Simonetti, M., Cannas, D. M., Simply-Baringo, X., Vitorica-Yrezabal, I. J. & Larrosa, I. Cyclometallated ruthenium catalyst allows late-stage directed arylation of prescription drugs. Nat. Chem. 10, 724–731 (2018).
Salazar, C. A. et al. Tailor-made quinones help high-turnover Pd catalysts for oxidative C-H arylation with O2. Science 370, 1454–1460 (2020).
DiRocco, D. A. et al. A multifunctional catalyst that stereoselectively assembles prodrugs. Science 356, 426–430 (2017).
Li, T. et al. Environment friendly, chemoenzymatic course of for manufacture of the Boceprevir bicyclic [3.1.0]proline intermediate primarily based on amine oxidase-catalyzed desymmetrization. J. Am. Chem. Soc. 134, 6467–6472 (2012).
Nielsen, L. P., Stevenson, C. P., Blackmond, D. G. & Jacobsen, E. N. Mechanistic investigation results in an artificial enchancment within the hydrolytic kinetic decision of terminal epoxides. J. Am. Chem. Soc. 126, 1360–1362 (2004).
van Dijk, L. et al. Mechanistic investigation of Rh(I)-catalysed uneven Suzuki–Miyaura coupling with racemic allyl halides. Nat. Catal. 4, 284–292 (2021).
Camasso, N. M. & Sanford, M. S. Design, synthesis, and carbon-heteroatom coupling reactions of organometallic nickel(IV) complexes. Science 347, 1218–1220 (2015).
Milo, A., Neel, A. J., Toste, F. D. & Sigman, M. S. A knowledge-intensive method to mechanistic elucidation utilized to chiral anion catalysis. Science 347, 737–743 (2015).
Butcher, T. W. et al. Desymmetrization of difluoromethylene teams by C-F bond activation. Nature 583, 548–553 (2020).
Cho, E. J. et al. The palladium-catalyzed trifluoromethylation of aryl chlorides. Science 328, 1679–1681 (2010).
Hutchinson, G., Alamillo-Ferrer, C. & Bures, J. Mechanistically guided design of an environment friendly and enantioselective aminocatalytic alpha-chlorination of aldehydes. J. Am. Chem. Soc. 143, 6805–6809 (2021).
Schreyer, L. et al. Confined acids catalyze uneven single aldolizations of acetaldehyde enolates. Science 362, 216–219 (2018).
Peters, B. Ok. et al. Scalable and secure artificial natural electroreduction impressed by Li-ion battery chemistry. Science 363, 838–845 (2019).
Michaelis, L. & Menten, M. L. Die Kinetik der Invertinwirkung. Biochem. Z. 49, 333–369 (1913).
Blackmond, D. G. Response progress kinetic evaluation: a robust methodology for mechanistic research of complicated catalytic reactions. Angew. Chem. Int. Ed. Engl. 44, 4302–4320 (2005).
Mathew, J. S. et al. Investigations of Pd-catalyzed ArX coupling reactions knowledgeable by response progress kinetic evaluation. J. Org. Chem. 71, 4711–4722 (2006).
Bures, J. A easy graphical methodology to find out the order in catalyst. Angew. Chem. Int. Ed. Engl. 55, 2028–2031 (2016).
Burés, J. Variable time normalization evaluation: basic graphical elucidation of response orders from focus profiles. Angew. Chem. Int. Ed. Engl. 55, 16084–16087 (2016).
Shi, Y., Prieto, P. L., Zepel, T., Grunert, S. & Hein, J. E. Automated experimentation powers information science in chemistry. Acc. Chem. Res. 54, 546–555 (2021).
Burger, B. et al. A cellular robotic chemist. Nature 583, 237–241 (2020).
Bedard, A. C. et al. Reconfigurable system for automated optimization of various chemical reactions. Science 361, 1220–1225 (2018).
Steiner, S. et al. Natural synthesis in a modular robotic system pushed by a chemical programming language. Science 363, eaav2211 (2019).
Clauset, A., Shalizi, C. R. & Newman, M. E. J. Energy-law distributions in empirical information. SIAM Rev. 51, 661–703 (2009).
