Reviews & Perspectives

Jorner, K.;  Tomberg, A.;  Bauer, C.;  Sköld, C.; Norrby, P.-O., Organic reactivity from mechanism to machine learning. Nature Reviews Chemistry 2021, 5 (4), 240-255.

Coley, C. W.;  Eyke, N. S.; Jensen, K. F., Autonomous Discovery in the Chemical Sciences Part I: Progress. Angewandte Chemie International Edition 2020, 59 (51), 22858-22893.

Coley, C. W.;  Eyke, N. S.; Jensen, K. F., Autonomous Discovery in the Chemical Sciences Part II: Outlook. Angewandte Chemie International Edition 2020, 59 (52), 23414-23436. 

de Almeida, A. F.; Moreira, R.; Rodrigues, T., Synthetic organic chemistry driven by artificial intelligence. Nature Reviews Chemistry 2019, 3 (10), 589-604. 

Santiago, C. B.; Guo, J.-Y.; Sigman, M. S., Predictive and mechanistic multivariate linear regression models for reaction development. Chemical Science 2018, 9 (9), 2398-2412.

Durand, D. J.; Fey, N., Computational Ligand Descriptors for Catalyst Design. Chemical Reviews 2019, 119 (11), 6561-6594.

Zahrt, A. F.; Athavale, S. V.; Denmark, S. E., Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future. Chemical Reviews 2020, 120 (3), 1620-1689.

Strieth-Kalthoff, F.; Sandfort, F.; Segler, M. H. S.; Glorius, F., Machine learning the ropes: principles, applications and directions in synthetic chemistry. Chemical Society Reviews 2020, 49 (17), 6154-6168. 

Kulik, H. J., Making machine learning a useful tool in the accelerated discovery of transition metal complexes. WIREs Computational Molecular Science 2020, 10 (1), e1439.

Schwaller, P.; Laino, T., Data-Driven Learning Systems for Chemical Reaction Prediction: An Analysis of Recent Approaches. In Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions, American Chemical Society: 2019; Vol. 1326, pp 61-79

Mahjour, B.;  Shen, Y.; Cernak, T., Ultrahigh-Throughput Experimentation for Information-Rich Chemical Synthesis. Accounts of Chemical Research 2021, 54 (10), 2337-2346.

Wodrich, M. D.;  Sawatlon, B.;  Busch, M.; Corminboeuf, C., The Genesis of Molecular Volcano Plots. Accounts of Chemical Research 2021, 54 (5), 1107-1117

dos Passos Gomes, G.;  Pollice, R.; Aspuru-Guzik, A., Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning. Trends in Chemistry 2021, 3 (2), 96-110. 

Szymkuć, S.; Gajewska, E. P.; Klucznik, T.; Molga, K.; Dittwald, P.; Startek, M.; Bajczyk, M.; Grzybowski, B. A., Computer-Assisted Synthetic Planning: The End of the Beginning. Angewandte Chemie International Edition 2016, 55 (20), 5904-5937. 

Foscato, M.; Jensen, V. R., Automated in Silico Design of Homogeneous Catalysts. ACS Catalysis 2020, 10 (3), 2354-2377. 

Dreher, S. D.; Krska, S. W., Chemistry Informer Libraries: Conception, Early Experience, and Role in the Future of Cheminformatics. Accounts of Chemical Research 2021, 54 (7), 1586-1596.

Wilbraham, L.;  Mehr, S. H. M.; Cronin, L., Digitizing Chemistry Using the Chemical Processing Unit: From Synthesis to Discovery. Accounts of Chemical Research 2021, 54 (2), 253-262 

Shi, Y.;  Prieto, P. L.;  Zepel, T.;  Grunert, S.; Hein, J. E., Automated Experimentation Powers Data Science in Chemistry. Accounts of Chemical Research 2021, 54 (3), 546-555.

Gallegos, L. C.;  Luchini, G.;  St. John, P. C.;  Kim, S.; Paton, R. S., Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties. Accounts of Chemical Research 2021, 54 (4), 827-836.

Walters, W. P.; Barzilay, R., Applications of Deep Learning in Molecule Generation and Molecular Property Prediction. Accounts of Chemical Research 2021, 54 (2), 263-270

Żurański, A. M.;  Martinez Alvarado, J. I.;  Shields, B. J.; Doyle, A. G., Predicting Reaction Yields via Supervised Learning. Accounts of Chemical Research 2021, 54 (8), 1856-1865.

Ley, S. V.; Fitzpatrick, D. E.; Ingham, R. J.; Myers, R. M., Organic Synthesis: March of the Machines. Angewandte Chemie International Edition 2015, 54 (11), 3449-3464.

Mazurenko, S.; Prokop, Z.; Damborsky, J., Machine Learning in Enzyme Engineering. ACS Catalysis 2020, 10 (2), 1210-1223. 

Yang, K. K.; Wu, Z.; Arnold, F. H., Machine-learning-guided directed evolution for protein engineering. Nature Methods 2019, 16 (8), 687-694.

Ma, S.; Liu, Z.-P., Machine Learning for Atomic Simulation and Activity Prediction in Heterogeneous Catalysis: Current Status and Future. ACS Catalysis 2020, 10 (22), 13213-13226.

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