MolGAN: Few-Step 3D Molecule Generation with Hierarchical Reasoning

Diffusion models generate high-quality three-dimensional molecules but typically require long iterative sampling trajectories. We introduce MolGAN, a few-step molecular generator that combines energy-based adversarial training with the multi-timescale computation of the Hierarchical Reasoning Model. This organization allows substantial latent computation without exposing a long sequence of molecular states. On QM9 and GEOM-DRUG, MolGAN achieves state-of-the-art few-step performance across molecular-quality and sampling-efficiency metrics. Its learned energy model additionally enables test-time scaling, improving molecular-property scores on GEOM-DRUG as the compute budget increases.