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Inspired by its success in natural language processing and computer vision, pre-training has attracted substantial attention in cheminformatics and bioinformatics, especially for molecule based tasks.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 1907
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A cluster separation measure
David L Davies and Donald W Bouldin · 1979
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Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
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Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
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Random decision forests
Tin Kam Ho · 1995
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Combining labeled and unlabeled data with co-training
Avrim Blum and Tom Mitchell · 1998
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Zinc - a free database of commercially available compounds for virtual screening
John J. Irwin and Brian K. Shoichet · 2005
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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 H Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, et al · 2006
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Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
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Deepergcn: All you need to train deeper gcns
Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem · 2006
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Learn molecular representations from large-scale unlabeled molecules for drug discovery
Pengyong Li, Jun Wang, Yixuan Qiao, Hao Chen, Yihuan Yu, Xiaojun Yao, Peng Gao, Guotong Xie, and Sen Song · 2012
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Computational exploration of molecular scaffolds in medicinal chemistry
Ye Hu, Dagmar Stumpfe, and Jürgen Bajorath · 2016
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Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity, 2016
Bing Huang and O Anatole Von Lilienfeld · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Computer-assisted retrosynthesis based on molecular similarity
Connor W Coley, Luke Rogers, William H Green, and Klavs F Jensen · 2017
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Retrosynthetic reaction prediction using neural sequence-to-sequence models
Bowen Liu, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Luu Nguyen, Stephen Ho, Jack Sloane, Paul Wender, and Vijay Pande · 2017
Cited alongside, same era.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Speech-transformer: A no-recurrence sequence-to-sequence model for speech recognition
Linhao Dong, Shuang Xu, and Bo Xu · 2018
Cited alongside, same era.
Image transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran · 2018
Cited alongside, same era.
Molecular representation learning with language models and domain-relevant auxiliary tasks
Benedek Fabian, Thomas Edlich, Héléna Gaspar, Marwin Segler, Joshua Meyers, Marco Fiscato, and Mohamed Ahmed · 2020
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Contrastive multi-view representation learning on graphs
Kaveh Hassani and Amir Hosein Khasahmadi · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
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PubChem in 2021: new data content and improved web interfaces
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, Leonid Zaslavsky, Jian Zhang, and Evan E Bolton · 2020
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
Cited alongside, same era.
Retrosynthesis prediction with conditional graph logic network
Hanjun Dai, Chengtao Li, Connor W Coley, Bo Dai, and Le Song · 2019
Cited alongside, same era.
Maha Elbayad, Jiatao Gu, Edouard Grave, and Michael Auli · 2019
Cited alongside, same era.
Smiles transformer: pre-trained molecular fingerprint for low data drug discovery
Shion Honda, Shoi Shi, and Hiroki R Ueda · 2019
Cited alongside, same era.
N-gram graph: Simple unsupervised representation for graphs, with applications to molecules
Shengchao Liu, Mehmet F Demirel, and Yingyu Liang · 2019
Cited alongside, same era.
Molecular property prediction: A multilevel quantum interactions modeling perspective
Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, and Lixin He · 2019
Cited alongside, same era.
Evaluating protein transfer learning with tape
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, and Yun S Song · 2019
Cited alongside, same era.
Multi-view graph neural networks for molecular property prediction
Hehuan Ma, Yatao Bian, Yu Rong, Wenbing Huang, Tingyang Xu, Weiyang Xie, Geyan Ye, and Junzhou Huang · 2020
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Molecule attention transformer
Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, and Stanisław Jastrzębski · 2020
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Transfer learning enables the molecular transformer to predict regio-and stereoselective reactions on carbohydrates
Giorgio Pesciullesi, Philippe Schwaller, Teodoro Laino, and Jean-Louis Reymond · 2020
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Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang · 2020
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Molgnn: Self-supervised motif learning graph neural network for drug discovery
Xiaoke Shen, Yang Liu, You Wu, and Lei Xie · 2020
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A graph to graphs framework for retrosynthesis prediction
Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, and Jian Tang · 2020
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State-of-the-art augmented nlp transformer models for direct and single-step retrosynthesis
Igor V Tetko, Pavel Karpov, Ruud Van Deursen, and Guillaume Godin · 2020
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Retroxpert: Decompose retrosynthesis prediction like a chemist
Chaochao Yan, Qianggang Ding, Peilin Zhao, Shuangjia Zheng, JINYU YANG, Yang Yu, and Junzhou Huang · 2020
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Incorporating bert into neural machine translation
Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, and Tie-Yan Liu · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Transformer neural network for structure constrained molecular optimization
Jiazhen He, Felix Mattsson, Marcus Forsberg, Esben Jannik Bjerrum, Ola Engkvist, Christian Tyrchan, Werngard Czechtizky, et al · 2021
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus · 2021
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Molclr: Molecular contrastive learning of representations via graph neural networks
Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani · 2021
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{IOT}: Instance-wise layer reordering for transformer structures
Jinhua Zhu, Lijun Wu, Yingce Xia, Shufang Xie, Tao Qin, Wengang Zhou, Houqiang Li, and Tie-Yan Liu · 2021
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