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Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery.
Hydrophobic docking: A proposed enhancement to molecular recognition techniques
I. A. Vakser and C. Aflalo · 1994
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The Protein Data Bank
H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne · 2000
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Protein docking using continuum electrostatics and geometric fit
J. G. Mandell, V. A. Roberts, M. E. Pique, V. Kotlovyi, J. C. Mitchell, E. Nelson, I. Tsigelny, and L. F. Ten Eyck · 2001
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ZDOCK: An initial-stage protein-docking algorithm
R. Chen, L. Li, and Z. Weng · 2003
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Pdb2pqr: an automated pipeline for the setup of poisson–boltzmann electrostatics calculations
T. J. Dolinsky, J. E. Nielsen, J. A. McCammon, and N. A. Baker · 2004
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PIPER: An FFT-based protein docking program with pairwise potentials
D. Kozakov, R. Brenke, S. R. Comeau, and S. Vajda · 2006
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Generalized neural-network representation of high-dimensional potential-energy surfaces
J. Behler and M. Parrinello · 2007
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Docking and scoring protein complexes: CAPRI 3rd Edition
M. F. Lensink, R. Méndez, and S. J. Wodak · 2007
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Integrating statistical pair potentials into protein complex prediction
J. Mintseris, B. Pierce, K. Wiehe, R. Anderson, R. Chen, and Z. Weng · 2007
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ZRANK: Reranking protein docking predictions with an optimized energy function
B. Pierce and Z. Weng · 2007
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Protein-protein docking using region-based 3D Zernike descriptors
V. Venkatraman, Y. D. Yang, L. Sael, and D. Kihara · 2009
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The HADDOCK web server for data-driven biomolecular docking
S. J. de Vries, M. van Dijk, and A. M. J. J. Bonvin · 2010
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Protein-protein docking benchmark version 4.0
H. Hwang, T. Vreven, J. Janin, and Z. Weng · 2010
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H. Zhou and J. Skolnick · 2011
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Optimized atomic statistical potentials: assessment of protein interfaces and loops
G. Q. Dong, H. Fan, D. Schneidman-Duhovny, B. Webb, and A. Sali · 2013
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Template-based structure modeling of protein-protein interactions
A. Szilagyi and Y. Zhang · 2013
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Protein-protein docking: From interaction to interactome
I. A. Vakser · 2014
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Updates to the integrated protein–protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2
T. Vreven, I. H. Moal, A. Vangone, B. G. Pierce, P. L. Kastritis, M. Torchala, R. Chaleil, B. Jiménez-García, P. A. Bates, J. Fernandez-Recio, A. M. Bonvin, and Z. Weng · 2015
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I. Wallach, M. Dzamba, and A. Heifets · 2015
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Group Equivariant Convolutional Networks
T. Cohen and M. Welling · 2016
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Deep convolutional networks for quality assessment of protein folds
G. Derevyanko, S. Grudinin, Y. Bengio, and G. Lamoureux · 2018
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N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials
R. Kondor · 2018
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Efficient flexible backbone protein–protein docking for challenging targets
N. A. Marze, S. S. Roy Burman, W. Sheffler, and J. J. Gray · 2018
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InterEvDock2: an expanded server for protein docking using evolutionary and biological information from homology models and multimeric inputs
C. Quignot, J. Rey, J. Yu, P. Tufféry, R. Guerois, and J. Andreani · 2018
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Horovod: fast and easy distributed deep learning in TensorFlow
A. Sergeev and M. Del Balso · 2018
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Progress and challenges in predicting protein interfaces
R. Esmaielbeiki, K. Krawczyk, B. Knapp, J.-C. Nebel, and C. M. Deane · 2016
Cited alongside, same era.
The ClusPro web server for protein-protein docking
D. Kozakov, D. R. Hall, B. Xia, K. A. Porter, D. Padhorny, C. Yueh, D. Beglov, and S. Vajda · 2016
Cited alongside, same era.
PPI4DOCK: large scale assessment of the use of homology models in free docking over more than 1000 realistic targets
J. Yu and R. Guerois · 2016
Cited alongside, same era.
DeepSite: protein-binding site predictor using 3D-convolutional neural networks
J. Jiménez, S. Doerr, G. Martínez-Rosell, A. S. Rose, and G. De Fabritiis · 2017
Cited alongside, same era.
Protein-Ligand Scoring with Convolutional Neural Networks
M. Ragoza, J. Hochuli, E. Idrobo, J. Sunseri, and D. R. Koes · 2017
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Protein-protein and peptide-protein docking and refinement using ATTRACT in CAPRI
C. E. M. Schindler, I. Chauvot de Beauchêne, S. J. de Vries, and M. Zacharias · 2017
Cited alongside, same era.
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
K. T. Schütt, P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, and K.-R. Müller · 2017
Cited alongside, same era.
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Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
N. Thomas, T. Smidt, S. Kearnes, L. Yang, L. Li, K. Kohlhoff, and P. Riley · 2018
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CORMORANT: Covariant Molecular Neural Networks
B. Anderson, T.-S. Hy, and R. Kondor · 2019
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iScore: a novel graph kernel-based function for scoring protein–protein docking models
C. Geng, Y. Jung, N. Renaud, V. Honavar, A. M. J. J. Bonvin, and L. C. Xue · 2019
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Protein model quality assessment using 3D oriented convolutional neural networks
G. Pagès, B. Charmettant, and S. Grudinin · 2019
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End-to-End Learning on 3D Protein Structure for Interface Prediction
R. J. L. Townshend, R. Bedi, P. Suriana, and R. O. Dror · 2019
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Protein docking model evaluation by 3D deep convolutional neural networks
X. Wang, G. Terashi, C. W. Christoffer, M. Zhu, and D. Kihara · 2019
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Energy-based graph convolutional networks for scoring protein docking models
Y. Cao and Y. Shen · 2020
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InterEvScore: a novel coarse-grained interface scoring function using a multi-body statistical potential coupled to evolution
J. Andreani, G. Faure, and R. Guerois · 2059
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SwarmDock: a server for flexible protein–protein docking
M. Torchala, I. H. Moal, R. A. G. Chaleil, J. Fernandez-Recio, and P. A. Bates · 2059
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