[21] Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang. Pre-training molecular graph representation with 3D geometry, 2021. URL https://arxiv.org/abs/2110.07728.
[22] Zhao Xu, Youzhi Luo, Xuan Zhang, Xinyi Xu, Yaochen Xie, Meng Liu, Kaleb Dickerson, Cheng Deng, Maho Nakata, and Shuiwang Ji. Molecule3D: A benchmark for predicting 3D geometries from molecular graphs, 2021. URL https://arxiv.org/abs/2110.01717.
[23] Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwooong Yoon, Devi Parikh, C. Lawrence Zitnick, and Zachary Ulissi. Open Catalyst 2020 (OC20) dataset and community challenges. ACS Catalysis, 11(10):6059-6072, May 2021. doi: 10.1021/acscatal.0c04525. URL https://doi.org/10.1021/acscatal.0c04525.
[24] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, pages 1096-1103, 2008.
[25] Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11(110):3371-3408, 2010. URL http://jmlr.org/papers/v11/ vincent10a.html.
[26] Yingbo Zhou, Devansh Arpit, Ifeoma Nwogu, and Venu Govindaraju. Is joint training better for deep auto-encoders?, 2014. URL https://arxiv.org/abs/1405.1380.
[27] 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, 2021. URL https://arxiv.org/abs/2106.07971.
[28] Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, and Jonathan Godwin. Pre-training via denoising for molecular property prediction. arXiv preprint arXiv:2206.00133, 2022.
[29] Shengchao Liu, Hongyu Guo, and Jian Tang. Molecular geometry pretraining with SE(3)-invariant denoising distance matching, 2022. URL https://arxiv.org/abs/2206.13602.
[30] Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, and O. Anatole von Lilienfeld. Quantum chemistry structures and properties of 134 kilo molecules. Scientific Data, 1(1), August 2014. doi: 10.1038/sdata.2014.22. URL https://doi.org/10.1038/sdata.2014.22.
[31] Colin A. Grambow, Lagnajit Pattanaik, and William H. Green. Deep learning of activation energies. The Journal of Physical Chemistry Letters, 11(8):2992-2997, March 2020. doi: 10.1021/acs.jpclett.0c00500. URL https://doi.org/10.1021/acs.jpclett.0c00500.
[32] Sereina Riniker and Gregory A. Landrum. Better informed distance geometry: Using what we know to improve conformation generation. Journal of Chemical Information and Modeling, 55 (12):2562-2574, November 2015. doi: 10.1021/acs.jcim.5b00654. URL https://doi.org/10.1021/acs.jcim.5b00654.
[33] RDKit Contributors. RDKit: Open-source cheminformatics, 2021. URL http://www.rdkit.org/. Accessed: 2023-03-19.
[34] 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, April 1996. doi: 10.1002/(sici)1096-987x(199604)17:5/6<490::aid-jcc1>3.0.co;2-p. URL https://doi.org/10.1002/(sici)1096-987x(199604)17:5/6<490::aid-jcc1>3.0.co;2-p.
[35] W. Kohn and L. J. Sham. Self-consistent equations including exchange and correlation effects. Phys. Rev., 140:A1133-A1138, Nov 1965. doi: 10.1103/PhysRev.140.A1133. URL https://link.aps.org/doi/10.1103/PhysRev.140.A1133.