The DOI matches its Crossref record; 94 of the 95 references it cites were found in a registry or an index.
Artificial intelligence in chemistry and drug design
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Cites
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Cited by
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Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473
“Further, AI models eliminate the toxicity problems, which arise due to off-target interactions [10].”
BackgroundCritical assessment of methods of protein structure prediction (CASP)—Round XIV
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Natural product drug discovery in the artificial intelligence era
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Metadata
- Authors
- Brown, N., Ertl, P., Lewis, R., Luksch, T., Reker, D., & Schneider, N.
- Affiliations
- BenevolentAI (United Kingdom) (GB)
- Novartis (Switzerland) (CH)
- Syngenta (Switzerland) (CH)
- Brigham and Women's Hospital (US)
- Corresponding author
- Lewis, Richard
- Luksch, Torsten
- Reker, Daniel
- Venue
- Journal of Computer-Aided Molecular Design
- Published
- 2020-05-29
- Volume
- 34
- Issue
- 7
- Pages
- 709-715
- ISSN
- 0920-654X (ISSN-L)
- 1573-4951
- OpenAlex
- W3031603244
- Cited by
- 165
- References
- 95
- Citations by year
- 2026: 19 · 2025: 22 · 2024: 34 · 2023: 26 · 2022: 29 · 2021: 24 · 2020: 10
- FWCI
- 11.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Computer-Aided Molecular Design (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- artificial intelligence, machine learning, drug design, QSAR, molecular design
- MeSH terms
- Machine Learning, Big Data, Algorithms, Artificial Intelligence, Humans, Drug Design, Drug Discovery, Databases, Chemical
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