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Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications.
The protein data bank
Helen M Berman, John Westbrook, Zukang Feng, Gary Gilliland, Talapady N Bhat, Helge Weissig, Ilya N Shindyalov, and Philip E Bourne · 2000
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Glide: a new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy
Richard A Friesner, Jay L Banks, Robert B Murphy, Thomas A Halgren, Jasna J Klicic, Daniel T Mainz, Matthew P Repasky, Eric H Knoll, Mee Shelley, Jason K Perry, et al · 2004
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The pdbbind database: methodologies and updates
Renxiao Wang, Xueliang Fang, Yipin Lu, Chao-Yie Yang, and Shaomeng Wang · 2005
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Deepdta: deep drug–target binding affinity prediction
Hakime Öztürk, Arzucan Özgür, and Elif Ozkirimli · 2018
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Improving language understanding by generative pre-training, 2018
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Schnet–a deep learning architecture for molecules and materials
Kristof T Schütt, Huziel E Sauceda, P-J Kindermans, Alexandre Tkatchenko, and K-R Müller · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Learning protein sequence embeddings using information from structure
Tristan Bepler and Bonnie Berger · 2019
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Deepaffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks
Mostafa Karimi, Di Wu, Zhangyang Wang, and Yang Shen · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Language models are unsupervised multitask learners, 2019
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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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
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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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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Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Johannes Gasteiger, Shankari Giri, Johannes T Margraf, and Stephan Günnemann · 2020
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Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures
Pedro Hermosilla, Marco Schäfer, Matej Lang, Gloria Fackelmann, Pere-Pau Vázquez, Barbora Kozlikova, Michael Krone, Tobias Ritschel, and Timo Ropinski · 2020
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Atom3d: Tasks on molecules in three dimensions
Raphael JL Townshend, Martin Vögele, Patricia Suriana, Alexander Derry, Alexander Powers, Yianni Laloudakis, Sidhika Balachandar, Bowen Jing, Brandon Anderson, Stephan Eismann, et al · 2020
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Prottrans: Toward understanding the language of life through self-supervised learning
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, et al · 2021
Cited alongside, same era.
Simple gnn regularisation for 3d molecular property prediction and beyond
Jonathan Godwin, Michael Schaarschmidt, Alexander L Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Veličković, James Kirkpatrick, and Peter Battaglia · 2021
Cited alongside, same era.
Ogb-lsc: A large-scale challenge for machine learning on graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
Cited alongside, same era.
Equivariant graph neural networks for 3d macromolecular structure
Bowen Jing, Stephan Eismann, Pratham N Soni, and Ron O Dror · 2021
Cited alongside, same era.
Pre-training molecular graph representation with 3d geometry
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang · 2021
3d infomax improves gnns for molecular property prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, and Pietro Liò · 2022
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Torchmd-net: equivariant transformers for neural network based molecular potentials
Philipp Thölke and Gianni De Fabritiis · 2022
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Discovering the representation bottleneck of graph neural networks from multi-order interactions
Fang Wu, Siyuan Li, Lirong Wu, Stan Z Li, Dragomir Radev, and Qiang Zhang · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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Pre-training via denoising for molecular property prediction
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Cited alongside, same era.
Frame averaging for invariant and equivariant network design
Omri Puny, Matan Atzmon, Edward J Smith, Ishan Misra, Aditya Grover, Heli Ben-Hamu, and Yaron Lipman · 2021
Cited alongside, same era.
E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
Cited alongside, same era.
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof Schütt, Oliver Unke, and Michael Gastegger · 2021
Cited alongside, same era.
Multi-scale representation learning on proteins
Vignesh Ram Somnath, Charlotte Bunne, and Andreas Krause · 2021
Cited alongside, same era.
Geom, energy-annotated molecular conformations for property prediction and molecular generation
Simon Axelrod and Rafael Gomez-Bombarelli · 2022
Cited alongside, same era.
Artificial intelligence for synthetic biology
Mohammed Eslami, Aaron Adler, Rajmonda S Caceres, Joshua G Dunn, Nancy Kelley-Loughnane, Vanessa A Varaljay, and Hector Garcia Martin · 2022
Cited alongside, same era.
Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
Cited alongside, same era.
Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, and Jonathan Godwin · 2022
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Protein representation learning by geometric structure pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, and Jian Tang · 2022
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The role of ai in drug discovery: challenges, opportunities, and strategies
Alexandre Blanco-Gonzalez, Alfonso Cabezon, Alejandro Seco-Gonzalez, Daniel Conde-Torres, Paula Antelo-Riveiro, Angel Pineiro, and Rebeca Garcia-Fandino · 2023
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Protein-ligand binding representation learning from fine-grained interactions
Shikun Feng, Minghao Li, Yinjun Jia, Weiying Ma, and Yanyan Lan · 2023
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Fractional denoising for 3d molecular pre-training
Shikun Feng, Yuyan Ni, Yanyan Lan, Zhi-Ming Ma, and Wei-Ying Ma · 2023
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Self-supervised pocket pretraining via protein fragment-surroundings alignment
Bowen Gao, Yinjun Jia, Yuanle Mo, Yuyan Ni, Weiying Ma, Zhiming Ma, and Yanyan Lan · 2023
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Energy-motivated equivariant pretraining for 3d molecular graphs
Rui Jiao, Jiaqi Han, Wenbing Huang, Yu Rong, and Yang Liu · 2023
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Unsupervised protein-ligand binding energy prediction via neural euler’s rotation equation
Wengong Jin, Siranush Sarkizova, Xun Chen, Nir Hacohen, and Caroline Uhler · 2023
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End-to-end full-atom antibody design
Xiangzhe Kong, Wenbing Huang, and Yang Liu · 2023
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Generalist equivariant transformer towards 3d molecular interaction learning
Xiangzhe Kong, Wenbing Huang, and Yang Liu · 2023
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Learning hierarchical protein representations via complete 3d graph networks
Limei Wang, Haoran Liu, Yi Liu, Jerry Kurtin, and Shuiwang Ji · 2023
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De novo design of protein structure and function with rfdiffusion
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al · 2023
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Unified molecular modeling via modality blending
Qiying Yu, Yudi Zhang, Yuyan Ni, Shikun Feng, Yanyan Lan, Hao Zhou, and Jingjing Liu · 2023
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Uni-mol: a universal 3d molecular representation learning framework, 2023
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke · 2023
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