Drug repurposing for SARS-CoV-2: a high-throughput molecular docking, molecular dynamics, machine learning, and DFT...

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.