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ORCIDSteven Kearnes
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- affiliation: Google Research, Mountain View, CA, USA
- affiliation (PhD): Stanford University, Department of Structural Biology, CA, USA
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Journal Articles
- 2023
[j5]Rocío Mercado
, Steven M. Kearnes, Connor W. Coley:
Data Sharing in Chemistry: Lessons Learned and a Case for Mandating Structured Reaction Data. J. Chem. Inf. Model. 63(14): 4253-4265 (2023)- 2016
- [j4]Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay S. Pande, Patrick Riley:
Molecular graph convolutions: moving beyond fingerprints. J. Comput. Aided Mol. Des. 30(8): 595-608 (2016) - [j3]Steven Kearnes, Vijay S. Pande:
ROCS-derived features for virtual screening. J. Comput. Aided Mol. Des. 30(8): 609-617 (2016) - [j2]Robert T. McGibbon, Carlos X. Hernández, Matthew P. Harrigan, Steven Kearnes, Mohammad M. Sultan, Stanislaw Jastrzebski, Brooke E. Husic, Vijay S. Pande:
Osprey: Hyperparameter Optimization for Machine Learning. J. Open Source Softw. 1(5): 34 (2016) - 2014
- [j1]Steven M. Kearnes, Imran S. Haque, Vijay S. Pande:
SCISSORS: Practical Considerations. J. Chem. Inf. Model. 54(1): 5-15 (2014)
Conference and Workshop Papers
- 2021
- [c1]Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai:
Towards understanding retrosynthesis by energy-based models. NeurIPS 2021: 10186-10194
Informal and Other Publications
- 2020
- [i8]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, Patrick F. Riley:
Machine learning on DNA-encoded libraries: A new paradigm for hit-finding. CoRR abs/2002.02530 (2020) - [i7]Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai:
Energy-based View of Retrosynthesis. CoRR abs/2007.13437 (2020) - [i6]Steven Kearnes:
Pursuing a Prospective Perspective. CoRR abs/2009.00707 (2020) - 2019
- [i5]Steven Kearnes, Li Li, Patrick Riley:
Decoding Molecular Graph Embeddings with Reinforcement Learning. CoRR abs/1904.08915 (2019) - 2018
- [i4]Nathaniel Thomas, Tess E. Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, Patrick Riley:
Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds. CoRR abs/1802.08219 (2018) - [i3]Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N. Zare, Patrick Riley:
Optimization of Molecules via Deep Reinforcement Learning. CoRR abs/1810.08678 (2018) - 2016
- [i2]Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay S. Pande, Patrick Riley:
Molecular Graph Convolutions: Moving Beyond Fingerprints. CoRR abs/1603.00856 (2016) - 2015
- [i1]Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David E. Konerding, Vijay S. Pande:
Massively Multitask Networks for Drug Discovery. CoRR abs/1502.02072 (2015)
Coauthor Index
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