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A universal system for digitization and automatic execution of the chemical synthesis literature
Open accessAbstract
Paper in, product out: A typical chemist running a known reaction will start by finding the method described in a published paper. Mehr et al. report a software platform that uses natural language processing to translate the organic chemistry literature directly into editable code, which in turn can be compiled to drive automated synthesis of the compound in the laboratory. The synthesis procedure is intended to be universally applicable to robotic systems operating in a batch reaction architecture. The full process is demonstrated for synthesis of an analgesic as well as common oxidizing and fluorinating agents. Science , this issue p. 101
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Cited by
Self-Driving Laboratories for Chemistry and Materials Science
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab
Seifrid, M., Pollice, R., Aguilar-Granda, A., Morgan Chan, Z., Hotta, K., Ser, C. T., Vestfrid, J., Wu, T. C., & Aspuru-Guzik, A. (2022) · Accounts of Chemical Research · doi:10.1021/acs.accounts.2c00220 · Cited by 294
A Multiagent-Driven Robotic AI Chemist Enabling Autonomous Chemical Research On Demand
Song, T., Luo, M., Zhang, X., Chen, L., Huang, Y., Cao, J., Zhu, Q., Liu, D., Zhang, B., Zou, G., et al. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.4c17738 · Cited by 171
Applications of natural language processing and large language models in materials discovery
Jiang, X., Wang, W., Tian, S., Wang, H., Lookman, T., & Su, Y. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01554-0 · Cited by 145
Large language models for chemistry robotics
Yoshikawa, N., Skreta, M., Darvish, K., Arellano-Rubach, S., Ji, Z., Bjørn Kristensen, L., Li, A. Z., Zhao, Y., Xu, H., Kuramshin, A., et al. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10136-2 · Cited by 112
Metadata
- Authors
- Mehr, S. H. M., Craven, M., Leonov, A. I., Keenan, G., & Cronin, L.
- Venue
- Science
- Published
- 2020-10-02
- Volume
- 370
- Issue
- 6512
- Pages
- 101-108
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W3091684735
- Cited by
- 270
- References
- 3
- FWCI
- 19.57
- Type
- journal article
- Language
- en
- Open access
- green
- Funders
- Defense Sciences Office, DARPA (AMD)
- H2020 European Research Council (project 670467 SMART-POM)
- Engineering and Physical Sciences Research Council (Grant Nos EP/H024107/1, EP/J00135X/1, EP/J015156/1, EP/K021966/1, EP/K023004/1, EP/L023652/1)
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- Mehr, S. H. M., Craven, M., Leonov, A. I., Keenan, G., & Cronin, L. (2020). A universal system for digitization and automatic execution of the chemical synthesis literature. Science. https://doi.org/10.1126/science.abc2986
Data sources
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
