[73] Yi-Lun Liao and Tess Smidt. Equformer: Equivariant graph attention transformer for 3d atomistic graphs. arXiv preprint arXiv:2206.11990, 2022. 5, 6, 33, 34, 36, 51
[74] Chen Lin, Ying Zou, Ji Qin, Xiangrong Liu, Yi Jiang, Caihuan Ke, and Quan Zou. Hierarchical classification of protein folds using a novel ensemble classifier. PloS one, 8(2):e56499, 2013. 8
[75] Chen Lin, Ying Zou, Ji Qin, Xiangrong Liu, Yi Jiang, Caihuan Ke, and Quan Zou. Hierarchical classification of protein folds using a novel ensemble classifier. PloS one, 8(2):e56499-e56499, 2013. 25
[76] Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, et al. Dig: A turnkey library for diving into graph deep learning research. The Journal of Machine Learning Research, 22(1):10873-10881, 2021. 2
[77] Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora M Oztekin, Xuan Zhang, and Shuiwang Ji. DIG: A turnkey library for diving into graph deep learning research. Journal of Machine Learning Research, 22(240):1-9, 2021. 2
[78] Shengchao Liu, Mehmet F Demirel, and Yingyu Liang. N-gram graph: Simple unsupervised representation for graphs, with applications to molecules. NeurIPS, 32, 2019. 1, 21
[79] Shengchao Liu, Weitao Du, Zhiming Ma, Hongyu Guo, and Jian Tang. A group symmetric stochastic differential equation model for molecule multi-modal pretraining. In International Conference on Machine Learning, 2023. 3, 5, 6, 10, 31, 34, 39, 52
[80] Shengchao Liu, Hongyu Guo, and Jian Tang. Molecular geometry pretraining with se (3)-invariant denoising distance matching. arXiv preprint arXiv:2206.13602, 2022. 3, 5, 39
[81] Shengchao Liu, Hongyu Guo, and Jian Tang. Molecular geometry pretraining with SE(3)-invariant denoising distance matching. In ICLR, 2023. 34, 52
[82] Shengchao Liu, Yingyu Liang, and Anthony Gitter. Loss-balanced task weighting to reduce negative transfer in multi-task learning. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 9977-9978, 2019. 3
[83] Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, and Anima Anandkumar. Multi-modal molecule structure-text model for text-based retrieval and editing. arXiv preprint arXiv:2212.10789, 2022. 10
[84] Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, and Jian Tang. Structured multi-task learning for molecular property prediction. In International Conference on Artificial Intelligence and Statistics, pages 8906-8920. PMLR, 2022. 3
[85] Shengchao Liu, Chengpeng Wang, Weili Nie, Hanchen Wang, Jiarui Lu, Bolei Zhou, and Jian Tang. Graphcg: Unsupervised discovery of steerable factors in graphs, 2023. 1
[86] Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang. Pre-training molecular graph representation with 3d geometry. arXiv preprint arXiv:2110.07728, 2021. 3, 5, 34, 39, 52
[87] Shengchao Liu, Jiongxiao Wang, Yijin Yang, Chengpeng Wang, Ling Liu, Hongyu Guo, and Chaowei Xiao. Chatgpt-powered conversational drug editing using retrieval and domain feedback. arXiv preprint arXiv:2305.18090, 2023. 10
[88] 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. 1, 10
[89] Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, and Shuiwang Ji. Spherical message passing for 3d graph networks. arXiv preprint arXiv:2102.05013, 2021. 5, 6, 7, 23, 34, 35, 51
[90] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. 23
[91] Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R Eguchi, Po-Ssu Huang, and Richard Socher. Progen: Language modeling for protein generation. arXiv preprint arXiv:2004.03497, 2020. 1