The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
A robotic platform for flow synthesis of organic compounds informed by AI planning
Abstract
Pairing prediction and robotic synthesis: Progress in automated synthesis of organic compounds has been proceeding along parallel tracks. One goal is algorithmic prediction of viable routes to a desired compound; the other is implementation of a known reaction sequence on a platform that needs little to no human intervention. Coley et al. now report preliminary integration of these two protocols. They paired a retrosynthesis prediction algorithm with a robotically reconfigurable flow apparatus. Human intervention was still required to supplement the predictor with practical considerations such as solvent choice and precise stoichiometry, although predictions should improve as accessible data accumulate for training. Science , this issue p. eaax1566
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Extended-Connectivity Fingerprints
Rogers, D., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,524
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
Planning chemical syntheses with deep neural networks and symbolic AI
Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847
Organic synthesis provides opportunities to transform drug discovery
Blakemore, D. C., Castro, L., Churcher, I., Rees, D. C., Thomas, A. W., Wilson, D. M., & Wood, A. (2018) · Nature Chemistry · doi:10.1038/s41557-018-0021-z · Cited by 1,485
Continuous‐Flow Technology—A Tool for the Safe Manufacturing of Active Pharmaceutical Ingredients
Gutmann, B., Cantillo, D., & Kappe, C. O. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201409318 · Cited by 1,469
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2017) · arXiv · doi:10.48550/arxiv.1712.01815 · Cited by 1,102
Deciding Whether To Go with the Flow: Evaluating the Merits of Flow Reactors for Synthesis
Hartman, R. L., McMullen, J. P., & Jensen, K. F. (2011) · Angewandte Chemie International Edition · doi:10.1002/anie.201004637 · Cited by 999
Self-citationOn-demand continuous-flow production of pharmaceuticals in a compact, reconfigurable system
Adamo, A., Beingessner, R. L., Behnam, M., Chen, J., Jamison, T. F., Jensen, K. F., Monbaliu, J.-C. M., Myerson, A. S., Revalor, E. M., Snead, D. R., et al. (2016) · Science · doi:10.1126/science.aaf1337 · Cited by 941
Self-citationSchneider, G. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.232 · Cited by 910 · cited 1×
“Synthesis is often a bottleneck in small-molecule drug discovery ( 1 ), where design – synthesize – test iter- ationshavecycletimesontheorderofweeksandwherethescopeofacompoundlibrarysynthe-sis can determine the accuracy of an empirical structure – activity relationship model ( 2 ).”
BackgroundAccelerating the discovery of materials for clean energy in the era of smart automation
Tabor, D. P., Roch, L. M., Saikin, S. K., Kreisbeck, C., Sheberla, D., Montoya, J. H., Dwaraknath, S., Aykol, M., Ortiz, C., Tribukait, H., et al. (2018) · Nature Reviews Materials · doi:10.1038/s41578-018-0005-z · Cited by 776
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Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726
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Boiko, D. A., MacKnight, R., Kline, B., & Gomes, G. (2023) · Nature · doi:10.1038/s41586-023-06792-0 · Cited by 1,050
An autonomous laboratory for the accelerated synthesis of inorganic materials
Szymanski, N. J., Rendy, B., Fei, Y., Kumar, R. E., He, T., Milsted, D., McDermott, M. J., Gallant, M., Cubuk, E. D., Merchant, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06734-w · Cited by 1,041
“When these precursors do not cover all the elements in the target, we use a masked precursor completion model 12 to account for such missing precursors.”
Augmenting large language models with chemistry tools
M. Bran, A., Cox, S., Schilter, O., Baldassari, C., White, A. D., & Schwaller, P. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00832-8 · Cited by 794
Metadata
- Authors
- 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., Hicklin, R. W., Plehiers, P. P., Byington, J., Piotti, J. S., Green, W. H., Hart, A. J., Jamison, T. F., & Jensen, K. F.
- Venue
- Science
- Published
- 2019-08-09
- Volume
- 365
- Issue
- 6453
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2968071222
- Cited by
- 1,052
- References
- 41
- FWCI
- 34.83
- Type
- journal article
- Language
- en
- Open access
- closed
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- 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., Hicklin, R. W., Plehiers, P. P., Byington, J., Piotti, J. S., Green, W. H., Hart, A. J., Jamison, T. F., & Jensen, K. F. (2019). A robotic platform for flow synthesis of organic compounds informed by AI planning. Science. https://doi.org/10.1126/science.aax1566
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