Biologically Active Compounds and Structure- Activity Relationship

Naturally occurring compounds are found to be the most prominent and

effective biological active compounds against various diseases. The majority of drugs

approved between 1983 to 1994 are derived from natural products. Still today, the

majority of pharmaceutical laboratories are hoping to get new drug candidates from

natural resources. The traditional method of drug discovery from naturally occurring

compounds has been upgraded by using advanced computer-based drug discovery.

In drug discovery, the initial efforts are to know the relationship between the biological

activity of natural compounds and their chemical structures. To be precise, the method

of structure-activity relationship aims to recognize the basic structural component

responsible for biological activity.

The computational modeling drug discovery using various tools plays a major role in

identifying the lead compounds. In this method, three major ways are utilized to

understand the structure-activity relationship.

The foremost one is the Quantitative Structure-Activity Relationship (QSAR). In this

method, the relationship was established using regression techniques between the

‘Predictor Variable (X)’ with the potency of the ‘Response Variable (Y)’. The predictor

variables are molecular descriptors, while the response variables represent the

biological activities of the molecules against the selected diseases. If the response

variable represents the chemical property, in that case, the model is called as

Quantitative Structure-Property Relationship (QSPR).

The second method is called “Inhibition Studies”. In this process, the designed

chemical entity is docked to the targeted enzyme using docking software. The basic

principle of this method is the executive competitive inhibition between the natural

inhibitor and the designed chemical entity. The law of thermodynamic is used to

understand the best-docked chemical entity by obtaining the value of binding energy

(ΔG kcal/mole) due to the complex formation between the chemical moiety and target

enzyme.

The third approach is very advanced and more accurate. It is called “The drug

discovery using Artificial Neural Network”. This is the recent technique adapted by

major international pharmaceutical research laboratories. In this method, the neural

network is designed and trained to identify the potent chemical compound against a

particular disease. The designing of the network can be achieved using the chemical

properties of a neuron, and output is related to the biological activity.

This chapter discussed all three methods in detail, along with examples. It also provides

the practical procedure to use available computational tools.

The final aim of this chapter is not only to provide the theoretical background of drug

discovery using structure-activity relationships but also to provide practical methods.

Cite as

The Chemistry inside Spices & Herbs: Research and Development