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Related Experiment Video

Updated: Aug 5, 2025

High-throughput Confocal Imaging of Quantum Dot-Conjugated SARS-CoV-2 Spike Trimers to Track Binding and Endocytosis in HEK293T Cells

High-throughput Confocal Imaging of Quantum Dot-Conjugated SARS-CoV-2 Spike Trimers to Track Binding and Endocytosis in HEK293T Cells

Published on: April 21, 2022

SARS-CoV2 billion-compound docking.

David M Rogers¹, Rupesh Agarwal^2,3, Josh V Vermaas^4,5

¹Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA. rogersdm@ornl.gov.

Scientific Data

|March 28, 2023

Summary

This summary is machine-generated.

This study docked 1.4 billion molecules against SARS-CoV-2 proteins, generating extensive data for drug discovery. The results offer valuable insights for developing novel antiviral compounds and training AI models.

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Use of Viral Entry Assays and Molecular Docking Analysis for the Identification of Antiviral Candidates against Coxsackievirus A16

Published on: July 15, 2019

Related Experiment Videos

Last Updated: Aug 5, 2025

High-throughput Confocal Imaging of Quantum Dot-Conjugated SARS-CoV-2 Spike Trimers to Track Binding and Endocytosis in HEK293T Cells

High-throughput Confocal Imaging of Quantum Dot-Conjugated SARS-CoV-2 Spike Trimers to Track Binding and Endocytosis in HEK293T Cells

Published on: April 21, 2022

Engineering Antiviral Agents via Surface Plasmon Resonance

Engineering Antiviral Agents via Surface Plasmon Resonance

Published on: June 14, 2022

Use of Viral Entry Assays and Molecular Docking Analysis for the Identification of Antiviral Candidates against Coxsackievirus A16

Use of Viral Entry Assays and Molecular Docking Analysis for the Identification of Antiviral Candidates against Coxsackievirus A16

Published on: July 15, 2019

Area of Science:

Computational chemistry
Structural biology
Drug discovery

Background:

SARS-CoV-2 presents a significant global health challenge, necessitating the identification of novel therapeutic agents.
Virtual screening of large compound libraries is crucial for accelerating the discovery of potential antiviral drugs.

Purpose of the Study:

To create a comprehensive dataset of ligand-protein interactions for SARS-CoV-2 targets.
To facilitate the identification of small molecules with potential therapeutic value against SARS-CoV-2.
To provide a benchmark for AI model training and large-scale docking data analysis.

Main Methods:

Docking of 1.4 billion molecules against five SARS-CoV-2 proteins (MPro, NSP15, PLPro, RDRP, Spike) using AutoDock-GPU.
Generation of 20 ligand binding poses per molecule with the Solis Wets search method.
Scoring and rescoring of ligand conformations using AutoDock, RFScore v3, and DUD-E models.

Main Results:

A dataset of 1.4 billion docked ligand conformations and associated scores against SARS-CoV-2 targets.
Inclusion of protein structures compatible with AutoDock-GPU and other docking software.
Demonstration of a methodology for organizing and processing ultra-large-scale docking data.

Conclusions:

The dataset serves as a valuable resource for identifying trends in small molecule-protein binding.
Enables the training of artificial intelligence models for drug discovery.
Provides a foundation for comparative analysis with existing SARS-CoV-2 inhibitor compounds.