DRUG Discovery Using Data Mining

Drug Discovery today conceptualizes on the involvement of chemo informatics to overcome the shortcomings of the traditional Drug development process. Drug Discovery with the use of Chemo informatics and Data Mining generates large numbers of related chemical compounds. It speeds up the drug discovery in two ways: It can generate several million structurally related chemical molecules. By increasing the chemicals available for testing, the chances of finding a drug lead may be higher. If you know the biological effects of different molecules, you can combine them to make chemicals with particular designed biological effects. Chemo informatics (Chemical Informatics) is the use of Computer and Information Technology, applied to a range of problems in the field of Chemistry.It transforms the data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug identification and optimization. With Data Mining, throughout drug discovery, data is collected relating chemical structures to each other. The Data Mining Technique “Clustering Process” divides the databases of unknown drugs in clusters based on their similarity. It makes use of Lipinski Rule which defines those compounds as Drug like which have properties implicit to drug likeness such as log p, Molecular Weight, Number of Hydrogen bond acceptors and donors in a molecule etc. The clusters of unknown similar drugs are evaluated and compared with the clusters of some specific (e.g. HIV) drugs to discover from unknown drugs those drugs that are similar in properties with the known drugs. This paper highlights the use of Chemo Informatics and Data Mining Techniques for exploiting the widespread database of unknown drugs to discover new Drug like compounds that contain functional groups Charanpreet Kaur & Shweta Bhardwaj 336 and have physical as well as therapeutic properties consistent with the majority of known drugs.