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.

通过引入分子超图和分子超图神经网络（MHNN），本研究针对化学有关任务中传统图模型无法足够表示高阶连接（如多中心键和共轭结构）的问题，提出了一种可以预测有机半导体的光电性质的 MHNN 算法，其中超边表示共轭结构。结果表明，MHNN 在 OPV、OCELOTv1 和 PCQM4Mv2 数据集的大多数任务中优于所有基准模型。值得注意的是，MHNN 在没有任何三维几何信息的情况下，超过了利用原子位置的基准模型。此外，在训练数据有限的情况下，MHNN 表现出更好的性能比预训练 GNNs，彰显其出色的数据效率。本研究为更一般的分子表示和高阶连接相关的属性预测任务提供了新的策略。