[33] Mario Krenn, Florian Häse, AkshitKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik. Self-referencing embedded strings (SELFIES): A  $100\%$  robust molecular string representation. Machine Learning: Science and Technology, 1(4):045024, oct 2020. doi: 10.1088/2632-2153/aba947. URL https://doi.org/10.1088/2F2632-2153%2Faba947.
[34] Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, and Seokho Kang. Uncertainty-aware prediction of chemical reaction yields with graph neural networks. Journal of Cheminformatics, 14:1-10, 2022.
[35] G. A. Landrum. Rdkit: Open-source cheminformatics software. http://www.rdkit.org, 2020.
[36] Itay Levy, Ben Bogin, and Jonathan Berant. Diverse demonstrations improve in-context compositional generalization, 2022.
[37] Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation. arXiv preprint arXiv:2305.01210, 2023.
[38] Zequn Liu, Wei Zhang, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Ming Zhang, and TieYan Liu. Molxpt: Wrapping molecules with text for generative pre-training. arXiv preprint arXiv:2305.10688, 2023.
[39] Daniel Mark Lowe. Extraction of chemical structures and reactions from the literature. PhD thesis, University of Cambridge, 2012.
[40] Frederic P Miller, Agnes F Vandome, and John McBrewster. Levenshtein distance: Information theory, computer science, string (computer science), string metric, damerau? levenshtein distance, spell checker, hamming distance, 2009.
[41] Harry L Morgan. The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service. Journal of chemical documentation, 5(2): 107-113, 1965.
[42] Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, and Eric Horvitz. Capabilities of gpt-4 on medical challenge problems. arXiv preprint arXiv:2303.13375, 2023.
[43] OpenAI. Gpt-4 technical report, 2023.
[44] Damith Perera, Joseph W Tucker, Shalini Brahmbhatt, Christopher J Helal, Ashley Chong, William Farrell, Paul Richardson, and Neal W Sach. A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science, 359(6374):429-434, 2018.
[45] Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Gunter Klambauer. Fréchet chemnet distance: a metric for generative models for molecules in drug discovery. Journal of chemical information and modeling, 58(9):1736-1741, 2018.
[46] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485-5551, 2020.
[47] Kohulan Rajan, Achim Zielesny, and Christoph Steinbeck. Stout: Smiles to iupac names using neural machine translation. Journal of Cheminformatics, 13(1):1-14, 2021.
[48] Mayk Caldas Ramos, Shane S Michtavy, Marc D Porosoff, and Andrew D White. Bayesian optimization of catalysts with in-context learning. arXiv preprint arXiv:2304.05341, 2023.
[49] David Ratcliff, John W.; Metzener. Pattern matching: The gestalt approach, 1988.
[50] Brandon J Reizman, Yi-Ming Wang, Stephen L Buchwald, and Klavs F Jensen. Suzuki-miyaura cross-coupling optimization enabled by automated feedback. Reaction chemistry & engineering, 1(6):658-666, 2016.
[51] Mandana Saebi, Bozhao Nan, John E Herr, Jessica Wahlers, Zhichun Guo, Andrzej M Zurański, Thierry Kogej, Per-Ola Norrby, Abigail G Doyle, Nitesh V Chawla, et al. On the use of real-world datasets for reaction yield prediction. Chemical Science, 2023.