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References

References#

AR15

Ali R. Al-Roomi. Unconstrained Single-Objective Benchmark Functions Repository. 2015. URL: https://www.al-roomi.org/benchmarks/unconstrained.

BKJ+20

Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, and Eytan Bakshy. Botorch: a framework for efficient monte-carlo bayesian optimization. arXiv, December 2020. arXiv:1910.06403 [cs, math, stat]. URL: http://arxiv.org/abs/1910.06403.

BKG+23

Lasse M Blaabjerg, Maher M Kassem, Lydia L Good, Nicolas Jonsson, Matteo Cagiada, Kristoffer E Johansson, Wouter Boomsma, Amelie Stein, and Kresten Lindorff-Larsen. Rapid protein stability prediction using deep learning representations. eLife, 12:e82593, May 2023. doi:10.7554/eLife.82593.

BF17

Wouter Boomsma and Jes Frellsen. Spherical convolutions and their application in molecular modelling. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017. URL: https://proceedings.neurips.cc/paper_files/paper/2017/file/1113d7a76ffceca1bb350bfe145467c6-Paper.pdf.

BFSV19

Nathan Brown, Marco Fiscato, Marwin H.S. Segler, and Alain C. Vaucher. Guacamol: benchmarking models for de novo molecular design. Journal of Chemical Information and Modeling, 59(3):1096–1108, March 2019. doi:10.1021/acs.jcim.8b00839.

DWaDE+22

Samuel Daulton, Xingchen Wan, and David Eriksson, Maximilian Balandat, Michael A. Osborne, and Eytan Bakshy. Bayesian optimization over discrete and mixed spaces via probabilistic reparameterization. In Advances in Neural Information Processing Systems 35. 2022.

DPAM02

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):182–197, April 2002. doi:10.1109/4235.996017.

EJ21

David Eriksson and Martin Jankowiak. High-dimensional Bayesian optimization with sparse axis-aligned subspaces. In Cassio de Campos and Marloes H. Maathuis, editors, Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, volume 161 of Proceedings of Machine Learning Research, 493–503. PMLR, 27–30 Jul 2021. URL: https://proceedings.mlr.press/v161/eriksson21a.html.

EPG+19

David Eriksson, Michael Pearce, Jacob Gardner, Ryan D Turner, and Matthias Poloczek. Scalable global optimization via local bayesian optimization. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. URL: https://proceedings.neurips.cc/paper/2019/hash/6c990b7aca7bc7058f5e98ea909e924b-Abstract.html.

ES09

Peter Ertl and Ansgar Schuffenhauer. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. Journal of Cheminformatics, 1(1):8, June 2009. doi:10.1186/1758-2946-1-8.

GFSC22

Wenhao Gao, Tianfan Fu, Jimeng Sun, and Connor W. Coley. Sample efficiency matters: a benchmark for practical molecular optimization. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. 2022. URL: https://openreview.net/forum?id=yCZRdI0Y7G.

GOST+22

Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp, José Miguel Hernández-Lobato, Andreas Bender, and Sergio Bacallado. Dockstring: easy molecular docking yields better benchmarks for ligand design. Journal of Chemical Information and Modeling, 62(15):3486–3502, August 2022. doi:10.1021/acs.jcim.1c01334.

GPB+18

Jacob R Gardner, Geoff Pleiss, David Bindel, Kilian Q Weinberger, and Andrew Gordon Wilson. Gpytorch: blackbox matrix-matrix gaussian process inference with gpu acceleration. In Advances in Neural Information Processing Systems. 2018.

GBWD+18

Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik. Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Science, 4(2):268–276, February 2018. doi:10.1021/acscentsci.7b00572.

HO96

N. Hansen and A. Ostermeier. Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In Proceedings of IEEE International Conference on Evolutionary Computation, volume, 312–317. 1996. doi:10.1109/ICEC.1996.542381.

HFG+21

Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik. Therapeutics data commons: machine learning datasets and tasks for drug discovery and development. Proceedings of Neural Information Processing Systems, NeurIPS Datasets and Benchmarks, 2021.

HHN24

Carl Hvarfner, Erik Orm Hellsten, and Luigi Nardi. Vanilla bayesian optimization performs great in high dimensions. 2024. arXiv:2402.02229.

