[19] Samuel Goldman, Jeremy Wohlwend, Martin Stražar, Guy Haroush, Ramnik J Xavier, and Connor W Coley. Annotating metabolite mass spectra with domain-inspired chemical formula transformers. Nature Machine Intelligence, pages 1-15, 2023.
[20] William L Hamilton. Graph representation learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 14(3):1-159, 2020.
[21] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8): 1735-1780, 1997.
[22] Martin A Hoffmann, Fleming Kretschmer, Marcus Ludwig, and Sebastian Böcker. Mad Hatter correctly annotates  $98\%$  of small molecule tandem mass spectra searching in PubChem. Metabolites, 13(3):314, 2023.
[23] Yuhui Hong, Sujun Li, Christopher J Welch, Shane Tichy, Yuzhen Ye, and Haixu Tang. 3dmolms: Prediction of tandem mass spectra from three dimensional molecular conformations. bioRxiv, pages 2023-03, 2023.
[24] Florian Huber, Lars Ridder, Stefan Verhoeven, Jurriaan H Spaaks, Faruk Diblen, Simon Rogers, and Justin J J van der Hooft. Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships. PLoS computational biology, 17(2):e1008724, 2021.
[25] Franziska Hufsky, Kerstin Scheubert, and Sebastian Böcker. Computational mass spectrometry for small-molecule fragmentation. *TrAC Trends in Analytical Chemistry*, 53:41–48, 2014.
[26] Hyun Woo Kim, Mingxun Wang, Christopher A Leber, Louis-Félix Nothias, Raphael Reher, Kyo Bin Kang, Justin JJ Van Der Hooft, Pieter C Dorrestein, William H Gerwick, and Garrison W Cottrell. Npclassifier: A deep neural network-based structural classification tool for natural products. Journal of natural products, 84(11):2795-2807, 2021.
[27] Sunghwan Kim, Paul A. Thiessen, Evan E. Bolton, Jie Chen, Gang Fu, Asta Gindulyte, Lianyi Han, Jane He, Siqian He, and Benjamin A. Shoemaker. PubChem substance and compound databases. Nucleic Acids Research, 44(D1):D1202–D1213, 2016.
[28] Adam R Kosiorek, Hyunjik Kim, and Danilo J Rezende. Conditional set generation with transformers. In Workshop on Object-Oriented Learning at ICML 2020, 2020.
[29] Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh. Set transformer: A framework for attention-based permutation-invariant neural networks. In Proceedings of the 36th International Conference on Machine Learning, pages 3744-3753, 2019.
[30] Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. Gated graph sequence neural networks. In International Conference on Learning Representations, 2016.
[31] Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica. Tune: A research platform for distributed model selection and training. ICML 2018 AutoML Workshop, 2018.
[32] Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. Object-centric learning with slot attention. In Advances in Neural Information Processing Systems 33, 2020.
[33] Marcus Ludwig, Louis-Félix Nothias, Kai Dührkop, Irina Koester, Markus Fleischauer, Martin A. Hoffmann, Daniel Petras, Fernando Vargas, Mustafa Morsy, and Lihini Aluwihare. Database-independent molecular formula annotation using Gibbs sampling through ZODIAC. Nature Machine Intelligence, 2(10):629-641, 2020.
[34] Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey, and Thomas Butler. Efficiently predicting high resolution mass spectra with graph neural networks. arXiv preprint arXiv:2301.11419, 2023.
[35] NIST. Tandem Mass Spectral Library. NIST, 2020. URL https://www.nist.gov/programs-projects/tandem-mass-spectral-library