[50] John Ingraham, Vikas K Garg, Regina Barzilay, and Tommi Jaakkola. Generative models for graph-based protein design. 2019.
[51] Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, and Yang Shen. Fold2seq: A joint sequence (1d)-fold (3d) embedding-based generative model for protein design. In International Conference on Machine Learning, pages 1261-1271. PMLR, 2021.
[52] Yufeng Liu, Lu Zhang, Weilun Wang, Min Zhu, Chenchen Wang, Fudong Li, Jiahai Zhang, Houqiang Li, Quan Chen, and Haiyan Liu. Rotamer-free protein sequence design based on deep learning and self-consistency. Nature Computational Science, 2(7):451-462, 2022.
[53] Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, and Alexander Rives. Learning inverse folding from millions of predicted structures. bioRxiv, 2022.
[54] Baldwin Dumortier, Antoine Liutkus, Clément Carré, and Gabriel Krouk. Petribert: Augmenting bert with tridimensional encoding for inverse protein folding and design. bioRxiv, pages 2022-08, 2022.
[55] Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379(6637):1123-1130, 2023.
[56] Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, and Quanquan Gu. Structure-informed language models are protein designers. bioRxiv, pages 2023-02, 2023.
[57] Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, and Anima Anandkumar. A text-guided protein design framework. arXiv preprint arXiv:2302.04611, 2023.
[58] Xuezhi Xie and Philip M Kim. Helixgan: A bidirectional generative adversarial network with search in latent space for generation under constraints, 2021.
[59] Mostafa Karimi, Shaowen Zhu, Yue Cao, and Yang Shen. De novo protein design for novel folds using guided conditional Wasserstein generative adversarial networks. Journal of chemical information and modeling, 60(12):5667-5681, 2020.
[60] Yufeng Liu, Linghui Chen, and Haiyan Liu. De novo protein backbone generation based on diffusion with structured priors and adversarial training. bioRxiv, pages 2022-12, 2022.
[61] Bo Ni, David L Kaplan, and Markus J Buehler. Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model. Chem, 2023.
[62] Yifei Qi and John ZH Zhang. Densecpd: improving the accuracy of neural-network-based computational protein sequence design with densenet. Journal of chemical information and modeling, 60(3):1245-1252, 2020.
[63] Zhangyang Gao, Cheng Tan, and Stan Z. Li. Pifold: Toward effective and efficient protein inverse folding. In International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=oMsN9TYwJ0j.
[64] Carl Pabo. Molecular technology: designing proteins and peptides. Nature, 301(5897):200-200, 1983.
[65] Christine A Orengo, Alex D Michie, Susan Jones, David T Jones, Mark B Swindells, and Janet M Thornton. Cath-a hierarchic classification of protein domain structures. Structure, 5 (8):1093-1109, 1997.
[66] Stephen K Burley, Charmi Bhikadiya, Chunxiao Bi, Sebastian Bittrich, Li Chen, Gregg V Crichlow, Cole H Christie, Kenneth Dalenberg, Luigi Di Costanzo, Jose M Duarte, et al. Rcsb protein data bank: powerful new tools for exploring 3d structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic acids research, 49(D1):D437-D451, 2021.