Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction

27 July 2022

Papers citing "Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction"

6 / 6 papers shown

Title
SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction Junchi Liu Ying Tang Sergei Tretiak Wenhui Duan Liujiang Zhou 87 0 0 01 May 2025
Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures Shuo-feng Zhang Yang Liu Lei Xie GNN 42 60 0 15 Nov 2020
Analyzing Learned Molecular Representations for Property Prediction Kevin Kaichuang Yang Kyle Swanson Wengong Jin Connor W. Coley Philipp Eiden ... Andrew Palmer Volker Settels Tommi Jaakkola K. Jensen Regina Barzilay 86 1,305 0 02 Apr 2019
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks Christopher Morris Martin Ritzert Matthias Fey William L. Hamilton J. E. Lenssen Gaurav Rattan Martin Grohe GNN 142 1,625 0 04 Oct 2018
How Powerful are Graph Neural Networks? Keyulu Xu Weihua Hu J. Leskovec Stefanie Jegelka GNN 188 7,554 0 01 Oct 2018
Convolutional Networks on Graphs for Learning Molecular Fingerprints David Duvenaud D. Maclaurin J. Aguilera-Iparraguirre Rafael Gómez-Bombarelli Timothy D. Hirzel Alán Aspuru-Guzik Ryan P. Adams GNN 170 3,337 0 30 Sep 2015