**3. Activity landscape modeling**

The methods of modeling the landscape based on properties of the compounds (property landscape modeling (PLM)) is at the interface between experimental sciences and computational chemistry, being a frequent strategy to systematically describe the structure-property relationships (SPR) of the compound data set [72]. PLM have been used in medicinal chemistry in the stages of drug discovery with a quantitative, descriptive, and statistical approach to activity cliffs [72–74]. Structure-activity relationships (SARs), using the concept of modeling the activity landscape (activity landscape modeling ALM), are an increasing common practice in the drug discovery process to identify the activity cliffs, guide the optimization of compound hits, and to avoid the deleterious effects of the activity cliffs in the studies of the classic models of QSAR and in the search of structural similarity. In this

**Figure 20.** *Structural similarity compared with activity cliffs in NPAs.*

**97**

**Figure 22.**

*plotter.*

*Chemoinformatic Approach: The Case of Natural Products of Panama*

research we analyze, through the web tool Activity Landscape Plotter (ALP) [72], a set of data from NPs from Panama with antimalarial activity against four strains of

*SAS maps of compounds with antimalarial activity ((a), (b), and (c)) through the web tool activity landscape* 

*DOI: http://dx.doi.org/10.5772/intechopen.87779*

**Figure 21.**

*Structural similarity compared with activity cliffs in GSK and Novartis (GNF).*

*Cheminformatics and Its Applications*

**3. Activity landscape modeling**

The methods of modeling the landscape based on properties of the compounds (property landscape modeling (PLM)) is at the interface between experimental sciences and computational chemistry, being a frequent strategy to systematically describe the structure-property relationships (SPR) of the compound data set [72]. PLM have been used in medicinal chemistry in the stages of drug discovery with a quantitative, descriptive, and statistical approach to activity cliffs [72–74]. Structure-activity relationships (SARs), using the concept of modeling the activity landscape (activity landscape modeling ALM), are an increasing common practice in the drug discovery process to identify the activity cliffs, guide the optimization of compound hits, and to avoid the deleterious effects of the activity cliffs in the studies of the classic models of QSAR and in the search of structural similarity. In this

**96**

**Figure 21.**

**Figure 20.**

*Structural similarity compared with activity cliffs in NPAs.*

*Structural similarity compared with activity cliffs in GSK and Novartis (GNF).*

research we analyze, through the web tool Activity Landscape Plotter (ALP) [72], a set of data from NPs from Panama with antimalarial activity against four strains of

**Figure 22.**

*SAS maps of compounds with antimalarial activity ((a), (b), and (c)) through the web tool activity landscape plotter.*

*Plasmodium falciparum* in the erythrocyte gametocyte stage (**Figures 20** and **24**).

The generation and comparison of structure-activity pairs, by structure-activity similarity maps (SAS map). The SAS map has been used to link up structure and biological activity, based on a systematic pairwise comparison of all the compounds in a data set analyzed. We compare the values of structure-activity similarity, the activity difference, and structure-activity landscape index (SALI) to find the pairs of compounds with high molecular similarity and the activity difference that are located in the upper right quadrant of the SAS map (activity cliffs) [72–76]. **Figures 17**–**21** show SAS map in NP of Panama, NP published, GSK, and GNF. In SAS maps, data points are colored by density (**Figure 22**).

The SAS maps using the molecular fingerprints EFCP-4, MACCS keys, and PubChem led to the identification of a total of 26 pairs of compounds with structure-activity similarity ratios >0.50 and structure-activity landscape index values varying between 0.3 and 5.0. The web application Activity Landscape Plotter [72] is a tool that allows us to perform QSAR. The SAS generated represent 55 natural products isolated in Panama with antimalarial activity which were analyzed and compared the biological activities against strains of *Plasmodium falciparum* sensitive, resistant and multiresistant. The analysis with the parameters the (SAS / Tanimoto index / ECFP-4), a total of twenty-six pairs of compounds showed similarity values greater than 70%, sixteen pairs greater than 80% and only two pairs of compounds gave a similarity greater than 85%. While with activity cliffs, only three pairs of compounds show structural similarity correlated with the values of pIC50 activity [72, 77].

SAS maps are color-coded according to their intensity and we observe that most pairs of compounds with antimalarial activity show an intense red color. A nalyzed are located in the region of little structural similarity, indicating that the natural products have high structural diversity and low difference in activity, attributed to having similar functional groups in their molecules.

**99**

interest.

