Biologically Active Compounds and Structure- Activity Relationship

<div&gt;Naturally occurring compounds are found to be the most prominent and&lt;/div&gt;<div&gt;effective biological active compounds against various diseases. The majority of drugs&lt;/div&gt;<div&gt;approved between 1983 to 1994 are derived from natural products. Still today, the&lt;/div&gt;<div&gt;majority of pharmaceutical laboratories are hoping to get new drug candidates from&lt;/div&gt;<div&gt;natural resources. The traditional method of drug discovery from naturally occurring&lt;/div&gt;<div&gt;compounds has been upgraded by using advanced computer-based drug discovery.</div&gt;<div&gt;<br&gt;In drug discovery, the initial efforts are to know the relationship between the biological&lt;/div&gt;<div&gt;activity of natural compounds and their chemical structures. To be precise, the method&lt;/div&gt;<div&gt;of structure-activity relationship aims to recognize the basic structural component&lt;/div&gt;<div&gt;responsible for biological activity.</div&gt;<div&gt;<br&gt;The computational modeling drug discovery using various tools plays a major role in&lt;/div&gt;<div&gt;identifying the lead compounds. In this method, three major ways are utilized to&lt;/div&gt;<div&gt;understand the structure-activity relationship.</div&gt;<div&gt;<br&gt;The foremost one is the Quantitative Structure-Activity Relationship (QSAR). In this&lt;/div&gt;<div&gt;method, the relationship was established using regression techniques between the&lt;/div&gt;<div&gt;'Predictor Variable (X)' with the potency of the 'Response Variable (Y)'. The predictor&lt;/div&gt;<div&gt;variables are molecular descriptors, while the response variables represent the&lt;/div&gt;<div&gt;biological activities of the molecules against the selected diseases. If the response&lt;/div&gt;<div&gt;variable represents the chemical property, in that case, the model is called as&lt;/div&gt;<div&gt;Quantitative Structure-Property Relationship (QSPR).</div&gt;<div&gt;<br&gt;The second method is called "Inhibition Studies". In this process, the designed&lt;/div&gt;<div&gt;chemical entity is docked to the targeted enzyme using docking software. The basic&lt;/div&gt;<div&gt;principle of this method is the executive competitive inhibition between the natural&lt;/div&gt;<div&gt;inhibitor and the designed chemical entity. The law of thermodynamic is used to&lt;/div&gt;<div&gt;understand the best-docked chemical entity by obtaining the value of binding energy&lt;/div&gt;<div&gt;(ΔG kcal/mole) due to the complex formation between the chemical moiety and target&lt;/div&gt;<div&gt;enzyme.<br&gt;<div&gt;<br&gt;The third approach is very advanced and more accurate. It is called "The drug&lt;/div&gt;<div&gt;discovery using Artificial Neural Network". This is the recent technique adapted by&lt;/div&gt;<div&gt;major international pharmaceutical research laboratories. In this method, the neural&lt;/div&gt;<div&gt;network is designed and trained to identify the potent chemical compound against a&lt;/div&gt;<div&gt;particular disease. The designing of the network can be achieved using the chemical&lt;/div&gt;<div&gt;properties of a neuron, and output is related to the biological activity.</div&gt;<div&gt;<br&gt;This chapter discussed all three methods in detail, along with examples. It also provides&lt;/div&gt;<div&gt;the practical procedure to use available computational tools.</div&gt;<div&gt;<br&gt;The final aim of this chapter is not only to provide the theoretical background of drug&lt;/div&gt;<div&gt;discovery using structure-activity relationships but also to provide practical methods.</div&gt;<br&gt;</div&gt;