JRHernandezGarcia+23

Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-Garc\' ıa, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, and Emmanuel Bengio. Multi-objective GFlowNets. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett, editors, Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, 14631–14653. PMLR, 23–29 Jul 2023. URL: https://proceedings.mlr.press/v202/jain23a.html.

JBJ20

Wengong Jin, Regina Barzilay, and Tommi Jaakkola. Multi-objective molecule generation using interpretable substructures. Proceedings of the 37 th International Conference on Machine Learning PMLR, 2020.

Kha09

Ahmed Khalifa. The mario ai framework. amidos2006/Mario-AI-Framework, 2009. Accessed: 20/03/2024.

KMH+19

Johannes Kirschner, Mojmir Mutny, Nicole Hiller, Rasmus Ischebeck, and Andreas Krause. Adaptive and safe bayesian optimization in high dimensions via one-dimensional subspaces. In Proceedings of the 36th International Conference on Machine Learning, 3429–3438. PMLR, May 2019. URL: https://proceedings.mlr.press/v97/kirschner19a.html.

LCRB20

Ben Letham, Roberto Calandra, Akshara Rai, and Eytan Bakshy. Re-examining linear embeddings for high-dimensional bayesian optimization. In Advances in Neural Information Processing Systems, volume 33, 1546–1558. Curran Associates, Inc., 2020. URL: https://proceedings.neurips.cc/paper/2020/hash/10fb6cfa4c990d2bad5ddef4f70e8ba2-Abstract.html.

LZL18

Yibo Li, Liangren Zhang, and Zhenming Liu. Multi-objective de novo drug design with conditional graph generative model. Journal of Cheminformatics, 10(1):33, July 2018. doi:10.1186/s13321-018-0287-6.

OBEC17

Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen. Molecular de-novo design through deep reinforcement learning. Journal of Cheminformatics, 9(1):48, September 2017. doi:10.1186/s13321-017-0235-x.

PNP22

missing booktitle in Papenmeier:BAxUS:2022

PNP24

Leonard Papenmeier, Luigi Nardi, and Matthias Poloczek. Bounce: reliable high-dimensional bayesian optimization for combinatorial and mixed spaces. 2024. arXiv:2307.00618.

PZSL+20

missing journal in Polykovskiy:MOSES:2020

RDK06

RDKit. Rdkit: open-source cheminformatics. rdkit/rdkit, 2006.

SSW+16

Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando De Freitas. Taking the human out of the loop: a review of bayesian optimization. Proceedings of the IEEE, 104(1):148–175, January 2016. doi:10.1109/JPROC.2015.2494218.

SR73

A. Shrake and J.A. Rupley. Environment and exposure to solvent of protein atoms. lysozyme and insulin. Journal of Molecular Biology, 79(2):351–371, Sep 1973. doi:10.1016/0022-2836(73)90011-9.

SMG+22

Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, and Andrew Gordon Wilson. Accelerating bayesian optimization for biological sequence design with denoising autoencoders. arXiv preprint arXiv:2203.12742, 2022.

SSMnonV16

Adam James Summerville, Sam Snodgrass, Michael Mateas, and Santiago Onta n'on Villar. The vglc: the video game level corpus. Proceedings of the 7th Workshop on Procedural Content Generation, 2016.

SB13

S. Surjanovic and D. Bingham. Optimization test functions and datasets. https://www.sfu.ca/ ssurjano/optimization.html, 2013. Accessed: 2024-04-12.

TKB10

Julian Togelius, Sergey Karakovskiy, and Robin Baumgarten. The 2009 mario ai competition. In IEEE Congress on Evolutionary Computation, volume, 1–8. 2010. doi:10.1109/CEC.2010.5586133.

VLS+18

Vanessa Volz, Simon M. Lucas, Jacob Schrum, Adam Smith, Jialin Liu, and Sebastian Risi. Evolving mario levels in the latent space of a deep convolutional generative adversarial network. GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference, pages 221–228, 2018. doi:10.1145/3205455.3205517.

BlankDeb20

J. Blank and K. Deb. Pymoo: multi-objective optimization in python. IEEE Access, 8():89497–89509, 2020.