Structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) study, collectively referred to as (Q)SAR, are theoretical models that can be used to predict the physicochemical, biological, and environmental fate properties of molecules.

Structure-Activity Relationship and Quantitative Structure Activity Relationship

INTRODUCTION

Structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) study, collectively referred to as (Q)SAR, are theoretical models that can be used to predict the physicochemical, biological, and environmental fate properties of molecules.

The aim of QSAR techniques is to develop correlations between any biological property form of activity, frequently biological activity, and their properties, usually, physicochemical properties of a set of molecules, in particular, substituent properties. However, in its most general form, QSAR has been adapted to cover correlations independent of actual physicochemical properties. QSAR started with similar correlations between chemical reactivity and structure. Ideally, the activities and properties are connected by some known mathematical function, F:

Biological activity = F (physicochemical properties)

Biological activity can be any measure of, such as C, Ki, IC50, ED50, and Km.

Physicochemical properties can be broadly classiﬁed into three general types such as electronic, steric, and hydrophobic property of biologically active molecules, for which an enormous range of properties and physicochemical parameters have been deﬁned. Ideally, the parameters selected should be orthogonal, that is, have minimal covariance. The relationship or function is usually (but not always) a mathematical expression derived by statistical and related techniques, for example, multiple linear regression (MLR). The parameters describing physicochemical properties are used as independent variables and the biological activities are dependent variables. In some cases, a function cannot be found, and this reﬂects the multivariate, nonlinear nature of biological and physical properties. Usage of such data may be possible with neural networks to deduce essential data for biological activities and then using them for prediction.

Usually, some data are used to generate a relationship (the training set), while another set of data is reserved as a test set on which predictions using the rule are made. In this manner, a model can be tested for validity. The complete range of techniques used to derive functional relationships between the data is collectively known as chemometrics.

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