Docking techniques in pharmacology: How much promising?

In the drug development process, large numbers of molecules are failing at a later stage due to safety to efficacy concerns which are generally investigated by conducting various experiments on animals but unfortunately, animal experimentation is associated with lots of limitations. Thus, there is an increasing interest in the development of alternative approaches that requires less number of animals. Docking is an alternative approach for screening of compounds before actually testing it on animals. It is the best option to predict the energetically favourable binding conformations of ligands in the active site cavity of particular receptor and has gained popularity in the scientific community to save time and money involved in the drug development process. The use of such techniques is increasing day by day among researchers due to availability of user friendly software's. Due to this, the number of papers related to docking has dramatically increased over the past decades. A variety of docking software's are currently available to predict the ligand binding pose in its particular receptor, but there is a lack of knowledge and confidence among scientists regarding accuracy of these softwares. There are many concerns to be taken care of, while carrying out any docking study such as selection of correct binding sites of the target protein, the selection of correct docking pose, lack of clarity over whether the compound is an antagonist or agonist, difficult to get best matching algorithms and scoring schemes. Many compounds show high dock score, but unfortunately fail in pre-clinical studies. These issues ought a chance to because for caution and concern before carrying out docking. Therefore, to ensure correct and effective application of docking techniques, it is necessary to understand the method's merits, demerits, the scope of application, and interpretation. In this review, we summarize fundamental concepts, why these techniques are needed, various docking methods and recent developments in this area. Further, current challenges and future perspectives of these tools are also discussed.