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

Drug Discovery: Overview01:26

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...

You might also read

Related Articles

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

Sort by
Same author

EpiMII: Structure-Aware Graph Neural Networks for MHC-II Epitope Generation.

Research (Washington, D.C.)·2026
Same author

Overcoming Resistance in the Androgen Receptor: Rational and Strategic Design of Advanced Antagonists.

Accounts of chemical research·2026
Same author

Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow.

Nature communications·2026
Same author

BioTD: An Online Database of Biotoxins.

Journal of chemical information and modeling·2026
Same author

A Multistage Virtual Screening Strategy Integrating Molecular Similarity, Deep Learning Scoring, and Molecular Docking toward the Discovery of Novel LRRK2 Inhibitors.

Journal of chemical information and modeling·2026
Same author

STE-DC2I Uncovers Driver Genes in Colorectal Cancer Subtypes Using Symbolic Trajectory-Embedded Dark Causal Inference.

Journal of chemical information and modeling·2026
Same journal

Photocatalytic low-temperature defluorination of PTFE.

Nature protocols·2026
Same journal

Multimodal imaging and quantification of lanthanide chelate-labeled micro- and nanoplastics in plants.

Nature protocols·2026
Same journal

Yeast nuclei-mediated precise delivery of synthetic megabase-scale human DNA into mammalian embryos.

Nature protocols·2026
Same journal

Direct inoculation of bioreactor-controlled stirred suspension culture with cryopreserved human pluripotent stem cells.

Nature protocols·2026
Same journal

Whole-organ spatial transcriptional analysis at cellular resolution using TRISCO.

Nature protocols·2026
Same journal

Solid-phase synthesis of sterically hindered peptides via ribosome-mimicking molecular reactors.

Nature protocols·2026
See all related articles
  1. Home
  3. Facilitating Structure-based Drug Discovery With An Artificial Intelligence-driven Virtual Screening Platform.
  1. Home
  3. Facilitating Structure-based Drug Discovery With An Artificial Intelligence-driven Virtual Screening Platform.

Related Experiment Video

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform.

Shukai Gu1,2, Xujun Zhang1, Mengwu Xiao3

  • 1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.

Nature Protocols
|June 24, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces the Comprehensive VS Platform with AI Engine (CVSP-AIE), a novel drug discovery tool that integrates three artificial intelligence (AI) models for efficient and accurate virtual screening (VS) of compound libraries.

More Related Videos

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

Related Experiment Videos

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Artificial Intelligence in Bioinformatics

Background:

  • Structure-based virtual screening (VS) using molecular docking is crucial for identifying potential drug candidates.
  • Artificial intelligence (AI) methods have shown promise in accelerating protein-ligand docking and scoring.
  • Challenges remain in selecting and efficiently implementing appropriate AI-driven VS methods for specific drug discovery applications.

Purpose of the Study:

  • To present the Comprehensive VS Platform with AI Engine (CVSP-AIE) for efficient drug discovery from compound libraries.
  • To integrate and demonstrate the hierarchical application of three AI models for balancing screening speed and accuracy.
  • To provide a user-friendly platform, available as a web server and local package, for initiating and managing VS workflows.

Main Methods:

  • Integration of three AI models: KarmaDock (fast docking), CarsiDock (accurate docking), and RTMScore (accurate scoring).
  • Hierarchical application of AI models to dynamically balance screening speed and accuracy.
  • Development of a workflow including protein structure repair, molecule standardization, binding pose and affinity prediction, and postprocessing analysis.

Main Results:

  • The CVSP-AIE platform enables hierarchical screening of 100,000 compounds in 30-45 minutes.
  • Outputs include ranked lists of molecules with predicted binding scores, interaction profiles, and chemical space analysis.
  • The platform is accessible as an online web server and a local software package for flexible deployment.

Conclusions:

  • CVSP-AIE offers an efficient and accurate solution for AI-powered virtual screening in drug discovery.
  • The hierarchical application of AI models provides a tunable balance between computational speed and predictive accuracy.
  • The platform's accessibility and comprehensive workflow facilitate the identification of bioactive compounds from large libraries.