The DOI matches its Crossref record; 121 of the 121 references it cites were found in a registry or an index.
A Structure-Based Drug Discovery Paradigm
Open accessAbstract
Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.
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Metadata
- Authors
- Batool, M., Ahmad, B., & Choi, S.
- Venue
- International Journal of Molecular Sciences
- Publisher
- MDPI AG
- Published
- 2019-06-06
- Volume
- 20
- Issue
- 11
- Pages
- 2783
- ISSN
- 1422-0067 (ISSN-L)
- OpenAlex
- W2948831670
- Cited by
- 744
- References
- 121
- FWCI
- 41.25
- Type
- journal article
- Language
- en
- Open access
- gold
- Funders
- National Research Foundation of Korea (2019R1H1A2039674)
- Ministry of Science and ICT (2018K000369)
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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.
