BondNet: Noise-Robust Molecular Bond Perception from 3D Coordinates with Explicit Hydrogen Context
Year: 2026
Type: preprint
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.
Source: ChemRxiv
Authors: Xiaochen Zhang, Hui Zeng, Xiao-Chen Zhang
Institution: Shangqiu Normal University
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7213226112