Cargando…
Describe Molecules by a Heterogeneous Graph Neural Network with Transformer-like Attention for Supervised Property Predictions
[Image: see text] Machine learning and deep learning have facilitated various successful studies of molecular property predictions. The rapid development of natural language processing and graph neural network (GNN) further pushed the state-of-the-art prediction performance of molecular property to...
Autores principales: | Deng, Daiguo, Lei, Zengrong, Hong, Xiaobin, Zhang, Ruochi, Zhou, Fengfeng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
|
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811943/ https://www.ncbi.nlm.nih.gov/pubmed/35128279 http://dx.doi.org/10.1021/acsomega.1c06389 |
Ejemplares similares
-
Deep scaffold hopping with multimodal transformer neural networks
por: Zheng, Shuangjia, et al.
Publicado: (2021) -
PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning
por: Deng, Yihe, et al.
Publicado: (2023) -
Graph Multihead Attention Pooling with Self-Supervised Learning
por: Wang, Yu, et al.
Publicado: (2022) -
Supervised biological network alignment with graph neural networks
por: Ding, Kerr, et al.
Publicado: (2023) -
MDGNN: Microbial Drug Prediction Based on Heterogeneous Multi-Attention Graph Neural Network
por: Pi, Jiangsheng, et al.
Publicado: (2022)