Make Diffusion Models Great Again in Structure-Based Drug Design

Recent Bayesian and flow-based generators have challenged diffusion's leading position in structure-based drug design (SBDD). This progress motivates us to re-examine the modeling choices within molecular diffusion, particularly its forward trajectories and intermediate atom-type representations. We introduce MEGA, a unified molecular diffusion framework that redesigns both for joint generation of coordinates and atom types. MEGA directly specifies Gaussian coordinate marginals and categorical parameter trajectories on the simplex, enabling explicit control over both forward noising paths. It separates atom-type parameter evolution from network observations, using stochastic Concrete samples to provide graded classwise evidence beyond hard one-hot observations. To further support learning under high noise and the sampling cold start, we augment MEGA with GeoRank, a geometric prior that provides persistent slot identities without requiring reference ligand conformations at inference. On CrossDocked2020, the MEGA framework leads the compared methods in 12 of 15 reported benchmark statistics, including all Vina evaluations, PoseBusters validity, and strain energy. It achieves 89.5% PoseBusters validity and a mean Vina score of -6.96 kcal/mol. With GeoRank, MEGA achieves the lowest weighted JSD for bond-length and bond-angle distributions among the compared methods, improving over MEGA alone by 20.2% and 15.1%, respectively. GeoRank also improves agreement with the reference distributions of ring sizes and functional groups. These results show that redesigning molecular diffusion can restore its leading performance in SBDD.