[2603.27113] Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation

Computer Science > Machine Learning arXiv:2603.27113 (cs) [Submitted on 28 Mar 2026] Title:Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation Authors:Urvi Awasthi, Alexander Arjun Lobo, Leonid Zhukov View a PDF of the paper titled Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation, by Urvi Awasthi and 2 other authors View PDF HTML (experimental) Abstract: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...