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Oxidative Medicine and Cellular Longevity
Volume 2017 (2017), Article ID 3789856, 12 pages
https://doi.org/10.1155/2017/3789856
Research Article

The Correlation between Chemical Structures and Antioxidant, Prooxidant, and Antitrypanosomatid Properties of Flavonoids

1Chemistry Institute, Federal University of Alfenas (Unifal-MG), 37130-001 Alfenas, MG, Brazil
2Human and Natural Sciences Centre, Federal University of ABC (UFABC), 09210-580 Santo André, SP, Brazil
3Parasitology and Pathology Department, Federal University of Alfenas (Unifal-MG), 37130-000 Alfenas, MG, Brazil
4Department of Structural Biology, Federal University of Alfenas (Unifal-MG), 37130-000 Alfenas, MG, Brazil
5Department of Pharmacy, Federal University of Sergipe (UFS-SE), 491000-000 São Cristóvão, SE, Brazil

Correspondence should be addressed to Daniela Aparecida Chagas-Paula; moc.liamg@aluapsagahc.ad

Received 6 January 2017; Revised 23 March 2017; Accepted 20 April 2017; Published 2 July 2017

Academic Editor: Giuseppe Cirillo

Copyright © 2017 João Luiz Baldim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

Supplementary session. Supplementary Table S1 – The substitution pattern of all the 40 flavonoids divided by classes. Supplementary Table S2 – TEAC values with more than one value from the literature. Supplementary Table S3 – Prooxidant values to some flavonoids. Supplementary Table S4 – PaDEL descriptors to each QSAR model, meaning and features. Supplementary Table S5 – PaDEL descriptors of flavonoids important to describe activity against L. donovani*. Supplementary Table S6 – PaDEL descriptors of flavonoids important to describe activity against T. brucei*. Supplementary Table S7 – PaDEL descriptors of flavonoids important to describe activity against T. cruzi*. Supplementary Table S8 – PaDEL descriptors of flavonoids important to describe activity against L6 cells*. Supplementary Table S9 – PaDEL descriptors of flavonoids important to describe antioxidant activity (TEAC)*. Supplementary Table S10 – Results of ANNs external validation*. Supplementary Table S11 – SMILES for each flavonoid used in this work. Supplementary Figure S1. Artificial Neural Networks (ANNs) built on Weka 3.8.0 using the classifier Multilayer Perceptron. They have applicability domain for prediction of IC50 of flavonoids in L. donovani (R2=0.75, Q2=0.62, P2=0.67), T. cruzi (R2=0.94, Q2=0.67, P2=0.92), T. brucei (R2=0.93, Q2=0.73, P2=0.66) and TEAC (R2=0.89, Q2=0.72, P2=0.66). All of them show R2, Q2 and P2 lower than 0.41 for scramble test. Except ANN built to predict toxicity against L6 cells that not show predictive ability even robustness (R2=0.63, Q2=0.33, P2=0.48).

  1. Supplementary Material
  2. Supplementary Material