The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models
Open accessAbstract
Using a text-based representation of molecules, chemical reactions are predicted with a neural machine translation model borrowed from language processing.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Weininger, D. (1988) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00057a005 · Cited by 7,436
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 5,839
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
Williams, R. J., & Zipser, D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.2.270 · Cited by 4,369
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.
Gómez‐Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera‐Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru‐Guzik, A. (2018) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.34134 · Cited by 1,703
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00512 · Cited by 1,537
Prediction of Organic Reaction Outcomes Using Machine Learning
Coley, C. W., Barzilay, R., Jaakkola, T. S., Green, W. H., & Jensen, K. F. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00064 · Cited by 751
Corey, E. J., & Wipke, W. T. (1969) · Science · doi:10.1126/science.166.3902.178 · Cited by 679
Cited by
Drug discovery with explainable artificial intelligence
Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00236-4 · Cited by 1,063
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
Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction
Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., & Lee, A. A. (2019) · ACS Central Science · doi:10.1021/acscentsci.9b00576 · Cited by 928
Self-citationRethinking drug design in the artificial intelligence era
Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0050-3 · Cited by 894
Machine Learning in Computer-Aided Synthesis Planning
Coley, C. W., Green, W. H., & Jensen, K. F. (2018) · Accounts of Chemical Research · doi:10.1021/acs.accounts.8b00087 · Cited by 704
Metadata
- Authors
- Schwaller, P., Gaudin, T., Lányi, D., Bekas, C., & Laino, T.
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2018
- Volume
- 9
- Issue
- 28
- Pages
- 6091-6098
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- OpenAlex
- W2769423117
- Cited by
- 441
- References
- 29
- FWCI
- 34.80
- Type
- journal article
- Language
- en
- Open access
- diamond
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- CoChat. (2026). Citation check: “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models. https://app.cochat.ai/papers/10.1039/c8sc02339e
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- Schwaller, P., Gaudin, T., Lányi, D., Bekas, C., & Laino, T. (2018). “Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models. Chemical Science. https://doi.org/10.1039/c8sc02339e
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
