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Molecular graph representation learning is a fundamental problem in modern drug and material discovery.
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Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
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Deep-learning-based drug–target interaction prediction
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Mutual information neural estimation
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Convolutional neural network based on SMILES representation of compounds for detecting chemical motif
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Momentum contrast for unsupervised visual representation learning
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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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Gpt-gnn: Generative pre-training of graph neural networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun · 2020
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Understanding and controlling short-and long-range electron/charge-transfer processes in electron donor–acceptor conjugates
Ramandeep Kaur, Fabio Possanza, Francesca Limosani, Stefan Bauroth, Robertino Zanoni, Timothy Clark, Giorgio Arrigoni, Pietro Tagliatesta, and Dirk M Guldi · 2020
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
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Glow: generative flow with invertible 1 × \times 1 convolutions
Diederik P Kingma and Prafulla Dhariwal · 2018
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Practical model selection for prospective virtual screening
Shengchao Liu, Moayad Alnammi, Spencer S Ericksen, Andrew F Voter, Gene E Ananiev, James L Keck, F Michael Hoffmann, Scott A Wildman, and Anthony Gitter · 2018
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N-gram graph: Simple unsupervised representation for graphs, with applications to molecules
Shengchao Liu, Mehmet Furkan Demirel, and Yingyu Liang · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Deepdta: deep drug–target binding affinity prediction
Hakime Öztürk, Arzucan Özgür, and Elif Ozkirimli · 2018
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Petar Veličković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2018
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Structured multi-view representations for drug combinations
Shengchao Liu, Andreea Deac, Zhaocheng Zhu, and Jian Tang · 2020
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Survae flows: Surjections to bridge the gap between vaes and flows
Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, and Max Welling · 2020
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Message passing networks for molecules with tetrahedral chirality
Lagnajit Pattanaik, Octavian-Eugen Ganea, Ian Coley, Klavs F Jensen, William H Green, and Connor W Coley · 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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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
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Evaluating scalable supervised learning for synthesize-on-demand chemical libraries
Moayad Alnammi, Shengchao Liu, Spencer S Ericksen, Gene E Ananiev, Andrew F Voter, Song Guo, James L Keck, F Michael Hoffmann, Scott A Wildman, and Anthony Gitter · 2021
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An analysis of attentive walk-aggregating graph neural networks
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Molecular contrastive learning with chemical element knowledge graph
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Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries
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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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Graph self-supervised learning: A survey
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