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AI in small-molecule drug discovery: a coming wave?
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“The impact of big data and AI in drug discovery, the subject of a 2020 review in this journal (4), continues to attract significant interest: Investors (5, 6), industrial (7, 8) and academic scientists (9, 10), and legislators (https://bit.ly/40LRAip) discuss the impact of AI for drug discovery (AI4DD).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [5]
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Computational approaches streamlining drug discovery
Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,424
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
Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
Tropsha, A., Isayev, O., Varnek, A. A., Schneider, G., & Cherkasov, A. R. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00832-0 · Cited by 447
Macrocycles in Drug Discovery─Learning from the Past for the Future
Jiménez, D. G., Poongavanam, V., & Kihlberg, J. O. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c00134 · Cited by 359
Big data in basic and translational cancer research
Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254
Metadata
- Authors
- Jayatunga, M. K. P., Xie, W., Ruder, L., Schulze, U., & Meier, C.
- Affiliations
- Boston Consulting Group (United States) (US)
- Corresponding author
- Meier, Christoph
- Venue
- Nature Reviews Drug Discovery
- Published
- 2022-02-07
- Volume
- 21
- Issue
- 3
- Pages
- 175-176
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W4210852924
- Cited by
- 187
- References
- 0
- Citations by year
- 2026: 29 · 2025: 56 · 2024: 48 · 2023: 42 · 2022: 12
- FWCI
- 21.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Reviews Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- artificial intelligence, small molecule drug discovery, machine learning, drug design
- MeSH terms
- Artificial Intelligence, Humans, Drug Discovery
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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.
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