Constrained Flow Optimization via Sequential Fine Tuning for Molecular Design

ArXi:2605.30610v1 Announce Type: new Adapting generative foundation models, in particular diffusion and flow models, to optimize given reward functions (e.g., binding affinity) while satisfying constraints (e.g., molecular synthesizability) is fundamental for their adoption in real-world scientific discovery applications such as molecular design or protein engineering. While recent works have