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

Protein-protein Interfaces02:04

Protein-protein Interfaces

12.4K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
12.4K
Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

20
Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
20
Protein Organization01:24

Protein Organization

6.0K
Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
6.0K

You might also read

Related Articles

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

Sort by
Same author

The evolutionary history and unique genetic diversity of Indigenous Americans.

Nature·2026
Same author

Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins.

Journal of chemical information and modeling·2025
Same author

Ribosomes modulate transcriptome abundance via generalized frameshift and out-of-frame mRNA decay.

Molecular cell·2025
Same author

Drug Release Nanoparticle System Design: Data Set Compilation and Machine Learning Modeling.

ACS applied materials & interfaces·2025
Same author

Artificial Intelligence-Driven Modeling for Hydrogel Three-Dimensional Printing: Computational and Experimental Cases of Study.

Polymers·2025
Same author

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR).

Polymers·2024

Related Experiment Video

Updated: May 13, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

60

IFPTML Multi-Output Model for Anti-Retroviral Compounds Including the Drug Structure and Target Protein Sequence

Emilia Vásquez-Domínguez1,2, Shan He1,3, Carlos Santolaria1

  • 1Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, 48940 Leioa, Spain.

Journal of Chemical Information and Modeling
|April 28, 2025
PubMed
Summary

This study introduces an advanced AI model that integrates viral protein sequences to improve antiretroviral drug discovery. The enhanced model accurately predicts drug activity against emerging viral mutations and strains.

More Related Videos

Multi-target Parallel Processing Approach for Gene-to-structure Determination of the Influenza Polymerase PB2 Subunit
22:10

Multi-target Parallel Processing Approach for Gene-to-structure Determination of the Influenza Polymerase PB2 Subunit

Published on: June 28, 2013

13.1K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

913

Related Experiment Videos

Last Updated: May 13, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

60
Multi-target Parallel Processing Approach for Gene-to-structure Determination of the Influenza Polymerase PB2 Subunit
22:10

Multi-target Parallel Processing Approach for Gene-to-structure Determination of the Influenza Polymerase PB2 Subunit

Published on: June 28, 2013

13.1K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

913

Area of Science:

  • Drug Discovery
  • Computational Biology
  • Virology

Background:

  • Retroviral infections necessitate the continuous development of antiretroviral (ARV) drugs.
  • Existing drug discovery models struggle to account for viral mutations and diverse biological conditions.
  • The ChEMBL database offers extensive ARV data but requires sophisticated analysis for effective drug discovery.

Purpose of the Study:

  • To develop an enhanced Artificial Intelligence/Machine Learning (AI/ML) model for accelerated ARV discovery.
  • To integrate viral protein sequence information into predictive models for ARV activity.
  • To address the limitations of current models in predicting drug efficacy against viral variants and mutations.

Main Methods:

  • Developed an enhanced Information Fusion Perturbation Theory and Machine Learning (IFPTML) model.
  • Incorporated sequence descriptors computed from retroviral proteomes into the IFPTML model.
  • Trained and validated the model on a comprehensive ChEMBL ARV dataset.

Main Results:

  • The enhanced IFPTML model achieved high performance metrics (Sensitivity: 72.0-88.0%, Specificity: 72.0-88.0%, Accuracy: 72.0-88.0%) during training and validation.
  • The model demonstrated effectiveness in predicting drug activity against known protein mutations.
  • Successfully integrated diverse data, including viral sequences, strains, cell lines, and assay organisms.

Conclusions:

  • The enhanced IFPTML model represents a significant advancement in ARV discovery by systematically incorporating protein sequence data.
  • This unified, multi-condition, multi-output model offers improved prediction of ARV activity against diverse viral challenges.
  • The approach facilitates the discovery of drugs effective against drug-resistant mutations and emerging viral strains.