*Chemoinformatic Approach: The Case of Natural Products of Panama*

DAS maps represent the pairwise activity differences for each possible pair of compounds in an evaluated data set, against two biological targets. These maps permitted to differentiate if a structural modification can increase or decrease the

With this web application, we have carried out a QSAR study in a fast, simple, and easily interpretable way, obtaining three natural products as leading computational compounds for their optimization as *Plasmodium falciparum* blockers,

The chemoinformatic analysis of the 20,364 compounds (1312 NPs and 19,052 synthetic (MMV, OSM, GNF, St. Jude, GSK, CHEMBL, and DrugBank)) indicates that so many natural products and synthetic products (S) share the same chemical space showing molecules that have similar structural properties. NPs present a greater diversity based on fingerprint than the synthetic compounds. Also,

and greater complexity, while synthetic products contain a greater proportion of aromatic atoms. Finally, concerning the properties related to cyclicity, relative shape, and flexibility, all have very similar values, which could explain the antimalarial activity of computationally determined compound hits in this work against *Plasmodium falciparum*-sensitive (3D7, D6, poW, D10) and chloroquine-resistant

The DAO acknowledges the SNI 2018 awards from SENACYT of Panama.

The authors declare that there are no financial or commercial conflicts of

hybridization

*DOI: http://dx.doi.org/10.5772/intechopen.87779*

activity under one target or other (**Figure 23**).

*Antimalarial compounds in NPs from Panama.*

**4. Conclusion**

**Figure 24.**

strains (W2, Dd).

**Acknowledgements**

**Conflict of interest**

which exhibit a gametocidal activity [78] (**Figure 24**).

NPs have a higher proportion of chiral carbons and atoms with sp3

**Figure 23.** *DAS map with MACCS key fingerprint.*

*Chemoinformatic Approach: The Case of Natural Products of Panama DOI: http://dx.doi.org/10.5772/intechopen.87779*

**Figure 24.** *Antimalarial compounds in NPs from Panama.*

DAS maps represent the pairwise activity differences for each possible pair of compounds in an evaluated data set, against two biological targets. These maps permitted to differentiate if a structural modification can increase or decrease the activity under one target or other (**Figure 23**).

With this web application, we have carried out a QSAR study in a fast, simple, and easily interpretable way, obtaining three natural products as leading computational compounds for their optimization as *Plasmodium falciparum* blockers, which exhibit a gametocidal activity [78] (**Figure 24**).

### **4. Conclusion**

*Cheminformatics and Its Applications*

*Plasmodium falciparum* in the erythrocyte gametocyte stage (**Figures 20** and **24**). The generation and comparison of structure-activity pairs, by structure-activity similarity maps (SAS map). The SAS map has been used to link up structure and biological activity, based on a systematic pairwise comparison of all the compounds in a data set analyzed. We compare the values of structure-activity similarity, the activity difference, and structure-activity landscape index (SALI) to find the pairs of compounds with high molecular similarity and the activity difference that are located in the upper right quadrant of the SAS map (activity cliffs) [72–76]. **Figures 17**–**21** show SAS map in NP of Panama, NP published, GSK, and GNF. In

The SAS maps using the molecular fingerprints EFCP-4, MACCS keys, and PubChem led to the identification of a total of 26 pairs of compounds with structure-activity similarity ratios >0.50 and structure-activity landscape index values varying between 0.3 and 5.0. The web application Activity Landscape Plotter [72] is a tool that allows us to perform QSAR. The SAS generated represent 55 natural products isolated in Panama with antimalarial activity which were analyzed and compared the biological activities against strains of *Plasmodium falciparum* sensitive, resistant and multiresistant. The analysis with the parameters the (SAS / Tanimoto index / ECFP-4), a total of twenty-six pairs of compounds showed similarity values greater than 70%, sixteen pairs greater than 80% and only two pairs of compounds gave a similarity greater than 85%. While with activity cliffs, only three pairs of compounds show structural similarity correlated with the values of pIC50 activity

SAS maps are color-coded according to their intensity and we observe that most pairs of compounds with antimalarial activity show an intense red color. A nalyzed are located in the region of little structural similarity, indicating that the natural products have high structural diversity and low difference in activity, attributed to

SAS maps, data points are colored by density (**Figure 22**).

having similar functional groups in their molecules.

**98**

**Figure 23.**

*DAS map with MACCS key fingerprint.*

[72, 77].

The chemoinformatic analysis of the 20,364 compounds (1312 NPs and 19,052 synthetic (MMV, OSM, GNF, St. Jude, GSK, CHEMBL, and DrugBank)) indicates that so many natural products and synthetic products (S) share the same chemical space showing molecules that have similar structural properties. NPs present a greater diversity based on fingerprint than the synthetic compounds. Also, NPs have a higher proportion of chiral carbons and atoms with sp3 hybridization and greater complexity, while synthetic products contain a greater proportion of aromatic atoms. Finally, concerning the properties related to cyclicity, relative shape, and flexibility, all have very similar values, which could explain the antimalarial activity of computationally determined compound hits in this work against *Plasmodium falciparum*-sensitive (3D7, D6, poW, D10) and chloroquine-resistant strains (W2, Dd).

### **Acknowledgements**

The DAO acknowledges the SNI 2018 awards from SENACYT of Panama.

### **Conflict of interest**

The authors declare that there are no financial or commercial conflicts of interest.

*Cheminformatics and Its Applications*
