[1] T. Langer and R.D. Hoffmann. Pharmacophores and Pharmacophore Searches. 08 2006.
[2] M. Afshar, A. Lanoue, and J. Sallantin. 4.30 - multiobjective/multicriteria optimization and decision support in drug discovery. In John B. Taylor and David J. Triggle, editors, Comprehensive Medicinal Chemistry II, pages 767-774. Elsevier, Oxford, 2007.
[3] John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon Kohl, Andrew Ballard, Andrew Cowie, Bernardino Romero-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, and Demis Hassabis. Highly accurate protein structure prediction with alphafold. Nature, 596:1-11, 08 2021.
[4] Zhangyang Gao, Cheng Tan, Stan Li, et al. Alphadesign: A graph protein design method and benchmark on alphafolddb. arXiv preprint arXiv:2202.01079, 2022.
[5] Yufei Huang, Lirong Wu, Haitao Lin, Jiangbin Zheng, Ge Wang, and Stan Z. Li. Data-efficient protein 3d geometric pretraining via refinement of diffused protein structure decoy, 2023.
[6] Lirong Wu, Yufei Huang, Haitao Lin, and Stan Z. Li. A survey on protein representation learning: Retrospect and prospect, 2022.
[7] Zhangyang Gao, Cheng Tan, and Stan Z Li. Pifold: Toward effective and efficient protein inverse folding. arXiv preprint arXiv:2209.12643, 2022.
[8] Cheng Tan, Zhangyang Gao, Jun Xia, and Stan Z Li. Generative de novo protein design with global context. arXiv preprint arXiv:2204.10673, 2022.
[9] Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, and Jianzhu Ma. Pocket2mol: Efficient molecular sampling based on 3d protein pockets. In International Conference on Machine Learning, 2022.
[10] Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, and Shuiwang Ji. Generating 3d molecules for target protein binding. In International Conference on Machine Learning, 2022.
[11] Cheng Tan, Zhangyang Gao, and Stan Z Li. Target-aware molecular graph generation. arXiv preprint arXiv:2202.04829, 2022.
[12] Shitong Luo, Jiaqi Guan, Jianzhu Ma, and Jian Peng. A 3D generative model for structure-based drug design. In Thirty-Fifth Conference on Neural Information Processing Systems, 2021.
[13] 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.
[14] Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lio, Carla Gomes, Max Welling, Michael Bronstein, and Bruno Correia. Structure-based drug design with equivariant diffusion models, 2022.
[15] Haitao Lin, Yufei Huang, Meng Liu, Xuanjing Li, Shuiwang Ji, and Stan Z. Li. Diffbp: Generative diffusion of 3d molecules for target protein binding, 2022.
[16] Carl Doersch. Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908, 2016.
[17] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In NIPS, pages 2672-2680, 2014.
[18] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real NVP. arXiv preprint arXiv:1605.08803, 2016.
[19] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. Advances in Neural Information Processing Systems, 31:10215-10224, 2018.