The field of score-based modeling and molecular generation is witnessing significant developments, with a focus on improving the efficiency and accuracy of sampling methods. Researchers are exploring new approaches to address the challenges of sampling from complex distributions, such as those encountered in molecular generation. One notable direction is the use of score-based transport modeling, which allows for the approximation of Wasserstein gradient flow and provides a built-in convergence diagnostic. Another area of research is the integration of pre-trained generative models with transition path sampling, enabling the discovery of physically realistic transition pathways. Furthermore, the development of novel architectures, such as integration flow models and graph-transformer-based frameworks, is advancing the state-of-the-art in molecular generation. Noteworthy papers include: Score-Based Deterministic Density Sampling, which proposes a deterministic sampling framework using score-based transport modeling, and Action-Minimization Meets Generative Modeling, which interprets candidate paths as trajectories sampled from stochastic dynamics induced by pre-trained generative models. Additionally, Integration Flow Models and JTreeformer are introducing new architectures for improved sampling and molecular generation capabilities. These advancements have the potential to significantly impact fields such as drug discovery and materials science.