van Gemmeren, M. & List, B. How and why crowd reviewing works. Synlett 32, 885–891 (2021).

Article  Google Scholar 

Zhang, Y., Chen, S., Liu, Y. & Wang, Q. Route evaluation and Ritter reaction based synthesis of oxazoline acaricide candidates FET-II-L and NK-12. Org. Process Res. Dev. 24, 216–227 (2020).

Article  CAS  Google Scholar 

Boda, K., Seidel, T. & Gasteiger, J. Structure and reaction based evaluation of synthetic accessibility. J. Comput. Aided Mol. Des. 21, 311–325 (2007).

Article  CAS  PubMed  Google Scholar 

Laird, T. Editorial reproducibility of results. Org. Process Res. Dev. 18, 921 (2014).

Article  Google Scholar 

Wethman, R. et al. An under-appreciated source of reproducibility issues in cross-coupling: solid-state decomposition of primary sodium alkoxides in air. ACS Catal. 11, 502–508 (2021).

Article  CAS  Google Scholar 

Kirklin, W. A. & Becker, W. W. Standardization in chemical industry. Anal. Chem. 23, 1556–1558 (1951).

Article  CAS  Google Scholar 

Schnitzer, T. et al. How subtle changes can make a difference: reproducibility in complex supramolecular systems. Angew. Chem. Int. Ed. 134, e202206738 (2022).

Article  Google Scholar 

Tiokhin, L. et al. Honest signaling in academic publishing. PLoS ONE 16, e0246675 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cook, C. Publication fraud, dishonesty and deceit. J. Man. Manip. Ther. 20, 57–58 (2012).

Article  PubMed  PubMed Central  Google Scholar 

Boström, J., Brown, D. G., Young, R. J. & Keserü, G. M. Expanding the medicinal chemistry synthetic toolbox. Nat. Rev. Drug Discov. 17, 709–727 (2018).

Article  PubMed  Google Scholar 

Schultz, D. & Campeau, L.-C. Harder, better, faster. Nat. Chem. 12, 661–664 (2020).

Article  PubMed  Google Scholar 

Bergman, R. G. & Danheiser, R. L. Reproducibility in chemical research. Angew. Chem. Int. Ed. 55, 12548–12549 (2016).

Article  CAS  Google Scholar 

Scott, S. L., Gunnoe, T. B., Fornasiero, P. & Crudden, C. M. To err is human; to reproduce takes time. ACS Catal. 12, 3644–3650 (2022).

Article  CAS  Google Scholar 

Baker, M. 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016).

Article  CAS  PubMed  Google Scholar 

Kozlowski, M. C. On the topic of substrate scope. Org. Lett. 24, 7247–7249 (2022).

Article  CAS  PubMed  Google Scholar 

Kozlov, M. Revealed: the millions of dollars in time wasted making papers fit journal guidelines. Nature https://doi.org/10.1038/d41586-023-01846-9 (2023).

Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

Article  PubMed  PubMed Central  Google Scholar 

Kearnes, S. M. et al. The Open Reaction database. J. Am. Chem. Soc. 143, 18820–18826 (2021).

Article  CAS  PubMed  Google Scholar 

Tremouilhac, P. et al. The repository Chemotion: infrastructure for sustainable research in chemistry. Angew. Chem. Int. Ed. 59, 22771–22778 (2020).

Article  CAS  Google Scholar 

Crystal‐Ornelas, R. et al. A guide to using GitHub for developing and versioning data standards and reporting formats. Earth Space Sci. 8, e2021EA001797 (2021).

Article  Google Scholar 

Strieth-Kalthoff, F. et al. Machine learning for chemical reactivity: the importance of failed experiments. Angew. Chem. Int. Ed. 61, e202204647 (2022).

Article  CAS  Google Scholar 

Schleinitz, J. et al. Machine learning yield prediction from NiCOlit, a small-size literature data set of Nickel catalyzed C-O couplings. J. Am. Chem. Soc. 144, 14722–14730 (2022).

Article  CAS  PubMed  Google Scholar 

Svejstrup, T. D. et al. Effects of light intensity and reaction temperature on photoreactions in commercial photoreactors. ChemPhotoChem 5, 808–814 (2021).

