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Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense.
Tm-align: a protein structure alignment algorithm based on the tm-score
Zhang, Y. and Skolnick, J · 2005
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Protein–protein interactions as targets for small molecule drug discovery
Fry, D. C · 2006
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Robust 3d shape correspondence in the spectral domain
Jain, V. and Zhang, H · 2006
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Laplace-beltrami eigenfunctions towards an algorithm that” understands” geometry
Lévy, B · 2006
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A lock-and-key model for protein–protein interactions
Morrison, J. L., Breitling, R., Higham, D. J., and Gilbert, D. R · 2006
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A concise and provably informative multi-scale signature based on heat diffusion
Sun, J., Ovsjanikov, M., and Guibas, L · 2009
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Protein–protein interactions essentials: key concepts to building and analyzing interactome networks
De Las Rivas, J. and Fontanillo, C · 2010
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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Proteins: structure and function
Whitford, D · 2013
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Dockq: a quality measure for protein-protein docking models
Basu, S. and Wallner, B · 2016
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Small molecules, big targets: drug discovery faces the protein–protein interaction challenge
Scott, D. E., Bayly, A. R., Abell, C., and Skidmore, J · 2016
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Protein-protein interaction (ppi) network: recent advances in drug discovery
Athanasios, A., Charalampos, V., Vasileios, T., and Md Ashraf, G · 2017
Cited alongside, same era.
Sequence-based prediction of protein protein interaction using a deep-learning algorithm
Sun, T., Zhou, B., Lai, L., and Pei, J · 2017
Cited alongside, same era.
Efficient curvature estimation for oriented point clouds
Cao, Y., Li, D., Sun, H., Assadi, A. H., and Zhang, S · 2019
Cited alongside, same era.
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Gainza, P., Sverrisson, F., Monti, F., Rodola, E., Boscaini, D., Bronstein, M., and Correia, B · 2020
Cited alongside, same era.
Luan, S., Zhao, M., Hua, C., Chang, X.-W., and Precup, D · 2020
Cited alongside, same era.
Revisiting heterophily for graph neural networks
Luan, S., Hua, C., Lu, Q., Zhu, J., Zhao, M., Zhang, S., Chang, X.-W., and Precup, D · 2022
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Scannet: an interpretable geometric deep learning model for structure-based protein binding site prediction
Tubiana, J., Schneidman-Duhovny, D., and Wolfson, H. J · 2022
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Protein representation learning by geometric structure pretraining
Zhang, Z., Xu, M., Jamasb, A., Chenthamarakshan, V., Lozano, A., Das, P., and Tang, J · 2022
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Hierarchical graph learning for protein–protein interaction
Gao, Z., Jiang, C., Zhang, J., Jiang, X., Li, L., Zhao, P., Yang, H., Huang, Y., and Li, J · 2023
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Mudiff: Unified diffusion for complete molecule generation
Hua, C., Luan, S., Xu, M., Ying, R., Fu, J., Ermon, S., and Precup, D · 2023
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Pointcontrast: Unsupervised pre-training for 3d point cloud understanding
Xie, S., Gu, J., Guo, D., Qi, C. R., Guibas, L., and Litany, O · 2020
Cited alongside, same era.
Structure-based protein function prediction using graph convolutional networks
Gligorijević, V., Renfrew, P. D., Kosciolek, T., Leman, J. K., Berenberg, D., Vatanen, T., Chandler, C., Taylor, B. C., Fisk, I. M., Vlamakis, H., et al · 2021
Cited alongside, same era.
Is heterophily a real nightmare for graph neural networks to do node classification?
Luan, S., Hua, C., Lu, Q., Zhu, J., Zhao, M., Zhang, S., Chang, X.-W., and Precup, D · 2021
Cited alongside, same era.
E (n) equivariant graph neural networks
Satorras, V. G., Hoogeboom, E., and Welling, M · 2021
Cited alongside, same era.
Fast end-to-end learning on protein surfaces
Sverrisson, F., Feydy, J., Correia, B. E., and Bronstein, M. M · 2021
Cited alongside, same era.
High-order pooling for graph neural networks with tensor decomposition
Hua, C., Rabusseau, G., and Tang, J · 2022
Cited alongside, same era.
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Structure-based drug design with geometric deep learning
Isert, C., Atz, K., and Schneider, G · 2023
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Jigsaw: Learning to assemble multiple fractured objects
Lu, J., Sun, Y., and Huang, Q · 2023
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Dips-plus: The enhanced database of interacting protein structures for interface prediction
Morehead, A., Chen, C., Sedova, A., and Cheng, J · 2023
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Deeprank-gnn: a graph neural network framework to learn patterns in protein–protein interfaces
Réau, M., Renaud, N., Xue, L. C., and Bonvin, A. M · 2023
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Fast and accurate protein structure search with foldseek
van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L., Söding, J., and Steinegger, M · 2023
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Learning harmonic molecular representations on riemannian manifold
Wang, Y., Shen, Y., Chen, S., Wang, L., Ye, F., and Zhou, H · 2023
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