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The concern of overconfident mis-predictions under distributional shift demands extensive reliability research on Graph Neural Networks used in critical tasks in drug discovery.
The inward rectification mechanism of the HERG cardiac potassium channel
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S7B Nonclinical Evaluation of the Potential for Delayed Ventricular Repolarization (QT Interval Prolongation) by Human Pharmaceuticals, 2005
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Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?
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Interaction Networks for Learning about Objects, Relations and Physics
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Molecular graph convolutions: moving beyond fingerprints
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Protein interface prediction using graph convolutional networks
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Neural Message Passing for Quantum Chemistry
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Improved training of Wasserstein GANs
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Graph Attention Networks
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Reliable graph neural networks via robust aggregation
Simon Geisler, Daniel Zügner, and Stephan Günnemann · 2020
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Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
Lior Hirschfeld, Kyle Swanson, Kevin Yang, Regina Barzilay, and Connor W Coley · 2020
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A benchmark study on reliable molecular supervised learning via Bayesian learning
Doyeong Hwang, Grace Lee, Hanseok Jo, Seyoul Yoon, and Seongok Ryu · 2020
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, and Balaji Lakshminarayanan · 2020
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Uncertainty estimation with infinitesimal jackknife, its distribution and mean-field approximation
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Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia · 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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Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D Sculley, Sebastian Nowozin, Joshua V Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Principal Neighbourhood Aggregation for Graph Nets
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Uncertainty-aware attention graph neural network for defending adversarial attacks
Boyuan Feng, Yuke Wang, Zheng Wang, and Yufei Ding · 2020
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Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding
Kevin McCloskey, Eric A Sigel, Steven Kearnes, Ling Xue, Xia Tian, Dennis Moccia, Diana Gikunju, Sana Bazzaz, Betty Chan, Matthew A Clark, John W Cuozzo, Marie-Aude Guié, John P Guilinger, Christelle Huguet, Christopher D Hupp, Anthony D Keefe, Christopher J Mulhern, Ying Zhang, and Patrick Riley · 2020
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Critical Assessment of Artificial Intelligence Methods for Prediction of hERG Channel Inhibition in the “Big Data” Era
Vishal B Siramshetty, Dac-Trung Nguyen, Natalia J Martinez, Noel T Southall, Anton Simeonov, and Alexey V Zakharov · 2020
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Uncertainty estimation using a single deep deterministic neural network
Joost Van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal · 2020
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Regularisation of neural networks by enforcing Lipschitz continuity
Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael J Cree · 2021
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On feature collapse and deep kernel learning for single forward pass uncertainty
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