Rapid technological improvements over the last decades have led to the rising popularity of advanced machine learning methods. These developments have also greatly influenced the field of DNDD with state-of-the-art methods including population-based metaheuristics, recurrent neural networks, generative adversarial networks, variational autoencoders and transformers. Moreover, concepts such as transfer, conditional and reinforcement learning are often applied to generate molecules with desired properties.