InvarNet: Molecular property prediction via rotation invariant graph neural networks

InvarNet: Molecular property prediction via rotation invariant graph neural networks

Predicting molecular properties is crucial in drug synthesis and screening, but traditional molecular dynamics methods are time-consuming and costly. Recently, deep learning methods, particularly Graph Neural Networks (GNNs), have significantly improved efficiency by capturing molecular structures’...

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Bibliographic Details
Main Authors: Danyan Chen, Gaoxiang Duan, Dengbao Miao, Xiaoying Zheng, Yongxin Zhu
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Machine Learning with Applications
Subjects:
Graph neural networks
Molecular property prediction
Rotation invariance
Data processing
Online Access:http://www.sciencedirect.com/science/article/pii/S266682702400063X
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