[120] Seongjoon Joo, Injin Cho, Hogyun Seo, Hyeoncheol Francis Son, Hye-Young Sagong, Tae Joo Shin, So Young Choi, Sang Yup Lee, and Kyung-Jin Kim. Structural insight into molecular mechanism of poly(ethylene terephthalate) degradation. Nature Communications, 9, 2018.
[121] Bing Liu, Lihui He, Liping Wang, Tao Li, Chang-Cheng Li, Huayi Liu, Yunzi Luo, and Rui Bao. Protein crystallography and site-direct mutagenesis analysis of the poly(ethylene terephthalate) hydrolase PETase from Ideonella sakaiensis. ChemBioChem, 19, 2018.
[122] Shosuke Yoshida, Kazumi Hiraga, Toshihiko Takehana, Ikuo Taniguchi, Hironao Yamaji, Yasuhito Maeda, Kiyotsuna Toyohara, Kenji Miyamoto, Yoshiharu Kimura, and Kohei Oda. A bacterium that degrades and assimilates poly (ethylene terephthalate). Science, 351(6278):1196-1199, 2016.
[123] Urszula Chajewska, Daphne Koller, and Ronald Parr. Making rational decisions using adaptive utility elicitation. In Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, pages 363-369, 2000.
[124] Johannes FURNKranz and Eyke Hullermeier. Pairwise preference learning and ranking. In Machine Learning: ECML 2003: 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, September 22-26, 2003. Proceedings 14, pages 145-156. Springer, 2003.
[125] Craig Boutilier. A POMDP formulation of preference elicitation problems. In Eighteenth National Conference on Artificial Intelligence, page 239-246. American Association for Artificial Intelligence, 2002.
[126] Johannes Furnkranz and Eyke Hüllermeier. Preference Learning. Springer Science & Business Media, 2010.
[127] G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins. Quantifying the chemical beauty of drugs. Nature Chemistry, 4(2):90-98, 2012.
[128] Ryan-Rhys Griffiths, Philippe Schwaller, and Alpha A Lee. Dataset bias in the natural sciences: a case study in chemical reaction prediction and synthesis design. arXiv preprint arXiv:2105.02637, 2021.
[129] Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher. GuacaMol: benchmarking models for de novo molecular design. Journal of chemical information and modeling, 59(3):1096-1108, 2019.
[130] Iiris Sundin, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos, Esben Jannik Bjerrum, Markus Heinonen, Atanas Patronov, Samuel Kaski, and Ola Engkvist. Human-in-the-loop assisted de novo molecular design. Journal of Cheminformatics, 14(1):1-16, 2022.
[131] Oh-Hyeon Choung, Riccardo Vianello, Marwin Segler, Nikolaus Stiefl, and José Jiménez-Luna. Learning chemical intuition from humans in the loop. ChemRxiv, 2023.
[132] Wei Chu and Zoubin Ghahramani. Preference learning with Gaussian processes. In Proceedings of the 22nd international conference on Machine learning, pages 137-144, 2005.
[133] Raul Astudillo, Zhiyuan Jerry Lin, Eytan Bakshy, and Peter Frazier. qEUBO: A decision-theoretic acquisition function for preferential Bayesian optimization. In International Conference on Artificial Intelligence and Statistics, pages 1093-1114. PMLR, 2023.
[134] Seung-Seok Choi, Sung-Hyuk Cha, Charles C Tappert, et al. A survey of binary similarity and distance measures. Journal of systemics, cybernetics and informatics, 8(1):43-48, 2010.
[135] Rafic Nader, Alain Bretto, Bassam Mourad, and Hassan Abbas. On the positive semi-definite property of similarity matrices. Theoretical Computer Science, 755:13-28, 2019.
[136] Daniel P Faith, Peter R Minchin, and Lee Belbin. Compositional dissimilarity as a robust measure of ecological distance. Vegetatio, 69:57-68, 1987.
[137] Stephen A Forbes. On the local distribution of certain Illinois fishes: an essay in statistical ecology, volume 7. Illinois State Laboratory of Natural History, 1907.