: The advancement of computing and technology has invaded all the dimensions of science. Artificial intelligence (AI) is one core branch of Computer Science, which has percolated to all the arenas of science and technology, from core engineering to medicines. Thus, AI has found its way for application in the field of medicinal chemistry and heath care. The conventional methods of drug design have been replaced by computer-aided designs of drugs in recent times. AI is being used extensively to improve the design techniques and required time of the drugs. Additionally, the target proteins can be conveniently identified using AI, which enhances the success rate of the designed drug. The AI technology is used in each step of the drug designing procedure, which decreases the health hazards related to preclinical trials and also reduces the cost substantially. The AI is an effective tool for data mining based on the huge pharmacological data and machine learning process. Hence, AI has been used in de novo drug design, activity scoring, virtual screening and in silico evaluation in the properties (absorption, distribution, metabolism, excretion and toxicity) of a drug molecule. Various pharmaceutical companies have teamed up with AI companies for faster progress in the field of drug development, along with the healthcare system. The review covers various aspects of AI (Machine learning, Deep learning, Artificial neural networks) in drug design. It also provides a brief overview of the recent progress by the pharmaceutical companies in drug discovery by associating with different AI companies.

Multiple potential drugs have been developed based on the heterocyclic molecules for the treatment of different symptoms. Among the existing heterocyclic molecules, quinazoline and quinazolinone derivatives have been found to exhibit extensive pharmacological and biological characteristics. One significant property of these molecules is their potency as anti-tubercular agents. Thus, both quinazoline and quinazolinone derivatives are modified using different functional groups as substituents for investigating their anti-tubercular activities. We present a summary of the reported anti-tubercular drugs, designed using quinazoline and quinazolinone derivatives, in this review.