**Author details**

*Cheminformatics and Its Applications*

to obtain eight new derivatives.

protozoan [94].

**Figure 10.**

**Acknowledgements**

required for antitrypanosomal activity, along with chemical intuition, allowed the introduction of substituents in one of the most active nitrofurans in the training set

*MEP maps (kcal/mol) for most promising nitrofurans in the prediction set against T. cruzi.*

PR models (PCA, HCA, KNN, SDA, and SIMCA) were constructed and demonstrated that 23 nitrofurans can be classified into two classes or groups: more active and less active according to their degrees of activity against *T. cruzi*. The properties QN1, gap energy, Mor05u, and MlogP are responsible for the classification into more active and less active studied nitrofurans. It is interesting to notice that these properties represent three distinct classes of interactions between the nitrofurans and the biological receptor: electronic (QN1 and gap energy), steric (Mor05u), and hydrophobic (MlogP). Here it is important to mention that Paulino et al.*,* studying the influence of molecular parameters on the activity of 5-nitrofurans against *T. cruzi,* reported the importance of electronic properties and molecular hydrophobicity as well as the variation of the nitrofurans electronic structure to explain the greater activity of these compounds as inhibitors of the growth of this

The results of the application of PR models on the validation set evidenced two nitrofurans (**25** and **30**) as more promising for synthesis and biological assays,

We gratefully acknowledge the financial support of the Brazilian agencies: Conselho Nacional de Desenvolvimento Científico e Tecnológico and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior. The authors would like to thank the Virtual Computational Chemistry Laboratory (VCCLAB–Munich) and the Swiss Center for Scientific Computing for the use of the DRAGON and MOLEKEL software, respectively. We employed computing facilities at the Laboratório de Química Teórica e Computacional (LQTC)–Universidade Federal do Pará.

which in the future can be used to validate our PR models.

**66**

Marcos Antônio B. dos Santos1 , Luã Felipe S. de Oliveira2 , Antônio Florêncio de Figueiredo3 , Fábio dos Santos Gil2 , Márcio de Souza Farias2 , Heriberto Rodrigues Bitencourt4 , José Ribamar B. Lobato2 , Raimundo Dirceu de P. Farreira<sup>2</sup> , Sady Salomão da S. Alves3 , Edilson Luiz C. de Aquino2 and José Ciríaco-Pinheiro2 \*

1 University of the State of Pará, Pará, Brazil

2 Computational and Theoretical Chemistry Laboratory, Federal University of Pará, Pará, Brazil

3 Federal Institute of Education, Science and Technology, Pará, Brazil

4 Group of Organic Chemistry, Federal University of Pará, Pará, Brazil

\*Address all correspondence to: ciriaco@ufpa.br

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
