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arXiv · 2603.27113

Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation

Urvi Awasthi·Alexander Arjun Lobo·Leonid Zhukov
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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 stricter checks.

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Urvi Awasthi, Alexander Arjun Lobo, Leonid Zhukov. 2026-03-28. Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation. https://arxiv.org/abs/2603.27113

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