Designing molecules with specific functions within an immense chemical space is a fundamental challenge in chemistry and materials science, as traditional methods often lack efficiency and exploratory capacity. The advent of generative deep learning has introduced automated and intelligent approaches that promise to transform molecular design. In this review, we summarizes the advancements in applying generative deep learning to molecular design. We first compare molecular representation methods, including SMILES notation, molecular graphs, and three-dimensional structural representations, highlight their respective advantages and limitations. We then critically evaluate three leading generative models: generative adversarial network (GAN), variational autoencoder (VAE), and denoising diffusion probabilistic model (DDPM), and discuss applications of generative models in object-oriented molecular design, with a particular focus on the differences among various models in terms of molecular generation quality and property optimization. Finally, we propose future research directions for leveraging generative models in molecular design, aiming to inspire further advancements in this rapidly evolving field.