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Protein-ligand binding prediction is a fundamental problem in AI-driven drug discovery.
A solution for the best rotation to relate two sets of vectors
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Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein- ligand complexes
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A combination of rescoring and refinement significantly improves protein docking performance
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Protein–ligand scoring with convolutional neural networks
M. Ragoza, J. Hochuli, E. Idrobo, J. Sunseri, and D. R. Koes · 2017
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Converging a knowledge-based scoring function: Drugscore2018
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Comparative assessment of scoring functions: the casf-2016 update
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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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Skempi 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation
J. Jankauskaitė, B. Jiménez-García, J. Dapkūnas, J. Fernández-Recio, and I. H. Moal · 2019
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Multi-scale representation learning on proteins
V. R. Somnath, C. Bunne, and A. Krause · 2021
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Maximum likelihood training of score-based diffusion models
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Ebm-fold: fully-differentiable protein folding powered by energy-based models
J. Wu, S. Luo, T. Shen, H. Lan, S. Wang, and J. Huang · 2021
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Diffdock: Diffusion steps, twists, and turns for molecular docking
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Learning inverse folding from millions of predicted structures
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Generative modeling by estimating gradients of the data distribution
Y. Song and S. Ermon · 2019
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Analyzing learned molecular representations for property prediction
K. Yang, K. Swanson, W. Jin, C. Coley, P. Eiden, H. Gao, A. Guzman-Perez, T. Hopper, B. Kelley, M. Mathea, et al · 2019
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Atom3d: Tasks on molecules in three dimensions
R. J. Townshend, M. Vögele, P. Suriana, A. Derry, A. Powers, Y. Laloudakis, S. Balachandar, B. Jing, B. Anderson, S. Eismann, et al · 2020
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Autodock vina 1.2.0: New docking methods, expanded force field, and python bindings
J. Eberhardt, D. Santos-Martins, A. F. Tillack, and S. Forli · 2021
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Independent SE(3)-equivariant models for end-to-end rigid protein docking
O.-E. Ganea, X. Huang, C. Bunne, Y. Bian, R. Barzilay, T. Jaakkola, and A. Krause · 2021
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Interactiongraphnet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions
D. Jiang, C.-Y. Hsieh, Z. Wu, Y. Kang, J. Wang, E. Wang, B. Liao, C. Shen, L. Xu, J. Wu, et al · 2021
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When attention meets fast recurrence: Training language models with reduced compute
T. Lei · 2021
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Denoising diffusion probabilistic models on so (3) for rotational alignment
A. Leach, S. M. Schmon, M. T. Degiacomi, and C. G. Willcocks · 2022
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Language models of protein sequences at the scale of evolution enable accurate structure prediction
Z. Lin, H. Akin, R. Rao, B. Hie, Z. Zhu, W. Lu, A. dos Santos Costa, M. Fazel-Zarandi, T. Sercu, S. Candido, et al · 2022
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Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction
W. Lu, Q. Wu, J. Zhang, J. Rao, C. Li, and S. Zheng · 2022
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Csm-ab: graph-based antibody–antigen binding affinity prediction and docking scoring function
Y. Myung, D. E. Pires, and D. B. Ascher · 2022
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Sabdab in the age of biotherapeutics: updates including sabdab-nano, the nanobody structure tracker
C. Schneider, M. I. Raybould, and C. M. Deane · 2022
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Equibind: Geometric deep learning for drug binding structure prediction
H. Stärk, O. Ganea, L. Pattanaik, R. Barzilay, and T. Jaakkola · 2022
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Structure-aware multimodal deep learning for drug–protein interaction prediction
P. Wang, S. Zheng, Y. Jiang, C. Li, J. Liu, C. Wen, A. Patronov, D. Qian, H. Chen, and Y. Yang · 2022
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Planet: A multi-objective graph neural network model for protein-ligand binding affinity prediction
X. Zhang, H. Gao, H. Wang, Z. Chen, Z. Zhang, X. Chen, Y. Li, Y. Qi, and R. Wang · 2023
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