[4] J. Chen and P. Baldi. No electron left-behind: a rule-based expert system to predict chemical reactions and reaction mechanisms. Journal of Chemical Information and Modeling, 49(9):2034-2043, 2009.
[5] Marwin HS Segler and Mark P Waller. Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chemistry-A European Journal, 23(25):5966-5971, 2017.
[6] James Law, Zsolt Zsoldos, Aniko Simon, Darryl Reid, Yang Liu, Sing Yoong Khew, A Peter Johnson, Sarah Major, Robert A Wade, and Howard Y Ando. Route designer: a retrosynthetic analysis tool utilizing automated retrosynthetic rule generation. Journal of chemical information and modeling, 49(3):593-602, 2009.
[7] Connor W Coley, William H Green, and Klavs F Jensen. Rdchiral: An rdkit wrapper for handling stereochemistry in retrosynthetic template extraction and application. Journal of chemical information and modeling, 59(6):2529-2537, 2019.
[8] Sara Szymkuć, Ewa P Gajewska, Tomasz Klucznik, Karol Molga, Piotr Dittwald, Michał Startek, Michał Bajczyk, and Bartosz A Grzybowski. Computer-assisted synthetic planning: the end of the beginning. Angewandte Chemie International Edition, 55(20):5904-5937, 2016.
[9] Zhengkai Tu and Connor W Coley. Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction. Journal of chemical information and modeling, 62(15):3503-3513, 2022.
[10] Kelong Mao, Xi Xiao, Tingyang Xu, Yu Rong, Junzhou Huang, and Peilin Zhao. Molecular graph enhanced transformer for retrosynthesis prediction. Neurocomputing, 457:193-202, 2021.
[11] Mohammadamin Tavakoli and Pierre Baldi. Continuous representation of molecules using graph variational autoencoder. arXiv preprint arXiv:2004.08152, 2020.
[12] Connor W Coley, Wengong Jin, Luke Rogers, Timothy F Jamison, Tommi S Jaakkola, William H Green, Regina Barzilay, and Klavs F Jensen. A graph-convolutional neural network model for the prediction of chemical reactivity. Chemical Science, 10(2):370–377, 2019.
[13] Connor W Coley, Regina Barzilay, Tommi S Jaakkola, William H Green, and Klavs F Jensen. Prediction of organic reaction outcomes using machine learning. ACS central science, 3(5):434-443, 2017.
[14] Marwin HS Segler, Mike Preuss, and Mark P Waller. Planning chemical syntheses with deep neural networks and symbolic ai. Nature, 555(7698):604-610, 2018.
[15] John Bradshaw, Matt J Kusner, Brooks Paige, Marwin HS Segler, and José Miguel Hernández-Lobato. A generative model for electron paths. arXiv preprint arXiv:1805.10970, 2018.
[16] Daniel Lowe. Chemical reactions from us patents (1976-sep2016).
[17] Mohammadamin Tavakoli, Aaron Mood, David Van Vranken, and Pierre Baldi. Quantum mechanics and machine learning synergies: Graph attention neural networks to predict chemical reactivity. arXiv preprint arXiv:2103.14536, 2021.
[18] Derek T Ahneman, Jesus G Estrada, Shishi Lin, Spencer D Dreher, and Abigail G Doyle. Predicting reaction performance in c–n cross-coupling using machine learning. Science, 360(6385):186-190, 2018.
[19] Damith Perera, Joseph W Tucker, Shalini Brahmbhatt, Christopher J Helal, Ashley Chong, William Farrell, Paul Richardson, and Neal W Sach. A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science, 359(6374):429-434, 2018.
[20] David Fooshee, Aaron Mood, Eugene Gutman, Mohammadamin Tavakoli, Gregor Urban, Frances Liu, Nancy Huynh, David Van Vranken, and Pierre Baldi. Deep learning for chemical reaction prediction. Molecular Systems Design & Engineering, 2018.
[21] Matthew A Kayala, Chloé-Agathe Azencott, Jonathan H Chen, and Pierre Baldi. Learning to predict chemical reactions. Journal of chemical information and modeling, 51(9):2209-2222, 2011.