The field of 3D molecular generation is moving towards more efficient, flexible, and controllable methods. Recent developments have focused on improving the generation quality and speed of diffusion-based models, as well as exploring alternative approaches such as autoregressive models. Noteworthy papers in this area include one that introduces a training-free framework for evolutionary guidance in diffusion models, enabling rapid and guided exploration of chemical space. Another paper presents a scalable autoregressive model that achieves substantial improvements in generation quality and competitiveness with state-of-the-art diffusion models. A third paper introduces a collaborative constrained graph diffusion model that generates molecules guaranteed to be chemically valid, outperforming state-of-the-art approaches while requiring fewer parameters.