{
  "abstract": "Recovering molecular graphs from atomic identities and 3D coordinates is a key step in coordinate-first molecular modelling and generation. We present Bond-Net, a two-stage framework in which a PaiNN model first predicts connectivity on a distance-derived candidate graph and a topology-aware network then classifies heavy–heavy bond types. Explicit hydrogens participate in message passing as local valence context but are excluded from bond-type supervision and scoring. On a fixed 26,940-molecule GEOM-DRUGS validation cohort, the near-clean model achieves pipeline macro-F1 = 0.9994 and correctly types every reference heavy–heavy bond in 99.4% of molecules. A historical ablation shows that removing hydrogen context reduces macro-F1 by 6.7 percentage points and the exact-type rate to 19.7%. With Gaussian coordinate-noise augmentation and candidate graphs rebuilt from the perturbed coordinates, BondNet retains pipeline macro-F1 = 0.9859 and a true-bond exact-type rate of 87.7% at σ = 0.1 Å; its stricter HH-graph exact match is 83.0% because extra predicted bonds are also penalized. BondNet’s pipeline macro-F1 exceeds RDKit DetermineBonds at every tested noise level on the common denominator. Residual strict-label disagreements are concentrated in resonance-ambiguous N–O and carboxyl motifs. An exact-molecule-leakage-controlled diversity study further shows that a curated multi-source set of 80,000 molecules transfers better than both a same-size random GEOM subset and a 3.4× larger single-source set. 1 Scientific Contribution. BondNet isolates geometric connectivity from topology-aware bond typing and quantifies how explicit-hydrogen context and noise augmentation affect robust graph recovery. The study also provides atom-pair-matched baselines, explicitly reports tool failures, and introduces an exact-molecule-leakage-controlled comparison of training-set diversity and scale.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer Science and Artificial Intelligence",
    "Shangqiu Normal University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Xiaochen Zhang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Hui Zeng"
    },
    {
      "name": "Xiao-Chen Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ezg7v4e9707f9.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008897/v1",
  "fulltext_url": null,
  "id": "ezg7v4e9707f9",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008897/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7213226112",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/ezg7v4e9707f9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15008897",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008897/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "BondNet: Noise-Robust Molecular Bond Perception from 3D Coordinates with Explicit Hydrogen Context",
  "title_text": "BondNet: Noise-Robust Molecular Bond Perception from 3D Coordinates with Explicit Hydrogen Context",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/ezg7v4e9707f9",
  "venue": "ChemRxiv",
  "versions": []
}