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.

翻译：图神经网络（GNNs）已在各种化学相关任务中展现出良好的性能。然而，传统图仅建模分子中的成对连接性，无法充分表示多中心键和共轭结构等高阶连接。为应对这一挑战，我们引入分子超图，并提出分子超图神经网络（MHNN）来预测有机半导体的光电性质，其中超边表示共轭结构。我们设计了一种适用于不规则高阶连接的通用算法，可高效处理具有不同阶次超边的分子超图。结果表明，在OPV、OCELOTv1和PCQM4Mv2数据集的大多数任务中，MHNN优于所有基线模型。值得注意的是，MHNN无需任何3D几何信息即可实现这一性能，超越了利用原子位置的基线模型。此外，在有限训练数据下，MHNN比预训练的GNN取得了更好的性能，凸显其出色的数据效率。本工作为更通用的分子表示及与高阶连接相关的性质预测任务提供了新策略。