通过分层拓扑规划生成更稳定的分子图，避免键合错误导致的结构失败。
Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation
- 采用分层潜空间规划全局拓扑结构，指导3D分子生成过程
- 在QM9上达到98.8%原子稳定性与92.9%有效唯一性，超越现有基线
- 无需后处理即可生成高精度分子，适合药物分子设计场景
生成化学有效的三维分子受限于离散的键合拓扑结构：微小的局部键错误可能引发全局失效（价态违规、断裂、不合理环结构），尤其在具有长程约束的类药物分子中更为显著。许多无条件3D生成模型侧重坐标生成后再推断键合或依赖后处理，导致拓扑可行性控制较弱。我们提出层次引导的潜空间拓扑流（HLTF），一种规划-执行模型，同时生成键图与3D坐标，利用潜空间多尺度规划提供全局上下文，并通过约束感知采样器抑制拓扑驱动的失败。在QM9数据集上，HLTF实现98.8%的原子稳定性与92.9%的有效且唯一性，使PoseBusters有效性达94.0%（比最强基线提升0.9）。在GEOM-DRUGS上，未经过后处理即达85.5%/85.0%的有效性/有效唯一新颖性，经标准松弛后提升至92.2%/91.2%，接近最佳后处理基线（差值<0.9）。显式拓扑生成还减少了通过RDKit净化但经更严格检测仍失败的‘假有效’样本。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。