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GNNs and chemical fingerprints are the predominant approaches to representing molecules for property prediction.
Byte pair encoding: A text compression scheme that accelerates pattern matching
Yusuxke Shibata, Takuya Kida, Shuichi Fukamachi, Masayuki Takeda, Ayumi Shinohara, Takeshi Shinohara, and Setsuo Arikawa · 1999
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Rdkit: Open-source cheminformatics
Greg Landrum et al · 2006
Earlier work this paper cites.
Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
Earlier work this paper cites.
Molecular graph convolutions: moving beyond fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Deepchem: Democratizing deep-learning for drug discovery, quantum chemistry, materials science and biology, 2016
B Ramsundar, P Eastman, E Feinberg, J Gomes, K Leswing, A Pappu, M Wu, and V Pande · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Earlier work this paper cites.
Convolutional embedding of attributed molecular graphs for physical property prediction
Connor W Coley, Regina Barzilay, William H Green, Tommi S Jaakkola, and Klavs F Jensen · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Seq2seq fingerprint: An unsupervised deep molecular embedding for drug discovery
Zheng Xu, Sheng Wang, Feiyun Zhu, and Junzhou Huang · 2017
Earlier work this paper cites.
Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
Cited alongside, same era.
Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
Cited alongside, same era.
Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller · 2018
Cited alongside, same era.
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.
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
Smiles-bert: large scale unsupervised pre-training for molecular property prediction
Sheng Wang, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang · 2019
Later among the works it cites.
Path-augmented graph transformer network
Benson Chen, Regina Barzilay, and Tommi Jaakkola · 2019
Later among the works it cites.
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 · 2019
Later among the works it cites.
Pubchem 2019 update: improved access to chemical data
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, et al · 2019
Later among the works it cites.
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Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2019
Cited alongside, same era.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2019
Cited alongside, same era.
A multiscale visualization of attention in the transformer model
Jesse Vig · 2019
Cited alongside, same era.
Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
Cited alongside, same era.
Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A Hunter, Costas Bekas, and Alpha A Lee · 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.
Mirac Suzgun, Sebastian Gehrmann, Yonatan Belinkov, and Stuart M Shieber · 2019
Later among the works it cites.
Learning the dyck language with attention-based seq2seq models
Xiang Yu, Ngoc Thang Vu, and Jonas Kuhn · 2019
Later among the works it cites.
Quantifying the carbon emissions of machine learning
Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres · 2019
Later among the works it cites.
Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning · 2020
Closest in time.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
Closest in time.
Self-referencing embedded strings (selfies): A 100% robust molecular string representation
Mario Krenn, Florian Hase, AkshatKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik · 2020
Closest in time.
Molecule attention transformer
Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, and Stanisław Jastrzębski · 2020
Closest in time.
Modelling drug-target binding affinity using a bert based graph neural network
Anonymous · 2021
Closest in time.