mlsanityData to 7 Oct 2026

Molecular Hypergraph Neural Networks

arXiv 2312.13136 ↗First posted 20 Dec 2023physics.chem-ph cs.LGcited by 7

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Abstract (arXiv)

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher-order connections like multi-center bonds and conjugated structures. To tackle this challenge, we introduce molecular hypergraphs and propose Molecular Hypergraph Neural Networks (MHNN) to predict the optoelectronic properties of organic semiconductors, where hyperedges represent conjugated structures. A general algorithm is designed for irregular high-order connections, which can efficiently operate on molecular hypergraphs with hyperedges of various orders. The results show that MHNN outperforms all baseline models on most tasks of OPV, OCELOTv1 and PCQM4Mv2 datasets. Notably, MHNN achieves this without any 3D geometric information, surpassing the baseline model that utilizes atom positions. Moreover, MHNN achieves better performance than pretrained GNNs under limited training data, underscoring its excellent data efficiency. This work provides a new strategy for more general molecular representations and property prediction tasks related to high-order connections.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 29 of its 59 references that are in the map. First posted on arXiv 20 Dec 2023.

Builds on

29 of 59 references.

References · 29

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17 more
  1. 2020Directional Message Passing for Molecular Graphscited by 556
  2. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 467
  3. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 373
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  5. 2019Hypergraph Convolution and Hypergraph Attentioncited by 226
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  9. 2021Geometric Deep Learning on Molecular Representationscited by 113
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  11. 2018Molecular Hypergraph Grammar with its Application to Molecular Optimizationcited by 56
  12. 2022Equivariant Hypergraph Diffusion Neural Operatorscited by 45
  13. 2021Edge Representation Learning with Hypergraphscited by 37
  14. 2018Message-passing neural networks for high-throughput polymer screeningcited by 30
  15. 2022Augmentations in Hypergraph Contrastive Learning: Fabricated and Generativecited by 28
  16. 2022HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Networkcited by 16
  17. 2022A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddingscited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 1; 2026: 2.

7 papers.

  1. 2024E(n) Equivariant Topological Neural Networkscited by 22
  2. 2024HC-GLAD: Dual Hyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly Detectioncited by 5
  3. 2024Generalizable, Fast, and Accurate DeepQSPR with fastpropcited by 3
  4. 2024SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learningcited by 2
  5. 2025ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discoverycited by 1
  6. 2026How well can off-the-shelf LLMs elucidate molecular structures from mass spectra using chain-of-thought reasoning?cited by 0
  7. 2026ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET predictioncited by 0