[15] Vineet John, Lili Mou, Hareesh Bahuleyan, and Olga Vechtomova. Disentangled representation learning for non-parallel text style transfer. arXiv preprint arXiv:1808.04339, 2018.
[16] Pengyu Cheng, Martin Renqiang Min, Dinghan Shen, Christopher Malon, Yizhe Zhang, Yitong Li, and Lawrence Carin. Improving disentangled text representation learning with information-theoretic guidance. arXiv preprint arXiv:2006.00693, 2020.
[17] Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf. Wasserstein auto-encoders. arXiv preprint arXiv:1711.01558, 2017.
[18] Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio. Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349, 2015.
[19] Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black, and Bernhard Scholkopf. From variational to deterministic autoencoders. arXiv preprint arXiv:1903.12436, 2019.
[20] Balachandra K Gorentla and Xiao-Ping Zhong. T cell receptor signal transduction in t lymphocytes. Journal of clinical & cellular immunology, 2012(Suppl 12):005, 2012.
[21] Kinjal Shah, Amr Al-Haidari, Jianmin Sun, and Julhash U Kazi. T cell receptor (tcr) signaling in health and disease. Signal transduction and targeted therapy, 6(1):1-26, 2021.
[22] Nicholas P Restifo, Mark E Dudley, and Steven A Rosenberg. Adoptive immunotherapy for cancer: harnessing the t cell response. Nature Reviews Immunology, 12(4):269-281, 2012.
[23] Ido Springer, Hanan Besser, Nili Tickotsky-Moskovitz, Shirt Dvorkin, and Yoram Louzoun. Prediction of specific tcr-peptide binding from large dictionaries of tcr-peptide pairs. Frontiers in immunology, page 1803, 2020.
[24] Anna Weber, Jannis Born, and María Rodríguez Martínez. Titan: T-cell receptor specificity prediction with bimodal attention networks. Bioinformatics, 37(Supplement_1):i237-i244, 2021.
[25] Yicheng Gao, Yuli Gao, Yuxiao Fan, Chengyu Zhu, Zhiting Wei, Chi Zhou, Guohui Chuai, Qinchang Chen, He Zhang, and Qi Liu. Pan-peptide meta learning for t-cell receptor-antigen binding recognition. Nature Machine Intelligence, pages 1-14, 2023.
[26] Alessandro Montemurro, Viktoria Schuster, Helle Rus Povlsen, Amalie Kai Bentzen, Vanessa Jurtz, William D Chronister, Austin Crinklaw, Sine R Hadrup, Ole Winther, Bjoern Peters, et al. Nettr-2.0 enables accurate prediction of tcr-peptide binding by using paired tcr $\alpha$  and  $\beta$  sequence data. Communications biology, 4(1):1-13, 2021.
[27] Filippo Grazioli, Pierre Machart, Anja Mösch, Kai Li, Leonardo V Castorina, Nico Pfeifer, and Martin Renqiang Min. Attentive variational information bottleneck for tcr-peptide interaction prediction. Bioinformatics, 39(1):btac820, 2023.
[28] Erik Procko, Geoffrey Y Berguig, Betty W Shen, Yifan Song, Shani Frayo, Anthony J Convertine, Daciana Margineantu, Garrett Booth, Bruno E Correia, Yuanhua Cheng, et al. A computationally designed inhibitor of an epstein-barr viral bcl-2 protein induces apoptosis in infected cells. Cell, 157(7):1644–1656, 2014.
[29] Jue Wang, Sidney Lisanza, David Juergens, Doug Tischer, Joseph L Watson, Karla M Castro, Robert Ragotte, Amijai Saragovi, Lukas F Milles, Minkyung Baek, et al. Scaffolding protein functional sites using deep learning. Science, 377(6604):387-394, 2022.
[30] Brian L Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, and Tommi Jaakkola. Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem. arXiv preprint arXiv:2206.04119, 2022.
[31] Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al. Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models. BioRxiv, pages 2022-12, 2022.
[32] Jin Sub Lee, Jisun Kim, and Philip M Kim. Score-based generative modeling for de novo protein design. Nature Computational Science, pages 1-11, 2023.
[33] Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Scholkopf, and Alexander Smola. A kernel two-sample test. The Journal of Machine Learning Research, 13(1):723-773, 2012.