[2] Amr Alhossary, Stephanus Daniel Handoko, Yuguang Mu, and Chee-Keong Kwoh. Fast, accurate, and reliable molecular docking with quickvina 2. Bioinformatics, 31(13):2214-2216, 2015.
[3] Ivan Anishchenko, Samuel J Pellock, Tamuka M Chidyausiku, Theresa A Ramelot, Sergey Ovchinnikov, Jingzhou Hao, Khushboo Bafna, Christoffer Norn, Alex Kang, Asim K Bera, et al. De novo protein design by deep network hallucination. Nature, 600(7889):547-552, 2021.
[4] Amos Bairoch. The enzyme database in 2000. Nucleic acids research, 28(1):304-305, 2000.
[5] Matthew J Bick, Per J Greisen, Kevin J Morey, Mauricio S Antunes, David La, Banumathi Sankaran, Luc Reymond, Kai Johnsson, June I Medford, and David Baker. Computational design of environmental sensors for the potent opioid fentanyl. *Elife*, 6:e28909, 2017.
[6] F Edward Boas and Pehr B Harbury. Potential energy functions for protein design. Current opinion in structural biology, 17(2):199-204, 2007.
[7] 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.
[8] Yaoxi Chen, Quan Chen, and Haiyan Liu. Depact and pacmatch: A workflow of designing de novo protein pockets to bind small molecules. Journal of Chemical Information and Modeling, 62(4):971-985, 2022.
[9] Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob J de Haas, Neville Bethel, et al. Robust deep learning-based protein sequence design using proteinmpnn. Science, 378(6615):49-56, 2022.
[10] Christopher M Dobson. Protein folding and misfolding. Nature, 426(6968):884-890, 2003.
[11] Jiayi Dou, Lindsey Doyle, Per Jr Greisen, Alberto Schena, Hahnbeom Park, Kai Johnsson, Barry L Stoddard, and David Baker. Sampling and energy evaluation challenges in ligand binding protein design. Protein Science, 26(12):2426-2437, 2017.
[12] Xing Du, Yi Li, Yuan-Ling Xia, Shi-Meng Ai, Jing Liang, Peng Sang, Xing-Lai Ji, and Shu-Qun Liu. Insights into protein-ligand interactions: mechanisms, models, and methods. International journal of molecular sciences, 17(2):144, 2016.
[13] Peter Eastman, Jason Swails, John D Chodera, Robert T McGibbon, Yutong Zhao, Kyle A Beauchamp, Lee-Ping Wang, Andrew C Simonetti, Matthew P Harrigan, Chaya D Stern, et al. Openmm 7: Rapid development of high performance algorithms for molecular dynamics. PLoS computational biology, 13(7):e1005659, 2017.
[14] Noelia Ferruz and Birte Höcker. Controllable protein design with language models. Nature Machine Intelligence, 4(6):521-532, 2022.
[15] Noelia Ferruz, Steffen Schmidt, and Birte Höcker. Protgpt2 is a deep unsupervised language model for protein design. Nature communications, 13(1):4348, 2022.
[16] Paul G Francoeur, Tomohide Masuda, Jocelyn Sunseri, Andrew Jia, Richard B Iovanisci, Ian Snyder, and David R Koes. Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design. Journal of chemical information and modeling, 60(9):4200-4215, 2020.
[17] Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J Gray. Deep learning in protein structural modeling and design. *Patterns*, 1(9):100142, 2020.
[18] Anum A Glasgow, Yao-Ming Huang, Daniel J Mandell, Michael Thompson, Ryan Ritterson, Amanda L Loshbaugh, Jenna Pellegrino, Cody Krivacic, Roland A Pache, Kyle A Barlow, et al. Computational design of a modular protein sense-response system. Science, 366(6468):1024-1028, 2019.
[19] Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma. 3d equivariant diffusion for target-aware molecule generation and affinity prediction. In International Conference on Learning Representations, 2023.
[20] 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. ICML, 2022.