**1. Introduction**

The planning and development of new drugs require high-risk and high-cost investments [1]. This process can involve, for example, studying about 5000–10,000 compounds, a period of 7–12 years, and spending about \$800 million for a single drug to be marketed [2]. Thus, alternatives that optimize the process and reduce these costs are considered promising [3].

Nevertheless, there are other issues to the success or failure of drugs that must be considered. The main factors responsible for the lack of success in the production of possible drugs during clinical trials are pharmacokinetic factors, such as absorption, distribution, metabolism, excretion, and toxicities [4].

With the evolution of biotechnology and bioinformatics, promising new approaches for drug planning and optimization have become possible [5]. To reduce costs, risks and have greater efficiency in the production process, the pharmaceutical industry has increasingly used *in silico* analysis, which enables the performance of various analytical tasks, such as the quantitative structure-activity relationship of a drug, definition of pharmacophores (region of a ligand molecule that is strongly bound to its receptors), and other forms of modeling [6].

The *in silico* approach to drug development assesses the properties and interactions of a given molecule using computational algorithms [7]. Research in the areas of "omics" (proteomics, genomics, and transcriptomics) has increased due to the use of computational analysis through spectrometry, crystallography, and magnetic resonance techniques, which allows detailed access to the structure of the molecule, thus enabling the planning of medications and also predict their effects [8]. Molecular docking, an *in silico* approach, has been widely used for the planning and development of new drugs [3].

In 1984, the Lock-Key model, proposed by Fisher, explained the theory of ligandreceptor interaction. The model suggested that the interaction between two corresponding structures (ligand and receptor) was due to geometric and energy affinity. In this model, both ligand and receptor were considered rigid structures. The Lock-Key model contributed to the understanding of the mechanism of action of drugs. Nonetheless, it does not explain the interactions in the environment or changes in the spatial conformation of the molecules. Considering these modifications is extremely important, as the conformation of structures can change before and after bonding. Consequently, modern molecular docking tools consider these factors.

Molecular docking assesses the interaction and recognition between macromolecules, in general proteins and ligands [9]. Besides, the algorithm can predict what would happen if these structures interacted in a microenvironment [10].

*Molecular Docking of Phytochemicals against* Streptococcus mutans *Virulence Targets… DOI: http://dx.doi.org/10.5772/intechopen.101506*

Prediction of these interactions allows for the creation of structure-based drug design, an advance in drug development as it allows screening of specific molecules for specific targets [11].

Therefore, computer-aided drug design (CADD) uses high-performance computational algorithms to design and optimize molecules to become new drugs. The use of CADD in drug development optimizes the development process, increasing success rate, decreasing laboratory and personnel costs, in addition to producing quick results [3].

Several drugs, currently available for use, have been discovered and improved with the aid of *in silico* tools, such as molecular dockings, zanamivir [12], imatinib [13], nelfinavir [14], and erdafitinib [15]. With the evolution of bioinformatics, biotechnology, and molecular biology, including the determination of protein structures by using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, it has become increasingly easier to use *in silico* tools to predict functioning drugs. Thus, in the last 20 years, more than 60 different molecular docking software were developed by universities and companies [16].

Molecular docking programs have different approaches and their characterization is according to incremental construction approaches, including shape-based algorithms, genetic algorithms, the Monte Carlo method, and systematic search techniques [17–20].

Despite the evidence of the effectiveness and advantage of using molecular docking for drug discovery, studies in this area are still incipient for oral diseases [5], which justifies the performance of new studies. Streptococcus mutans (*S. mutans*), a gram-positive, aciduric, and acidogenic bacterium, is the most prevalent in the dental biofilm [21] and the most studied [22]. This microorganism has relevant virulence factors that enable colonization of the tooth surface, including its high capacity to form biofilms, causing the development of carious lesions [23]. The dominant defense systems of S. mutans for biofilm formation and caries development are its ability to adhere to the surface of teeth and produce acids, associated with its resistance to this environment without suffering damage [24]. Thus, preventing the formation of this microbial complex is one of the most targeted strategies for caries control [23].

Additionally, natural products have been a promising source of positive molecules for drug development over the years [25]. Therefore, plants are a promising source of new chemical compounds (phytochemicals) with high biological potential. Phytochemicals are a class of organic compounds synthesized in small amounts from secondary plant metabolism and are related to plant defense, growth, reproduction, and adaptation, among others. Its main classes of compounds are terpenes, alkaloids, and phenolic compounds [26, 27].

In consequence, in this chapter, we performed, by molecular docking, a screening of molecules from plants that showed results of *in vitro* antimicrobial activity against *S. mutans*, to verify the possibility of interaction and inactivation of virulence factors of this bacteria.

### **2. Molecular docking between phytochemicals and S. mutans targets**

#### **2.1 Selection of the ligands**

Ligands were selected from a literature search on phytoconstituents or plants with antimicrobial activity, *in vitro*, against *S. mutans*. The search was performed in the

Pubmed database (http://www.ncbi.nlm.nih.gov/pubmed), using the following terms as keywords: S. mutans, natural products, and anti-cariogenic effects, without language specification or deadline. All articles that addressed the antimicrobial activity of phytoconstituents (isolated molecules) with action on S. mutans, or with activity related to the reduction of cariogenic dental biofilm, were considered relevant. After these filters, 24 articles remained that had defined chemical structures of molecules with an inhibitory effect against S. mutans. The molecules identified and selected for the study in these articles are shown in **Figure 1**.
