[71] A. Rives, J. Meier, T. Sercu, S. Goyal, Z. Lin, J. Liu, D. Guo, M. Ott, C. L. Zitnick, J. Ma, et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences, 118(15):e2016239118, 2021.
[72] C. Roos, T. Terlaky, and J.-P. Vial. Interior point methods for linear optimization. 2005.
[73] J. A. Ruffolo and J. J. Gray. Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Biophysical Journal, 121(3):155a-156a, 2022.
[74] J. A. Ruffolo, J. J. Gray, and J. Sulam. Deciphering antibody affinity maturation with language models and weakly supervised learning. arXiv preprint arXiv:2112.07782, 2021.
[75] V. G. Satorras, E. Hoogeboom, and M. Welling. E (n) equivariant graph neural networks. In International conference on machine learning, pages 9323-9332. PMLR, 2021.
[76] A. Shanehsazzadeh, S. Bachas, G. Kasun, J. M. Sutton, A. K. Steiger, R. Shuai, C. Kohnert, A. Morehead, A. Brown, C. Chung, et al. Unlocking de novo antibody design with generative artificial intelligence. bioRxiv, pages 2023-01, 2023. URL https://www.biorxiv.org/content/10.1101/2023.01.08.523187v2.
[77] W. Sheffler and D. Baker. Rosettaholes: rapid assessment of protein core packing for structure prediction, refinement, design, and validation. Protein Science, 18(1):229-239, 2009.
[78] C. Shi, C. Wang, J. Lu, B. Zhong, and J. Tang. Protein sequence and structure co-design with equivariant translation. arXiv preprint arXiv:2210.08761, 2022.
[79] E. L. Shrock, R. T. Timms, T. Kula, E. L. Mena, A. P. West Jr, R. Guo, I.-H. Lee, A. A. Cohen, L. G. McKay, C. Bi, et al. Germline-encoded amino acid-binding motifs drive immunodominant public antibody responses. Science, 380(6640):eadc9498, 2023.
[80] R. W. Shuai, J. A. Ruffolo, and J. J. Gray. Generative language modeling for antibody design. bioRxiv, pages 2021-12, 2021.
[81] I. Sillitoe, N. Bordin, N. Dawson, V. P. Waman, P. Ashford, H. M. Scholes, C. S. Pang, L. Woodridge, C. Rauer, N. Sen, et al. Cath: increased structural coverage of functional space. *Nucleic acids research*, 49(D1):D266–D273, 2021.
[82] J. M. Singer, S. Novotney, D. Strickland, H. K. Haddox, N. Leiby, G. J. Rocklin, C. M. Chow, A. Roy, A. K. Bera, F. C. Motta, et al. Large-scale design and refinement of stable proteins using sequence-only models. PloS one, 17(3):e0265020, 2022.
[83] F. L. Sirota, S. Maurer-Stroh, Z. Li, F. Eisenhaber, and B. Eisenhaber. Functional classification of super-large families of enzymes based on substrate binding pocket residues for biocatalysis and enzyme engineering applications. Frontiers in bioengineering and biotechnology, page 674, 2021.
[84] J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015.
[85] S. Stanton, W. Maddox, N. Gruver, P. Maffettone, E. Delaney, P. Greenside, and A. G. Wilson. Accelerating bayesian optimization for biological sequence design with denoising autoencoders. In International Conference on Machine Learning, pages 20459-20478. PMLR, 2022.
[86] I. O. Tolstikhin, N. Houlsby, A. Kolesnikov, L. Beyer, X. Zhai, T. Unterthiner, J. Yung, A. Steiner, D. Keysers, J. Uszkoreit, et al. Mlp-mixer: An all-mlp architecture for vision. Advances in Neural Information Processing Systems, 34:24261–24272, 2021.
[87] B. L. Trippe, J. Yim, D. Tischer, T. Broderick, D. Baker, R. Barzilay, and T. Jaakkola. Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem. arXiv preprint arXiv:2206.04119, 2022.
[88] C. Vignac, I. Krawczuk, A. Siraudin, B. Wang, V. Cevher, and P. Frossard. Digress: Discrete denoising diffusion for graph generation. arXiv preprint arXiv:2209.14734, 2022.