Article  CAS  Google Scholar 

Wills, A. G., Poole, D. L., Alder, C. M. & Reid, M. A mechanistic and cautionary case study on the use of alternating potential in electrochemical reactions. ChemElectroChem 7, 2771–2776 (2020).

Article  CAS  Google Scholar 

Kingston, C. et al. A survival guide for the ‘Electro-curious’. Acc. Chem. Res. 53, 72–83 (2020).

Article  CAS  PubMed  Google Scholar 

Leech, M. C. & Lam, K. A practical guide to electrosynthesis. Nat. Rev. Chem. 6, 275–286 (2022).

Article  PubMed  Google Scholar 

Beil, S. B., Pollok, D. & Waldvogel, S. R. Reproducibility in electroorganic synthesis—myths and misunderstandings. Angew. Chem. Int. Ed. 60, 14750–14759 (2021).

Article  CAS  Google Scholar 

Hone, C. A. & Kappe, C. O. Towards the standardization of flow chemistry protocols for organic reactions. Chem. Methods 1, 454–467 (2021).

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  PubMed  Google Scholar 

Collins, K. D. & Glorius, F. A robustness screen for the rapid assessment of chemical reactions. Nat. Chem. 5, 597–601 (2013).

Article  CAS  PubMed  Google Scholar 

Dreher, S. D. & Krska, S. W. Chemistry informer libraries: conception, early experience and role in the future of cheminformatics. Acc. Chem. Res. 54, 1586–1596 (2021).

Article  CAS  PubMed  Google Scholar 

Kutchukian, P. S. et al. Chemistry informer libraries: a chemoinformatics enabled approach to evaluate and advance synthetic methods. Chem. Sci. 7, 2604–2613 (2016).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bess, E. N., Bischoff, A. J. & Sigman, M. S. Designer substrate library for quantitative, predictive modeling of reaction performance. Proc. Natl Acad. Sci. USA 111, 14698–14703 (2014).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kariofillis, S. K. et al. Using data science to guide aryl bromide substrate scope analysis in a Ni/photoredox-catalyzed cross-coupling with acetals as alcohol-derived radical sources. J. Am. Chem. Soc. 144, 1045–1055 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Stevens, J. M. et al. Advancing base metal catalysis through data science: insight and predictive models for Ni-catalyzed borylation through supervised machine learning. Organometallics 41, 1847–1864 (2022).

Article  CAS  Google Scholar 

Gensch, T. et al. Design and application of a screening set for monophosphine ligands in cross-coupling. ACS Catal. 12, 7773–7780 (2022).

Article  CAS  Google Scholar 

Calvo-Flores, F. G. Sustainable chemistry metrics. Chem. Sus. Chem 2, 905–919 (2009).

Article  CAS  Google Scholar 

Constable, D. J. C., Curzons, A. D. & Cunningham, V. L. Metrics to ‘green’ chemistry—which are the best? Green Chem. 4, 521–527 (2002).

Article  CAS  Google Scholar 

Curzons, A. D., Mortimer, D. N., Constable, D. J. C. & Cunningham, V. L. So you think your process is green, how do you know?—Using principles of sustainability to determine what is green—a corporate perspective. Green Chem. 3, 1–6 (2001).

Article  CAS  Google Scholar 

van Aken, K., Strekowski, L. & Patiny, L. EcoScale, a semi-quantitative tool to select an organic preparation based on economical and ecological parameters. Beilstein J. Org. Chem. 2, 3 (2006).

PubMed  PubMed Central  Google Scholar 

Anastas, P. T. & Lankey, R. L. Life cycle assessment and green chemistry: the yin and yang of industrial ecology. Green Chem. 2, 289–295 (2000).

Article  CAS  Google Scholar 

Sheldon, R. A. Metrics of green chemistry and sustainability: past, present and future. ACS Sustain. Chem. Eng. 6, 32–48 (2018).

Article  CAS  Google Scholar 

Anastas, P. T. & Warner, J. C. Green Chemistry. Theory and Practice 1st edn (Oxford Univ. Press, 1998).

Trost, B. M. The atom economy—a search for synthetic efficiency. Science 254, 1471–1477 (1991).

Article  CAS  PubMed  Google Scholar 

Trost, B. M. On inventing reactions for atom economy. Acc. Chem. Res. 35, 695–705 (2002).