**1. Introduction**

This chapter presents *in silico* approaches used in protein structure prediction and drug dis‐ covery research.

The structural and functional diversity of animal toxins are interesting tools for therapeutic drug design. This diversity is also of great interest in the search for natural or synthetic in‐ hibitors against these animal toxins.

Computational techniques are highly important in drug design. They are used in the search for candidate ligands binding to a receptor.

Drug design based on structure has become a highly developed technology and is used in large pharmaceutical companies. Firstly, the structure of the protein of interest must be known. Therefore, molecular modelling plays an important role in the discovery of new drugs.

If the structure of the receptor is known, then the application is essentially a problem of structure-based drug design. These methods have specific goals, such as attempting to iden‐ tify the location of the active site of the ligand and the geometry of the ligand in the active site. Another goal is to select a number of related binders in terms of affinity or evaluation of the binding free energy.

The strategy of virtual screening has been used to contribute to the increase in hit rate in the selection of new drug candidates.

Virtual screening (VS) is a modern methodology that has been used in the identification of new bioactive substances. It is an *in silico* method that aims to identify small molecules con‐ tained in large databases of compounds with high potential for interaction with target pro‐ teins for subsequent biochemical analyses.

© 2013 Giuliatti; licensee InTech. This is an open access article 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. © 2013 Giuliatti; licensee InTech. This is a paper 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.

The strategy of VS can be divided into *ligand-based virtual screening* (LBVS), where a large number of molecules can be evaluated based on the similarity of known ligands, and *struc‐ ture-based virtual screening* (SBVS), where a number of molecules can be evaluated for specifi‐ cally binding to the active sites of target proteins (Figure 1).

gand, the next step in the process is molecular docking, which involves the coupling of the ligands with the receptor. At this stage, various conformations and orientations are generat‐ ed and classified according to the score function. The target protein can be obtained from a

Computer-Based Methods of Inhibitor Prediction

http://dx.doi.org/10.5772/ 52334

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**Figure 2.** Stages of SBVS. The receptor (the target protein) can be obtained from a database or by modelling. Molecu‐

Knowledge of the target protein structure is essential for structure-based drug design. The determination of the 3-dimensional structure of the protein may be achieved experimentally by diffraction of X-rays or by magnetic resonance. If the structure of the target protein has already been solved, it can easily be found deposited in public databases such as PDB [37]

However, sometimes the structure of the target is not known, and this poses a problem in the drug design process. This situation can be resolved by making use of computational

Such methods are divided into 2 groups: those based on templates and those that are tem‐ plate-free. The first group includes comparative or homology modelling and threading. The second group includes methods that do not depend on templates to build the model, such as

database or by modelling.

lar docking completes the structure-based virtual screening.

**2.1. Obtaining the Structure of the Protein Target**

methods for predicting protein structure.

*ab initio* modelling (Figure 3).

which contains more than 80,000 experimentally solved structures.

**Figure 1.** Virtual screening can be divided into ligand-based virtual screening (LBVS) and structure-based virtual screen‐ ing (SBVS).

Molecular docking is used to determine the best orientation and conformation of a ligand in its receptor site. The aim is to generate a range of conformations of the protein-ligand com‐ plex and sort them according to their scores, which are based on their stabilities. In order to do this, the protein structure and a database of ligands (potential candidates) are used as in‐ puts to the docking software. Thus, large collections of virtual compounds are subjected to docking into a protein-binding site and sorted according to their affinities for the macromo‐ lecular target, as suggested by the score function.

The focus of this chapter is to present the strategy of SBVS and the basic concepts of the methodologies involved. Examples of these approaches that have been applied to the identi‐ fication of animal venom inhibitors have been presented at the end of the chapter.
