A dynamic fusion diffusion model for molecular generation

Molecular generation aims to construct valid, diverse, and novel molecular graphs by transforming noise into structured atom and bond representations through reverse diffusion. Because each reverse step relies on accurate denoising, the denoising network must recover both local chemical connectivity and global graph topology across noise levels. We propose DFDM, a dynamic fusion diffusion model for 2D molecular graph generation. DFDM combines a GINE-based spatial branch with a Chebyshev spectral branch and uses signal-to-noise-ratio-guided, layer-specific weights to adapt their contributions during reverse generation. Across three independent seeded generation-and-evaluation runs on QM9 and ZINC250k, DFDM achieved the lowest mean Frechet ChemNet Distance and the highest mean validity without correction among the compared methods. Ablations on both datasets showed that dynamic fusion provided a better overall metric balance than fixed weighting, while QM9 noise-stratified analysis revealed a systematic transition from spectral emphasis at high noise to spatial emphasis near the final denoising stage. DFDM therefore links noise-dependent denoising with molecular graph quality by integrating complementary local and global representations throughout generation.