The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
A Structure-Based Platform for Predicting Chemical Reactivity
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
MoleculeNet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107
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
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966
Machine-learning-assisted materials discovery using failed experiments
Raccuglia, P., Elbert, K. C., Adler, P. D. F., Falk, C., Wenny, M. B., Mollo, A., Zeller, M., Friedler, S. A., Schrier, J., & Norquist, A. J. (2016) · Nature · doi:10.1038/nature17439 · Cited by 1,727
Impact of high-throughput screening in biomedical research
Macarron, R., Banks, M. N., Bojanic, D., Burns, D. J., Cirovic, D. A., Garyantes, T., Green, D. V. S., Hertzberg, R. P., Janzen, W. P., Paslay, J. W., et al. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3368 · Cited by 1,364
Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships
Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500747n · Cited by 1,159
Predicting reaction performance in C–N cross-coupling using machine learning
Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D., & Doyle, A. G. (2018) · Science · doi:10.1126/science.aar5169 · Cited by 1,058
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
Cited by
Prediction of chemical reaction yields using deep learning
Schwaller, P., Vaucher, A. C., Laino, T., & Reymond, J.-L. (2021) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/abc81d · Cited by 291
Henle, J. J., Zahrt, A. F., Rose, B. T., Darrow, W. T., Wang, Y., & Denmark, S. E. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c04715 · Cited by 93
Schleinitz, J., Langevin, M., Smail, Y., Wehnert, B., Grimaud, L., & Vuilleumier, R. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.2c05302 · Cited by 88
Sagawa, T., & Kojima, R. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01075-4 · Cited by 15
Metadata
- Authors
- Sandfort, F., Strieth-Kalthoff, F., Kühnemund, M., Beecks, C., & Glorius, F.
- Venue
- Chem
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 6
- Issue
- 6
- Pages
- 1379-1390
- ISSN
- 2451-9294 (ISSN-L)
- OpenAlex
- W3012519883
- Cited by
- 288
- References
- 16
- FWCI
- 20.25
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Structure-Based Platform for Predicting Chemical Reactivity. https://app.cochat.ai/papers/10.1016/j.chempr.2020.02.017
- The paper
- Sandfort, F., Strieth-Kalthoff, F., Kühnemund, M., Beecks, C., & Glorius, F. (2020). A Structure-Based Platform for Predicting Chemical Reactivity. Chem. https://doi.org/10.1016/j.chempr.2020.02.017
Data sources
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.
