**6. Conclusions**

Analysis of the metabolic fate and chemical toxicity of the compounds can be accomplished using the software programs DEREK and METEOR [11]. DEREK predicts whether a given chemical is toxic to humans, mammals, and bacteria. METEOR uses the knowledge of me‐ tabolism rules to predict the metabolic fate of chemicals, assisting in the choice of more effi‐

An Integrated View of the Molecular Recognition and Toxinology - From Analytical Procedures to Biomedical

Other methods can also be used for screening databases of compounds, such as those based on ligands (LBSV). In this case, a similarity search can be made between known bioactive compounds and molecules contained in databases. LBVS techniques include methods based on the pharmacophore and quantitative structure-activity relationship (QSAR) modelling.

In pharmacophore-based virtual screening, a hypothetical pharmacophore is taken as a template. The goal of screening is to identify molecules that show chemical similarities to

QSAR is based on the similarity between structures. It is a quantitative relationship between a biological activity and the molecular descriptors that are used to predict the activity. QSAR searches for similarities between known ligands and each structure in a database, investigat‐ ing how the biological activity of the ligands can be correlated to their structural features [8].

**5. Examples of Virtual Screening / Molecular Docking in Animal Venom**

[38] performed a virtual screening against α-Cobratoxin. The neurotoxin α-Cobratoxin (Cbtx), isolated from the venom of the Thai cobra *Naja kaouthia*, causes paralysis by prevent‐ ing acetylcholine (ACh) binding to nicotinic acetylcholine receptors (nAChRs). A search for α- Cobratoxin structures was carried out in the PDB, and the virtual screening of 1990 com‐

bungarotoxin, NSC121865 (compound 23) was most potent in binding with Ac (K*d* = 16.26 nM; K*d* = 36.63 nM). The results showed that, in clinical applications, NSC121865 would be a very useful potential lead in the development of a new treatment for snakebite victims. This inhibitor can be used for the development of a more potent and specific anti-cobratoxin.

[14] investigated the effects of protease inhibitors, including phenylmethylsulfonyl fluoride (PMSF), benzamidine (BMD), and their derivatives on the activity of recombinant gloshedo‐ bin, a snake venom thrombin-like enzyme (SVTLE), from the snake *Gloydius shedaoensis*. The structural model of gloshedobin was built by homology modelling using modelling package MODELLER. The stereochemical quality of the homology model was assessed using the PROCHECK program and the software AutoDock was used to dock inhibitors onto the structural model of gloshedobin. The docking results indicated that the strongest inhibitor,

H]epibatidine and on [125I] α-

pounds was performed using the program AutoDock. On [3

PMSF, bound covalently to the catalytic Ser195.

cient molecules.

Applications

84

the template [40].

**4. Ligand-Based Virtual Screening (LBVS)**

*In silico* approaches used in protein structure prediction and in drug discovery research have been presented in this chapter.

Computational methods used in the search for inhibitors play an essential role in the process of discovering new drugs.

The application of protein modelling methods has contributed significantly in cases where the structure of the target protein has not been solved, allowing the SBVS process be completed.

Good results obtained by virtual screening depend on the quality of structures, databases to be scanned, the search algorithms, and scoring functions. Therefore, there must be a good interaction and exchange of information between *in silico* and experimental methods. Care‐ ful application of these strategies is necessary for successful drug design.


Table 2 presents a list of software tools and server web sites.


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**Table 2.** Software tools and server web sites.

### **Acknowledgements**

The author would like to thank CAPES-PROEX and CNPq for financial support.
