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

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
Quantitative Analysis01:12

Quantitative Analysis

Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the method...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Quadratic Models01:23

Quadratic Models

Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...

You might also read

Related Articles

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

Sort by
Same author

Microbial pollution disables the chemical defenses of sea fans.

Marine pollution bulletin·2026
Same author

Unilateral Proptosis Mimicking an Eye Tumour: A Case Report.

Cureus·2026
Same author

Identification of a mouse-virulent recombinant type I/III Toxoplasma gondii strain in liver cytology of an immunosuppressed cat infected with FeLV-C subgroup.

Veterinary research communications·2025
Same author

Remodeling of Gut Microbial Networks After Sulforaphane Supplementation in Patients with Chronic Kidney Disease.

Life (Basel, Switzerland)·2025
Same author

Long-Term Impact of the Largest Environmental Disaster in Latin America (Fundão Dam Failure) on Microbial Communities in Lakes of the Doce River Basin, Brazil.

Environmental microbiology·2025
Same author

Reprogramming CD8+ T-cell Branched N-Glycosylation Limits Exhaustion, Enhancing Cytotoxicity and Tumor Killing.

Cancer immunology research·2025
Same journal

OpenStats: how to combine statistics and research data management (RDM) to leverage efficient scientific data analysis by guided statistics.

Journal of cheminformatics·2026
Same journal

Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.

Journal of cheminformatics·2026
Same journal

Count your bits: fingerprint benchmarking to assess broad chemical space representation.

Journal of cheminformatics·2026
Same journal

Sampling out-of-distribution chemical spaces via Bayesian flow.

Journal of cheminformatics·2026
Same journal

Hold on tight: the kinetic profiling of opioid receptor ligands using the CORAL-MD.

Journal of cheminformatics·2026
Same journal

Transformer-accelerated discovery of inhibitors targeting the RpsA<sub>Δ438</sub> deletion in PZA-resistant tuberculosis.

Journal of cheminformatics·2026
See all related articles

Related Experiment Video

Updated: May 30, 2026

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

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment.

Jonna C Stålring1, Lars A Carlsson, Pedro Almeida

  • 1Computational Toxicology, Global Safety Assessment, AstraZeneca R&D, Pepparedsleden 1, 431 53 Mölndal, Sweden. Jonna.Stalring@astrazeneca.com.

Journal of Cheminformatics
|July 30, 2011
PubMed
Summary
This summary is machine-generated.

AZOrange is an Open Source machine learning platform for developing accurate quantitative structure-activity relationship (QSAR) models. It offers automated workflows and high-performance algorithms, accessible without programming knowledge, supporting regulatory quality standards.

More Related Videos

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
14:34

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

Related Experiment Videos

Last Updated: May 30, 2026

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

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
14:34

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

Area of Science:

  • Computational chemistry
  • cheminformatics
  • Machine learning

Background:

  • Machine learning is increasingly used in quantitative structure-activity relationship (QSAR) modeling.
  • Growing QSAR datasets necessitate computationally efficient and accessible machine learning algorithms.
  • Open Source solutions align with scientific principles of transparency and reproducibility, gaining regulatory acceptance.

Purpose of the Study:

  • To introduce AZOrange, an Open Source machine learning platform for QSAR model development.
  • To provide a high-performance tool that supports the full workflow of QSAR modeling.
  • To enable researchers without extensive machine learning expertise to build regulatory-quality QSAR models.

Main Methods:

  • Implementation of the AZOrange Open Source machine learning package.
  • Development of an automated workflow for QSAR model generation, including descriptor calculation, model building, validation, and selection.
  • Customization and interfacing of multiple high-performance machine learning algorithms.
  • Generalized, automated model hyper-parameter selection process.

Main Results:

  • AZOrange supports batch generation of QSAR models through a comprehensive workflow.
  • The platform facilitates efficient, dataset-specific selection of machine learning algorithms to enhance model accuracy.
  • AZOrange allows users to create applications via scripting or graphical programming, requiring no prior programming knowledge.

Conclusions:

  • AZOrange addresses the need for an Open Source, high-performance machine learning platform for QSAR modeling.
  • The platform facilitates the efficient development of accurate QSAR models that meet regulatory requirements.
  • AZOrange promotes accessibility and usability for researchers in the QSAR community.