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

You might also read

Related Articles

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

Sort by
Same author

AI-Enforced Ultra-Large Virtual Screening Discovers Potent CD28 Binders.

Journal of chemical information and modeling·2026
Same author

Structure-based screening and a conformational biosensor identify a GPR183 inverse agonist and an activation switch.

Nature communications·2026
Same author

PyRMD Studio: A Unified Suite for Next-Generation, AI-Powered Virtual Screening.

Journal of chemical information and modeling·2026
Same author

Synthesis of New Asymmetrical Chalcones and Evaluation of Their Use in Combination with Curcumin Against Rhodesain of <i>T. brucei rhodesiense</i>.

International journal of molecular sciences·2026
Same author

StabLyzeGraph: High-throughput screening of combinatorial mutations using graph neural networks.

Protein science : a publication of the Protein Society·2026
Same author

Coupling PD-L1 Inhibition and Lysosomal Degradation: Innovative Anti-PD-L1 Peptides for NSCLC Immunotherapy.

Journal of medicinal chemistry·2026

Related Experiment Video

Updated: Jul 19, 2025

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

337

Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach.

Michele Roggia1, Benito Natale1, Giorgio Amendola1

  • 1DiSTABiF, University of Campania Luigi Vanvitelli, Via Vivaldi 43, 81100 Caserta, Italy.

Journal of Chemical Information and Modeling
|August 8, 2023
PubMed
Summary

A new protocol, PyRMD2Dock, accelerates drug discovery by combining Ligand-Based Virtual Screening (LBVS) with GPU-accelerated docking. This method efficiently screens large databases to identify potential drug candidates with high binding affinity.

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

96
Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
11:06

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

2.0K

Related Experiment Videos

Last Updated: Jul 19, 2025

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

337
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

96
Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
11:06

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

2.0K

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Virtual screening is crucial for identifying drug candidates.
  • Existing methods can be limited by throughput and computational cost.
  • AI-powered tools offer potential for enhanced screening.

Purpose of the Study:

  • Introduce PyRMD2Dock, a novel computational protocol.
  • Enhance the throughput of virtual screening campaigns for drug discovery.
  • Facilitate rapid screening of massive chemical databases.

Main Methods:

  • Combined Ligand-Based Virtual Screening (LBVS) tool PyRMD with AutoDock-GPU (AD4-GPU).
  • Developed the PyRMD2Dock protocol for high-throughput virtual screening.
  • Benchmarked and conducted screening experiments to assess performance.

Main Results:

  • PyRMD2Dock enables rapid screening of large chemical databases.
  • Identified compounds with high predicted binding affinity to target proteins.
  • Demonstrated the predictive power and speed of the PyRMD2Dock protocol.

Conclusions:

  • PyRMD2Dock significantly accelerates the discovery of novel drug candidates.
  • Combining AI-powered LBVS tools with docking software is effective for ultralarge database screening.
  • PyRMD2Dock is available as an open-source tool on GitHub.