Introduction:
Traditional approaches for designing drugs are lengthy and very expensive. Presently, rational drug design is being used to discover new drug molecules where a researcher exploits new technologies to speed up the drug development process. The use of computational techniques invades all aspects of modern drug discovery. Chemoinformatics is a new discipline and modern technique that integrates the applications of mathematics, computer, and Internet in solving chemistry problems. According to F.K. Brown the Chemoinformatics is the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and optimization.
Recent years have seen an explosion in the amount of chemical compounds and their related biological information. Some compounds are naturally occurring in biological system, they called as biological compounds. These compounds can have a wide variety of biological functions, they act as cofactor, serving as signaling molecules, and act as activator/effector in many enzymatic reactions. These compounds have different types of physiochemical properties so they affect the binding site with target proteins and responsible for biochemical function. They involved in numbers of pharmacokinetics and pharmacodynamics properties. In response to the increased demand for new compounds by biologists, chemists started using Chemoinformatics approaches to predict and designing new drug molecules.
Scope of Chemoinformatics
Using all the advanced chemoinformatics system, it enhances the drug discovery rapidly and with low cost and helps to eminent scientists to synthesize the chemical molecules, which leads to help the society.
Application
Computational chemistry or Chemoinformatics is used in storage and retrieval of chemical structures and information in form of database. This approach widely used in prediction of the physiochemical or biological properties of compounds. It is used in identification and optimization of new lead compounds and in establishment of quantitative structure-activity relationships (QSAR). It also used in docking and screening of small chemical molecules.
Tools and Techniques used in Chemoinformatics
Several Chemoinformatics tools and techniques are developed and used to support on going drug discovery programs. There are several tools and techniques are available in computer aided drug designing.
Target identification
Target identification is the first step in drug discovery, and it begins with the discovery of a relevant drug target, which believed to be essential for the biological function and survival of a pathogen. One of the major challenges for bioinformaticians is to discover effective drugs using in silico approaches. The target identification includes in silico identification of drug target, identifying new target classes and their functions, predicting disease relevant genes, and constructing gene networks and protein interaction networks.
Drugs/chemical database
In ancient times, natural products obtained from various sources like plants were used to test whether they have bio- logical activity. These natural products were major sources for discovering therapeutics. Subsequently, synthetic chemists have synthesized large number of chemical compounds and generated library of synthetic molecules in the last century. Presently, there are number of databases and repositories that are managing comprehensive information about millions of chemicals. This section describes major source of molecules that are freely available for public use. Chemical databases/resources are the backbone of computer-aided drug discovery, whether it is chemoinformatics or pharmacoinformatics or bioinformatics. These databases provide information that is used to build knowledge-based models for discovering and designing drug molecules.
Molecular Editors
Molecular editors are commonly used tools in the field of cheminformatics, to draw and manipulate chemical structures. These tools provide a number of facilities like geometry optimization, structure visualization, and energy minimization.
Combinatorial chemistry/analogs generation
Virtual library generation approaches have a major impact on drug designing process where small therapeutic molecules are generated from basic scaffolds with attach- ments sites and lists of R-groups. Here we briefly summarized software packages available for the combinatorial enumeration of virtual chemical compound libraries. All these software packages are based on similar methodology to generate virtual chemical libraries. Basically, all these packages required three basic substructures: core scaffolds, linkers and building blocks (R-groups).
Structure optimization
The pharmacological properties of small therapeutics molecules depend on their specific conformation. In chemoinformatics, various techniques such as structure-based screening, ligand-based screening, molecular modeling and molecular docking requires suitable multi-conformer stuctures (2D/3D) of compounds to facilitate the drug discovery process. The compound conformers are the basic need in medicinal chemistry to explore more complex structural motifs and different topologies, because there is a relationship between different conformers and function.
Chemical clustering
Clustering of chemical is playing a very crucial role in computational chemistry. The chemical clustering is used to identify the outliers in a given dataset, to understand the behavior of a particular functional group, and also in identification of a common scaffold etc. Numbers of approach have been used for clustering the chemical compounds like the binary fingerprints based, graph properties based, maximum common substructure based.
Descriptors calculation
Molecular descriptors repersent the characterstics or features of a molecule in numerical values. Descriptor can be defined as an outcome of logical procedure where chemical information is represented in the form of some values or numbers for a property in considerations. A key steps in classical QSAR modeling is the encoding of a chemical compound into a vector of numerical descriptors. These molecular descriptors may be result of some experiment, for example logP and are highly correlated with that property of chemicals. Based on these descriptors QSAR/QSPR model are developed, which are helpful in designing new chemical entity (NCE) having the property similar to used dataset.
Molecular Docking
Molecular docking technique is most preferably used to predict the preferred orientation of molecule with in the active site of target molecule where it binds to form a stable complex. So, it is widely used in hit identification and lead optimization. Mostly docking algorithm generates the large number of possible structures and finally selects the most favorable structure geometry by scoring function. Depending on the interacting partner of the proteins, docking can be divided into two classes: Protein-protein docking, where two different protein molecules interact with each other and this is mainly rigid body docking and protein-ligand docking, where protein binds with small molecules.
Pharmacophore
Pharmacophore search is a key component of drug discovery programs that could be used as alternative method to molecular docking for fast and efficient screening of compound library. It represents the spatial arrangement of chemical features that is essential for a molecule to interact with a specific target receptor. Pharmacophore search is an established and effective mechanism of virtual screening.