[60] Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, and Tommi S. Jaakkola. Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=6TxBxqNME1Y.
[61] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[62] Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang. Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315, 2019.
[63] Sheng Wang, Jian Peng, Jianzhu Ma, and Jinbo Xu. Protein secondary structure prediction using deep convolutional neural fields. Scientific reports, 6(1):1-11, 2016.
[64] Andrew M. Watkins, Richard Bonneau, and Paramjit S. Arora. Side-chain conformational preferences govern protein-protein interactions. Journal of the American Chemical Society, 138 33:10386-9, 2016.
[65] Andrew M. Watkins, Timothy W. Craven, P. Douglas Renfrew, Paramjit S. Arora, and Richard Bonneau. Rotamer libraries for the high-resolution design of  $\beta$ -amino acid foldamers. bioRxiv, 2016.
[66] 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, 2022.
[67] Kevin E Wu, Kevin K Yang, Rianne van den Berg, James Y Zou, Alex X Lu, and Ava P Amini. Protein structure generation via folding diffusion. arXiv preprint arXiv:2209.15611, 2022.
[68] Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, and Qiang Liu. Diffusion-based molecule generation with informative prior bridges. arXiv preprint arXiv:2209.00865, 2022.
[69] Gang Xu, Qinghua Wang, and Jianpeng Ma. Opus-rota3: Improving protein side-chain modeling by deep neural networks and ensemble methods. Journal of chemical information and modeling, 2020.
[70] Gang Xu, Qinghua Wang, and Jianpeng Ma. Opus-rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors. Briefings in Bioinformatics, 23, 2021.
[71] Jinbo Xu and Bonnie Berger. Fast and accurate algorithms for protein side-chain packing. J. ACM, 53:533-557, 2006.
[72] Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang. Geodiff: A geometric diffusion model for molecular conformation generation. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=PzcvxEMzvQC.
[73] Chen Yanover, Ora Schueler-Furman, and Yair Weiss. Minimizing and learning energy functions for side-chain prediction. Journal of computational biology : a journal of computational molecular cell biology, 15 7:899-911, 2007.
[74] Jason Yim, Brian L. Trippe, Valentin De Bortoli, Emile Mathieu, A. Doucet, Regina Barzilay, and T. Jaakkola. Se(3) diffusion model with application to protein backbone generation. *ArXiv*, abs/2302.02277, 2023.
[75] Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, and Jian Tang. Protein representation learning by geometric structure pretraining. In International Conference on Learning Representations, 2023.