[1] R. Akbar, P. A. Robert, M. Pavlovic, J. R. Jeliazkov, I. Snapkov, A. Slabodkin, C. R. Weber, L. Scheffer, E. Miho, I. H. Haff, et al. A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding. Cell Reports, 34(11):108856, 2021.
[2] R. F. Alford, A. Leaver-Fay, J. R. Jeliazkov, M. J. O'Meara, F. P. DiMaio, H. Park, M. V. Shapovalov, P. D. Renfrew, V. K. Mulligan, K. Kappel, et al. The rosetta all-atom energy function for macromolecular modeling and design. Journal of chemical theory and computation, 13(6):3031–3048, 2017.
[3] N. Anand, R. Eguchi, I. I. Mathews, C. P. Perez, A. Derry, R. B. Altman, and P.-S. Huang. Protein sequence design with a learned potential. Nature communications, 13(1):1-11, 2022.
[4] I. Anishchenko, S. J. Pellock, T. M. Chidyausiku, T. A. Ramelot, S. Ovchinnikov, J. Hao, K. Bafna, C. Norn, A. Kang, A. K. Bera, et al. De novo protein design by deep network hallucination. Nature, 600(7889):547-552, 2021.
[5] J. Austin, D. D. Johnson, J. Ho, D. Tarlow, and R. van den Berg. Structured denoising diffusion models in discrete state-spaces. Advances in Neural Information Processing Systems, 34: 17981-17993, 2021.
[6] S. Bachas, G. Rakocevic, D. Spencer, A. V. Sastry, R. Haile, J. M. Sutton, G. Kasun, A. Stachyra, J. M. Gutierrez, E. Yassine, et al. Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness. bioRxiv, 2022.
[7] S. Bond-Taylor, P. Hessey, H. Sasaki, T. P. Breckon, and C. G. Willcocks. Unleashing transformers: Parallel token prediction with discrete absorbing diffusion for fast high-resolution image generation from vector-quantized codes. In European Conference on Computer Vision (ECCV), 2022.
[8] V. D. Bortoli, E. Mathieu, M. J. Hutchinson, J. Thornton, Y. W. Teh, and A. Doucet. Riemannian score-based generative modelling. In A. H. Oh, A. Agarwal, D. Belgrave, and K. Cho, editors, Advances in Neural Information Processing Systems, 2022.
[9] B. Buchfink, K. Reuter, and H.-G. Drost. Sensitive protein alignments at tree-of-life scale using diamond. Nature methods, 18(4):366-368, 2021.
[10] A. V. Collis, A. P. Brouwer, and A. C. Martin. Analysis of the antigen combining site: correlations between length and sequence composition of the hypervariable loops and the nature of the antigen. Journal of molecular biology, 325(2):337-354, 2003.
[11] J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. Ragotte, L. Milles, B. Wicky, A. Courbet, R. de Haas, N. Bethel, et al. Robust deep learning based protein sequence design using proteinmpnn. bioRxiv, 2022.
[12] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
[13] J. Dunbar, K. Krawczyk, J. Leem, T. Baker, A. Fuchs, G. Georges, J. Shi, and C. M. Deane. Sabdab: the structural antibody database. *Nucleic acids research*, 42(D1):D1140–D1146, 2014.
[14] R. L. Dunbrack Jr. Rotamer libraries in the 21st century. Current opinion in structural biology, 12(4):431-440, 2002.
[15] R. R. Eguchi, C. A. Choe, and P.-S. Huang. Ig-vae: Generative modeling of protein structure by direct 3d coordinate generation. Biorxiv, pages 2020–08, 2022.
[16] A. Elnaggar, M. Heinzinger, C. Dallago, G. Rihawi, Y. Wang, L. Jones, T. Gibbs, T. Feher, C. Angerer, M. Steinegger, et al. Protrans: towards cracking the language of life's code through self-supervised deep learning and high performance computing. arXiv preprint arXiv:2007.06225, 2020.
[17] R. A. Engh and R. Huber. Structure quality and target parameters. International Tables for Crystallography, 2006.