Generative artificial intelligence (AI) is changing the way scientists discover new small-molecule drugs. It helps design new molecules, speeds up testing, and predicts how molecules will act by analyzing large data sets. This chapter gives an overview of generative AI, compares it to older AI methods, and explains how deep learning, transformers, and reinforcement learning are used to find, improve, and reuse drug candidates. It also explains why small molecules matter, what makes a good drug candidate, and how formats like SMILES, molecular graphs, and 3D structures are used in AI research. The chapter discusses challenges such as data quality, bias, understanding results, whether molecules can be made in practice, and how AI fits into current drug development. It also covers ethical, legal, and resource concerns. Finally, the chapter introduces new approaches, such as improved ways to describe molecules, graph neural networks, lab automation, and federated learning, all aimed at making AI-driven drug discovery more reliable, private, and repeatable.