The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow
Abstract
A reaction screen in flowing solvent: Chemists charged with manufacturing pharmaceuticals have recently been exploring the efficiency advantages of continuous flow techniques. Perera et al. now show that a flow apparatus can also accelerate reaction optimization earlier in the drug discovery process. They modified a high-performance liquid chromatography system to screen a wide variety of solvent, ligand, and base combinations to optimize carbon-carbon bond formation. Injecting stock solution aliquots of the catalyst and reactants into a carrier solvent stream let the authors vary the main solvent efficiently and scale up the optimal conditions for product isolation. Science , this issue p. 429
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Cites
Innovation in the pharmaceutical industry: New estimates of R&D costs
DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016) · Journal of Health Economics · doi:10.1016/j.jhealeco.2016.01.012 · Cited by 3,359
The Medicinal Chemist’s Toolbox: An Analysis of Reactions Used in the Pursuit of Drug Candidates
Roughley, S. D., & Jordan, A. M. (2011) · Journal of Medicinal Chemistry · doi:10.1021/jm200187y · Cited by 2,725
The Hitchhiker’s Guide to Flow Chemistry
Plutschack, M. B., Pieber, B., Gilmore, K., & Seeberger, P. H. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.7b00183 · Cited by 1,995
Brown, D. G., & Boström, J. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01409 · Cited by 1,964
Magano, J., & Dunetz, J. R. (2011) · Chemical Reviews · doi:10.1021/cr100346g · Cited by 1,860
Catalysts for Suzuki−Miyaura Coupling Processes: Scope and Studies of the Effect of Ligand Structure
Barder, T. E., Walker, S. D., Martinelli, J. R., & Buchwald, S. L. (2005) · Journal of the American Chemical Society · doi:10.1021/ja042491j · Cited by 1,796
Multiphase microfluidics: from flow characteristics to chemical and materials synthesis
Günther, A., & Jensen, K. F. (2006) · Lab Chip · doi:10.1039/b609851g · Cited by 983
Nanomole-scale high-throughput chemistry for the synthesis of complex molecules
Buitrago Santanilla, A., Regalado, E. L., Pereira, T., Shevlin, M., Bateman, K., Campeau, L.-C., Schneeweis, J., Berritt, S., Shi, Z.-C., Nantermet, P., et al. (2015) · Science · doi:10.1126/science.1259203 · Cited by 603
Wide Bite Angle Diphosphines: Xantphos Ligands in Transition Metal Complexes and Catalysis
Kamer, P. C. J., van Leeuwen, P. W. N. M., & Reek, J. N. H. (2001) · Accounts of Chemical Research · doi:10.1021/ar000060+ · Cited by 551
Micromixing of Miscible Liquids in Segmented Gas−Liquid Flow
Günther, A., Jhunjhunwala, M., Thalmann, M., Schmidt, M. A., & Jensen, K. F. (2005) · Langmuir · doi:10.1021/la0482406 · Cited by 438
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A robotic platform for flow synthesis of organic compounds informed by AI planning
Coley, C. W., Thomas, D. A., Lummiss, J. A. M., Jaworski, J. N., Breen, C. P., Schultz, V., Hart, T., Fishman, J. S., Rogers, L., Gao, H., et al. (2019) · Science · doi:10.1126/science.aax1566 · Cited by 1,052
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The importance of synthetic chemistry in the pharmaceutical industry
Campos, K. R., Coleman, P. J., Alvarez, J. C., Dreher, S. D., Garbaccio, R. M., Terrett, N. K., Tillyer, R. D., Truppo, M. D., & Parmee, E. R. (2019) · Science · doi:10.1126/science.aat0805 · Cited by 668
Metadata
- Authors
- Perera, D., Tucker, J. W., Brahmbhatt, S., Helal, C. J., Chong, A., Farrell, W., Richardson, P., & Sach, N. W.
- Venue
- Science
- Published
- 2018-01-26
- Volume
- 359
- Issue
- 6374
- Pages
- 429-434
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2784918212
- Cited by
- 465
- References
- 24
- FWCI
- 16.52
- Type
- journal article
- Language
- en
- Open access
- closed
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- Perera, D., Tucker, J. W., Brahmbhatt, S., Helal, C. J., Chong, A., Farrell, W., Richardson, P., & Sach, N. W. (2018). A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science. https://doi.org/10.1126/science.aap9112
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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