Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation

Urvi Awasthi, Alexander Arjun Lobo, Leonid Zhukov

Keywords: molecular generation, flow matching, graph generation, hierarchical learning, 3D molecular design, drug discovery, molecular graph generation, continuous-time categorical dynamics, hierarchical latent planning, equivariant generative models, topology prediction

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.

Submission Track: Full Paper

Submission Category: AI-Guided Design

Submission Number: 49