TOPVAE: REDUCING DARK AREAS IN 3D MOLECULAR LATENT DIFFUSION

Latent diffusion is a scalable framework for 3D molecular generation, but it requires a latent space that stays valid beyond the posterior samples. A molecular VAE decoder can reconstruct every posterior latent exactly and still return disconnected or chemically invalid graphs a short distance away from it, which is where a diffusion prior draws its samples. We call these regions dark areas. They are widest where conditioning or size extrapolation builds a latent code no training molecule supplied. Therefore, we propose TopVAE, a topology-optimized VAE whose decoder emits adjacency before atom and bond types and is trained with connectivity and chemical constraints inside the decoding path, so that the region of latents that decode to valid molecules extends well past the posterior. TopVAE matches the strongest baseline at the posterior and keeps returning valid, connected molecules far outside it, where that baseline has collapsed. It continues to return sanitizable products when a scaffold is grown well past its training maximum in atom count. Paired with a standard diffusion transformer it is competitive on de novo generation on QM9 and GEOM-Drugs, with the lowest GEOM-Drugs FCD-3D of the methods scored through one pipeline.