[21] Zijie Geng, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Jie Wang, Yongdong Zhang, Feng Wu, and Tie-Yan Liu. De novo molecular generation via connection-aware motif mining. In International Conference on Learning Representations, 2023.
[22] Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamin Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspiru-Guzik. Automatic chemical design using a data-driven continuous representation of molecules. ACS central science, 4(2):268-276, 2018.
[23] Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik. Objective-reinforced generative adversarial networks (organ) for sequence generation models. arXiv preprint arXiv:1705.10843, 2017.
[24] Jiazhen He, Eva Nittinger, Christian Tyrchan, Werngard Czechtizky, Atanas Patronov, Esben Jannik Bjerrum, and Ola Engkvist. Transformer-based molecular optimization beyond matched molecular pairs. Journal of cheminformatics, 14(1):18, 2022.
[25] Hauke S Hillen, Goran Kokic, Lucas Farnung, Christian Dienemann, Dimitry Tegunov, and Patrick Cramer. Structure of replicating sars-cov-2 polymerase. Nature, 584(7819):154–156, 2020.
[26] Ross Irwin, Spyridon Dimitriadis, Jiazhen He, and Esben Jannik Bjerrum. Chemformer: a pre-trained transformer for computational chemistry. Machine Learning: Science and Technology, 3(1):015022, 2022.
[27] Kevin Maik Jablonka, Philippe Schwaller, and Berend Smit. Is gpt-3 all you need for machine learning for chemistry? In AI for Accelerated Materials Design NeurIPS 2022 Workshop, 2022.
[28] Jan H Jensen. A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space. Chemical science, 10(12):3567-3572, 2019.
[29] Wengong Jin, Regina Barzilay, and T. Jaakkola. Multi-objective molecule generation using interpretable substructures. In International Conference on Machine Learning, page 4849-4859. PMLR, 2020.
[30] Wengong Jin, Regina Barzilay, and Tommi Jaakkola. Junction tree variational autoencoder for molecular graph generation. In International conference on machine learning, pages 2323-2332. PMLR, 2018.
[31] Yibo Li, Liangren Zhang, and Zhenming Liu. Multi-objective de novo drug design with conditional graph generative model. Journal of cheminformatics, 10(1):1-24, 2018.
[32] Long-Ji Lin. Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine learning, 8(3):293-321, 1992.
[33] Youzhi Luo and Shuiwang Ji. An autoregressive flow model for 3d molecular geometry generation from scratch. In International Conference on Learning Representations (ICLR), 2022.
[34] Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, and Shimon Whiteson. Maven: Multi-agent variational exploration. Advances in Neural Information Processing Systems, 32, 2019.
[35] Krzysztof Maziarz, Henry Jackson-Flux, Pashmina Cameron, Finton Sirockin, Nadine Schneider, Nikolaus Stiefl, Marwin Segler, and Marc Brockschmidt. Learning to extend molecular scaffolds with structural motifs. In International Conference on Learning Representations (ICLR), 2022.
[36] David Mendez, Anna Gaulton, A Patrícia Bento, Jon Chambers, Marleen De Veij, Eloy Félix, María Paula Magariños, Juan F Mosquera, Prudence Mutowo, Michal Nowotka, et al. Chembl: towards direct deposition of bioassay data. Nucleic acids research, 47(D1):D930–D940, 2019.
[37] Henry Moss, David Leslie, Daniel Beck, Javier Gonzalez, and Paul Rayson. Boss: Bayesian optimization over string spaces. Advances in neural information processing systems, 33:15476-15486, 2020.
[38] Varnavas D Mouchlis, Antreas Afantitis, Angela Serra, Michele Fratello, Anastasios G Papadiamantis, Vassilis Aidinis, Iseult Lynch, Dario Greco, and Georgia Melagraki. Advances in de novo drug design: From conventional to machine learning methods. International journal of molecular sciences, 22(4):1676, 2021.
[39] Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen. Molecular de-novo design through deep reinforcement learning. Journal of Cheminformatics, 9, 2017.
[40] Jerzy Osipiuk, Saara-Anne Azizi, Steve Dvorkin, Michael Endres, Robert Jedrzejczak, Krysten A Jones, Soowon Kang, Rahul S Kathayat, Youngchang Kim, Vladislav G Lisnyak, et al. Structure of papain-like protease from sars-cov-2 and its complexes with non-covalent inhibitors. Nature communications, 12(1):743, 2021.