**3. MEP and PR techniques as tools to design nitrofuran compounds with biological activity against** *T. cruzi*

#### **3.1 Computational**

*Cheminformatics and Its Applications*

phenomena is well documented [2–18].

directly measured property of the object [19].

accumulated perceptions in the MEP studies.

**of bioactive compounds: a brief review**

Reports of theoretical bases of MEP and the development of efficient computational methods state that MEP has become an important reactivity index in studies of a large variety of molecular interactions [1]. The usefulness of this theoretical approach in studies and interpretation of chemical, biochemical, and related

Chemometrics is a discipline that collects mathematical, statistical, information theory, and computer science tools to deal with complex chemical data [19–22]. PR techniques were introduced in the chemistry, at the beginning of the 1970s, to analyze various types of spectroscopic data. Since then, PR became part of chemometrics and has been an excellent tool to aid in the interpretation of chemical data to obtain relevant information in different application sectors of chemical science [19, 20]. PR techniques are especially useful for the classification of objects into discrete classes on the basis of measured features. A set of characteristic features of an object is considered as an abstract pattern that contains information about a not

The MEP and PR techniques have been used as independent strategies in the study of active compounds and lead to the proposal of new molecules for synthesis and biological testing. The joint applications of these powerful tools were described carefully, to unravel the structure-activity relationship of bioactive compounds, consequently proposing new molecules. Therefore, a more intense exploration of its

The design of molecules with a desired property is one of the objectives of chemoinformatics. In this chapter, we present a study of the application of MEP and PR techniques to design nitrofuran compounds with potential activity against *T. cruzi.* In the first step of our study, MEP maps will be used in an attempt to identify the key structural features of nitrofuran compounds that are necessary for their activities and investigate their probable interactions with a molecular receptor through recognition in a biological process. Subsequently, PR techniques are used to construct models that will be applied later to a forecast set constructed with the

potentials is needed in order to design biologically active compounds.

**2. MEP and chemometrics techniques as tools for the design** 

According to the literature, MEP [1, 3] has been a tool of quantum chemistry used by researchers for several decades to study and understand the relationships between structure and activity of molecules. Among the papers that point out the importance of this tool in the matter, and consequently in the planning of bioactive compounds, we can mention those reported by Bernardinelli et al. [23] and by

Another tool, in the form of a set of techniques has been used emphatically over the years in the understanding of the structure-activity relationship of molecules is Chemometrics [25–27]. This set of techniques has also enables the planning of new biologically active compounds, and most of the developed research is focused on the construction of QSAR (quantitative structure-activity relationship) models. The combination of MEP and chemometrics as tools for designing new bioactive compounds has almost always been focused on the elaboration of quantitative models, for example, the CoMFA methodology [28]. This methodology was developed in the late 1980s by Cramer et al. [29]. Its application is richly extensive and recently it has been used in several studies of structure–activity relationships of bioactive

**1. Introduction**

**48**

Jefford et al. [24].

## *3.1.1 Biological recognition process ligand/receptor through the molecular electrostatic potential*

The MEP is also suitable for analyzing processes based on the "recognition" of one molecule by another as in drug-receptor and enzyme-substrate interactions, because it is through their potentials that the two species first "see" each other [2, 3, 44–46].

MEP for the electronic density is a very useful property for understanding the site of electrophilic attack and nucleophilic reactions as well as the hydrogen bonding interactions [46]. The MEP at a given point (x, y, z) in the vicinity of a molecule is defined in terms of the interaction energy between the electrical charge generated from the molecule's electrons and nuclei and a positive charge test (a proton) located at <sup>→</sup> *r*. Being a real physical property, MEP can be determined experimentally by diffraction or by computational tools [3]. For the studied nitrofuran molecules, the MEP values were computed through Eq. (1) [45] ρ(→*r*′) *d*→ \_*r*′

## different or by computational tools [ $\beta$ ]. For the studied attractor and becomes  $\mathbf{MEP}$  values were computed through Eq. (1) [45]

$$\mathbf{V}(\vec{r}) = \sum\_{j=1}^{K} \frac{Z\_j}{|\vec{R}\_j - \vec{r}|} - \int \frac{\rho(\vec{r}')d\vec{r}'}{|\vec{r}' - \vec{r}|}\tag{1}$$

where K is the number of nuclei with charges *Zj*, located at position *Rj* and *ρ (*<sup>→</sup> *r)* is the electronic charge density. The first term on the right side of Eq. (1) represents the contribution of the nuclei, which is positive; the second term brings in the effect of the electrons, which is negative. In the investigation of the reactive sites of nitrofuran compounds, the MEP was evaluated through of the HF/6-31G method.

## *3.1.2 RP techniques*

In this section, we will make a brief presentation of the PR techniques used in this chapter. A deeper and detailed description of these matters can be found elsewhere [47–66].

## *3.1.2.1 Principal component analysis (PCA) technique*

When computing large multivariate data, it is mandatory to find and reduce unknown data trends using exploratory tools. The main idea of the PCA technique is to reduce the dimensionality of a data set consisting of large numbers of interrelated variables while retaining the variation present in the data set as much as possible. This can be achieved by transforming them into a new set of variables, the PCs, which are uncorrelated and ordered so that the first few retain most of the variation present in all of the original variables. As the final result, the PCA technique performs the selection of a small number of variables (molecular properties) considered better related to the dependent property or feature [67], in this study, the biological activity against *T. cruzi*.
