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 Experiment Video

Updated: Oct 28, 2025

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

323

Efficient machine learning model for predicting drug-target interactions with case study for Covid-19.

Heba El-Behery1, Abdel-Fattah Attia1, Nawal El-Feshawy2

  • 1Department of Computer Science and Engineering, Faculty of Engineering, Kafrelsheikh University, Kafr_El_Sheikh, Egypt.

Computational Biology and Chemistry
|July 16, 2021
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Pharmacokinetics: Drug–Food and Drug–Viral Interactions01:26

Pharmacokinetics: Drug–Food and Drug–Viral Interactions

40
A drug interaction occurs when the concurrent use of another drug, food, or an external substance alters the pharmacological activity of a drug. This interaction can modify the action of the original drug, affecting its effectiveness and safety.Drug–food interactions are significant as they impact drug absorption, metabolism, and excretion. For example, grapefruit juice is a well-known disruptor of drug metabolism. It inhibits the cytochrome P450 3A4 enzyme, crucial for the metabolism of...
40
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.1K
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...
14.1K
Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

360
Drug interactions are a critical aspect of pharmacology and can occur when two or more drugs compete for the same binding site. This competition can result in one drug displacing another, altering the effect of the displaced drug. Drug interactions are complex processes that rely heavily on how much of the displacer drug is present and how strongly it can bind to the same sites as the displaced drug.
Displacement interactions can have varying outcomes, ranging from toxicity to virtually...
360
Drug Discovery: Overview01:26

Drug Discovery: Overview

9.9K
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...
9.9K
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.4K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
1.4K
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

6.6K
Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue....
6.6K

You might also read

Related Articles

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

Sort by
Same author

Parametric resonance and chaos in a duffing-type oscillator with periodic inertia modulation.

Scientific reports·2026
Same author

An explainable deep learning framework for few shot crop disease detection in rice and sugarcane using CNN based feature extraction.

Scientific reports·2026
Same author

BiGKbhb: a bi-directional gated recurrent unit model for predicting lysine β-hydroxybutyrylation sites.

BMC genomics·2026
Same author

A deep learning framework for lysine 2-hydroxyisobutyrylation site prediction using evolutionary feature representation.

Scientific reports·2025
Same author

An efficient machine-learning framework for predicting protein post-translational modification sites.

Scientific reports·2025
Same author

An efficient IoT-based crop damage prediction framework in smart agricultural systems.

Scientific reports·2025

This study introduces a new computational model for predicting drug target interactions (DTIs) using protein and drug structures. The model achieves high accuracy, aiding in drug repositioning and COVID-19 treatment discovery.

Area of Science:

  • Bioinformatics
  • Computational Chemistry
  • Drug Discovery

Background:

  • Discovering drug target interactions (DTIs) is crucial for drug development and repositioning.
  • Traditional experimental methods for DTI identification are costly and time-consuming.
  • Existing computational methods often have limitations in accuracy and rarely integrate both protein and drug structural data.

Purpose of the Study:

  • To develop an accurate computational model for predicting drug target interactions (DTIs).
  • To leverage both protein structural features and drug molecular structures for enhanced prediction accuracy.
  • To provide a practical tool for drug repositioning and identifying potential treatments for diseases like COVID-19.

Main Methods:

  • Utilized ensemble learning algorithms for DTI prediction.
Keywords:
Covid-19Deep-learningDrug-target interactionsDrugsMachine learningPredictionProteins

More Related Videos

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.4K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.1K

Related Experiment Videos

Last Updated: Oct 28, 2025

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

323
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.4K
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.1K
  • Extracted features from protein amino acid sequences using physicochemical properties.
  • Encoded drug structures from Simplified Molecular Input Line Entry System (SMILES) strings.

    • Main Results:

    • Achieved 98% accuracy and 0.97 f-score, outperforming existing methods (94% accuracy, 0.92 f-score).
    • Successfully predicted novel drug-target interactions, demonstrating utility in drug repositioning.
    • Identified specific drug interactions with ACE2 protein (e.g., DB00691, DB05203) relevant to COVID-19 treatment.

    Conclusions:

    • The proposed model offers a highly accurate and efficient approach for DTI prediction.
    • The model's ability to integrate structural and feature data enhances prediction capabilities.
    • This computational tool can accelerate drug discovery and aid in developing treatments for infectious diseases like COVID-19.