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Extracting informative representations of molecules using Graph neural networks (GNNs) is crucial in AI-driven drug discovery.
The properties of known drugs. 1. molecular frameworks
Guy W Bemis and Mark A Murcko · 1996
Earlier work this paper cites.
Crafting papers on machine learning
P. Langley · 2000
Earlier work this paper cites.
Chiral toxicology: it’s the same thing… only different
Silas W Smith · 2009
Earlier work this paper cites.
Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
Earlier work this paper cites.
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, et al · 2012
Earlier work this paper cites.
Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
Earlier work this paper cites.
Rdkit: Open-source cheminformatics software, 2016
G Landrum · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Representation learning on graphs: Methods and applications
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Alchemy: A quantum chemistry dataset for benchmarking ai models
Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh, Chee-Kong Lee, Benben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, et al · 2019
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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 · 2019
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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 · 2019
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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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Pre-training molecular graph representation with 3d geometry
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang · 2021
Later among the works it cites.
Mocl: data-driven molecular fingerprint via knowledge-aware contrastive learning from molecular graph
Mengying Sun, Jing Xing, Huijun Wang, Bin Chen, and Jiayu Zhou · 2021
Later among the works it cites.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
Later among the works it cites.
Self-supervised on graphs: Contrastive, generative, or predictive
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Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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A simple framework for contrastive learning of visual representations
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Contrastive multi-view representation learning on graphs
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Momentum contrast for unsupervised visual representation learning
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Gpt-gnn: Generative pre-training of graph neural networks
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Towards deeper graph neural networks
Meng Liu, Hongyang Gao, and Shuiwang Ji · 2020
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Synthetically accessible virtual inventory (savi)
Hitesh Patel, Wolf Ihlenfeldt, Philip Judson, Yurii S Moroz, Yuri Pevzner, Megan Peach, Nadya Tarasova, and Marc Nicklaus · 2020
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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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Do transformers really perform bad for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
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Graph contrastive learning with adaptive augmentation
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2021
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Structure-aware transformer for graph representation learning
Dexiong Chen, Leslie O’Bray, and Karsten Borgwardt · 2022
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Pure transformers are powerful graph learners
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Simgrace: A simple framework for graph contrastive learning without data augmentation
Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, and Stan Z Li · 2022
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