Creation of drugs using computers has been impacted by computational intelligence. This growth continues to be fuelled by, amongst other factors, the widespread application of machine learning, and especially deep learning, throughout an array of academic regions, in addition to advances in computing software and hardware. Medical chemistry is benefiting as a consequence of the early scepticism related to the use of AI in pharmaceutical discovery starting to dissipate. The use of deep learning has promise for the development of drugs because it enables advanced image comprehension, molecule function, and structure forecasting, including the computerised generation of new chemical compounds having particular characteristics. Overview of the present situation with AI in chemoinformatics. De novo molecular design, structure-based modelling, and chemical reaction prediction are among the topics covered in this chapter. To satisfy the demand for an innovative narrative of the computational language of molecular sciences, there’s an appetite for “explainable” methods for deep learning. The most significant and explainable artificial intelligence concepts are laid out in this chapter, which additionally expects potential uses and an array of unsolved challenges. I also hope that this chapter stimulates additional research on developing and implementation of methods of artificial intelligence in drug discovery.