Target-driven drug discovery is a process in which a known target is used to search for small molecules that interfere with it or influence its role in cells. These methods function well for easily druggable targets with a well-defined structure and well-understood interactions within the cell. However, due to the complexity of cellular interactions and a lack of understanding of intricate cellular pathways, these methods are severely limited. By detecting novel associations and inferring the functional significance of various components of a cellular pathway, AI will conquer these obstacles. It extracts useful knowledge from a broad dataset using complex algorithms and machine learning techniques. QSAR modeling based on structure, de-novo drug design, automated synthesis planning is just a few of the cutting-edge applications available. Screening of the compounds along with optimizing the lead compound, goal validation and selection, nonclinical research and studies, and clinical drug trials are all places where AI is used extensively.