AUTODIFF, a novel diffusion-based fragment-wise autoregressive generation model, can generate realistic molecules with valid structures and conformations for structure-based drug design.
TACOGFN, a Generative Flow Network conditioned on protein pocket structure, efficiently explores the chemical space to generate novel molecules with high binding affinity, drug-likeness, and synthesizability.