Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation

TL;DR

This paper introduces HLTF, a novel model for generating chemically valid 3D molecules with correct topology, significantly improving validity and stability over previous methods without requiring post-processing.

Contribution

HLTF is a hierarchy-guided latent flow model that explicitly generates molecular topology and coordinates, reducing topology errors and increasing validity in 3D molecule generation.

Findings

Achieves 98.8% atom stability on QM9.

Attains 92.9% valid-and-unique molecules on QM9.

Outperforms baseline validity metrics on GEOM-DRUGS.

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.