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Updated: Nov 30, 2025
Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
Published on: June 20, 2025
Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19
A Acharya1, R Agarwal2,3,4, M Baker5
1School of Physics, Georgia Institute of Technology, Atlanta, GA 30332.
We developed a supercomputer pipeline for in-silico drug discovery targeting SARS-CoV-2 proteins. This method uses enhanced sampling molecular dynamics (MD) and ensemble docking for rapid identification of potential drug candidates.
Area of Science:
- Computational chemistry
- Drug discovery
- Virology
Background:
- SARS-CoV-2 poses a significant global health threat.
- Efficient in-silico methods are crucial for rapid drug discovery.
- Protein targets within the SARS-CoV-2 proteome require extensive investigation.
Conclusions:
- The developed pipeline offers a powerful and efficient approach for in-silico drug discovery.
- Preliminary results show promise for identifying SARS-CoV-2 drug candidates.
- Future work will integrate quantum mechanical, machine learning, and AI methods for pipeline enhancement.
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