.png){kind=icon}
返回
Molecular hypergraph neural networks
.png){kind=icon}
OA
AI
DOI：10.1063/5.0193557.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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, such as multi-center bonds and conjugated structures. To tackle this challenge, we introduce molecular hypergraphs and propose Molecular Hypergraph Neural Networks (MHNNs) 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 organic photovoltaic, OCELOT chromophore v1, 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.
Keyword:
DELOCALIZATION
REPRESENTATION
期刊
IF:
3.1
论文数:
7.2W
被引数:
23.2W
机构
引用论文
The Construction of a Neurodynamic Structure of an Adaptive Sensor for Measuring Physical Parameters
PREAI
分享收藏E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentialsE(3)-等变图神经网络，用于数据高效和精确的原子间势.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven ChemistryPubChemQC项目: 用于数据驱动化学的大型第一原理电子结构数据库
PREAI
分享收藏Delineation of the intratemporal facial nerve in a cadaveric specimen on diffusion tensor imaging using a 9.4 T magnetic resonance imaging scanner: a technical note在9.4 T磁共振成像扫描仪上，通过扩散张量成像对尸检标本中颞骨内面神经进行勾画：一项技术说明
PREAI
分享收藏A graph-convolutional neural network model for the prediction of chemical reactivity用于预测化学反应性的图卷积神经网络模型CHEMICAL SCIENCEOAAI
CHEMICAL SCIENCEIF7.4
OAAI分享收藏Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategyCHEMICAL SCIENCEOAAI
CHEMICAL SCIENCEIF7.4
OAAI分享收藏Enhancement patterns of the normal facial nerve on three-dimensional T1W fast spin echo MRI正常面神经在三维T1加权快速自旋回波MRI上的增强模式OAAI
OAAI分享收藏
查看更多内容