The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
In silico methods and tools for drug discovery
Abstract
In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.
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Digital twins for health: a scoping review
Katsoulakis, E., Wang, Q., Wu, H., Shahriyari, L., Fletcher, R., Liu, J., Achenie, L., Liu, H., Jackson, P., Xiao, Y., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01073-0 · Cited by 760
Metadata
- Authors
- Shaker, B., Ahmad, S., Lee, J., Jung, C., & Na, D.
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 137
- Pages
- 104851
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W3196609471
- Cited by
- 607
- References
- 23
- FWCI
- 41.86
- Type
- journal article
- Language
- en
- Open access
- closed
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- Shaker, B., Ahmad, S., Lee, J., Jung, C., & Na, D. (2021). In silico methods and tools for drug discovery. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2021.104851
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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.
