The DOI matches its Crossref record; 105 of the 105 references it cites were found in a registry or an index.
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Open accessAbstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Cites
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Cited by
Artificial intelligence in drug discovery and development
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.010 · Cited by 1,695
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
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“They also intend to establish a uniform data format, which is technically challenging [161].”
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Drug discovery with explainable artificial intelligence
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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
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Metadata
- Authors
- Yang, X., Wang, Y., Byrne, R., Schneider, G., & Yang, S.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-07-11
- Volume
- 119
- Issue
- 18
- Pages
- 10520-10594
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W2959938226
- Cited by
- 1,022
- References
- 105
- FWCI
- 4.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
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