Spherical Message Passing for 3D Molecular Graphs
暂无分享,去创建一个
S. Ji | Bora Oztekin | Meng Liu | Limei Wang | Yi Liu | Yu-Ching Lin | Xuan Zhang
[1] P. Battaglia,et al. Simple GNN Regularisation for 3D Molecular Property Prediction&Beyond , 2021, 2106.07971.
[2] Jonathan P. Mailoa,et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials , 2021, Nature Communications.
[3] Shuiwang Ji,et al. Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery. , 2020, Bioinformatics.
[4] Simon Axelrod,et al. GEOM: Energy-annotated molecular conformations for property prediction and molecular generation , 2020, ArXiv.
[5] Shuiwang Ji,et al. Graph U-Nets , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Florian Becker,et al. GemNet: Universal Directional Graph Neural Networks for Molecules , 2021, NeurIPS.
[7] Joan Bruna,et al. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges , 2021, ArXiv.
[8] Shuiwang Ji,et al. DIG: A Turnkey Library for Diving into Graph Deep Learning Research , 2021, J. Mach. Learn. Res..
[9] J. Leskovec,et al. ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations , 2021, ArXiv.
[10] Michael Gastegger,et al. Equivariant message passing for the prediction of tensorial properties and molecular spectra , 2021, ICML.
[11] Shuiwang Ji,et al. Topology-Aware Graph Pooling Networks , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] Max Welling,et al. Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs , 2020, ICLR.
[13] Mohammed Bennamoun,et al. Deep Learning for 3D Point Clouds: A Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Johannes T. Margraf,et al. Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules , 2020, ArXiv.
[15] Weihua Hu,et al. The Open Catalyst 2020 (OC20) Dataset and Community Challenges , 2020, ACS Catalysis.
[16] Frederick R. Manby,et al. OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features , 2020, The Journal of chemical physics.
[17] Shuiwang Ji,et al. Deep Learning of High-Order Interactions for Protein Interface Prediction , 2020, KDD.
[18] Andreas Loukas,et al. Building powerful and equivariant graph neural networks with message-passing , 2020, ArXiv.
[19] Andreas Loukas,et al. Building powerful and equivariant graph neural networks with structural message-passing , 2020, NeurIPS.
[20] Fabian B. Fuchs,et al. SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks , 2020, NeurIPS.
[21] Stephan Günnemann,et al. Directional Message Passing for Molecular Graphs , 2020, ICLR.
[22] Jure Leskovec,et al. Learning to Simulate Complex Physics with Graph Networks , 2020, ICML.
[23] José Miguel Hernández-Lobato,et al. Reinforcement Learning for Molecular Design Guided by Quantum Mechanics , 2020, ICML.
[24] Stefanie Jegelka,et al. Generalization and Representational Limits of Graph Neural Networks , 2020, ICML.
[25] Qi Liu,et al. Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective , 2019, AAAI.
[26] Risi Kondor,et al. Cormorant: Covariant Molecular Neural Networks , 2019, NeurIPS.
[27] Lei Wang,et al. Appendix for : Graph Attention Convolution for Point Cloud Semantic Segmentation , 2019 .
[28] Chao Chen,et al. ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Yaron Lipman,et al. Provably Powerful Graph Networks , 2019, NeurIPS.
[30] Mohamed Boussaha,et al. Point Cloud Oversegmentation With Graph-Structured Deep Metric Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Markus Meuwly,et al. PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges. , 2019, Journal of chemical theory and computation.
[32] Max Welling,et al. Gauge Equivariant Convolutional Networks and the Icosahedral CNN 1 , 2019 .
[33] Nathanael Perraudin,et al. DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications , 2018, Astron. Comput..
[34] Jure Leskovec,et al. How Powerful are Graph Neural Networks? , 2018, ICLR.
[35] Raphael J. L. Townshend,et al. End-to-End Learning on 3D Protein Structure for Interface Prediction , 2018, NeurIPS.
[36] Yingyu Liang,et al. N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules , 2018, NeurIPS.
[37] Yue Wang,et al. Dynamic Graph CNN for Learning on Point Clouds , 2018, ACM Trans. Graph..
[38] Zhengyang Wang,et al. Large-Scale Learnable Graph Convolutional Networks , 2018, KDD.
[39] Razvan Pascanu,et al. Relational inductive biases, deep learning, and graph networks , 2018, ArXiv.
[40] Yixin Chen,et al. An End-to-End Deep Learning Architecture for Graph Classification , 2018, AAAI.
[41] K. Müller,et al. Towards exact molecular dynamics simulations with machine-learned force fields , 2018, Nature Communications.
[42] Li Li,et al. Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds , 2018, ArXiv.
[43] Dong Tian,et al. Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[44] Martin Simonovsky,et al. Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[45] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[46] Jeffrey C Grossman,et al. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. , 2017, Physical review letters.
[47] Alex Fout,et al. Protein Interface Prediction using Graph Convolutional Networks , 2017, NIPS.
[48] Klaus-Robert Müller,et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions , 2017, NIPS.
[49] Nikos Komodakis,et al. Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[51] Vijay S. Pande,et al. MoleculeNet: a benchmark for molecular machine learning , 2017, Chemical science.
[52] Klaus-Robert Müller,et al. Machine learning of accurate energy-conserving molecular force fields , 2016, Science Advances.
[53] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[54] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[55] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[56] Pavlo O. Dral,et al. Quantum chemistry structures and properties of 134 kilo molecules , 2014, Scientific Data.
[57] Kurt Mehlhorn,et al. Weisfeiler-Lehman Graph Kernels , 2011, J. Mach. Learn. Res..
[58] F. Scarselli,et al. A new model for learning in graph domains , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[59] E. Villaseñor. Introduction to Quantum Mechanics , 2008, Nature.