[16] Jules Leguy, Thomas Cauchy, Marta Glavatskikh, Béatrice Duval, and Benoit Da Mota. Evomol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation. Journal of Cheminformatics, 12(1):1-19, 2020.
[17] Yongbeom Kwon and Juyong Lee. Molfinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using smiles. Journal of cheminformatics, 13(1):1-14, 2021.
[18] Wenhao Gao, Tianfan Fu, Jimeng Sun, and Connor W Coley. Sample efficiency matters: A benchmark for practical molecular optimization. arXiv preprint arXiv:2206.12411, 2022.
[19] 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, 2020.
[20] Mario Krenn, Qianxiang Ai, Senja Barthel, Nessa Carson, Angelo Frei, Nathan C Frey, Pascal Friederich, Théophile Gaudin, Alberto Alexander Gayle, Kevin Maik Jablonka, et al. Selfies and the future of molecular string representations. arXiv preprint arXiv:2204.00056, 2022.
[21] Philipp Renz, Dries Van Rompaey, Jörg Kurt Wegner, Sepp Hochreiter, and Günter Klambauer. On failure modes in molecule generation and optimization. *Drug Discovery Today: Technologies*, 32:55–63, 2019.
[22] Tobiasz Cieplinski, Tomasz Danel, Sabina Podlewka, and Stanislaw Jastrzebski. Generative models should at least be able to design molecules that dock well: A new benchmark. Journal of Chemical Information and Modeling, 63(11):3238-3247, 2023. PMID: 37224003.
[23] Gisbert Schneider and David E. Clark. Automated de novo drug design: Are we nearly there yet? Angew. Chem. Int. Ed., 58(32):10792-10803, 2019.
[24] Woosung Jeon and Dongsup Kim. Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors. Scientific reports, 10(1):22104, 2020.
[25] Sarvesh Mehta, Siddhartha Laghuvarapu, Yashaswi Pathak, Aaftaab Sethi, Mallika Alvala, and U. Deva Priyakumar. Memes: Machine learning framework for enhanced molecular screening. Chem. Sci., 12:11710-11721, 2021.
[26] Morgan Thomas, Robert T Smith, Noel M O'Boyle, Chris de Graaf, and Andreas Bender. Comparison of structure-and ligand-based scoring functions for deep generative models: a gpcr case study. Journal of Cheminformatics, 13(1):1-20, 2021.
[27] Tiago Sousa, João Correia, Vitor Pereira, and Miguel Rocha. Generative deep learning for targeted compound design. Journal of Chemical Information and Modeling, 61(11):5343-5361, 2021. PMID: 34699719.
[28] Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf H 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. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021.
[29] 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, 0(0):null, 2022.
[30] Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen. Molecular de-novo design through deep reinforcement learning. Journal of cheminformatics, 9(1):1-14, 2017.
[31] 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, Jan 2018.