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Updated: Dec 22, 2025
05:50
Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
Published on: September 26, 2025
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Knowledge-based structural models of SARS-CoV-2 proteins and their complexes with potential drugs
Atsushi Hijikata1, Clara Shionyu-Mitsuyama1, Setsu Nakae1
1Faculty of Bioscience, Nagahama Institute of Bio-Science and Technology, Japan.
FEBS Letters
|May 8, 2020
Summary
Researchers modeled SARS-CoV-2 proteins to identify potential COVID-19 treatments. Theoretical models suggest drugs like carfilzomib and sinefungin for further investigation as coronavirus therapeutics.
Area of Science:
- Virology
- Drug Discovery
- Computational Biology
Background:
- The World Health Organization declared coronavirus disease 2019 (COVID-19) a global pandemic.
- No definitive antiviral therapeutics are currently confirmed for treating COVID-19.
- Urgent need for effective treatments to combat the SARS-CoV-2 virus.
Purpose of the Study:
- To aid structure-based drug discovery for COVID-19 treatment.
- To identify potential drug candidates for repurposing against SARS-CoV-2.
- To explore existing drugs and natural compounds as COVID-19 therapeutics.
Main Methods:
- Construction of knowledge-based models for SARS-CoV-2 proteins.
- Comparison of ligand molecules within template structures.
- Screening against databases of approved/experimental drugs and natural medicine components.
Main Results:
- Identification of several potential therapeutic agents through theoretical modeling.
- Specific drugs suggested for further investigation include carfilzomib, sinefungin, tecadenoson, and trabodenoson.
- Theoretical models provide a basis for experimental validation.
Conclusions:
- The study presents promising drug candidates for COVID-19 treatment.
- Further research and experimental validation are warranted for the identified drugs.
- Computational modeling can accelerate the discovery of novel antiviral therapies.
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