Martinez-Carrion, A. et al. Kinetic therapies for catalyst activation and deactivation processes primarily based on variable time normalization evaluation. Angew. Chem. Int. Ed. Engl. 58, 10189–10193 (2019).
Bernacki, J. P. & Murphy, R. M. Mannequin discrimination and mechanistic interpretation of kinetic information in protein aggregation research. Biophys. J. 96, 2871–2887 (2009).
Pfluger, P. M. & Glorius, F. Molecular machine studying: the way forward for artificial chemistry? Angew. Chem. Int. Ed. Engl. 59, 18860–18865 (2020).
Segler, M. H. S., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610 (2018).
Raissi, M., Yazdani, A. & Karniadakis, G. E. Hidden fluid mechanics: studying velocity and strain fields from move visualizations. Science 367, 1026–1030 (2020).
Hermann, J., Schatzle, Z. & Noe, F. Deep-neural-network resolution of the digital Schrodinger equation. Nat. Chem. 12, 891–897 (2020).
Shields, B. J. et al. Bayesian response optimization as a device for chemical synthesis. Nature 590, 89–96 (2021).
Tunyasuvunakool, Ok. et al. Extremely correct protein construction prediction for the human proteome. Nature 596, 590–596 (2021).
Jumper, J. et al. Extremely correct protein construction prediction with AlphaFold. Nature 596, 583–589 (2021).
Hueffel, J. A. et al. Accelerated dinuclear palladium catalyst identification by way of unsupervised machine studying. Science 374, 1134–1140 (2021).
Haitao, X., Junjie, W. & Lu, L. In Proc. 1st Worldwide Convention on E-Enterprise Intelligence 303–309 (Atlantis Press, 2010).
Batista, G. E. A. P. A. et al. In Advances in Clever Information Evaluation 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. Professional Syst. Appl. 37, 3799–3809 (2010).
Alberton, A. L., Schwaab, M., Schmal, M. & Pinto, J. C. Experimental errors in kinetic assessments and its affect on the precision of estimated parameters. Half I—evaluation of first-order reactions. Chem. Eng. J. 155, 816–823 (2009).
Pacheco, H., Thiengo, F., Schmal, M. & Pinto, J. C. A household of kinetic distributions for interpretation of experimental fluctuations in kinetic issues. Chem. Eng. J. 332, 303–311 (2018).
Storer, A. C., Darlison, M. G. & Cornish-Bowden, A. The character of experimental error in enzyme kinetic measurments. Biochem. J 151, 361–367 (1975).
Valkó, É. & Turányi, T. In Lindner, E., Micheletti, A. & Nunes, C. (eds) Mathematical Modelling in Actual Life Issues. Arithmetic in Business https://doi.org/10.1007/978-3-030-50388-8_3 (2020).
Thiel, V., Wannowius, Ok. J., Wolff, C., Thiele, C. M. & Plenio, H. Ring-closing metathesis reactions: interpretation of conversion-time information. Chem. Eur. J. 19, 16403–16414 (2013).
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).
Knapp, S. M. M. et al. Mechanistic research of alkene isomerization catalyzed by CCC-pincer complexes of iridium. Organometallics 33, 473–484 (2014).
Stroek, W., Keilwerth, M., Pividori, D. M., Meyer, Ok. & Albrecht, M. An iron-mesoionic carbene complicated for catalytic intramolecular C-H amination using natural azides. J. Am. Chem. Soc. 143, 20157–20165 (2021).
Lehnherr, D. et al. Discovery of a photoinduced darkish catalytic cycle utilizing in situ LED-NMR spectroscopy. J. Am. Chem. Soc. 140, 13843–13853 (2018).
Ludwig, J. R., Zimmerman, P. M., Gianino, J. B. & Schindler, C. S. Iron(III)-catalysed carbonyl-olefin metathesis. Nature 533, 374–379 (2016).
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).
Janse van Rensburg, W., Steynberg, P. J., Meyer, W. H., Kirk, M. M. & Forman, G. S. DFT prediction and experimental commentary of substrate-induced catalyst decomposition in ruthenium-catalyzed olefin metathesis. J. Am. Chem. Soc. 126, 14332–14333 (2004).
van der Eide, E. F. & Piers, W. E. Mechanistic insights into the ruthenium-catalysed diene ring-closing metathesis response. Nat. Chem. 2, 571–576 (2010).