Chematica: A Story of Computer Code That Started to Think like a Chemist
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BackgroundMethodDeep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP
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He, S., Leanse, L. G., & Feng, Y. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.113922 · Cited by 141 · see citing works
Li, X., Zhang, S., Xu, L., & Hong, X. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.202000959 · Cited by 132 · see citing works
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Harren, T., Matter, H., Heßler, G., Rarey, M., & Grebner, C. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01263 · Cited by 65 · see citing works
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Wang, Z., Zhao, W., Hao, G., & Song, B. (2020) · Organic Chemistry Frontiers · doi:10.1039/d0qo00946f · Cited by 37 · see citing works
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BackgroundTeixeira, R. I., Andresini, M., Luisi, R., & Benyahia, B. (2024) · JACS Au · doi:10.1021/jacsau.4c00624 · Cited by 24 · see citing works
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Wang, H., Chen, J., Liu, W., Wang, D., Song, Y., Hong, H., Wang, T., Anastas, P. T., & Zimmerman, J. B. (2026) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00828 · Cited by 19 · see citing works
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Schwaller, P., & Laino, T. (2019) · ACS symposium series · doi:10.1021/bk-2019-1326.ch004 · Cited by 16 · see citing works
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Zhong, Q., Tan, X., Du, R., Liu, J., Liao, L., Wang, C., Sun, R., Tang, Z., Ren, J., Mebrahtu, C., et al. (2023) · Preprints.org · doi:10.20944/preprints202302.0292.v1 · Cited by 11 · see citing works
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Lin, K., Pei, J., Lai, L., & Xu, Y. (2019) · ChemRxiv · doi:10.26434/chemrxiv.8168354.v1 · Cited by 8 · see citing works
Ida, T., Kojima, H., & Hori, Y. (2023) · Chemical Communications · doi:10.1039/d3cc03890d · Cited by 7 · see citing works
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Backgroundde Oliveira, A. S. (2025) · RSC Medicinal Chemistry · doi:10.1039/d5md00700c · Cited by 7 · see citing works
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Paliwal, D., Gudhanti, S. N. K. R., Yadav, D., & Raj, P. (2023) · Letters in Drug Design & Discovery · doi:10.2174/1570180820666230427151812 · Cited by 6 · see citing works
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Staddon, J., Smit, J. J., Skoufa, Z., et al. (2022) · Johnson Matthey Technology Review · doi:10.1595/205651322x16594453018855 · Cited by 3 · see citing works
Toniato, A., Unsleber, J. P., Vaucher, A. C., et al. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-gd0q9 · Cited by 3 · see citing works
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Martinelli, C., Repetto, M., & Curigliano, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13671-9.00012-0 · Cited by 3 · see citing works
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Plutschack, M. B., & Seeberger, P. H. (2024) · Green Chemistry · doi:10.1039/d4gc90024c · Cited by 2 · see citing works
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Self-citationHull, R., Manabile, M. A., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-21506-3_11
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- Authors
- Grzybowski, B. A., Szymkuć, S., Gajewska, E. P., Molga, K., Dittwald, P., Wołos, A., & Klucznik, T.
- Venue
- Chem
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 4
- Issue
- 3
- Pages
- 390-398
- ISSN
- 2451-9294 (ISSN-L)
- OpenAlex
- W2789913372
- Cited by
- 114
- References
- 0
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 19 · 2023: 32 · 2022: 7 · 2021: 15 · 2020: 11 · 2019: 7
- FWCI
- 6.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Chem (publishedVersion)
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
- Funders
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- cheminformatics software, artificial intelligence in chemistry, retrosynthesis
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 ›
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