*3.2.3.5 SIMCA model*

The SIMCA model were built with the same descriptors as PCA, HCA, KNN, and SDA models and used two (2) PCs in the modeling of the two classes: more active nitrofurans (**4–7**, **10–12**, **18,** and **23**) and less active (**1–3, 8, 9, 13–17,** and **19–22**) nitrofurans. In **Table 7**, the obtained results for the SIMCA model are shown. In this case, the information percentage was also 100%. According to the PCA, HCA, KNN, SDA, and SIMCA models, we can also notice that the QN1, gap energy, Mor05u, and MlogP descriptors are key properties for explaining the anti-*T. cruzi* activity of the nitrofurans training set (**Figure 2**).

As QN1, gap energy, Mor05u, and MlogP properties were selected in the chemometric modeling as the most important characteristics to describe the antitrypanosomal activity, some considerations about them may be relevant to the understanding of the behavior of more active nitrofurans. According to classical chemical theory, chemical interactions can be classified in two categories: electrostatic (polar) or orbital (covalent). Electrical charges in the molecule are indubitably the impelling cause of electrostatic interactions. It has been demonstrated that local electron densities or charges are important in many chemical reactions, physicochemical properties, and ligand–receptor interactions [89, 90]. Thus, charge-based parameters have been widely employed as chemical reactivity indices or as measures of weak intermolecular interactions. Many quantum–chemical descriptors are derived from the partial charge distribution in a molecule or from the electron densities on particular atoms [91]. From **Table 2**, we can observe that, in general, QN1 for more active analogues must present lower values than the less active ones. This is an indication that biological processes can occur through electrostatic interactions between the more active nitrofurans and an eventual biological receptor.

Gap energy is an important stability index. A large gap energy implies high stability for the molecule in the sense of its lower reactivity in chemical reactions.


#### **Table 7.**

*Classification obtained by using SIMCA technique.*

It is an approximation of the lowest excitation energy of the molecule and can be used for the definition of absolute and activation hardness [89, 90]. In **Table 2**, we can observe that, in general, the more active nitrofurans present lower gap energy than the less active ones. This indicates that the more active nitrofurans have a great probability of interacting with the biological receptor through a charge transfer mechanism.

Mor05u is a 3D-MoRSE descriptor based on the idea of obtaining information from 3D atomic coordinates through the transformed used in electrons diffraction studies [91] and is strictly related to the stereochemistry of the compounds [92]. According to **Table 2**, the more active nitrofurans present lower values of Mor5u. This may be, in general, an indication of the importance of the stereochemical properties of the more active nitrofurans in a possible mechanism of action of its own.

MlogP is an important hydrophobic descriptor in diverse biochemical, pharmacological, and toxicological processes involved in drug absorption [93]. As identified in **Table 2**, the more active reported nitrofurans exhibit the higher MlogP values. This is an indication that in processes involving nitrofurans and a biological receptor, hydrophobic interactions may be important in the mechanism of action of these compounds.

Knowing the performance of the RP models constructed for the 23 studied nitrofurans, we decided to apply them to a series of eight compounds (**Figure 3**) designed to maintain the key structural features that are necessary for their biological activities evidenced by the MEP maps of the compounds of the training set. The basic nucleus of these compounds corresponds to that of the most active nitrofurans with double unsaturation, containing vicinal O atom to carbonyl (see compounds **10**–**12**). The eight molecules proposed for the study of prediction of activity were drawn with the help of one of the collaborators of this work, who belong to the research group in organic chemistry of the Federal University of Pará, Brazil, and the most promising syntheses are in progress. In the future, antitrypanosomal tests with the most promising nitrofurans can be used to validate our RP models.

The results obtained of the application of the PR models (PCA, HCA, KNN, SDA, and SIMCA) and the descriptors for the compounds of the prediction set are summarized in **Tables 8** and **9**, respectively. In **Table 8**, the compounds **25** and **30** were predicted as more active against *T. cruzi* with the five models. Only the KNN model predicted compound **26** as the most active. Meanwhile, only the PCA and HCA models predicted compound **31** as the most active. On the other hand, all models, except the SDA model, predicted compounds **24**, **27**, and **28** as the most active. In turn, the SIMCA model did not classify compounds **29** and **31** into any of the two classes. Thus, we can consider nitrofurans **25** and **30** as potentially more active in a future test against *T. cruzi*. For the values reported for compounds **25** and **30** (**Table 9**), it can be shown that in order to design more active nitrofurans we must combine smaller values for the descriptors QN1, gap energy, and Mor05u with higher value for the descriptor MlogP.

**65**

process.

**Table 8.**

**Table 9.**

**3.3 Concluding remarks**

*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive…*

*Results of application of chemometric models for the nitrofurans of the prediction set.*

**Nitrofuran PCA model HCA model KNN model SDA model SIMCA model** MA MA MA LA MA MA MA MA MA MA LA LA MA LA LA MA MA MA LA MA MA MA MA LA MA MA MA MA MA 0 MA MA MA MA MA MA MA LA LA 0

**Nitrofuran QN1 Gap energy (kcal/mol) Mor05u MLogP** 0.165 205.2 −6.352 3.155 0.165 203.3 −7.332 2.250 0.165 204.6 −5.835 1.146 0.169 203.9 −6.164 2.508 0.166 203.9 −7.146 1.875 0.164 229.7 −8.201 3.854 0.164 229.7 −6.421 3.373 0.164 223.4 −5.525 2.167

*DOI: http://dx.doi.org/10.5772/intechopen.89113*

*3.2.4 MEP maps for compounds of the prediction set*

*Values for descriptors for the prediction set.*

the range + 4.84 to +57.58 kcal/mol (green and blue).

