC, Quiles MG, Dias LG, Silva JLF da. Screening of the Role of the Chemical Structure in the Electrochemical Stability Window of Ionic Liquids: DFT Calculations Combined with Data Mining [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 19): 4702–4712.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.2c00748
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
Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC
Assuntos: 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
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: 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
Energy Decomposition to Access the Stability Changes Induced by CO Adsorption on Transition-Metal 13-Atom Clusters (2021)
Batista, Krys E. A.; Soares, Marinalva D.; Quiles, Marcos G.; Piotrowski, Maurício J.; Da Silva, Juarez Lopes Ferreira
Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC
Assuntos: METAIS, ADSORÇÃO, FÍSICO-QUÍMICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BATISTA, Krys E. A. et al. Energy Decomposition to Access the Stability Changes Induced by CO Adsorption on Transition-Metal 13-Atom Clusters. Journal of Chemical Information and Modeling, v. 61, n. 5, p. 2294–2301, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.1c00097. Acesso em: 08 out. 2025.
APA
Batista, K. E. A., Soares, M. D., Quiles, M. G., Piotrowski, M. J., & Da Silva, J. L. F. (2021). Energy Decomposition to Access the Stability Changes Induced by CO Adsorption on Transition-Metal 13-Atom Clusters. Journal of Chemical Information and Modeling, 61( 5), 2294–2301. doi:10.1021/acs.jcim.1c00097
NLM
Batista KEA, Soares MD, Quiles MG, Piotrowski MJ, Da Silva JLF. Energy Decomposition to Access the Stability Changes Induced by CO Adsorption on Transition-Metal 13-Atom Clusters [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 5): 2294–2301.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c00097
Vancouver
Batista KEA, Soares MD, Quiles MG, Piotrowski MJ, Da Silva JLF. Energy Decomposition to Access the Stability Changes Induced by CO Adsorption on Transition-Metal 13-Atom Clusters [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 5): 2294–2301.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c00097
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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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
Fonte: Journal of Chemical Information and Modeling. Unidades: IQSC, IFSC
Assunto: METAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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
Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition (2021)
Azevedo, Luis Cesar de; Pinheiro, Gabriel A.; Quiles, Marcos G.; Silva, Juarez Lopes Ferreira da ; Prati, Ronaldo C.
Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC
Assuntos: QUÍMICA QUÂNTICA, ALGORITMOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
AZEVEDO, Luis Cesar de et al. Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition. Journal of Chemical Information and Modeling, v. 61, p. 4210−4223, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.1c00503. Acesso em: 08 out. 2025.
APA
Azevedo, L. C. de, Pinheiro, G. A., Quiles, M. G., Silva, J. L. F. da, & Prati, R. C. (2021). Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition. Journal of Chemical Information and Modeling, 61, 4210−4223. doi:10.1021/acs.jcim.1c00503
NLM
Azevedo LC de, Pinheiro GA, Quiles MG, Silva JLF da, Prati RC. Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 4210−4223.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c00503
Vancouver
Azevedo LC de, Pinheiro GA, Quiles MG, Silva JLF da, Prati RC. Systematic Investigation of Error Distribution in Machine Learning Algorithms Applied to the Quantum-Chemistry QM9 Data Set Using the Bias and Variance Decomposition [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 4210−4223.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c00503
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
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
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: 08 out. 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 out. 08 ] 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 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.1c00515
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
Fonte: Journal of Chemical Information and Modeling. Unidade: IFSC
Assuntos: CORONAVIRUS, COVID-19, 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. 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

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