In the rapid developing AI area, thanks to both the ever-increasing computational resources and ever growing data sets, AI/ML-assisted strategies for automated molecular design show great promise to outperform traditional methods. Unlike some common ML models which make predictions, generative AI creates new data in various formats, such as text, image, video, audio, 3D models, etc. by learning the data distributions from known data sets. This unique feature satisfies the goal of molecular design to intentionally search for tailored molecules or materials for practical applications in unknown chemical space. Moreover, the self-learning characteristics of generative AI makes molecule generation autonomous without human supervision, which can avoid biases, leading to more objective and better decision-making. Another advantage of generative models is the large number of possible rational outputs, which provides large molecular samples for downstream tasks, e.g. wet-lab synthesis, computational analyses of structure-property relationships, or even building new ML models using the generated structures.