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Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science.
Smiles. 2. algorithm for generation of unique smiles notation
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Daniel Mark Lowe · 2012
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Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
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Gated graph sequence neural networks
Yujia Li, Richard Zemel, Marc Brockschmidt, and Daniel Tarlow · 2016
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Prediction of organic reaction outcomes using machine learning
Connor W Coley, Regina Barzilay, Tommi S Jaakkola, William H Green, and Klavs F Jensen · 2017
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The chembl database in 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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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Predicting organic reaction outcomes with weisfeiler-lehman network
Wengong Jin, Connor Coley, Regina Barzilay, and Tommi Jaakkola · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Predicting reaction performance in c–n cross-coupling using machine learning
Derek T Ahneman, Jesús G Estrada, Shishi Lin, Spencer D Dreher, and Abigail G Doyle · 2018
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Interpretable drug target prediction using deep neural representation
Kyle Yingkai Gao, Achille Fokoue, Heng Luo, Arun Iyengar, Sanjoy Dey, and Ping Zhang · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Learning multimodal graph-to-graph translation for molecule optimization
Wengong Jin, Kevin Yang, Regina Barzilay, and Tommi Jaakkola · 2018
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Drug similarity integration through attentive multi-view graph auto-encoders
Tengfei Ma, Cao Xiao, Jiayu Zhou, and Fei Wang · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 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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A graph-convolutional neural network model for the prediction of chemical reactivity
Connor W Coley, Wengong Jin, Luke Rogers, Timothy F Jamison, Tommi S Jaakkola, William H Green, Regina Barzilay, and Klavs F Jensen · 2019
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Graph transformation policy network for chemical reaction prediction
Kien Do, Truyen Tran, and Svetha Venkatesh · 2019
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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
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Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Heterogeneous graph neural network
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V Chawla · 2019
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Gnn-film: Graph neural networks with feature-wise linear modulation
Marc Brockschmidt · 2020
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Se (3)-transformers: 3d roto-translation equivariant attention networks
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling · 2020
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Graseq: graph and sequence fusion learning for molecular property prediction
Zhichun Guo, Wenhao Yu, Chuxu Zhang, Meng Jiang, and Nitesh V Chawla · 2020
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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
Mocl: Contrastive learning on molecular graphs with multi-level domain knowledge
Mengying Sun, Jing Xing, Huijun Wang, Bin Chen, and Jiayu Zhou · 2021
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Property-aware relation networks for few-shot molecular property prediction
Yaqing Wang, Abulikemu Abuduweili, Quanming Yao, and Dejing Dou · 2021
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An end-to-end framework for molecular conformation generation via bilevel programming
Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, and Jian Tang · 2021
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Safedrug: Dual molecular graph encoders for recommending effective and safe drug combinations
Chaoqi Yang, Cao Xiao, Fenglong Ma, Lucas Glass, and Jimeng Sun · 2021
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Deep molecular representation learning via fusing physical and chemical information
Shuwen Yang, Ziyao Li, Guojie Song, and Lingsheng Cai · 2021
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Self-supervised learning on graphs: Deep insights and new direction
Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, and Jiliang Tang · 2020
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Hierarchical generation of molecular graphs using structural motifs
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Multi-objective molecule generation using interpretable substructures
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Self-referencing embedded strings (selfies): A 100% robust molecular string representation
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik · 2020
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Kgnn: Knowledge graph neural network for drug-drug interaction prediction
Xuan Lin, Zhe Quan, Zhi-Jie Wang, Tengfei Ma, and Xiangxiang Zeng · 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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Motif-based graph self-supervised learning for molecular property prediction
Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee · 2021
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Molecular contrastive learning with chemical element knowledge graph
Yin Fang, Qiang Zhang, Haihong Yang, Xiang Zhuang, Shumin Deng, Wen Zhang, Ming Qin, Zhuo Chen, Xiaohui Fan, and Huajun Chen · 2022
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Geomgcl: Geometric graph contrastive learning for molecular property prediction
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Graph rationalization with environment-based augmentations
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Pre-training molecular graph representation with 3d geometry
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Spherical message passing for 3d molecular graphs
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Learning to extend molecular scaffolds with structural motifs
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3d infomax improves gnns for molecular property prediction
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Chemical-reaction-aware molecule representation learning
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Comenet: Towards complete and efficient message passing for 3d molecular graphs
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Learning substructure invariance for out-of-distribution molecular representations
Nianzu Yang, Kaipeng Zeng, Qitian Wu, Xiaosong Jia, and Junchi Yan · 2022
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Unified 2d and 3d pre-training of molecular representations
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Boosting graph neural networks via adaptive knowledge distillation
Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, and Nitesh Chawla · 2023
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Rdkit: Open-source cheminformatics software
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On the use of real-world datasets for reaction yield prediction
Mandana Saebi, Bozhao Nan, John E Herr, Jessica Wahlers, Zhichun Guo, Andrzej M Zurański, Thierry Kogej, Per-Ola Norrby, Abigail G Doyle, Nitesh V Chawla, et al · 2023
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Molerec: Combinatorial drug recommendation with substructure-aware molecular representation learning
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