Herein we mainly discuss several of the most common generative models used in molecular design: variational autoencoders (VAEs), generative adversarial networks (GANs) and diffusion models. A general overview of generative models has been provided elsewhere , so we only focus on molecular design related to metals, like organometallics, inorganic materials, crystal structures, etc. Reviews on generative models for drug-like organic molecules have also appeared recently. The overarching goal of generative models is to implement advanced algorithms embedded by the architecture, in order to model the latent representation of the high-dimensional probability distribution allowing for the generation of novel data within the existing distribution.