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Artigo de periodico
Flexibility and hydration of the Qo site determine multiple pathways for proton transfer in cytochrome bc1 (2025)
Camilo, Sofia Rodrigues Guedes; Arantes, Guilherme Menegon
Source: Journal of Chemical Information and Modeling. Unidade: IQ
Subjects: BIOQUÍMICA INORGÂNICA, MOLÉCULA, PEPTÍDEOS, PROTEÍNAS
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ABNT
CAMILO, Sofia Rodrigues Guedes e ARANTES, Guilherme Menegon. Flexibility and hydration of the Qo site determine multiple pathways for proton transfer in cytochrome bc1. Journal of Chemical Information and Modeling, v. 65, n. 12, p. 6184-6197, 2025Tradução . . Disponível em: https://dx.doi.org/10.1021/acs.jcim.5c00655. Acesso em: 08 out. 2025.
APA
Camilo, S. R. G., & Arantes, G. M. (2025). Flexibility and hydration of the Qo site determine multiple pathways for proton transfer in cytochrome bc1. Journal of Chemical Information and Modeling, 65( 12), 6184-6197. doi:10.1021/acs.jcim.5c00655
NLM
Camilo SRG, Arantes GM. Flexibility and hydration of the Qo site determine multiple pathways for proton transfer in cytochrome bc1 [Internet]. Journal of Chemical Information and Modeling. 2025 ; 65( 12): 6184-6197.[citado 2025 out. 08 ] Available from: https://dx.doi.org/10.1021/acs.jcim.5c00655
Vancouver
Camilo SRG, Arantes GM. Flexibility and hydration of the Qo site determine multiple pathways for proton transfer in cytochrome bc1 [Internet]. Journal of Chemical Information and Modeling. 2025 ; 65( 12): 6184-6197.[citado 2025 out. 08 ] Available from: https://dx.doi.org/10.1021/acs.jcim.5c00655
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
Source: Journal of Chemical Information and Modeling. Unidade: IFSC
Subjects: INTELIGÊNCIA ARTIFICIAL, MODELAGEM MOLECULAR, MOLÉCULA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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
Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder (2022)
Oliveira, Andre F ; Silva, Juarez Lopes Ferreira da ; Quiles, Marcos Gonçalves
Source: Journal of Chemical Information and Modeling. Unidade: IQSC
Subjects: 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
Source: Journal of Chemical Information and Modeling. Unidade: IQSC
Subjects: MODELAGEM MOLECULAR, MOLÉCULA, QUÍMICA TEÓRICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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
Self-assembly of phosphocholine derivatives using the ELBA coarse-grained model: micelles, bicelles, and reverse micelles (2020)
Souza, Rafael Maglia de ; Ratochinski, Rafael Henrique; Karttunen, Mikko; Dias, Luis Gustavo
Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP
Subjects: LIPÍDEOS, ELETROSTÁTICA, MOLÉCULA, QUÍMICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUZA, Rafael Maglia de et al. Self-assembly of phosphocholine derivatives using the ELBA coarse-grained model: micelles, bicelles, and reverse micelles. Journal of Chemical Information and Modeling, v. 60, n. 2, p. 522-536, 2020Tradução . . Disponível em: https://doi.org/10.1021/acs.jcim.9b00790. Acesso em: 08 out. 2025.
APA
Souza, R. M. de, Ratochinski, R. H., Karttunen, M., & Dias, L. G. (2020). Self-assembly of phosphocholine derivatives using the ELBA coarse-grained model: micelles, bicelles, and reverse micelles. Journal of Chemical Information and Modeling, 60( 2), 522-536. doi:10.1021/acs.jcim.9b00790
NLM
Souza RM de, Ratochinski RH, Karttunen M, Dias LG. Self-assembly of phosphocholine derivatives using the ELBA coarse-grained model: micelles, bicelles, and reverse micelles [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 2): 522-536.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.9b00790
Vancouver
Souza RM de, Ratochinski RH, Karttunen M, Dias LG. Self-assembly of phosphocholine derivatives using the ELBA coarse-grained model: micelles, bicelles, and reverse micelles [Internet]. Journal of Chemical Information and Modeling. 2020 ; 60( 2): 522-536.[citado 2025 out. 08 ] Available from: https://doi.org/10.1021/acs.jcim.9b00790

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