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Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design.
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Wang, M.; Zheng, D.; Ye, Z.; Gan, Q.; Li, M.; Song, X.; Zhou, J.; Ma, C.; Yu, L.; Gai, Y.; Xiao, T.; He, T.; Karypis, G.; Li, J.; and Zhang, Z. 2019 · 1909
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Computational modeling of β \beta -secretase 1 (BACE-1) inhibitors using ligand based approaches
Subramanian, G.; Ramsundar, B.; Pande, V.; and Denny, R. A. 2016 · 1949
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The properties of known drugs. 1. Molecular frameworks
Bemis, G. W.; and Murcko, M. A. 1996 · 1996
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ESOL: estimating aqueous solubility directly from molecular structure
Delaney, J. S. 2004 · 2004
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Extended-Connectivity Fingerprints
Rogers, D.; and Hahn, M. 2010 · 2010
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A Bayesian Approach to in Silico Blood-Brain Barrier Penetration Modeling
Martins, I. F.; Teixeira, A. L.; Pinheiro, L.; and Falcão, A. O. 2012 · 2012
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On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
Cho, K.; van Merrienboer, B.; Bahdanau, D.; and Bengio, Y. 2014 · 2014
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FreeSolv: a database of experimental and calculated hydration free energies, with input files
Mobley, D. L.; and Guthrie, J. P. 2014 · 2014
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Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?
Bajusz, D.; Rácz, A.; and Héberger, K. 2015 · 2015
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Convolutional Networks on Graphs for Learning Molecular Fingerprints
Duvenaud, D.; Maclaurin, D.; Aguilera-Iparraguirre, J.; Gómez-Bombarelli, R.; Hirzel, T.; Aspuru-Guzik, A.; and Adams, R. P. 2015 · 2015
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ZINC 15 - Ligand Discovery for Everyone
Sterling, T.; and Irwin, J. J. 2015 · 2015
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A data-driven approach to predicting successes and failures of clinical trials
Gayvert, K. M.; Madhukar, N. S.; and Elemento, O. 2016 · 2016
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Jastrzebski, S.; Lesniak, D.; Czarnecki, W. M.; and . 2016 · 2016
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Molecular graph convolutions: moving beyond fingerprints
Kearnes, S. M.; McCloskey, K.; Berndl, M.; Pande, V. S.; and Riley, P. 2016 · 2016
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The SIDER database of drugs and side effects
Kuhn, M.; Letunic, I.; Jensen, L. J.; and Bork, P. 2016 · 2016
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ToxCast chemical landscape: paving the road to 21st century toxicology
Richard, A. M.; Judson, R. S.; Houck, K. A.; Grulke, C. M.; Volarath, P.; Thillainadarajah, I.; Yang, C.; Rathman, J.; Martin, M. T.; Wambaugh, J. F.; et al. 2016 · 2016
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Order Matters: Sequence to sequence for sets
Vinyals, O.; Bengio, S.; and Kudlur, M. 2016 · 2016
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Tox21 challenge
2017 · 2017
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Neural Message Passing for Quantum Chemistry
Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; and Dahl, G. E. 2017 · 2017
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Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N.; and Welling, M. 2017 · 2017
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Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery
Xu, Z.; Wang, S.; Zhu, F.; and Huang, J. 2017 · 2017
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Modeling Relational Data with Graph Convolutional Networks
Schlichtkrull, M. S.; Kipf, T. N.; Bloem, P.; van den Berg, R.; Titov, I.; and Welling, M. 2018 · 2018
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Contrastive Multi-View Representation Learning on Graphs
Hassani, K.; and Ahmadi, A. H. K. 2020 · 2020
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Strategies for Pre-training Graph Neural Networks
Hu, W.; Liu, B.; Gomes, J.; Zitnik, M.; Liang, P.; Pande, V. S.; and Leskovec, J. 2020 · 2020
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KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction
Lin, X.; Quan, Z.; Wang, Z.; Ma, T.; and Zeng, X. 2020 · 2020
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Self-Supervised Graph Transformer on Large-Scale Molecular Data
Rong, Y.; Bian, Y.; Xu, T.; Xie, W.; Wei, Y.; Huang, W.; and Huang, J. 2020 · 2020
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Communicative Representation Learning on Attributed Molecular Graphs
Song, Y.; Zheng, S.; Niu, Z.; Fu, Z.; Lu, Y.; and Yang, Y. 2020 · 2020
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InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
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Graph Attention Networks
Velickovic, P.; Cucurull, G.; Casanova, A.; Romero, A.; Liò, P.; and Bengio, Y. 2018 · 2018
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Learning deep representations by mutual information estimation and maximization
Hjelm, R. D.; Fedorov, A.; Lavoie-Marchildon, S.; Grewal, K.; Bachman, P.; Trischler, A.; and Bengio, Y. 2019 · 2019
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N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
Liu, S.; Demirel, M. F.; Liang, Y.; and . 2019 · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Kopf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; and Chintala, S. 2019 · 2019
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Pre-training of Graph Augmented Transformers for Medication Recommendation
Shang, J.; Ma, T.; Xiao, C.; and Sun, J. 2019 · 2019
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RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Sun, Z.; Deng, Z.; Nie, J.; and Tang, J. 2019 · 2019
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Sun, F.; Hoffmann, J.; Verma, V.; and Tang, J. 2020 · 2020
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Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes
Sun, K.; Lin, Z.; and Zhu, Z. 2020 · 2020
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Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
Yasunaga, M.; and Liang, P. 2020 · 2020
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Graph Contrastive Learning with Augmentations
You, Y.; Chen, T.; Sui, Y.; Chen, T.; Wang, Z.; and Shen, Y. 2020 · 2020
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Unsupervised Representation Learning From Pathology Images With Multi-Directional Contrastive Predictive Coding
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Learning Attributed Graph Representations with Communicative Message Passing Transformer
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Building a Knowledge Graph from public databases and scientific literature to extract associations between chemicals and diseases
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Knowledge-aware Contrastive Molecular Graph Learning
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MOTIF-Driven Contrastive Learning of Graph Representations
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MoCL: Contrastive Learning on Molecular Graphs with Multi-level Domain Knowledge
Sun, M.; Xing, J.; Wang, H.; Chen, B.; and Zhou, J. 2021 · 2021
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Graph Contrastive Learning Automated
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