Although no single drug has been designed solely by computer techniques, the contribution of these methods to drug discovery is no longer a matter of dispute. All the world’s major pharmaceutical and biotechnology companies use computational design tools. In particular, in the fields of drug discovery and development, artificial intelligence techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The course aims at providing the students with advanced understanding of artificial intelligence in the area of drug discovery. After finishing the course the students will be familiar with a range of ligand and structure based computational methods and will be able to perform computational modeling tasks using state of the art software.