[29] Tapio Pahikkala, Antti Airola, Sami Pietilä, Sushil Shakyawar, Agnieszka Szwajda, Jing Tang, and Tero Aittokallio. Toward more realistic drug-target interaction predictions. *Briefings Bioinform.*, 16(2):325-337, 2015.
[30] Greg Landrum. Rdkit documentation. Release, 1(1-79):4, 2013.
[31] Elena S Salmina, Norbert Haider, and Igor V Tetko. Extended functional groups (efg): an efficient set for chemical characterization and structure-activity relationship studies of chemical compounds. Molecules, 21(1):1, 2015.
[32] Wengong Jin, Regina Barzilay, and Tommi S. Jaakkola. Junction tree variational autoencoder for molecular graph generation. In ICML, volume 80 of Proceedings of Machine Learning Research, pages 2328-2337. PMLR, 2018.
[33] Daylight Chemical Information Systems, Inc. Daylight Theory Manual. http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html.
[34] Jonathan Clayden, Nick Greeves, and Stuart Warren. Organic chemistry. Oxford university press, 2012.
[35] Jörg Degen, Christof Wegscheid-Gerlach, Andrea Zaliani, and Matthias Rarey. On the art of compiling and using 'drug-like' chemical fragment spaces. *ChemMedChem: Chemistry Enabling Drug Discovery*, 3(10):1503-1507, 2008.
[36] Reinhard Diestel. Graph Theory, 4th Edition, volume 173 of Graduate texts in mathematics. Springer, 2012.
[37] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? In ICLR, 2019.
[38] Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, and Kaiming He. Masked autoencoders as spatiotemporal learners. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, NeurIPS, 2022.
[39] Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. Simplifying graph convolutional networks. In ICML, volume 97 of Proceedings of Machine Learning Research, pages 6861-6871. PMLR, 2019.
[40] Lei Chen, Zhengdao Chen, and Joan Bruna. On graph neural networks versus graph-augmented mlps. In ICLR. OpenReview.net, 2021.
[41] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, volume 37 of JMLR Workshop and Conference Proceedings, pages 448-456. JMLR.org, 2015.
[42] Teague Sterling and John J. Irwin. ZINC 15 - ligand discovery for everyone. J. Chem. Inf. Model., 55(11):2324-2337, 2015.
[43] Ali Razavi, Aäron van den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with VQ-VAE-2. In NeurIPS, pages 14837–14847, 2019.
[44] Thin Nguyen, Hang Le, Thomas P. Quinn, Tri Nguyen, Thuc Duy Le, and Svetha Venkatesh. Graphdata: predicting drug-target binding affinity with graph neural networks. Bioinform., 37(8):1140-1147, 2021.
[45] Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang. Pre-training molecular graph representation with 3d geometry. In ICLR. OpenReview.net, 2022.
[46] Simon Axelrod and Rafael Gómez-Bombarelli. GEOM: energy-annotated molecular conformations for property prediction and molecular generation. CoRR, abs/2006.05531, 2020.
[47] Quantum machine. http://quantum-machine.org/datasets/. Accessed 2023-03.
[48] 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. Nat. Mach. Intell., 4(2):127-134, 2022.
[49] Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael M. Bronstein, and Federico Monti. SIGN: scalable inception graph neural networks. CoRR, abs/2004.11198, 2020.