Combinação de Fatores de Escalonamentos de Deslocamentos Químicos de RMN de 13C e 1H (baseados em Regressões Lineares) e de Redes Neurais para Auxiliar a Determinação Estrutural da Savinina / Combination of Scaling Factors of 13C and 1H NMR Chemical Shifts (based on Linear Regressions) and Neural Networks to Aid the Structural Determination of Savinin

Authors

  • Evani Ferreira Cardoso Brazilian Journals Publicações de Periódicos, São José dos Pinhais, Paraná
  • Geomar Souza Alves
  • Sara Sâmitha Souza
  • Ana Carolina Ferreira de Albuquerque
  • Roberto Carlos Campos Martins
  • Rodrigo de Souza Miranda
  • Fernanda Barbosa da Silva
  • Marcelo Ricardo Souza Siqueira
  • Fernando Martins dos Santos Junior
  • Gunar Vingre da Silva Mota
  • Antonio Maia de Jesus Chaves Neto
  • Fabio Luiz Paranhos Costa

DOI:

https://doi.org/10.34117/bjdv7n3-147

Keywords:

ANN-PRA, Savinina, GIAO

Abstract

Este é um trabalho teórico-experimental, onde a molécula de foco do estudo é a savinina, um lignano do tipo dibenzilbutyrolactónico, substâncias que podem ser encontradas em vários géneros, um dos quais com maior ocorrência é o género Acanthopanax (Araliaceae) que é tradicionalmente utilizado como analgésico e estimulante do sistema imunitário, para além de exibir uma potente actividade insecticida e citotóxica para células do carcinoma do cólon humano HCT116. Foi isolado e aqui apresentamos a sua caracterização experimental e teórica através de dados 13C e 1H NMR e a possível confirmação da estrutura utilizando a ferramenta de rede neural (ANN-PRA). O objectivo deste trabalho é utilizar cálculos teóricos de 13C e 1H NMR e dados experimentais para a resolução da estrutura da savana, e a utilização da ferramenta de rede neural (ANN-PRA) para confirmar a estrutura da molécula.

References

. Charlton, J.L. 1998. Antiviral activity of lignans. Journal Natural Products, 61(11), p.1447-1451

. Moss, G.P. 2000. Nomenclature of lignanas and neolignans. Pure and Applied Chemistry, 72(8), p.1493-1523.

. Jain, R., Bally, T. and Rablen, P.R. 2009. Calculating Accurate Proton Chemical Shifts of Organic Molecules with Density Functional Methods and Modest Basis Sets. The Journal of Organic Chemistry, 74(11), p.4017–4023.

de Albuquerque, A.C.F., Ribeiro, D.J. and de Amorim, M.B. 2016. Structural determination of complex natural products by quantum mechanical calculations of 13C NMR chemical shifts: development of a parameterized protocol for terpenes. Journal of Molecular Modeling, 22(8), pp.183-189.

Costa, F.L.P, Fernandes, S.B.deO., Fingolo, C.E., Boylan, F., de Jesus Chaves Neto, A.M., Mota, G.V.daS., Soares, B.A., Siqueira, M.R.S and Giacomello, T.F., 2020. Experimental and Theoretical Nuclear Magnetic Resonance Data from Tryptanthrin, an Alkaloid with Potential Activity Against Human Coronavirus. Advance Science, Engineering and Medicine, 12 (7), pp.963-969.

Jalowitzki, S.A, Giacomello, T.F., Mota, G.V.daS., de Jesus Chaves Neto, A.M. and Costa, F.L.P., 2020. An Application of the PCM Model for Obtaining Chalcones Magnetic Properties. Advance Science, Engineering and Medicine, 12 (7), pp.939-950.

Cardoso, E.F., de Albuquerque, A.C.F., de Jesus Chaves Neto, A.M., Mota, G.V.daS. and Costa, F.L.P., 2020. Gauge-Including-Atomic-Orbitals-mPW1PW91/6-31G(d) Scaling Factor as a Satisfactory Cost-Effectiveness Ratio for H-1 Nuclear Magnetic Resonance Chemical Shift Calculations. Advance Science, Engineering and Medicine, 12 (8), pp. 1095-1101.

Rocha, R. A. deM., Giacomello, T.F., de Jesus Chaves Neto, A.M., Mota, G.V.daS. and Costa, F.L.P., 2020. Diferenciação de Triterpenos Regiosoméricos por Meio de Cálculos de Deslocamento Químico de RMN de 13C. Revista Virtual de Química, 12(4), pp. 969-980.

