[10] Patrick Bryant, Gabriele Pozzati, and Arne Elofsson. Improved prediction of protein-protein interactions using alphafold2. Nature communications, 13(1):1-11, 2022.
[11] Can Chen, Xi Chen, Chen Ma, Zixuan Liu, and Xue Liu. Gradient-based bi-level optimization for deep learning: A survey. arXiv preprint arXiv:2207.11719, 2022.
[12] Can Chen, Jingbo Zhou, Fan Wang, Xue Liu, and Dejing Dou. Structure-aware protein self-supervised learning. Bioinformatics, 39(4):btad189, 2023.
[13] Rong Chen, Li Li, and Zhiping Weng. Zdock: an initial-stage protein-docking algorithm. Proteins: Structure, Function, and Bioinformatics, 52(1):80-87, 2003.
[14] Yihong Chen, Bei Chen, Xiangnan He, Chen Gao, Yong Li, Jian-Guang Lou, and Yue Wang. λopt: Learn to regularize recommender models in finer levels. In KDD, pages 978-986, 2019.
[15] Charles Christoffer, Siyang Chen, Vijay Bharadwaj, Tunde Aderinwale, Vidhur Kumar, Matin Hormati, and Daisuke Kihara. Lzerd webserver for pairwise and multiple protein-protein docking. *Nucleic Acids Research*, 49(W1):W359-W365, 2021.
[16] Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi S Jaakkola. Diffdock: Diffusion steps, twists, and turns for molecular docking. In NeurIPS, 2022.
[17] Bowen Dai and Chris Bailey-Kellogg. Protein interaction interface region prediction by geometric deep learning. Bioinformatics, 37(17):2580-2588, 2021.
[18] Ana Davila, Zichang Xu, Songling Li, John Rozewicki, Jan Wilamowski, Sergei Kotelnikov, Dima Kozakov, Shunsuke Teraguchi, and Daron M Standley. Abadapt: an adaptive approach to predicting antibody-antigen complex structures from sequence. Bioinformatics Advances, 2(1):vbac015, 2022.
[19] Sjoerd J De Vries, Marc Van Dijk, and Alexandre MJJ Bonvin. The haddock web server for data-driven biomolecular docking. Nature protocols, 5(5):883-897, 2010.
[20] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
[21] Jerome Eberhardt, Diogo Santos-Martins, Andreas F Tillack, and Stefano Forli. Autodock vina 1.2.0: New docking methods, expanded force field, and python bindings. Journal of Chemical Information and Modeling, 61(8):3891-3898, 2021.
[22] Stephan Eismann, Raphael JL Townshend, Nathaniel Thomas, Milind Jagota, Bowen Jing, and Ron O Dror. Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes. Proteins: Structure, Function, and Bioinformatics, 89(5):493-501, 2021.
[23] Richard Evans, Michael O'Neill, Alexander Pritzel, Natasha Antropova, Andrew W Senior, Timothy Green, Augustin Žídek, Russell Bates, Sam Blackwell, Jason Yim, et al. Protein complex prediction with alphafold-multimer. *BioRxiv*, 2021.
[24] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, pages 1126–1135. PMLR, 2017.
[25] Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, and Andreas Krause. Independent se(3)-equivariant models for end-to-end rigid protein docking. In ICLR, 2022.
[26] Ian R Humphreys, Jimin Pei, Minkyung Baek, Aditya Krishnakumar, Ivan Anishchenko, Sergey Ovchinnikov, Jing Zhang, Travis J Ness, Sudeep Banjade, Saket R Bagde, et al. Computed structures of core eukaryotic protein complexes. Science, 374(6573):eabm4805, 2021.
[27] Wengong Jin, Regina Barzilay, and Tommi Jaakkola. Antibody-antigen docking and design via hierarchical structure refinement. In ICML, pages 10217–10227. PMLR, 2022.