About Authors:
Rakesh Bhatia*
School of Pharmaceutical Sciences,
Department of Pharmaceutical Chemistry,
Jaipur National University,
Jaipur-302025 (Rajasthan), India


Chemical synthesis data and their biological screening have provided a vast amount of experimental data. As a result of that, availability of large amount of biological data information through molecular biology has made drug discovery and development a more complex method. To combat these problems, Quantitative structure-activity relationships (QSAR) emerged as a very useful tool in drug design. QSAR has been applied extensively and successfully over several decades to find predictive models for activity of bioactive agents. QSAR have brought revolution in drug discovery process by thedevelopment of mathematicalrelationships linking chemical structures and pharmacological activity in quantitative manner of series of compounds. Description of the molecular structure, electronic orbital reactivity and the role of structural and steric components has been the subject of mathematical and statistical analysis. Computational drug design method in QSAR is a rapidly growing field which is now a very important component in the discipline of medicinal chemistry. Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complimenting the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationships (QSAR) analysis, a field with established methodology and successful history.By characterize a specific aspect of a molecule that is numbers containing structural information derived from the structural representation used for molecules under study called “Molecular descriptors” to find appropriate representations of the molecular structure of drug compoundsto obtain the structure-activity relationships in which these theoretical and computational methods are based, the ability to predict physicochemical, pharmacokinetic and toxicological properties of these leads are becoming increasingly important in reducing the number of expensive methods and late development failures. Thus thereby, QSAR certainly decreases the number of compounds to be synthesized by easing the selection of the most promising candidates. This review seeks to provide a review on role of molecular descriptors in the drug design in QSAR.

Reference Id: PHARMATUTOR-ART-1330

The main paradigm of medicinal chemistry is that biological activities, as well as physical, physicochemical and chemical properties, of organic compounds depend on their molecular structure [1, 2]. Based on this paradigm Crum-Brown and Fraser published the first quantitative structure activity relationship in 1868 [3]. Despite the great advances produced in theoretical drug design even today this paradigm is guiding the discovery of new lead compounds [4-6].QSAR (Quantitative Structure–Activity relationships) thus have been applied for decades in the development of relationships among physicochemical properties of chemical substances and their biological activities to obtain a reliable mathematical and statistical model for prediction of the activities of new chemical entities. QSAR have helped the scientists in the development of mathematicalrelationships linking chemical structures and pharmacological activity in quantitative manner of series of compound.The fundamental principal underlying the QSAR is that the differences in structural properties are responsible for the variations in biological activities of the compounds. In the classical QSAR studies, affinities of ligands to their binding sites, inhibition constants, rate constants, and other biological end points, with atomic, group or molecular properties such as lipophilicity, polarizability, electronic and steric properties (Hansch analysis) or with certain structural features (Free-Wilson analysis) have been correlated. QSAR certainly decreases the number of compounds to be synthesized by simplifying the selection of the most promising candidates. However, the most traditionally used method of drug discovery is the mass screening. It consists on massively screening of chemicals on a battery of biological assays. A recent advance in this field is the use of robotics to screen thousand to millions of compounds in an automated form. This technology is known today as high-throughput screening (HTS) [7-9]. With combinatorial chemistry, it allows for synthesis and rapid activity assessment of vast number of small-molecule compounds [10, 11]. With the increasing of experience by using these technologies, the focus has shifted from screening through large, diverse molecule collections to more rationally designed libraries [12].Virtual screening [13]of chemical libraries has emerged as a complementary approach to HTS [14, 15]. By this means, computational techniques are used to select a reduced number of potentially active compounds from large available chemical or combinatorial libraries called structural databases. The main objective of this approach is to discriminate potent candidate molecules from inactive or less potent molecules.The computer-aided drug discovery(CADD) approach offers an alternative to the realworld of synthesis and screening [16]. It “involves all computer-assisted techniques used to discover, design andoptimize compounds with desired structure and properties” [17]. This approach has advanced rapidly over the pastdecade [18], and has played a key role in the development of a number of drugs that are now on the market or are inclinical trials.It has reduced the cost of synthesis and bioassays which are made only after exploring the initial concepts with computational models.Molecular descriptorsare “terms that characterize a specific aspect of a molecule” [17].Molecular descriptors are numerical values that characterize properties of molecules. Molecular descriptors encoded structural features of molecules as numerical descriptors vary in complexity of encoded information and in compute time. This review article will focus on the different descriptors of QSAR approaches employed within the current drug discovery process to construct predictive structure-activity relationships.

The QSAR method involves recognition that a molecule (organic, peptide, protein, etc.) is really a three-dimensional distribution of properties. The most important of these properties are steric (e.g. shape and volume), electronic (e.g. electric charge and electrostatic potential) and lipophilic properties (how polar or non-polar the sections of molecular are, usually exemplified by the log of the n-octanol-water partition coefficient, log P). Scientists are used to visualizing mainly steric properties of molecules. However, molecules look different when viewed in electrostatic or lipophilic space (Figure 1).

Figure 1: A smallorganic molecule (glucopyranose) viewed in steric (left), electrostatic (centre) and lipophilic (right) space.

The QSAR method (and analogously quantitative structure-toxicity relationships (QSTR) and Quantitative structure-property relationships (QSPR)) involves several key steps:
1. Converting molecular structures into mathematical descriptors that encapsulate the key properties of the molecules relevant to the activity or property being modeled.
2. Selecting the best descriptors from a larger set of accessible, relevant descriptors.
3. Mapping the molecular descriptors into the properties, preferably using a model-free mapping system in which no assumptions are needed as to the functional form of the structure–activity relationships. These relationships are often complex, unknown and non-linear.
4. Validating the model to determine how predictive it is, and how well it will generalize to new molecules not in the date set used to generate the model (the training set).

The relationship among molecular structure and some biological response, BR (e.g. IC50, LD50, and ED90) can be expressed as: [19]

Log (BR) = f (x1, x2,…., xN)

Where, f is usually an unknown complex, non-linear function, and x1, … , xN are molecular descriptors. Building of a QSAR model via the four steps outlined above involves finding the best form of function f.



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