Molecular Hypergraph Neural Networks
This work addresses the limitation of conventional graph neural networks in modeling higher-order molecular connections for chemists and materials scientists, offering a new strategy for molecular representation and property prediction.
The authors tackled the problem of representing higher-order molecular connections like conjugated structures in neural networks by introducing molecular hypergraphs and Molecular Hypergraph Neural Networks (MHNN) for predicting optoelectronic properties, achieving superior performance on OPV, OCELOTv1, and PCQM4Mv2 datasets without 3D geometric information and with better data efficiency than pretrained GNNs.
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.