Fetching the paper…
Reading the bibliography…
The prediction of physicochemical properties from molecular structures is a crucial task for artificial intelligence aided molecular design.
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
Earlier work this paper cites.
The pdbbind database: Collection of binding affinities for protein- ligand complexes with known three-dimensional structures
Renxiao Wang, Xueliang Fang, Yipin Lu, and Shaomeng Wang · 2004
Earlier work this paper cites.
Molecular modeling and simulation: an interdisciplinary guide
Tamar Schlick · 2010
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
Earlier work this paper cites.
Molecular graph convolutions: moving beyond fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley · 2016
Earlier work this paper cites.
Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
Earlier work this paper cites.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Earlier work this paper cites.
Quantum-chemical insights from deep tensor neural networks
Kristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R Müller, and Alexandre Tkatchenko · 2017
Earlier work this paper cites.
Principled multilayer network embedding
Weiyi Liu, Pin-Yu Chen, Sailung Yeung, Toyotaro Suzumura, and Lingli Chen · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
Earlier work this paper cites.
Atomic convolutional networks for predicting protein-ligand binding affinity
Joseph Gomes, Bharath Ramsundar, Evan N Feinberg, and Vijay S Pande · 2017
Cited alongside, same era.
The rise of deep learning in drug discovery
Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, and Thomas Blaschke · 2018
Cited alongside, same era.
Schnetpack: A deep learning toolbox for atomistic systems
KT Schutt, Pan Kessel, Michael Gastegger, KA Nicoli, Alexandre Tkatchenko, and K-R Muller · 2018
Cited alongside, same era.
Scalable multiplex network embedding
Hongming Zhang, Liwei Qiu, Lingling Yi, and Yangqiu Song · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
Cited alongside, same era.
Layer communities in multiplex networks
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Later among the works it cites.
Cormorant: Covariant molecular neural networks
Brandon Anderson, Truong Son Hy, and Risi Kondor · 2019
Later among the works it cites.
Molecular property prediction: A multilevel quantum interactions modeling perspective
Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, and Lixin He · 2019
Later among the works it cites.
Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges
Oliver T Unke and Markus Meuwly · 2019
Later among the works it cites.
Graph networks as a universal machine learning framework for molecules and crystals
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong · 2019
Later among the works it cites.
Molecular property prediction: A multilevel quantum interactions modeling perspective
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ta-Chu Kao and Mason A Porter · 2018
Cited alongside, same era.
Schnetpack: A deep learning toolbox for atomistic systems
KT Schutt, Pan Kessel, Michael Gastegger, KA Nicoli, Alexandre Tkatchenko, and K-R Muller · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2018
Cited alongside, same era.
Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges
Oliver T Unke and Markus Meuwly · 2019
Cited alongside, same era.
Graph networks as a universal machine learning framework for molecules and crystals
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong · 2019
Cited alongside, same era.
Representation learning for attributed multiplex heterogeneous network
Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, and Jie Tang · 2019
Cited alongside, same era.
Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, and Lixin He · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Later among the works it cites.
Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
Later among the works it cites.
Cormorant: Covariant molecular neural networks
Brandon Anderson, Truong Son Hy, and Risi Kondor · 2019
Later among the works it cites.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
Closest in time.
Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2020
Closest in time.
Heterogeneous molecular graph neural networks for predicting molecule properties
Zeren Shui and George Karypis · 2020
Closest in time.
Heterogeneous multi-layered network model for omics data integration and analysis
Bohyun Lee, Shuo Zhang, Aleksandar Poleksic, and Lei Xie · 2020
Closest in time.
Abstract diagrammatic reasoning with multiplex graph networks
Duo Wang, Mateja Jamnik, and Pietro Lio · 2020
Closest in time.
A graph to graphs framework for retrosynthesis prediction
Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, and Jian Tang · 2020
Closest in time.
Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2020
Closest in time.