Organic reaction mechanism classification using machine learning – Nature.com

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Simonetti, M., Cannas, D. M., Just-Baringo, X., Vitorica-Yrezabal, I. J. & Larrosa, I. Cyclometallated ruthenium catalyst enables late-stage directed arylation of pharmaceuticals. Nat. Chem. 10, 724–731 (2018).Article 
CAS 

Google Scholar 
Salazar, C. A. et al. Tailored quinones support high-turnover Pd catalysts for oxidative C-H arylation with O2. Science 370, 1454–1460 (2020).Article 
CAS 

Google Scholar 
DiRocco, D. A. et al. A multifunctional catalyst that stereoselectively assembles prodrugs. Science 356, 426–430 (2017).Article 
CAS 

Google Scholar 
Li, T. et al. Efficient, chemoenzymatic process for manufacture of the Boceprevir bicyclic [3.1.0]proline intermediate based on amine oxidase-catalyzed desymmetrization. J. Am. Chem. Soc. 134, 6467–6472 (2012).Article 
CAS 

Google Scholar 
Nielsen, L. P., Stevenson, C. P., Blackmond, D. G. & Jacobsen, E. N. Mechanistic investigation leads to a synthetic improvement in the hydrolytic kinetic resolution of terminal epoxides. J. Am. Chem. Soc. 126, 1360–1362 (2004).Article 
CAS 

Google Scholar 
van Dijk, L. et al. Mechanistic investigation of Rh(I)-catalysed asymmetric Suzuki–Miyaura coupling with racemic allyl halides. Nat. Catal. 4, 284–292 (2021).Article 

Google Scholar 
Camasso, N. M. & Sanford, M. S. Design, synthesis, and carbon-heteroatom coupling reactions of organometallic nickel(IV) complexes. Science 347, 1218–1220 (2015).Article 
CAS 

Google Scholar 
Milo, A., Neel, A. J., Toste, F. D. & Sigman, M. S. A data-intensive approach to mechanistic elucidation applied to chiral anion catalysis. Science 347, 737–743 (2015).Article 
CAS 

Google Scholar 
Butcher, T. W. et al. Desymmetrization of difluoromethylene groups by C-F bond activation. Nature 583, 548–553 (2020).Article 
CAS 

Google Scholar 
Cho, E. J. et al. The palladium-catalyzed trifluoromethylation of aryl chlorides. Science 328, 1679–1681 (2010).Article 
CAS 

Google Scholar 
Hutchinson, G., Alamillo-Ferrer, C. & Bures, J. Mechanistically guided design of an efficient and enantioselective aminocatalytic alpha-chlorination of aldehydes. J. Am. Chem. Soc. 143, 6805–6809 (2021).Article 
CAS 

Google Scholar 
Schreyer, L. et al. Confined acids catalyze asymmetric single aldolizations of acetaldehyde enolates. Science 362, 216–219 (2018).Article 
CAS 

Google Scholar 
Peters, B. K. et al. Scalable and safe synthetic organic electroreduction inspired by Li-ion battery chemistry. Science 363, 838–845 (2019).Article 
CAS 

Google Scholar 
Michaelis, L. & Menten, M. L. Die Kinetik der Invertinwirkung. Biochem. Z. 49, 333–369 (1913).CAS 

Google Scholar 
Blackmond, D. G. Reaction progress kinetic analysis: a powerful methodology for mechanistic studies of complex catalytic reactions. Angew. Chem. Int. Ed. Engl. 44, 4302–4320 (2005).Article 
CAS 

Google Scholar 
Mathew, J. S. et al. Investigations of Pd-catalyzed ArX coupling reactions informed by reaction progress kinetic analysis. J. Org. Chem. 71, 4711–4722 (2006).Article 
CAS 

Google Scholar 
Bures, J. A simple graphical method to determine the order in catalyst. Angew. Chem. Int. Ed. Engl. 55, 2028–2031 (2016).Article 
CAS 

Google Scholar 
Burés, J. Variable time normalization analysis: general graphical elucidation of reaction orders from concentration profiles. Angew. Chem. Int. Ed. Engl. 55, 16084–16087 (2016).Article 

Google Scholar 
Shi, Y., Prieto, P. L., Zepel, T., Grunert, S. & Hein, J. E. Automated experimentation powers data science in chemistry. Acc. Chem. Res. 54, 546–555 (2021).Article 
CAS 

Google Scholar 
Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).Article 
CAS 

Google Scholar 
Bedard, A. C. et al. Reconfigurable system for automated optimization of diverse chemical reactions. Science 361, 1220–1225 (2018).Article 
CAS 

Google Scholar 
Steiner, S. et al. Organic synthesis in a modular robotic system driven by a chemical programming language. Science 363, eaav2211 (2019).Article 
CAS 

Google Scholar 
Clauset, A., Shalizi, C. R. & Newman, M. E. J. Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009).Article 
MATH 

Google Scholar 
Martinez-Carrion, A. et al. Kinetic treatments for catalyst activation and deactivation processes based on variable time normalization analysis. Angew. Chem. Int. Ed. Engl. 58, 10189–10193 (2019).Article 
CAS 

Google Scholar 
Bernacki, J. P. & Murphy, R. M. Model discrimination and mechanistic interpretation of kinetic data in protein aggregation studies. Biophys. J. 96, 2871–2887 (2009).Article 
CAS 

