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Predicting molecular properties with data-driven methods has drawn much attention in recent years.
Self-consistent equations including exchange and correlation effects
Walter Kohn and Lu Jeu Sham · 1965
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Catastrophic forgetting in connectionist networks
Robert M French · 1999
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Ron Milo, Shai Shen-Orr, Shalev Itzkovitz, Nadav Kashtan, Dmitri Chklovskii, and Uri Alon · 2002
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Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs
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Nemofinder: Dissecting genome-wide protein-protein interactions with meso-scale network motifs
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On the art of compiling and using’drug-like’chemical fragment spaces
Jörg Degen, Christof Wegscheid-Gerlach, Andrea Zaliani, and Matthias Rarey · 2008
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Application of molecular dynamics simulations in molecular property prediction ii: diffusion coefficient
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Revisiting frank-wolfe: Projection-free sparse convex optimization
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Rdkit documentation
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Get your atoms in order an open-source implementation of a novel and robust molecular canonicalization algorithm
Nadine Schneider, Roger A Sayle, and Gregory A Landrum · 2015
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Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Prediction errors of molecular machine learning models lower than hybrid dft error
Felix A Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S Schoenholz, George E Dahl, Oriol Vinyals, Steven Kearnes, Patrick F Riley, and O Anatole Von Lilienfeld · 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
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Machine learning of dynamic electron correlation energies from topological atoms
James L McDonagh, Arnaldo F Silva, Mark A Vincent, and Paul LA Popelier · 2017
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Quantum-chemical insights from deep tensor neural networks
Kristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R Müller, and Alexandre Tkatchenko · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Analyzing learned molecular representations for property prediction
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, et al · 2019
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A simple framework for contrastive learning of visual representations
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Graphcl: Contrastive self-supervised learning of graph representations
Hakim Hafidi, Mounir Ghogho, Philippe Ciblat, and Ananthram Swami · 2020
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Asgn: An active semi-supervised graph neural network for molecular property prediction
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Junction tree variational autoencoder for molecular graph generation
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
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Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
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Moleculenet: a benchmark for molecular machine learning
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Strategies for pre-training graph neural networks
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Gpt-gnn: Generative pre-training of graph neural networks
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Directional message passing for molecular graphs
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Towards deeper graph neural networks
Meng Liu, Hongyang Gao, and Shuiwang Ji · 2020
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Graph representation learning via graphical mutual information maximization
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Gcc: Graph contrastive coding for graph neural network pre-training
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Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang · 2020
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When does self-supervision help graph convolutional networks?
Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen · 2020
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Motif-driven contrastive learning of graph representations
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