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Self-supervised learning holds promise to revolutionize molecule property prediction - a central task to drug discovery and many more industries - by enabling data efficient learning from scarce experimental data.
Unsupervised universal self-attention network for graph classification
Dai Quoc Nguyen, Tu Dinh Nguyen, and Dinh Phung · 1909
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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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Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
Christopher A Lipinski, Franco Lombardo, Beryl W Dominy, and Paul J Feeney · 1997
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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ChEMBL: a large-scale bioactivity database for drug discovery
Anna Gaulton, Louisa J. Bellis, A. Patricia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, and John P. Overington · 2011
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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 K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarelli, Timothy Hirzel, Alan Aspuru-Guzik, and Ryan P Adams · 2015
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Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
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Stanisław Jastrzębski, Damian Leśniak, and Wojciech Marian Czarnecki · 2016
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Molecular graph convolutions: Moving beyond fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Rdkit: Open-source cheminformatics software
Greg Landrum · 2016
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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
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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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Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets
Alexandru Korotcov, Valery Tkachenko, Daniel P. Russo, and Sean Ekins · 2017
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A structured self-attentive sentence embedding
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio · 2017
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SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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BERT: Pre-training of deep bidirectional transformers for language understanding, 2018
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Fine-tuned language models for text classification
J. Howard and Sebastian Ruder · 2018
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Large-scale comparison of machine learning methods for drug target prediction on chembl
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Marvin Steijaert, Jörg K. Wegner, Hugo Ceulemans, Djork-Arné Clevert, and Sepp Hochreiter · 2018
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Horovod: fast and easy distributed deep learning in tensorflow
Alexander Sergeev and Mike Del Balso · 2018
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Edge Attention-based Multi-Relational Graph Convolutional Networks
Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi, and Jinbo Bi · 2018
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Self-attention with relative position representations
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani · 2018
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Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
Marc Finzi, Samuel Stanton, Pavel Izmailov, and Andrew Gordon Wilson · 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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Strategies for Pre-training Graph Neural Networkss
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
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Improve transformer models with better relative position embeddings
Zhiheng Huang, Davis Liang, Peng Xu, and Bing Xiang · 2020
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Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2020
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Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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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
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Cormorant: Covariant molecular neural networks
Brandon Anderson, Truong-Son Hy, and Risi Kondor · 2019
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On identifiability in transformers
Gino Brunner, Yang Liu, Damian Pascual, Oliver Richter, Massimiliano Ciaramita, and Roger Wattenhofer · 2019
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Advancing drug discovery via artificial intelligence
H.C. Stephen Chan, Hanbin Shan, Thamani Dahoun, Horst Vogel, and Shuguang Yuan · 2019
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Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov · 2019
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SMILES transformer: Pre-trained molecular fingerprint for low data drug discovery, 2019
Shion Honda, Shoi Shi, and Hiroki R. Ueda · 2019
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Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, and Stanisław Jastrzębski · 2020
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Relevance of rotationally equivariant convolutions for predicting molecular properties
Benjamin Kurt Miller, Mario Geiger, Tess E Smidt, and Frank Noé · 2020
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Comparison of atom representations in graph neural networks for molecular property prediction
Agnieszka Pocha, Tomasz Danel, and Łukasz Maziarka · 2020
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Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction
MC Robinson, RC Glen, and AA Lee · 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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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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John Ingraham, Vikas Kamur Garg, Regina Barzilay, and Tommi S Jaakkola · 2021
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Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models
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