Google Scholar 
Pfluger, P. M. & Glorius, F. Molecular machine learning: the future of synthetic chemistry? Angew. Chem. Int. Ed. Engl. 59, 18860–18865 (2020).Article 

Google Scholar 
Segler, M. H. S., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610 (2018).Article 
CAS 

Google Scholar 
Raissi, M., Yazdani, A. & Karniadakis, G. E. Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations. Science 367, 1026–1030 (2020).Article 
CAS 
MATH 

Google Scholar 
Hermann, J., Schatzle, Z. & Noe, F. Deep-neural-network solution of the electronic Schrodinger equation. Nat. Chem. 12, 891–897 (2020).Article 
CAS 

Google Scholar 
Shields, B. J. et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature 590, 89–96 (2021).Article 
CAS 

Google Scholar 
Tunyasuvunakool, K. et al. Highly accurate protein structure prediction for the human proteome. Nature 596, 590–596 (2021).Article 
CAS 

Google Scholar 
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).Article 
CAS 

Google Scholar 
Hueffel, J. A. et al. Accelerated dinuclear palladium catalyst identification through unsupervised machine learning. Science 374, 1134–1140 (2021).Article 
CAS 

Google Scholar 
Haitao, X., Junjie, W. & Lu, L. In Proc. 1st International Conference on E-Business Intelligence 303–309 (Atlantis Press, 2010).Batista, G. E. A. P. A. et al. In Advances in Intelligent Data Analysis VI (eds Fazel Famili, A. et al.) 24–35 (Springer, 2005).Wei, J.-M., Yuan, X.-J., Hu, Q.-H. & Wang, S.-Q. A novel measure for evaluating classifiers. Expert Syst. Appl. 37, 3799–3809 (2010).Article 

Google Scholar 
Alberton, A. L., Schwaab, M., Schmal, M. & Pinto, J. C. Experimental errors in kinetic tests and its influence on the precision of estimated parameters. Part I—analysis of first-order reactions. Chem. Eng. J. 155, 816–823 (2009).Article 
CAS 

Google Scholar 
Pacheco, H., Thiengo, F., Schmal, M. & Pinto, J. C. A family of kinetic distributions for interpretation of experimental fluctuations in kinetic problems. Chem. Eng. J. 332, 303–311 (2018).Article 
CAS 

Google Scholar 
Storer, A. C., Darlison, M. G. & Cornish-Bowden, A. The nature of experimental error in enzyme kinetic measurments. Biochem. J 151, 361–367 (1975).Article 
CAS 

Google Scholar 
Valkó, É. & Turányi, T. In Lindner, E., Micheletti, A. & Nunes, C. (eds) Mathematical Modelling in Real Life Problems. Mathematics in Industry https://doi.org/10.1007/978-3-030-50388-8_3 (2020).Thiel, V., Wannowius, K. J., Wolff, C., Thiele, C. M. & Plenio, H. Ring-closing metathesis reactions: interpretation of conversion-time data. Chem. Eur. J. 19, 16403–16414 (2013).Article 
CAS 

Google Scholar 
Joannou, M. V., Hoyt, J. M. & Chirik, P. J. Investigations into the mechanism of inter- and intramolecular iron-catalyzed [2 + 2] cycloaddition of alkenes. J. Am. Chem. Soc. 142, 5314–5330 (2020).Article 
CAS 

Google Scholar 
Knapp, S. M. M. et al. Mechanistic studies of alkene isomerization catalyzed by CCC-pincer complexes of iridium. Organometallics 33, 473–484 (2014).Article 
CAS 

Google Scholar 
Stroek, W., Keilwerth, M., Pividori, D. M., Meyer, K. & Albrecht, M. An iron-mesoionic carbene complex for catalytic intramolecular C-H amination utilizing organic azides. J. Am. Chem. Soc. 143, 20157–20165 (2021).Article 
CAS 

Google Scholar 
Lehnherr, D. et al. Discovery of a photoinduced dark catalytic cycle using in situ LED-NMR spectroscopy. J. Am. Chem. Soc. 140, 13843–13853 (2018).Article 
CAS 

Google Scholar 
Ludwig, J. R., Zimmerman, P. M., Gianino, J. B. & Schindler, C. S. Iron(III)-catalysed carbonyl-olefin metathesis. Nature 533, 374–379 (2016).Article 
CAS 

Google Scholar 
Albright, H. et al. Catalytic carbonyl-olefin metathesis of aliphatic ketones: iron(III) homo-dimers as Lewis acidic superelectrophiles. J. Am. Chem. Soc. 141, 1690–1700 (2019).Article 
CAS 

Google Scholar 
Janse van Rensburg, W., Steynberg, P. J., Meyer, W. H., Kirk, M. M. & Forman, G. S. DFT prediction and experimental observation of substrate-induced catalyst decomposition in ruthenium-catalyzed olefin metathesis. J. Am. Chem. Soc. 126, 14332–14333 (2004).Article 

Google Scholar 
van der Eide, E. F. & Piers, W. E. Mechanistic insights into the ruthenium-catalysed diene ring-closing metathesis reaction. Nat. Chem. 2, 571–576 (2010).Article 

Google Scholar 

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