ReP USP - Resultado da busca

Artigo de periodico
Tunneling and nonadiabatic effects on a proton-coupled electron transfer model for the Qo site in cytochrome bc1 (2021)
Camilo, Sofia Rodrigues Guedes; Curtolo, Felipe ; Galassi, Vanesa V; Arantes, Guilherme Menegon
Fonte: Journal of Chemical Information and Modeling. Unidade: IQ
Assuntos: ELÉTRONS, PEPTÍDEOS, PROTEÍNAS
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ABNT
CAMILO, Sofia Rodrigues Guedes et al. Tunneling and nonadiabatic effects on a proton-coupled electron transfer model for the Qo site in cytochrome bc1. Journal of Chemical Information and Modeling, v. 61, p. 1840−1849, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.1c00008. Acesso em: 09 nov. 2025.
APA
Camilo, S. R. G., Curtolo, F., Galassi, V. V., & Arantes, G. M. (2021). Tunneling and nonadiabatic effects on a proton-coupled electron transfer model for the Qo site in cytochrome bc1. Journal of Chemical Information and Modeling, 61, 1840−1849. doi:10.1021/acs.jcim.1c00008
NLM
Camilo SRG, Curtolo F, Galassi VV, Arantes GM. Tunneling and nonadiabatic effects on a proton-coupled electron transfer model for the Qo site in cytochrome bc1 [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 1840−1849.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.1c00008
Vancouver
Camilo SRG, Curtolo F, Galassi VV, Arantes GM. Tunneling and nonadiabatic effects on a proton-coupled electron transfer model for the Qo site in cytochrome bc1 [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 1840−1849.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.1c00008
Artigo de periodico
How the strain origin of Zika Virus NS1 protein impacts its dynamics and implications to their differential virulence (2021)
Poveda Cuevas, Sergio Alejandro; Silva, Fernando Luís Barroso da ; Etchebest, Catherine
Fonte: Journal of Chemical Information and Modeling. Unidades: FCFRP, Interunidades em Bioinformática
Assuntos: ZIKA VÍRUS, VIRULÊNCIA, FLAVIVIRUS
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ABNT
POVEDA CUEVAS, Sergio Alejandro e SILVA, Fernando Luís Barroso da e ETCHEBEST, Catherine. How the strain origin of Zika Virus NS1 protein impacts its dynamics and implications to their differential virulence. Journal of Chemical Information and Modeling, v. 61, n. 3, p. 1516-1530, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.0c01377. Acesso em: 09 nov. 2025.
APA
Poveda Cuevas, S. A., Silva, F. L. B. da, & Etchebest, C. (2021). How the strain origin of Zika Virus NS1 protein impacts its dynamics and implications to their differential virulence. Journal of Chemical Information and Modeling, 61( 3), 1516-1530. doi:10.1021/acs.jcim.0c01377
NLM
Poveda Cuevas SA, Silva FLB da, Etchebest C. How the strain origin of Zika Virus NS1 protein impacts its dynamics and implications to their differential virulence [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 3): 1516-1530.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.0c01377
Vancouver
Poveda Cuevas SA, Silva FLB da, Etchebest C. How the strain origin of Zika Virus NS1 protein impacts its dynamics and implications to their differential virulence [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 3): 1516-1530.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.0c01377
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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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: 09 nov. 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 nov. 09 ] 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 nov. 09 ] 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
Fonte: Journal of Chemical Information and Modeling. Unidade: IFSC
Assuntos: 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: 09 nov. 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 nov. 09 ] 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 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.1c00166
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
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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: 09 nov. 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 nov. 09 ] 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 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.1c00097
Artigo de periodico
Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters (2021)
Mucelini, Johnatan ; Quiles, Marcos Gonçalves ; Prati, Ronaldo Cristiano ; Silva, Juarez Lopes Ferreira da
Fonte: Journal of Chemical Information and Modeling. Unidade: IQSC
Assuntos: QUÍMICA QUÂNTICA, MINERAÇÃO DE DADOS, FRAMEWORKS
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ABNT
MUCELINI, Johnatan et al. Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters. Journal of Chemical Information and Modeling, v. 61, p. 1125-1135, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.0c01267. Acesso em: 09 nov. 2025.
APA
Mucelini, J., Quiles, M. G., Prati, R. C., & Silva, J. L. F. da. (2021). Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters. Journal of Chemical Information and Modeling, 61, 1125-1135. doi:10.1021/acs.jcim.0c01267
NLM
Mucelini J, Quiles MG, Prati RC, Silva JLF da. Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 1125-1135.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.0c01267
Vancouver
Mucelini J, Quiles MG, Prati RC, Silva JLF da. Correlation-based framework for extraction of insights from quantum chemistry databases: Applications for nanoclusters [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61 1125-1135.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.0c01267
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
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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: 09 nov. 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 nov. 09 ] 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 nov. 09 ] 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
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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: 09 nov. 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 nov. 09 ] 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 nov. 09 ] 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
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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: 09 nov. 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 nov. 09 ] 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 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.1c00460
Artigo de periodico
The SistematX web portal of natural products: an update (2021)
Costa, Renan P. O.; Lucena, Lucas F.; Silva, Lorena Mara A.; Zocolo, Guilherme Julião; Herrera-Acevedo, Chonny; Scotti, Luciana; Costa, Fernando Batista da ; Ionov, Nikita; Poroikov, Vladimir; Muratov, Eugene N.; Scotti, Marcus T.
Fonte: Journal of Chemical Information and Modeling. Unidade: FCFRP
Assuntos: PRODUTOS NATURAIS, METABÓLITOS SECUNDÁRIOS, FÁRMACOS, BANCO DE DADOS
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ABNT
COSTA, Renan P. O. et al. The SistematX web portal of natural products: an update. Journal of Chemical Information and Modeling, v. 61, n. 6, p. 2516-2522, 2021Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.1c00083. Acesso em: 09 nov. 2025.
APA
Costa, R. P. O., Lucena, L. F., Silva, L. M. A., Zocolo, G. J., Herrera-Acevedo, C., Scotti, L., et al. (2021). The SistematX web portal of natural products: an update. Journal of Chemical Information and Modeling, 61( 6), 2516-2522. doi:10.1021/acs.jcim.1c00083
NLM
Costa RPO, Lucena LF, Silva LMA, Zocolo GJ, Herrera-Acevedo C, Scotti L, Costa FB da, Ionov N, Poroikov V, Muratov EN, Scotti MT. The SistematX web portal of natural products: an update [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 6): 2516-2522.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.1c00083
Vancouver
Costa RPO, Lucena LF, Silva LMA, Zocolo GJ, Herrera-Acevedo C, Scotti L, Costa FB da, Ionov N, Poroikov V, Muratov EN, Scotti MT. The SistematX web portal of natural products: an update [Internet]. Journal of Chemical Information and Modeling. 2021 ; 61( 6): 2516-2522.[citado 2025 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.1c00083
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
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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: 09 nov. 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 nov. 09 ] 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 nov. 09 ] 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
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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: 09 nov. 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 nov. 09 ] 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 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.1c00683

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