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Updated: Sep 24, 2025
Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
Published on: June 20, 2025
Drug repurposing for SARS-CoV-2: a high-throughput molecular docking, molecular dynamics, machine learning, and DFT
Jatin Kashyap1, Dibakar Datta1
1Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, NJ 07103 USA.
Abstract:
A micro-molecule of dimension 125 nm has caused around 479 million human infections (80 M for the USA) and 6.1 million human deaths (977,000 for the USA) worldwide and slashed the global economy by US$ 8.5 Trillion over two years period. The only other events in recent history that caused comparative human life loss through direct usage (either by human or nature, respectively) of structure-property relations of 'nano-structures' (either human-made or nature, respectively) were nuclear bomb attacks during World War II and 1918 Flu Pandemic. This molecule is called SARS-CoV-2, which causes a disease known as COVID-19. The high liability cost of the pandemic had incentivized various private, government, and academic entities to work towards finding a cure for this and emerging diseases. As an outcome, multiple vaccine candidates are discovered to avoid the infection in the first place. But so far, there has been no success in finding fully effective therapeutic candidates. In this paper, we attempted to provide multiple therapy candidates based upon a sophisticated multi-scale in-silico framework, which increases the probability of the candidates surviving an in-vivo trial. We have selected a group of ligands from the ZINC database based upon previously partially successful candidates, i.e., Hydroxychloroquine, Lopinavir, Remdesivir, Ritonavir. We have used the following robust framework to screen the ligands; Step-I: high throughput molecular docking, Step-II: molecular dynamics analysis, Step-III: density functional theory analysis. In total, we have analyzed 242,000(ligands)*9(proteins) = 2.178 million unique protein binding site/ligand combinations. The proteins were selected based on recent experimental studies evaluating potential inhibitor binding sites. Step-I had filtered that number down to 10 ligands/protein based on molecular docking binding energy, further screening down to 2 ligands/protein based on drug-likeness analysis. Additionally, these two ligands per protein were analyzed in Step-II with a molecular dynamic modeling-based RMSD filter of less than 1Å. It finally suggested three ligands (ZINC001176619532, ZINC000517580540, ZINC000952855827) attacking different binding sites of the same protein(7BV2), which were further analyzed in Step-III to find the rationale behind comparatively higher ligand efficacy.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s10853-022-07195-8.
Insights
This study screened over 2 million molecular combinations to identify potential SARS-CoV-2 therapeutics. Three promising drug candidates were identified for treating COVID-19, offering hope for effective treatments.
Area of Science:
- Computational chemistry and drug discovery.
- Infectious disease research.
Background:
- The COVID-19 pandemic, caused by SARS-CoV-2, has resulted in millions of infections and deaths globally, alongside significant economic impact.
- Despite extensive efforts, effective therapeutic candidates for COVID-19 remain elusive, highlighting the need for novel treatment strategies.
Purpose of the Study:
- To identify novel therapeutic candidates for SARS-CoV-2 infection using a sophisticated in-silico framework.
- To computationally screen a large library of ligands against key SARS-CoV-2 proteins to predict potential drug efficacy.
Main Methods:
- A multi-scale in-silico framework was employed, integrating high-throughput molecular docking, molecular dynamics analysis, and density functional theory.
- Over 2.178 million unique protein-ligand combinations were analyzed, focusing on potential inhibitor binding sites of SARS-CoV-2 proteins.
- Ligands were filtered based on binding energy, drug-likeness, and molecular dynamics stability (RMSD < 1Å).
Main Results:
- The screening process identified three potential therapeutic ligands (ZINC001176619532, ZINC000517580540, ZINC000952855827) targeting different binding sites of the SARS-CoV-2 protein 7BV2.
- These selected ligands demonstrated promising binding affinities and stability in silico.
- Further analysis using density functional theory provided insights into the higher efficacy of these identified ligands.
Conclusions:
- The study successfully identified three novel drug candidates with potential therapeutic value against SARS-CoV-2.
- The employed in-silico framework effectively predicted promising ligands, increasing the likelihood of success in subsequent in-vivo trials.
- These findings offer a promising avenue for the development of effective COVID-19 therapies.
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