**1. Introduction**

Computer-aided drug design [1, 2] has the potential to lower the cost, decrease the failure rates and speed up the discovery process. Computational tools play various roles in medicinal chemistry ranging from optimization of protein-ligand interactions for drug discovery to the design of new drugs. These methods are broadly classified as structure based and ligand based methods. For structural methods the computational studies are carried out for molecular dynamics, proteinligand docking and calculation of free binding energies. For the ligand based methods, the computational calculations help to predict the biological response about known active and inactive ligands which include quantitative structure–activity relationships, activity cliffs analysis, and similarity search. In recent years, discovery of new molecules that could be more effective with fewer unwanted side effects is a constant concern of pharmaceutical industry. So, new developed research methods are used to predict the properties and activities of molecules even before they are synthesized. The significant development of computational tools as well as theoretical studies of quantum chemistry allow

researchers to obtain more precise physicochemical and quantum parameters of compounds in a shorter time. These techniques move towards the synthesis of a very large number of molecules simultaneously and to test their actions on therapeutic targets. Density Functional theory (DFT) has become a powerful tool to study the electronic and geometric characteristics of the drugs. Conceptual density functional theory (CDFT), originally developed by Parr and collaborators [3–8], with several global and local reactivity descriptors help us to understand various physicochemical processes. As Global reactivity descriptors are connected with several electronic structure principles so they play very important role in the physico-chemical information of the complexes. The understanding of the relationship between the structure and activity of the drug, the pharmacokinetic parameters responsible for bioavailability and the toxicity are evaluated by other computational tools. In this way the drugs with higher efficiency is obtained. These studies give a complete picture to design new drug molecules and its physicochemical parameters, drug-likeness and cytotoxicity evaluation in a shorter time. Recent advances in these methods increase the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the medicinal world. The correlations and patterns from large amounts of complex drug data should be performed by machine learning algorithms to extract knowledge and insights from the accumulated data. Databases are used to design new molecular descriptors and the models are validated with external test sets [9].

Modern artificial intelligence (AI) has the potential to significantly enhance the role of computational methods and machine learning in pharmaceutical industry [10]. According to the World Economic Forum, a combination of big data and AI are considered as the fourth paradigm of science and the fourth industrial revolution. Interestingly, with machine learning and AI solutions to some of the most complex drug related problems, drug discovery has created a potential breakthrough in medicinal world.
