PairReg: A method for enhancing the learning of molecular structure representation in equivariant graph neural networks
Saved in:
Main Authors: | Zhen Ren, Yu Liu, Sen Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2025-01-01
|
Series: | PLoS ONE |
Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12312963/?tool=EBI |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Dielectric tensor of perovskite oxides at finite temperature using equivariant graph neural network potentials
by: Alex Kutana, et al.
Published: (2025-12-01) -
The Dynamic Connection Layer (DCL): Enhancing Topological Representation in Chemical Graph Neural Networks
by: Wenyuan Zhang, et al.
Published: (2025-06-01) -
Fingerprint-enhanced hierarchical molecular graph neural networks for property prediction
by: Shuo Liu, et al.
Published: (2025-06-01) -
Dual Attention Equivariant Network for Weakly Supervised Semantic Segmentation
by: Guanglun Huang, et al.
Published: (2025-06-01) -
A permutation-equivariant deep learning model for quantum state characterization
by: D. Maragnano, et al.
Published: (2025-06-01)