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）在多种化学相关任务中展现了出色的性能。然而，传统图结构仅能建模分子中的成对连接，难以充分表示多中心键、共轭结构等高阶连接关系。为解决这一问题，我们引入分子超图概念，并提出分子超图神经网络（MHNN）用于预测有机半导体的光电性质，其中超边表示共轭结构。我们设计了一种针对不规则高阶连接的通用算法，该算法可高效处理包含不同阶超边的分子超图。实验结果表明，在OPV、OCELOTv1和PCQM4Mv2数据集的大多数任务中，MHNN均优于所有基线模型。值得注意的是，MHNN无需任何三维几何信息即可超越使用原子位置的基线模型。此外，在训练数据有限的情况下，MHNN比预训练GNNs具有更优性能，展现出卓越的数据效率。这项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。