[27] Justin S Smith, Alexandr Isayev, and Adrian E Roitberg. ANI-1, A Data Set of 20 Million Calculated Off-Equilibrium Conformations for Organic Molecules. Scientific data, 2017.
[28] Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter Battaglia. Learning to simulate complex physics with graph networks. In International conference on machine learning, pages 8459-8468. PMLR, 2020.
[29] Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L Dyer, Remi Munos, Petar Velicković, and Michal Valko. Large-scale representation learning on graphs via bootstrapping. arXiv preprint arXiv:2102.06514, 2021.
[30] Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. Graph contrastive learning with augmentations. Advances in neural information processing systems, 33:5812-5823, 2020.
[31] Jonathan Godwin, Michael Schaarschmidt, Alexander Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velicković, James Kirkpatrick, and Peter Battaglia. Simple gnn regularisation for 3d molecular property prediction & beyond. arXiv preprint arXiv:2106.07971, 2021.
[32] Ryoma Sato, Makoto Yamada, and Hisashi Kashima. Random features strengthen graph neural networks. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pages 333-341. SIAM, 2021.
[33] Baoyu Jing, Shengyu Feng, Yuejia Xiang, Xi Chen, Yu Chen, and Hanghang Tong. X-goal: Multiplex heterogeneous graph prototypical contrastive learning. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 894-904, 2022.
[34] Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, and Haifeng Wang. Chemr-gem: Geometry enhanced molecular representation learning for property prediction. arXiv preprint arXiv:2106.06130, 2021.
[35] Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Gunnemann, and Pietro Lio. 3d infomax improves gnns for molecular property prediction. In International Conference on Machine Learning, pages 20479-20502. PMLR, 2022.
[36] Rui Jiao, Jiaqi Han, Wenbing Huang, Yu Rong, and Yang Liu. 3d equivariant molecular graph pretraining. arXiv preprint arXiv:2207.08824, 2022.
[37] Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec. Distance encoding: Design provably more powerful neural networks for graph representation learning. Advances in Neural Information Processing Systems, 33:4465-4478, 2020.
[38] Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, and Stefanie Jegelka. Sign and basis invariant networks for spectral graph representation learning. arXiv preprint arXiv:2202.13013, 2022.
[39] Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou. Rethinking graph transformers with spectral attention. Advances in Neural Information Processing Systems, 34:21618-21629, 2021.
[40] Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. Do transformers really perform badly for graph representation? Advances in Neural Information Processing Systems, 34:28877-28888, 2021.
[41] Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. Graph neural networks with learnable structural and positional representations. arXiv preprint arXiv:2110.07875, 2021.
[42] Shengjie Luo, Tianlang Chen, Yixian Xu, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, and Di He. One transformer can understand both 2d & 3d molecular data. arXiv preprint arXiv:2210.01765, 2022.