bioRxivBioinformaticsPreprint
Potentially highly potent drugs for 2019-nCoV
Duc Duy NguyeniD, Kaifu GaoiD, Jiahui CheniD, Rui WangiD, Guo‐Wei WeiiD
Abstract
The World Health Organization (WHO) has declared the 2019 novel coronavirus (2019-nCoV) infection outbreak a global health emergency. Currently, there is no effective anti-2019-nCoV medication. The sequence identity of the 3CL proteases of 2019-nCoV and SARS is 96%, which provides a sound foundation for structural-based drug repositioning (SBDR). Based on a SARS 3CL protease X-ray crystal structure, we construct a 3D homology structure of 2019-nCoV 3CL protease. Based on this structure and existing experimental datasets for SARS 3CL protease inhibitors, we develop an SBDR model based on machine learning and mathematics to screen 1465 drugs in the DrugBank that have been approved by the U.S. Food and Drug Administration (FDA). We found that many FDA approved drugs are potentially highly potent to 2019-nCoV.
- research focus: drug response / drug discovery
- approach: statistical model
- approach: machine learning (classical)
Abstract summary
Claude Opus 5.5 · xhigh effort · 2 Nov 2026 · from the abstract
Structure-based drug repositioning using a homology model of the 2019-nCoV 3CL protease screens 1465 FDA-approved DrugBank compounds to identify candidate therapeutics against the virus.
Despite the 2019 novel coronavirus (2019-nCoV) outbreak being declared a global health emergency, no effective antiviral medication currently exists. The 96% sequence identity between the 3CL proteases of 2019-nCoV and severe acute respiratory syndrome (SARS) coronavirus enables structure-based drug repositioning of existing approved compounds.
Key results
SuggestedScreening approved pharmaceuticals with a structure-based drug repositioning model identifies multiple U.S. Food and Drug Administration (FDA)-approved compounds with predicted potency against 2019-nCoV.
The model was built from a 3D homology structure of the 2019-nCoV 3CL protease (sharing 96% sequence identity with the SARS 3CL protease) and experimental SARS 3CL protease inhibitor datasets, then applied to 1465 FDA-approved drugs in DrugBank. · Abstract
Methods, data and reproducibility
A 3D homology structure of the 2019-nCoV 3CL protease was constructed from an X-ray crystal structure of the SARS 3CL protease. This homology model was combined with experimental SARS 3CL protease inhibitor datasets to train a mathematical and machine learning model for screening FDA-approved compounds in DrugBank.
- FDA-approved drugs from DrugBank · n = 1465 drugs
- SARS 3CL protease X-ray crystal structure and experimental SARS 3CL protease inhibitor datasets · SARS coronavirus
- 2019-nCoV 3CL protease sequence · 2019-nCoV
- Methods & tools
- DrugBank
- Reproducibility gaps
- The abstract does not provide links to the homology model, machine learning code, or list of candidate drugs.
Limitations (2)
- The abstract does not name the specific candidate drugs or report quantitative potency predictions or experimental validation against 2019-nCoV. · noted in this summary
- Predictions rely on a 3D homology model derived from the SARS 3CL protease and SARS inhibitor datasets rather than an experimental 2019-nCoV structure or direct assay data. · noted in this summary
Read this if you are interested in computational structure-based drug repurposing targeting the 2019-nCoV 3CL protease.
- Citations
- 58
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