In the context of elucidating the underlying physicochemical properties of chemical structures of drugs is required for understanding the origin of their biological activity and pharmacological effects. QSAR is used to address these issues in which the biological activities of several compounds are studied using empirical mathematical approaches that seeks to discern the relationship between the chemical structures and the measured activity. Each compound is represented by a set of chemical descriptors, mathematical representation of chemical structures. This is because at a latter stage the model can then be used as a tool to predict the activities of new compounds before they are synthesized without the need for any physical measurements on them. Many approaches can be used to represent the chemical structures: counting the presence and absence of certain chemical groups, identifying the presence or absence of predified structural fragments in molecules or advanced methods that reflect the physiochemical properties of the chemical compounds. These descriptors were then used to build a mode by relating it to measured biological activities using machine learning approaches. With that, it is possible to reveal what influence a change in the chemical structure of a compound has on its biological activity by considering the biological activity of a compound is dependent on the compounds chemical space.