Concept of Structure-Activity Relationships SAR

SAR work can be classified into two categories QSAR (quantitative structure-activity relationships) and qSAR (qualitative structure-activity relationships). In QSAR analysis, biological activity is quantitatively expressed as a function of physico-chemical properties of molecules. QSAR involves modeling a continuous activity for quantitative prediction of the activity of new compounds. qSAR aims to separate the compounds into a number of discrete types, such as active and inactive or good and bad. It involves modeling a discrete activity for qualitative prediction of the activity of new compounds. 

In the SAR research work, one has to deal with two difficult problems feature selection and model selection for a particular data set with finite number of samples and multiple descriptors. 

As an effective way to overcome the problem of overfitting, support vector machine (SVM) [61 132], as a newly developed method, has been introduced in the field of SAR. The paper of Burbidge reported the pioneering work in this field. In this work, it was reported that the prediction ability of support vector classification (SVC) was significantly better than that of artificial neural network and decision tree in the SAR computation for the prediction of the inhibition of dihydrofolatase by pyrimidines [16]. In this chapter, more SAR work using both support vector classification (SVC) and support vector regression (SVR) methods will be described. 

SVC or SVR are compared with that of some other algorithms, and it can be seen that the prediction abilities of SVM in these examples are all better than those of other algorithms. 

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One of the sources of the fuzziness surrounding these concepts may well be the implicit assumption in structure-activity relationship (SAR) studies that molecular structure contains (i.e. encodes) the information on the biological activity of a given compound. Such an assumption cannot be incorrect, since this would imply the fallacy of SAR studies. However, the assumption becomes misleading if not properly qualified to the effect that the molecular structure of a given compound contains only part of the information on its bioactivity. Indeed, what the structure of a compound encodes is information about the molecular features accounting... 

The concept of hormesis should be formally considered in structure-activity-relationship (SAR) evaluation studies. If the low-dose stimulatory response is... 

The third part, perhaps the most significant, contains the synthesis of various important members treated individually, brief description of the synthesis, therapeutic applications of each compound, together with its dosage in various diseases, and routes of administration. The dosage for adults and children have been separately mentioned. The usual and maintenance doses, wherever applicable, have also been specified. The mode of action of various classes of medicinal compounds in addition to the structure-activity relationship (SAR) have also been elaborated wherever relevant. Greater emphasis has been laid on the chemistry of various compounds treated in this book, so that an undergraduate student may acquire a comprehensive knowledge on the basic concepts of the medicinal chemistry. 

The relationship between a chemical s structure and its biological action has been studied extensively for over a century (16). In cases where there is not a complete understanding of the mechanism/mode of action or where the influence of functional groups is not known or obvious, there is a vast body of knowledge on how different structural features within a class of chemicals may correlate with various levels of hazard. Structure-activity relations (SAR) or their mathematical treatment. Quantitative SAR (QSAR) have been developed for myriad endpoints including cancer, developmental and reproductive effects, aquatic toxicity, boiling points, water solubility and many others hazard endpoints. An instructor therefore has many opportunities to incorporate the concept of SAR at several points in the curriculum. 

Numerous relationships exist among the structural characteristics, physicochemical properties, and/or biological qualities of classes of related compounds. Simple examples include bivariate correlations between physicochemical properties such as aqueous solubility and octanol-water partition coefficients (Jtow) and correlations between equilibrium constants of related sets of compounds. Perhaps the best-known attribute relationships to chemists are the correlations between reaction rate constants and equilibrium constants for related reactions commonly known as linear free-energy relationships or LFERs. The LFER concept also leads to the broader concepts of property-activity and structure-activity relationships (PARs and SARs), which seek to predict the environmental fate of related compounds or their bioactivity (bioaccumulation, biodegradation, toxicity) based on correlations with physicochemical properties or structural features of the compounds. Table 1 summarizes the types of attribute relationships that have been used in chemical fate studies and defines some important terms used in these relationships. 

The most difficult problem in pattern recognition applications to SAR-classifications is the formulation of meaningful descriptors that describe the molecular structure and are correlated with the classification problem. The widely used concept of a linear, binary classifier assumes a linear relationship between the structural properties (pattern components) x. and the biological activity. 

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