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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

266
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
266
Aggregates Classification01:29

Aggregates Classification

395
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
395
Classification of Systems-I01:26

Classification of Systems-I

335
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
335

You might also read

Related Articles

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

Sort by
Same author

Computational Phenotypic Drug Discovery for Anticancer Chemotherapy: PTML Modeling of Multi-Cell Inhibitors of Colorectal Cancer Cell Lines.

International journal of molecular sciences·2025
Same author

Computational and spectrofluorimetric validation on glyphosate interactions with zebrafish (Danio rerio) acetylcholinesterase: Mechanistic and ecotoxicological implications.

Toxicology in vitro : an international journal published in association with BIBRA·2025
Same author

In Silico Approach for Early Antimalarial Drug Discovery: De Novo Design of Virtual Multi-Strain Antiplasmodial Inhibitors.

Microorganisms·2025
Same author

Perturbation-Theory Machine Learning for Multi-Target Drug Discovery in Modern Anticancer Research.

Current issues in molecular biology·2025
Same author

Interactions of Protein with Grafted Poly(ethylene oxide) Layer in Two Setups: A Molecular Dynamics Simulation Study.

Biomacromolecules·2025
Same author

Optimizing Vanadium-Catalyzed Epoxidation Reactions: Machine-Learning-Driven Yield Predictions and Data Augmentation.

Journal of chemical information and modeling·2025

Related Experiment Video

Updated: Sep 23, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

924

Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?

Amit Kumar Halder1,2, Ana S Moura1, Maria Natália D S Cordeiro1

  • 1LAQV@REQUIMTE, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.

International Journal of Molecular Sciences
|May 14, 2022
PubMed
Summary

Multitasking in silico modeling integrates diverse data for predictive models, outperforming single-task approaches. This review explores its applications, challenges, and opportunities in scientific research.

Keywords:
moving average approachmultitasking in silico modelingsoftwarevirtual screening

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

842
Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

670

Related Experiment Videos

Last Updated: Sep 23, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

924
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

842
Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

670

Area of Science:

  • Computational chemistry and cheminformatics
  • In silico modeling and simulation
  • Data science and machine learning

Background:

  • Conventional in silico modeling often focuses on single endpoints, limiting its scope.
  • Multitasking or multitarget in silico modeling integrates diverse data for enhanced predictive power.
  • The Box-Jenkins moving average approach is a key technique in multitarget modeling.

Purpose of the Study:

  • To review the current state of multitasking in silico modeling.
  • To discuss existing challenges and future opportunities in the field.
  • To highlight applications and software advancements in multitarget modeling.

Main Methods:

  • Integration of multiple input data types for model development.
  • Application of the Box-Jenkins moving average approach.
  • Development of classification-based models and new chemical entity design.

Main Results:

  • Multitasking models demonstrate ability in holistic and reliable in silico classification.
  • Successful application in drug design, materials science, environmental sciences, and nanotechnology.
  • Emergence of software to automate and accelerate multitarget modeling processes.

Conclusions:

  • Multitasking in silico modeling offers a powerful and versatile approach for scientific prediction and design.
  • The field presents significant opportunities for advancing computational methodologies.
  • This review serves as a guide for researchers utilizing in silico tools.