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We consider representation learning for proteins with 3D structures.
Structural patterns in globular proteins
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Medium-and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins
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Enzyme nomenclature 1992 : recommendations of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology on the nomenclature and classification of enzymes
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Updates to the integrated protein–protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2
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Protein interface prediction using graph convolutional networks
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Neural message passing for quantum chemistry
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Semi-supervised classification with graph convolutional networks
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Deep convolutional networks for quality assessment of protein folds
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DeepSF: deep convolutional neural network for mapping protein sequences to folds
Jie Hou, Badri Adhikari, and Jianlin Cheng · 2018
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DeepDTA: deep drug–target binding affinity prediction
Hakime Öztürk, Arzucan Özgür, and Elif Ozkirimli · 2018
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C Grossman · 2018
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Learning protein sequence embeddings using information from structure
Tristan Bepler and Bonnie Berger · 2019
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SIFTS: updated structure integration with function, taxonomy and sequences resource allows 40-fold increase in coverage of structure-based annotations for proteins
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Graph U-nets
Hongyang Gao and Shuiwang Ji · 2019
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Generative models for graph-based protein design
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
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PyTorch: An imperative style, high-performance deep learning library
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Evaluating protein transfer learning with tape
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, and Yun Song · 2019
Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik · 2021
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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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DIG: a turnkey library for diving into graph deep learning research
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Multi-scale representation learning on proteins
Vignesh Ram Somnath, Charlotte Bunne, and Andreas Krause · 2021
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Fast end-to-end learning on protein surfaces
Freyr Sverrisson, Jean Feydy, Bruno E Correia, and Michael M Bronstein · 2021
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End-to-end learning on 3D protein structure for interface prediction
Raphael Townshend, Rishi Bedi, Patricia Suriana, and Ron Dror · 2019
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Machine-learning-guided directed evolution for protein engineering
Kevin K Yang, Zachary Wu, and Frances H Arnold · 2019
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Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Pablo Gainza, Freyr Sverrisson, Frederico Monti, Emanuele Rodola, D Boscaini, MM Bronstein, and BE Correia · 2020
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Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, 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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Biochemistry, essential amino acids
Michael J Lopez and Shamim S Mohiuddin · 2020
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ATOM3D: Tasks on molecules in three dimensions
Raphael John Lamarre Townshend, Martin Vögele, Patricia Adriana Suriana, Alexander Derry, Alexander Powers, Yianni Laloudakis, Sidhika Balachandar, Bowen Jing, Brandon M. Anderson, Stephan Eismann, Risi Kondor, Russ Altman, and Ron O. Dror · 2021
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Protein sequence design with deep generative models
Zachary Wu, Kadina E Johnston, Frances H Arnold, and Kevin K Yang · 2021
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Using deep learning to annotate the protein universe
Maxwell L Bileschi, David Belanger, Drew H Bryant, Theo Sanderson, Brandon Carter, D Sculley, Alex Bateman, Mark A DePristo, and Lucy J Colwell · 2022
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Unifying structural descriptors for biological and bioinspired nanoscale complexes
Minjeong Cha, Emine Sumeyra Turali Emre, Xiongye Xiao, Ji-Young Kim, Paul Bogdan, J Scott VanEpps, Angela Violi, and Nicholas A Kotov · 2022
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Independent SE(3)-equivariant models for end-to-end rigid protein docking
Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, and Andreas Krause · 2022
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GOOD: A graph out-of-distribution benchmark
Shurui Gui, Xiner Li, Limei Wang, and Shuiwang Ji · 2022
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Contrastive representation learning for 3D protein structures, 2022
Pedro Hermosilla and Timo Ropinski · 2022
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Directed weight neural networks for protein structure representation learning
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Spherical message passing for 3D molecular graphs
Yi Liu, Limei Wang, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, and Shuiwang Ji · 2022
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Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction
Wei Lu, Qifeng Wu, Jixian Zhang, Jiahua Rao, Chengtao Li, and Shuangjia Zheng · 2022
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One transformer can understand both 2D & 3D molecular data
Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, and Di He · 2022
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EquiBind: Geometric deep learning for drug binding structure prediction
Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola · 2022
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AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models
Mihaly Varadi, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, Oana Stroe, Gemma Wood, Agata Laydon, et al · 2022
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Periodic graph transformers for crystal material property prediction
Keqiang Yan, Yi Liu, Yuchao Lin, and Shuiwang Ji · 2022
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GraphFM: Improving large-scale GNN training via feature momentum
Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, and Shuiwang Ji · 2022
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On the expressive power of geometric graph neural networks
Chaitanya K Joshi, Cristian Bodnar, Simon V Mathis, Taco Cohen, and Pietro Liò · 2023
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Protein representation learning by geometric structure pretraining
Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, and Jian Tang · 2023
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