The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods
Open accessAbstract
SARS-CoV-2 has caused tens of thousands of infections and more than one thousand deaths. There are currently no registered therapies for treating coronavirus infections. Because of time consuming process of new drug development, drug repositioning may be the only solution to the epidemic of sudden infectious diseases. We systematically analyzed all the proteins encoded by SARS-CoV-2 genes, compared them with proteins from other coronaviruses, predicted their structures, and built 19 structures that could be done by homology modeling. By performing target-based virtual ligand screening, a total of 21 targets (including two human targets) were screened against compound libraries including ZINC drug database and our own database of natural products. Structure and screening results of important targets such as 3-chymotrypsin-like protease (3CLpro), Spike, RNA-dependent RNA polymerase (RdRp), and papain like protease (PLpro) were discussed in detail. In addition, a database of 78 commonly used anti-viral drugs including those currently on the market and undergoing clinical trials for SARS-CoV-2 was constructed. Possible targets of these compounds and potential drugs acting on a certain target were predicted. This study will provide new lead compounds and targets for further in vitro and in vivo studies of SARS-CoV-2, new insights for those drugs currently ongoing clinical studies, and also possible new strategies for drug repositioning to treat SARS-CoV-2 infections.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Self-citationClinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Self-citationA novel coronavirus from patients with pneumonia in China, 2019
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001017 · Cited by 28,244
Self-citationWang, M., Cao, R., Zhang, L., Yang, X., Liu, J., Xu, M., Shi, Z., Hu, Z., Zhong, W., & Xiao, G. (2020) · Cell Research · doi:10.1038/s41422-020-0282-0 · Cited by 6,958
Self-citationFirst case of 2019 novel coronavirus in the United States
Holshue, M. L., DeBolt, C., Lindquist, S., Lofy, K. H., Wiesman, J., Bruce, H., Spitters, C., Ericson, K., Wilkerson, S., Tural, A., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001191 · Cited by 6,030
Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus
Li, W., Moore, M. J., Vasilieva, N., Sui, J., Wong, S. K., Berne, M. A., Somasundaran, M., Sullivan, J. L., Luzuriaga, K., Greenough, T. C., et al. (2003) · Nature · doi:10.1038/nature02145 · Cited by 5,923
Isolation of a Novel Coronavirus from a Man with Pneumonia in Saudi Arabia
Zaki, A. M., van Boheemen, S., Bestebroer, T. M., Osterhaus, A. D. M. E., & Fouchier, R. A. M. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1211721 · Cited by 5,735
Wan, Y., Shang, J., Graham, R., Baric, R. S., & Li, F. (2020) · Journal of Virology · doi:10.1128/jvi.00127-20 · Cited by 4,341 · cited 2×
“Recent researches speculated that SARS-CoV-2 could also bind to ACE2, and this was verified by computational docking and ELISA measurement 24,25 .”
Emerging coronaviruses: Genome structure, replication, and pathogenesis
Chen, Y., Liu, Q., & Guo, D. (2020) · Journal of Medical Virology · doi:10.1002/jmv.25681 · Cited by 3,062
A Major Outbreak of Severe Acute Respiratory Syndrome in Hong Kong
Lee, N., Hui, D., Wu, A., Chan, P., Cameron, P., Joynt, G. M., Ahuja, A., Yung, M. Y., Leung, C. B., To, K. F., et al. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa030685 · Cited by 2,628
Cited by
Naqvi, A. A. T., Fatima, K., Mohammad, T., Fatima, U., Singh, I. K., Singh, A., Atif, S. M., Hariprasad, G., Hasan, G. M., & Hassan, M. I. (2020) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2020.165878 · Cited by 1,114
SARS-CoV-2 (COVID-19) by the numbers
Bar-On, Y. M., Flamholz, A., Phillips, R., & Milo, R. (2020) · eLife · doi:10.7554/elife.57309 · Cited by 962
The SARS-CoV-2 main protease as drug target
Ullrich, S., & Nitsche, C. (2020) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2020.127377 · Cited by 772
Toward Nanotechnology-Enabled Approaches against the COVID-19 Pandemic
Weiss, C., Carriere, M., Fusco, L., Capua, I., Regla-Nava, J. A., Pasquali, M., Scott, J. A., Vitale, F., Unal, M. A., Mattevi, C., et al. (2020) · ACS Nano · doi:10.1021/acsnano.0c03697 · Cited by 601
Ortiz-Prado, E., Simbaña-Rivera, K., Gómez- Barreno, L., Rubio-Neira, M., Guaman, L. P., Kyriakidis, N. C., Muslin, C., Jaramillo, A. M. G., Barba-Ostria, C., Cevallos-Robalino, D., et al. (2020) · Diagnostic Microbiology and Infectious Disease · doi:10.1016/j.diagmicrobio.2020.115094 · Cited by 458
Metadata
- Authors
- Wu, C., Liu, Y., Yang, Y., Zhang, P., Zhong, W., Wang, Y., Wang, Q., Xu, Y., Li, M., Li, X., Zheng, M., Chen, L., & Li, H.
- Venue
- Acta Pharmaceutica Sinica B
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 10
- Issue
- 5
- Pages
- 766-788
- ISSN
- 2211-3835 (ISSN-L)
- OpenAlex
- W3008142620
- Cited by
- 2,330
- References
- 53
- FWCI
- 247.27
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. https://app.cochat.ai/papers/10.1016/j.apsb.2020.02.008
- The paper
- Wu, C., Liu, Y., Yang, Y., Zhang, P., Zhong, W., Wang, Y., Wang, Q., Xu, Y., Li, M., Li, X., Zheng, M., Chen, L., & Li, H. (2020). Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharmaceutica Sinica B. https://doi.org/10.1016/j.apsb.2020.02.008
Data sources
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.