**Figure 10** shows the MEP maps for the most active nitrofurans in the validation set (**25** and **30**). Also, in these compounds, as can be seen, raising the carbon chain increases the electron density in the atoms of the nitro group, extending through the O of the furan ring to the O atoms of the ester group accompanying the unsaturated chain. In these compounds, the MEP maps show more negative values between −74.27 and − 1.76 kcal/mol (red and yellow). They exhibit positive MEP in

The negative MEP region of compounds **25** and **30**, similar to the more active compounds in the training set, is susceptible to attack in a biological recognition

MEP and chemometric techniques in the last decades have become efficient tools in the study of the structure–activity relationships of bioactive molecules. The use of such tools has occurred through the inherent principles of each or combining their potentials to more efficiently unravel information about the structure–activity relationships of pharmacologically interesting compounds. This chapter is circumscribed in this second possibility. MEP maps were constructed for 23 nitrofurans with activity against *T. cruzi* reported in the literature. The key structural features

*Molecular Electrostatic Potential and Chemometric Techniques as Tools to Design Bioactive… DOI: http://dx.doi.org/10.5772/intechopen.89113*


#### **Table 8.**

*Cheminformatics and Its Applications*

TOTAL 23

*Classification obtained by using SIMCA technique.*

mechanism.

**Table 7.**

action of its own.

these compounds.

It is an approximation of the lowest excitation energy of the molecule and can be used for the definition of absolute and activation hardness [89, 90]. In **Table 2**, we can observe that, in general, the more active nitrofurans present lower gap energy than the less active ones. This indicates that the more active nitrofurans have a great probability of interacting with the biological receptor through a charge transfer

**Category Number of compounds Correct classification**

Class: more active 9 9 Class: less active 14 14

% correct information 100

Mor05u is a 3D-MoRSE descriptor based on the idea of obtaining information from 3D atomic coordinates through the transformed used in electrons diffraction studies [91] and is strictly related to the stereochemistry of the compounds [92]. According to **Table 2**, the more active nitrofurans present lower values of Mor5u. This may be, in general, an indication of the importance of the stereochemical properties of the more active nitrofurans in a possible mechanism of

MlogP is an important hydrophobic descriptor in diverse biochemical, pharmacological, and toxicological processes involved in drug absorption [93]. As identified in **Table 2**, the more active reported nitrofurans exhibit the higher MlogP values. This is an indication that in processes involving nitrofurans and a biological receptor, hydrophobic interactions may be important in the mechanism of action of

Knowing the performance of the RP models constructed for the 23 studied nitrofurans, we decided to apply them to a series of eight compounds (**Figure 3**) designed to maintain the key structural features that are necessary for their biological activities evidenced by the MEP maps of the compounds of the training set. The basic nucleus of these compounds corresponds to that of the most active nitrofurans with double unsaturation, containing vicinal O atom to carbonyl (see compounds **10**–**12**). The eight molecules proposed for the study of prediction of activity were drawn with the help of one of the collaborators of this work, who belong to the research group in organic chemistry of the Federal University of Pará, Brazil, and the most promising syntheses are in progress. In the future, antitrypanosomal tests

with the most promising nitrofurans can be used to validate our RP models.

The results obtained of the application of the PR models (PCA, HCA, KNN, SDA, and SIMCA) and the descriptors for the compounds of the prediction set are summarized in **Tables 8** and **9**, respectively. In **Table 8**, the compounds **25** and **30** were predicted as more active against *T. cruzi* with the five models. Only the KNN model predicted compound **26** as the most active. Meanwhile, only the PCA and HCA models predicted compound **31** as the most active. On the other hand, all models, except the SDA model, predicted compounds **24**, **27**, and **28** as the most active. In turn, the SIMCA model did not classify compounds **29** and **31** into any of the two classes. Thus, we can consider nitrofurans **25** and **30** as potentially more active in a future test against *T. cruzi*. For the values reported for compounds **25** and **30** (**Table 9**), it can be shown that in order to design more active nitrofurans we must combine smaller values for the descriptors QN1, gap energy, and Mor05u with

**64**

higher value for the descriptor MlogP.

*Results of application of chemometric models for the nitrofurans of the prediction set.*


#### **Table 9.**

*Values for descriptors for the prediction set.*

### *3.2.4 MEP maps for compounds of the prediction set*

**Figure 10** shows the MEP maps for the most active nitrofurans in the validation set (**25** and **30**). Also, in these compounds, as can be seen, raising the carbon chain increases the electron density in the atoms of the nitro group, extending through the O of the furan ring to the O atoms of the ester group accompanying the unsaturated chain. In these compounds, the MEP maps show more negative values between −74.27 and − 1.76 kcal/mol (red and yellow). They exhibit positive MEP in the range + 4.84 to +57.58 kcal/mol (green and blue).

The negative MEP region of compounds **25** and **30**, similar to the more active compounds in the training set, is susceptible to attack in a biological recognition process.

#### **3.3 Concluding remarks**

MEP and chemometric techniques in the last decades have become efficient tools in the study of the structure–activity relationships of bioactive molecules. The use of such tools has occurred through the inherent principles of each or combining their potentials to more efficiently unravel information about the structure–activity relationships of pharmacologically interesting compounds. This chapter is circumscribed in this second possibility. MEP maps were constructed for 23 nitrofurans with activity against *T. cruzi* reported in the literature. The key structural features

**Figure 10.** *MEP maps (kcal/mol) for most promising nitrofurans in the prediction set against T. cruzi.*

required for antitrypanosomal activity, along with chemical intuition, allowed the introduction of substituents in one of the most active nitrofurans in the training set to obtain eight new derivatives.

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 protozoan [94].

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