Giacomello, T.F., Mota, G.V.daS., de Jesus Chaves Neto, A.M. and Costa, F.L.P., 2020. Use of Replaced Chalcones to Generate a 13C Chemical Shift Staging Factor for Chalcone and Its Derivate. Advance Science, Engineering and Medicine, 12(4), pp.464-472.

Souza, S.S., Martins, M.A.deS., Neto, A.M.deJ.C.; Mota, G.V.daS. And Costa, F.L.P., 2020. Systematic Gauge-Including Atomic Orbital-Hybrid Density Functional Theory Linear Regressions for 13C NMR Chemical Shifts Calculation. Advanced Science, Engineering and Medicine, 12(3), pp.364-370.

Prado, G.daS., Giacomello, T.F., Wulff, L.B., Siqueira, M.R.S., Mota, G.V.daS., Neto, A.M. deJ.C., Moraes, E.dosS. and Costa, F.L.P., 2019. Chemical discharge of 13C calculated for efavirenz. Brazilian Journal of Development, 5(11), pp.25698-25703.

Wulff, L.B., Prado, G.daS., Giacomello, T.F., Siqueira, M.R.S., Mota, G.V.daS., Neto, A.M. deJ.C., Moraes, E.dosS. and Costa, F.L.P., 2019. Oseltamivir, a 13C chemical displacement correlation. Brazilian Journal of Development, 5(12), pp. 32129-32135.

Costa, F.L.P., Fernandes, S.B.deO.; Fingolo, C.E., Boylan, F. and Mota G.V.daS., 2017. Tryptanthrin (indolo [2,1-b] quinazoline-6,12-dione) Isolation from Leaves of Couroupita guianensis and Its Characterization by NMR Experimental and GIAO-DFT Data. Journal of Computational and Theoretical Nanoscience, 14(5), pp.2383-2388.

Costa, F.L.P., Giacomello, T.F., de Morais Rocha, R.A., de Jesus Chaves Neto, A.M. and Mota, G.V.D.S., 2017. Very Fast and Surprisingly Accurate GIAO-mPW1PW91/3-21G//PM7 Scaling Factor for 13C NMR Chemical Shifts Calculation. Advance Science, Engineering and Medicine, 9(3), pp.254–61.

Giacomello, T.F., de Morais Rocha, R.A., de Jesus Chaves Neto, A.M., Mota, G.V.daS. and Costa, F.L.P., 2017. Protocol for Calculating 13C Nuclear Magnetic Resonance Chemical Shifts of Flexible Organic Molecules. Advance Science, Engineering and Medicine, 9(8), pp. 640–647.

Costa, F.L.P., de Fernandes, S.B.O., Fingolo, C.E., Boylan, F., de Albuquerque, A.C.F., dos Santos Junior, F.M., de Amorim, M.B., 2016. Isolation, Identification, Relative Configuration and Conformational Analysis of Loliolide by GIAO-HDFT 1H and 13C NMR Chemical Shifts Calculations, Quantum Matter, 5(5), pp.675-679.

Costa, F.L.P., de Albuquerque, A.C.F., Borges, R.M., dos Santos Junior, F.M. and de Amorim, M.B., 2014. High Cost-Effectiveness Ratio: GIAO-MPW1PW91/6-31G(d)//MPW1PW91/6-31G(d) Scaling Factor for 13C Nuclear Magnetic Resonance Chemical Shifts Calculation. Journal of Computational and Theoretical Nanoscience, 11(1), pp.219–225.

Costa, F.L.P. and de Amorim, M.B., 2011. GIAO-B3LYP low computational cost scaling factor for 13C NMR chemical shifts calculation. Journal of Computational and Theoretical Nanoscience, 8(7), pp.1166-1172.

Costa, F.L.P., De Albuquerque, A.C.F., Dos Santos, F.M. and de Amorim, M.B., 2010. GIAO-HDFT scaling factor for 13C NMR chemical shifts calculation. Journal of Physical Organic Chemistry, 23(10), pp.972–977.

Dos s., F., Velozo, L., De Carvalho, E., M., A., Borges, R., Trindade, A., Dos Santos, M., de Albuquerque, A., Costa, F., Kaplan, M., De Amorim, M., 2013. 3-Ishwarone, a Rare Ishwarane Sesquiterpene from Peperomia scandens Ruiz. Molecules (Basel. Online), 18 (11), pp. 13520-13529.

