[62] Veronica Salmaso and Stefano Moro. Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview. Frontiers in pharmacology, 2018. 5
[63] Linyuan Lu and Tao Zhou. Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications, 2011. 5
[64] Gerta Rücker and Christoph Rücker. Substructure, subgraph, and walk counts as measures of the complexity of graphs and molecules. Journal of Chemical Information and Computer Sciences, 41(6):1457-1462, 2001. 5
[65] Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Kyoham Shin, and Seokho Kang. Compressed graph representation for scalable molecular graph generation. Journal of Cheminformatics, 2020.
[66] Ryuichiro Hataya, Hideki Nakayama, and Kazuki Yoshizoe. Graph energy-based model for substructure preserving molecular design. arXiv:2102.04600, 2021.
[67] Xian-bin Ye, Quanlong Guan, Weiqi Luo, Liangda Fang, Zhao-Rong Lai, et al. Molecular substructure graph attention network for molecular property identification in drug discovery. Pattern Recognition, 128:108659, 2022. 5
[68] John E McMurry. Organic chemistry with biological applications. Cengage Learning, 2014. 5
[69] Li Jing, Pascal Vincent, Yann LeCun, and Yuandong Tian. Understanding dimensional collapse in contrastive self-supervised learning. In ICLR, 2022. 6
[70] Tiago Janela and Jurgen Bajorath. Simple nearest-neighbour analysis meets the accuracy of compound potency predictions using complex machine learning models. Nature Machine Intelligence, pages 1-10, 2022. 6
[71] Tianyu Hua, Wenxiao Wang, Zihui Xue, Sucheng Ren, Yue Wang, et al. On feature decorrelation in self-supervised learning. In ICCV, 2021. 6
[72] Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, et al. You can have better graph neural networks by not training weights at all: Finding untrained graph tickets. In Learning on Graphs Conference, 2021. 6
[73] Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Gunnemann, and Pietro Liò. 3d infomax improves gnns for molecular property prediction. In ICML, 2022. 7
[74] Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola. Equibind: Geometric deep learning for drug binding structure prediction. In ICML, 2022.
[75] Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, and Jianzhu Ma. Pocket2mol: Efficient molecular sampling based on 3d protein pockets. In ICML, 2022. 7
[76] Deli Chen, Yankai Lin, Wei Li, Peng Li, et al. Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In AAAI, 2020. 8
[77] Thomas A Halgren. Merck molecular force field. i. basis, form, scope, parameterization, and performance of mmff94. Journal of computational chemistry, 17(5-6):490-519, 1996. 17
[78] Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, and Haifeng Wang. Geometry-enhanced molecular representation learning for property prediction. Nature Machine Intelligence, 4(2):127-134, 2022. 17
[79] AG Donchev, VD Ozrin, MV Subbotin, OV Tarasov, and VI Tarasov. A quantum mechanical polarizable force field for biomolecular interactions. PNAS, 2005. 17