Fetching the paper…
Reading the bibliography…
Molecule property prediction is a fundamental problem for computer-aided drug discovery and materials science.
Atom pairs as molecular features in structure-activity studies: definition and applications
Raymond E. Carhart, Dennis H. Smith, and R. Venkataraghavan · 1985
Earlier work this paper cites.
Density-functional thermochemistry. III. The role of exact exchange
Axel D. Becke · 1993
Earlier work this paper cites.
A new model for learning in graph domains
M. Gori, G. Monfardini, and F. Scarselli · 2005
Earlier work this paper cites.
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Jörg Behler and Michele Parrinello · 2007
Earlier work this paper cites.
970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13
Lorenz C. Blum and Jean-Louis Reymond · 2009
Earlier work this paper cites.
The Graph Neural Network Model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
Earlier work this paper cites.
Extended-Connectivity Fingerprints
David Rogers and Mathew Hahn · 2010
Earlier work this paper cites.
Perspective on density functional theory
Kieron Burke · 2012
Earlier work this paper cites.
Molecular Fingerprint-Based Artificial Neural Networks QSAR for Ligand Biological Activity Predictions
Kyaw-Zeyar Myint, Lirong Wang, Qin Tong, and Xiang-Qun Xie · 2012
Earlier work this paper cites.
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, and O. Anatole von Lilienfeld · 2012
Earlier work this paper cites.
Machine learning of molecular electronic properties in chemical compound space
Grégoire Montavon, Matthias Rupp, Vivekanand Gobre, Alvaro Vazquez-Mayagoitia, Katja Hansen, Alexandre Tkatchenko, Klaus-Robert Müller, and O. Anatole von Lilienfeld · 2013
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
Cited alongside, same era.
Convolutional Networks on Graphs for Learning Molecular Fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, and Ryan P Adams · 2015
Cited alongside, same era.
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
Katja Hansen, Franziska Biegler, Raghunathan Ramakrishnan, Wiktor Pronobis, O. Anatole von Lilienfeld, Klaus-Robert Müller, and Alexandre Tkatchenko · 2015
Cited alongside, same era.
Electronic spectra from TDDFT and machine learning in chemical space
Raghunathan Ramakrishnan, Mia Hartmann, Enrico Tapavicza, and O. Anatole von Lilienfeld · 2015
Cited alongside, same era.
Interaction Networks for Learning about Objects, Relations and Physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, and koray kavukcuoglu · 2016
Cited alongside, same era.
Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Later among the works it cites.
3d Graph Neural Networks for RGBD Semantic Segmentation
Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, and Raquel Urtasun · 2017
Later among the works it cites.
Quantum-Chemical Insights from Deep Tensor Neural Networks
Kristof T. Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R. Müller, and Alexandre Tkatchenko · 2017
Later among the works it cites.
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
Later among the works it cites.
Applying machine learning techniques to predict the properties of energetic materials
Daniel C. Elton, Zois Boukouvalas, Mark S. Butrico, Mark D. Fuge, and Peter W. Chung · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Molecular Graph Convolutions: Moving Beyond Fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley · 2016
Cited alongside, same era.
Gated Graph Sequence Neural Networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
Cited alongside, same era.
PointNet: Deep Learning on Point Sets for 3d Classification and Segmentation
R. Qi Charles, Hao Su, Mo Kaichun, and Leonidas J. Guibas · 2017
Cited alongside, same era.
Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel E. Sauceda, Igor Poltavsky, Kristof T. Schütt, and Klaus-Robert Müller · 2017
Cited alongside, same era.
Language Modeling with Gated Convolutional Networks
Yann N. Dauphin, Angela Fan, Michael Auli, and David Grangier · 2017
Cited alongside, same era.
Protein Interface Prediction using Graph Convolutional Networks
Alex Fout, Jonathon Byrd, Basir Shariat, and Asa Ben-Hur · 2017
Cited alongside, same era.
Semi-supervised User Geolocation via Graph Convolutional Networks
Afshin Rahimi, Trevor Cohn, and Timothy Baldwin · 2018
Later among the works it cites.
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
Daniil Sorokin and Iryna Gurevych · 2018
Later among the works it cites.
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Later among the works it cites.
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
Later among the works it cites.
Link Prediction Based on Graph Neural Networks
Muhan Zhang and Yixin Chen · 2018
Later among the works it cites.
Deep Learning for the Life Sciences
Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, Karl Leswing, and Zhenqin Wu · 2019
Closest in time.