Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation

ArXi:2411.05472v2 Announce Type: replace The paradigm shift toward structure-driven molecule generation has been propelled by advances in deep generative models, such as variational auto-encoders and diffusion models. However, these generative models for molecular design remain constrained by exposure bias, error accumulation, and suboptimal handling of activity cliffs. Here, we