Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Solvent-Mediated Control of Nanocellulose Dispersion: An Integrated Computational and Experimental Investigation.

ACS nano·2026
Same author

SARS-CoV-2 Spike Protein's Structural Dynamics Affect the Activity of the Bebtelovimab Antibody.

Journal of chemical information and modeling·2026
Same author

The scientific legacy of Martin Karplus from the perspective of his collaborators.

Biophysical journal·2026
Same author

IRIS: A Machine Learning-Based Pose Reranking Tool for RNA-Ligand Docking.

ACS omega·2026
Same author

Using AI to enhance healthcare resource management and allocation: A focus on the autism community in Alabama.

PloS one·2026
Same author

In-Silico Predictions of Drug Resistance in Lung Cancers With EGFR Mutation.

Proceedings of the Platform for Advanced Scientific Computing Conference·2026

Related Experiment Video

Updated: May 10, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

VinaMPI: facilitating multiple receptor high-throughput virtual docking on high-performance computers.

Sally R Ellingson1, Jeremy C Smith, Jerome Baudry

  • 1Genome Science and Technology, University of Tennessee, Knoxville, Tennessee, USA.

Journal of Computational Chemistry
|July 2, 2013
PubMed
Summary
This summary is machine-generated.

VinaMPI accelerates virtual drug screening on supercomputers by distributing tasks across many cores. This massively parallel program significantly reduces the time needed for large-scale drug discovery, making it more efficient.

More Related Videos

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Related Experiment Videos

Last Updated: May 10, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Area of Science:

  • Computational chemistry
  • Drug discovery
  • High-performance computing

Background:

  • Virtual drug screening is crucial for identifying potential drug candidates.
  • Large-scale screens require significant computational resources and time.
  • Optimizing computational efficiency is key to advancing the drug discovery pipeline.

Purpose of the Study:

  • To develop VinaMPI, a massively parallel program for large-scale virtual drug screens.
  • To leverage leadership-class computing resources for faster drug discovery.
  • To enhance the efficiency of the drug discovery pipeline through high-throughput inverse docking.

Main Methods:

  • VinaMPI is a Message Passing Interface (MPI) program based on Autodock Vina.
  • It utilizes task distribution based on chemical compound complexity (rotatable bonds).
  • Multithreading speeds up individual docking tasks, while MPI distributes tasks across workers.

Main Results:

  • VinaMPI successfully scaled to 84,672 cores, demonstrating reduced completion times with increased core count.
  • Efficient handling of multiple proteins enables high-throughput inverse docking.
  • A task-to-worker ratio of at least 100 ensures optimal load balance and job completion time.

Conclusions:

  • VinaMPI significantly decreases the time-to-completion for massively large virtual drug screens.
  • The program offers new opportunities for improving drug discovery pipeline efficiency.
  • VinaMPI is freely available, promoting wider adoption in computational drug discovery.