Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation

Generating chemically valid 3D molecules is hindered by discrete bond topology: small local bond errors can cause global failures (valence violations, disconnections, implausible rings), especially for drug-like molecules with long-range constraints. Many unconditional 3D generators emphasize coordinates and then infer bonds or rely on post-processing, leaving topology feasibility weakly controlled. We propose Hierarchy-Guided Latent Topology Flow (HLTF), a planner–executor model that generates bond graphs with 3D coordinates, using a latent multi-scale plan for global context and a constraint-aware sampler to suppress topology-driven failures. On QM9, HLTF achieves 98.8\% atom stability and 92.9\% valid-and-unique, improving PoseBusters validity to 94.0\% +0.9 over the strongest reported baseline). On GEOM-DRUGS, HLTF attains 85.5\%/85.0\% validity/valid–unique–novel without post-processing and 92.2\%/91.2\% after standardized relaxation, within 0.9 points of the best post-processed baseline. Explicit topology generation also reduces "false-valid" samples that pass RDKit sanitization but fail stricter checks.