Andac Demir, Baris Coskunuzer, Yulia Gel, Ignacio Segovia-Dominguez, Yuzhou Chen, and Bulent Kiziltan. Todd: Topological compound fingerprinting in computer-aided drug discovery. Advances in Neural Information Processing Systems, 35:27978-27993, 2022.
David Rogers and Mathew Hahn. Extended-connectivity fingerprints. Journal of chemical information and modeling, 50(5):742-754, 2010.
Tamal Krishna Dey and Yusu Wang. Computational topology for data analysis. Cambridge University Press, 2022.
Herbert Edelsbrunner and John L Harer. Computational topology: an introduction. American Mathematical Society, 2022.
Nina Otter, Mason A Porter, Ulrike Tillmann, Peter Grindrod, and Heather A Harrington. A roadmap for the computation of persistent homology. EPJ Data Science, 6:1-38, 2017.
Henry Adams, Tegan Emerson, Michael Kirby, Rachel Neville, Chris Peterson, Patrick Shipman, Sofya Chepushtanova, Eric Hanson, Francis Motta, and Lori Ziegelmeier. Persistence images: A stable vector representation of persistent homology. Journal of Machine Learning Research, 18, 2017.
Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, and Karsten Borgwardt. Topological graph neural networks, 2022.
Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Yusu Wang, and Chao Chen. Neural approximation of graph topological features. In Advances in Neural Information Processing Systems, 2022.
Weihua Hu*, Bowen Liu*, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. Strategies for pre-training graph neural networks. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=HJ1WWJSFDH.
Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, and Shuiwang Ji. Self-supervised learning of graph neural networks: A unified review. IEEE transactions on pattern analysis and machine intelligence, 45(2):2412-2429, 2022.
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang. Pretraining molecular graph representation with 3d geometry. In International Conference on Learning Representations, 2022a. URL https://openreview.net/forum?id=xQUe1pOKPam.
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.
Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, and Payel Das. Molformer: Large scale chemical language representations capture molecular structure and properties. 2022.
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke. Uni-mol: A universal 3d molecular representation learning framework. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=6K2RM6wVqKu.
Dongki Kim, Jinheon Baek, and Sung Ju Hwang. Graph self-supervised learning with accurate discrepancy learning. arXiv preprint arXiv:2202.02989, 2022.
Yuyang Wang, Rishikesh Magar, Chen Liang, and Amir Barati Farimani. Improving molecular contrastive learning via faulty negative mitigation and decomposed fragment contrast. Journal of Chemical Information and Modeling, 62(11):2713-2725, 2022b.
Kaiwen Zha, Peng Cao, Yuzhe Yang, and Dina Katabi. Supervised contrastive regression. arXiv preprint arXiv:2210.01189, 2022.
Saeid A Taghanaki, Kristy Choi, Amir Hosein Khasahmadi, and Anirudh Goyal. Robust representation learning via perceptual similarity metrics. In International Conference on Machine Learning, pages 10043-10053. PMLR, 2021.