Mota, E.A.V., Neto, A.F.G., Marques, F.C., Mota, G.V.S., Martins, M.G., Costa, F.L.P., Borges, R.S. and Neto, A.M.J.C. 2018. Time-Dependent Density Functional Theory Analysis of Triphenylamine-Functionalized Graphene Doped with Transition Metals for Photocatalytic Hydrogen Production. Journal of Nanoscience and Nanotechnology, 18(7) pp.4987-4991.

Costa, F., 2006. Electronic structure study of the reaction C2H4+?C2H2++H2. International Journal of Quantum Chemistry, 106(13), pp.2763–2771.

Mota, G.V. daS., Oliveira, C. X., Neto, A.M.J.C. and Costa, F.L.P. Inclusion Complexation of Praziquantel and -Cyclodextrin, Combined Molecular Mechanic and Monte Carlo Simulation, 2012. Journal of Computational and Theoretical Nanoscience, 9(8), pp.1090-1095.

Costa, F.L. P. and de Amorim, M.B. Theoretical Study on Styrenes Planarity: Styrene and p-Hydroxi-Styrene, 2010. Advanced Science Letters, 3(4) pp.507-511.

Costa, F.L.P, Gomes, P.F., Silva, A.K. and L. M. Lião, L. M. Conformational Analysis, Experimental and GIAO-DFT 13C NMR Chemical Shift Calculation on 2'-Hydroxy-3,4,5-trimethoxy-chalcone, 2017. Journal of the Brazilian Chemical Society, 28(11) pp.2130-2135.

. Mansoor, T.A., Borralho, P.M., Luo, X., Mulhovo, S., Rodrigues, M.P. and Ferreira, M.U. 2013. Apoptosis inducing activity ofbenzophenanthridine-type alkaloids and 2 arylbenzofuran neolignans in HCT116 colon carcinoma cells. Phytomedicine, 20(10), p.923–929.

. Zanardi, M. M. and Sarotti, A. M.; J. 2015. GIAO C–H COSY simulations merged with artificial neural networks pattern recognition analysis. The Journal of organic chemistry, 80(19), p.9371-9378.

Frisch, M.J., Trucks, G.W., Schlegel, H.B., Scuseria, G.E., Robb, M.A., Cheeseman, J.R., Scalmani, G., Barone, V., Mennucci, B., Petersson, G.A., Nakatsuji, H., Caricato, M., Li, X., Hratchian, H.P., Izmaylov, A.F., Bloino, J., Zheng, G., Sonnenberg, J.L., Hada, M., Ehara, M., Toyota, K., Fukuda, R., Hasegawa, J., Ishida, M., Nakajima, T., Honda,Y., Kitao, O., Nakai, H., Vreven, T., Montgomery, J.A., Peralta, J.E., Ogliaro, F., Bearpark, M., Heyd, J.J., Brothers, E., Kudin, K.N., Straroverov, V.N., Kobayashi, R., Normand, J., Raghavachari, K., Rendell, A., Burant, J.C., Iyengar, S.S., Tomasi, J., Cossi, M., Rega, N., Millam, J.M., Klene, M., Knox, J.E., Cross, J.B., Bakken, V., Adamo, C., Jaramillo, J., Gomperts, R., Stratmann, R.E., Yazyev, O., Austin, A.J., Cammi, R., Pomelli, C., Ochterski, J.W., Martin, R.L., Morokuma, K., Zakrzewski, V.G., Voth, G.A., Salvador, P., Dannenberg, J.J., Dapprich, S., Daniels, A.D., Farkas, Foresman, J.B., Ortiz, J.V., Cioslowski, J. and Fox, D.J., 2009. Gaussian 09, revision b.01.

Published

2021-03-08

How to Cite

Cardoso, E. F., Alves, G. S., Souza, S. S., de Albuquerque, A. C. F., Martins, R. C. C., Miranda, R. de S., da Silva, F. B., Siqueira, M. R. S., Junior, F. M. dos S., Mota, G. V. da S., Neto, A. M. de J. C., & Costa, F. L. P. (2021). Combinação de Fatores de Escalonamentos de Deslocamentos Químicos de RMN de 13C e 1H (baseados em Regressões Lineares) e de Redes Neurais para Auxiliar a Determinação Estrutural da Savinina / Combination of Scaling Factors of 13C and 1H NMR Chemical Shifts (based on Linear Regressions) and Neural Networks to Aid the Structural Determination of Savinin. Brazilian Journal of Development, 7(3), 22930–22939. https://doi.org/10.34117/bjdv7n3-147

Issue

Section

Original Papers