Problem
Research questions and friction points this paper is trying to address.
molecular graph generation
3D molecule
bond topology
chemical validity
topology feasibility
Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation
🤖 AI Summary
This work addresses key challenges in unconditional 3D molecular generation—specifically, valency violations, disconnected fragments, and implausible ring structures arising from discrete bond topologies, which are particularly pronounced in drug-like molecules with long-range constraints. To tackle these issues, the authors propose a planner-executor architecture that integrates multiscale latent planning to capture global context with a constraint-aware sampler that explicitly generates bond graphs endowed with 3D coordinates. This approach uniquely unifies explicit topology generation with hierarchical planning, enforcing strong topological feasibility. The resulting Hierarchically Guided Latent Topology Flow (HLTF) model enables end-to-end joint generation, achieving 98.8% atomic stability and 92.9% valid, unique molecules on QM9. On GEOM-DRUGS, it attains 85.5% validity without post-processing, improving to 92.2% after relaxation—approaching state-of-the-art baselines.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
molecular graph generation
3D molecule generation
topology-aware modeling
latent flow
hierarchical planning
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