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Molecular representation pretraining is critical in various applications for drug and material discovery due to the limited number of labeled molecules, and most existing work focuses on pretraining on 2D molecular graphs.
Merck molecular force field. i. basis, form, scope, parameterization, and performance of mmff94
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Combining labeled and unlabeled data with co-training
Avrim Blum and Tom Mitchell · 1998
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Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2001
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Exploring potential energy surfaces for chemical reactions: an overview of some practical methods
H Bernhard Schlegel · 2003
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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan · 2005
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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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Energy-based view of retrosynthesis
Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, and Bo Dai · 2007
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling, 2013
Greg Landrum et al · 2013
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Markov state models of biomolecular conformational dynamics
John D Chodera and Frank Noé · 2014
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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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Convolutional networks on graphs for learning molecular fingerprints
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Protein binding pocket dynamics
Antonia Stank, Daria B Kokh, Jonathan C Fuller, and Rebecca C Wade · 2016
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Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, Igor Poltavsky, Kristof T Schütt, and Klaus-Robert Müller · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof T Schütt, Pieter-Jan Kindermans, Huziel E Sauceda, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Applied chemoinformatics: achievements and future opportunities
Thomas Engel and Johann Gasteiger · 2018
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N-gram graph: Simple unsupervised representation for graphs, with applications to molecules
Shengchao Liu, Mehmet Furkan Demirel, and Yingyu Liang · 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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Representation learning with contrastive predictive coding
Aaron Van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Guacamol: benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data
Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Thanh Nguyen, and Sai-Kit Yeung · 2019
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Analyzing learned molecular representations for property prediction
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, et al · 2019
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Non-autoregressive electron redistribution modeling for reaction prediction
Hangrui Bi, Hengyi Wang, Chence Shi, Connor W. Coley, Jian Tang, and Hongyu Guo · 2021
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Geometric and physical quantities improve e(3) equivariant message passing
Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik Bekkers, and Max Welling · 2021
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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Chemrl-gem: Geometry enhanced molecular representation learning for property prediction
Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, and Haifeng Wang · 2021
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Graph energy-based model for molecular graph generation
Ryuichiro Hataya, Hideki Nakayama, and Kazuki Yoshizoe · 2021
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Simon Axelrod and Rafael Gomez-Bombarelli · 2020
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A hierarchical graph network for 3d object detection on point clouds
Jintai Chen, Biwen Lei, Qingyu Song, Haochao Ying, Danny Z Chen, and Jian Wu · 2020
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Improved contrastive divergence training of energy based models
Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch · 2020
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Se(3)-transformers: 3d roto-translation equivariant attention networks
Fabian B Fuchs, Daniel E Worrall, Volker Fischer, and Max Welling · 2020
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Functional regularization for representation learning: A unified theoretical perspective
Siddhant Garg and Yingyu Liang · 2020
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Learning to navigate the synthetically accessible chemical space using reinforcement learning
Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Simon Blackburn, Karam Thomas, Connor Coley, Jian Tang, et al · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Gemnet: Universal directional graph neural networks for molecules
Johannes Klicpera, Florian Becker, and Stephan Günnemann · 2021
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E(n) equivariant graph neural networks
Victor 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 T Schütt, Oliver T Unke, and Michael Gastegger · 2021
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Learning gradient fields for molecular conformation generation
Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang · 2021
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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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Saint: Improved neural networks for tabular data via row attention and contrastive pre-training
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C Bayan Bruss, and Tom Goldstein · 2021
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How to train your energy-based models
Yang Song and Diederik P Kingma · 2021
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Self-supervised on graphs: Contrastive, generative, or predictive
Lirong Wu, Haitao Lin, Zhangyang Gao, Cheng Tan, Stan Li, et al · 2021
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Self-supervised learning of graph neural networks: A unified review
Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, and Shuiwang Ji · 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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Molgensurvey: A systematic survey in machine learning models for molecule design
Yuanqi Du, Tianfan Fu, Jimeng Sun, and Shengchao Liu · 2022
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e3nn: Euclidean neural networks
Mario Geiger and Tess Smidt · 2022
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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 · 2022
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Self-supervised pre-training for protein embeddings using tertiary structures
Yuzhi Guo, Jiaxiang Wu, Hehuan Ma, and Junzhou Huang · 2022
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GraphCG: Unsupervised discovery of steerable factors in graphs
Shengchao Liu, Chengpeng Wang, Weili Nie, Hanchen Wang, Jiarui Lu, Bolei Zhou, and Jian Tang · 2022
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Pre-training via denoising for molecular property prediction
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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A text-guided protein design framework
Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, and Anima Anandkumar · 2023
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