Integration of bioinformatics, computational chemistry, and artificial intelligence resulted in the advancement of drug discovery programs through various biological databases and data mining techniques. With the increasing volume of data in recent years, traditional techniques have failed to efficiently process the large amount of data, prompting the development of data mining and the storage of biological data in biological databases. The role of biologic databases and data mining of interdisciplined approaches in evolving the landscape of drug innovation has been discussed herewith. Focus on the integration of artificial intelligence in biological databases for drug innovation by using dense machine learning (ML) techniques further explains the challenges and future perspectives of the study.