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Many real-world data can be modeled as 3D graphs, but learning representations that incorporates 3D information completely and efficiently is challenging.
Protein function prediction via graph kernels
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Quantum chemistry structures and properties of 134 kilo molecules
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Convolutional networks on graphs for learning molecular fingerprints
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PubChemQC project: a large-scale first-principles electronic structure database for data-driven chemistry
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Tian Xie and Jeffrey C Grossman · 2018
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Matthias Fey and Jan E. Lenssen · 2019
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Graph U-Nets
Hongyang Gao and Shuiwang Ji · 2019
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Molecular property prediction: A multilevel quantum interactions modeling perspective
Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, and Lixin He · 2019
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Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2019
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Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Molecular machine learning with conformer ensembles
Simon Axelrod and Rafael Gomez-Bombarelli · 2020
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Correction to “the open catalyst 2020 (oc20) dataset and community challenges”
Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, et al · 2020
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Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Learning ground states of quantum hamiltonians with graph networks
Dmitrii Kochkov, Tobias Pfaff, Alvaro Sanchez-Gonzalez, Peter Battaglia, and Bryan K Clark · 2021
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DIG: A turnkey library for diving into graph deep learning research
Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora M Oztekin, Xuan Zhang, and Shuiwang Ji · 2021
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof Schütt, Oliver Unke, and Michael Gastegger · 2021
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Open catalyst 2020 (OC20) dataset and community challenges
Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, et al · 2020
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SE(3)-Transformers: 3D roto-translation equivariant attention networks
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling · 2020
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Directional message passing for molecular graphs
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2020
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Johannes Klicpera, Shankari Giri, Johannes T Margraf, and Stephan Günnemann · 2020
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Deep learning of high-order interactions for protein interface prediction
Yi Liu, Hao Yuan, Lei Cai, and Shuiwang Ji · 2020
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Reinforcement learning for molecular design guided by quantum mechanics
Gregor N. C. Simm, Robert Pinsler, and José Miguel Hernández-Lobato · 2020
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A deep learning approach to antibiotic discovery
Jonathan M Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M Donghia, Craig R MacNair, Shawn French, Lindsey A Carfrae, Zohar Bloom-Ackermann, et al · 2020
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Rotation invariant graph neural networks using spin convolutions
Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das, Aditya Grover, Anuroop Sriram, Zachary Ulissi, and C Lawrence Zitnick · 2021
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Symmetry-aware actor-critic for 3D molecular design
Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, and José Miguel Hernández-Lobato · 2021
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Molecule3D: A benchmark for predicting 3D geometries from molecular graphs
Zhao Xu, Youzhi Luo, Xuan Zhang, Xinyi Xu, Yaochen Xie, Meng Liu, Kaleb Dickerson, Cheng Deng, Maho Nakata, and Shuiwang Ji · 2021
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Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
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Learning 3D representations of molecular chirality with invariance to bond rotations
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Simple GNN regularisation for 3D molecular property prediction and beyond
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Equivariant diffusion for molecule generation in 3D
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Torsional diffusion for molecular conformer generation
Bowen Jing, Gabriele Corso, Regina Barzilay, and Tommi S. Jaakkola · 2022
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Spherical message passing for 3D molecular graphs
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Geometric transformers for protein interface contact prediction
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Advanced graph and sequence neural networks for molecular property prediction and drug discovery
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