[7] K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller. SchNet – a deep learning architecture for molecules and materials. The Journal of Chemical Physics, 148(24): 241722, June 2018. doi: 10.1063/1.5019779. URL https://doi.org/10.1063/1.5019779.
[8] Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, and Regina Barzilay. Analyzing learned molecular representations for property prediction. Journal of Chemical Information and Modeling, 59(8):3370-3388, July 2019. doi: 10.1021/acs.jcim.9b00237. URL https://doi.org/10.1021/acs.jcim.9b00237.
[9] Zhaoping Xiong, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, Xiaozhe Wan, Xutong Li, Zhaojun Li, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, and Mingyue Zheng. Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. Journal of Medicinal Chemistry, 63(16):8749-8760, August 2019. doi: 10.1021/acs.jmedchem.9b00959. URL https://doi.org/10.1021/acs.jmedchem.9b00959.
[10] Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. Graph neural networks: A review of methods and applications. AI Open, 1:57–81, 2020. doi: 10.1016/j.aiopen.2021.01.001. URL https://doi.org/10.1016/j.aiopen.2021.01.001.
[11] Seokhyun Moon, Wonho Zhung, Soojung Yang, Jaechang Lim, and Woo Youn Kim. PIGNet: a physics-informed deep learning model toward generalized drug-target interaction predictions. Chemical Science, 13(13):3661-3673, 2022. doi: 10.1039/d1sc06946b. URL https://doi.org/10.1039/d1sc06946b.
[12] Jun Hyeong Kim, Hyeonsu Kim, and Woo Youn Kim. Effect of molecular representation on deep learning performance for prediction of molecular electronic properties. Bulletin of the Korean Chemical Society, 43(5):645-649, March 2022. doi: 10.1002/bkcs.12516. URL https://doi.org/10.1002/bkcs.12516.
[13] Johannes Gasteiger, Janek Groß, and Stephan Gunnemann. Directional message passing for molecular graphs, 2020. URL https://arxiv.org/abs/2003.03123.
[14] Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, and Shuiwang Ji. Spherical message passing for 3D graph networks, 2021. URL https://arxiv.org/abs/2102.05013.
[15] Víctor Garcia Satorras, Emiel Hoogeboom, and Max Welling. E(n) equivariant graph neural networks. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 9323-9332. PMLR, 18-24 Jul 2021. URL https://proceedings.mlr.press/v139/satorras21a.html.
[16] Yi-Lun Liao and Tess Smidt. Equformer: Equivariant graph attention transformer for 3D atomistic graphs, 2022. URL https://arxiv.org/abs/2206.11990.
[17] 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, 2022.
[18] C. Lawrence Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi, Anuroop Sriram, Zachary Ulissi, and Brandon Wood. Spherical channels for modeling atomic interactions, 2022. URL https://arxiv.org/abs/2206.14331.
[19] Jianing Lu, Cheng Wang, and Yingkai Zhang. Predicting molecular energy using force-field optimized geometries and atomic vector representations learned from an improved deep tensor neural network. Journal of Chemical Theory and Computation, 15(7):4113-4121, May 2019. doi: 10.1021/acs.jctc.9b00001. URL https://doi.org/10.1021/acs.jctc.9b00001.
[20] Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Gunnemann, and Pietro Lio. 3D Infomax improves gnns for molecular property prediction. 2021. doi: 10.48550/ARXIV.2110.04126. URL https://arxiv.org/abs/2110.04126.