- Young, T. A.; Silcock, J. J.; Sterling, A. J.; Duarte, F., autodE: Automated Calculation of Reaction Energy Profiles— Application to Organic and Organometallic Reactions. Angewandte Chemie International Edition n/a (n/a).
- Dhayalan, V.; Gadekar, S. C.; Alassad, Z.; Milo, A., Unravelling mechanistic features of organocatalysis with in situ modifications at the secondary sphere. Nature Chemistry 2019, 11 (6), 543-551.
- Zahrt, A. F.; Henle, J. J.; Rose, B. T.; Wang, Y.; Darrow, W. T.; Denmark, S. E., Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning. Science 2019, 363 (6424), eaau5631.
- Henle, J. J.; Zahrt, A. F.; Rose, B. T.; Darrow, W. T.; Wang, Y.; Denmark, S. E., Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis. Journal of the American Chemical Society 2020, 142 (26), 11578-11592.
- Tomberg, A.; Johansson, M. J.; Norrby, P.-O., A Predictive Tool for Electrophilic Aromatic Substitutions Using Machine Learning. The Journal of Organic Chemistry 2019, 84 (8), 4695-4703.
- Jorner, K.; Brinck, T.; Norrby, P.-O.; Buttar, D., Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies. Chemical Science 2021.
- Ahneman, D. T.; Estrada, J. G.; Lin, S.; Dreher, S. D.; Doyle, A. G., Predicting reaction performance in C–N cross-coupling using machine learning. Science 2018, 360 (6385), 186-190.
- Burai Patrascu, M.; Pottel, J.; Pinus, S.; Bezanson, M.; Norrby, P.-O.; Moitessier, N., From desktop to benchtop with automated computational workflows for computer-aided design in asymmetric catalysis. Nature Catalysis 2020, 3 (7), 574-584.
- Zhou, Z.; Li, X.; Zare, R. N., Optimizing Chemical Reactions with Deep Reinforcement Learning. ACS Central Science 2017, 3 (12), 1337-1344.
- Fu, Z.; Li, X.; Wang, Z.; Li, Z.; Liu, X.; Wu, X.; Zhao, J.; Ding, X.; Wan, X.; Zhong, F.; Wang, D.; Luo, X.; Chen, K.; Liu, H.; Wang, J.; Jiang, H.; Zheng, M., Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki–Miyaura cross-coupling reaction. Organic Chemistry Frontiers 2020, 7 (16), 2269-2277.
- Kondo, M.; Wathsala, H. D. P.; Sako, M.; Hanatani, Y.; Ishikawa, K.; Hara, S.; Takaai, T.; Washio, T.; Takizawa, S.; Sasai, H., Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence. Chemical Communications 2020, 56 (8), 1259-1262.
- Perera, D.; Tucker, J. W.; Brahmbhatt, S.; Helal, C. J.; Chong, A.; Farrell, W.; Richardson, P.; Sach, N. W., A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science 2018, 359 (6374), 429-434.
- Nielsen, M. K.; Ahneman, D. T.; Riera, O.; Doyle, A. G., Deoxyfluorination with Sulfonyl Fluorides: Navigating Reaction Space with Machine Learning. Journal of the American Chemical Society 2018, 140 (15), 5004-5008.
- Maley, Steven M.; Kwon, D.-H.; Rollins, N.; Stanley, J. C.; Sydora, O. L.; Bischof, S. M.; Ess, D. H., Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization. Chemical Science 2020, 11 (35), 9665-9674.
- Friederich, P.; dos Passos Gomes, G.; De Bin, R.; Aspuru-Guzik, A.; Balcells, D., Machine learning dihydrogen activation in the chemical space surrounding Vaska’s complex. Chemical Science 2020, 11 (18), 4584-4601.
- See, X. Y.; Wen, X.; Wheeler, T. A.; Klein, C. K.; Goodpaster, J. D.; Reiner, B. R.; Tonks, I. A., Iterative Supervised Principal Component Analysis Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis. ACS Catalysis 2020, 10 (22), 13504-13517.
- Wu, K.; Doyle, A. G., Parameterization of phosphine ligands demonstrates enhancement of nickel catalysis via remote steric effects. Nature Chemistry 2017, 9 (8), 779-784.
- Wu, Z.; Kan, S. B. J.; Lewis, R. D.; Wittmann, B. J.; Arnold, F. H., Machine learning-assisted directed protein evolution with combinatorial libraries. Proceedings of the National Academy of Sciences 2019, 116 (18), 8852-8858.