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

Mutagenicity and Carcinogenicity01:25

Mutagenicity and Carcinogenicity

1.2K
Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...
1.2K

You might also read

Related Articles

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

Sort by
Same author

A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning models.

Journal of computer-aided molecular design·2026
Same author

AI and ML for small molecule drug discovery in the big data era IV.

Molecular diversity·2026
Same author

Machine Learning-Based Quantitative Structure Activity Relationship Modeling of Repeated Dose Toxicity: A Data-Driven Approach Following Organisation for Economic Co-operation and Development Test Guidelines 407, 408, and 422 Supported by Experimental Validation.

Chemical research in toxicology·2026
Same author

Nanoinformatics: spanning scales, systems and solutions.

Beilstein journal of nanotechnology·2026
Same author

Comparative machine learning and deep learning frameworks for robust carcinogenicity prediction and activity cliff analysis.

Environmental science. Processes & impacts·2026
Same author

Machine learning-based q-RASAR modelling for the in silico identification of novel α7nAChR agonists for anti-Alzheimer's drug discovery.

SAR and QSAR in environmental research·2025

Related Experiment Video

Updated: May 23, 2025

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
00:05

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

Published on: August 28, 2019

13.8K

The multiclass ARKA framework for developing improved q-RASAR models for environmental toxicity endpoints.

Arkaprava Banerjee1, Kunal Roy1

  • 1Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India. arka.banerjee16@gmail.com.

Environmental Science. Processes & Impacts
|April 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces ARKA-RASAR, an improved workflow for chemical toxicity prediction, enhancing Quantitative Structure-Activity Relationship (QSAR) models. The new method offers more accurate predictions for filling crucial toxicity data gaps.

More Related Videos

Author Spotlight: High-Throughput Toxicity Screening Using Zebrafish Embryo Startle Response Assay
06:25

Author Spotlight: High-Throughput Toxicity Screening Using Zebrafish Embryo Startle Response Assay

Published on: January 12, 2024

1.3K
Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

2.6K

Related Experiment Videos

Last Updated: May 23, 2025

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
00:05

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

Published on: August 28, 2019

13.8K
Author Spotlight: High-Throughput Toxicity Screening Using Zebrafish Embryo Startle Response Assay
06:25

Author Spotlight: High-Throughput Toxicity Screening Using Zebrafish Embryo Startle Response Assay

Published on: January 12, 2024

1.3K
Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

2.6K

Area of Science:

  • Computational toxicology and cheminformatics
  • Environmental science and risk assessment
  • Quantitative Structure-Activity Relationship (QSAR) modeling

Background:

  • Accurate chemical toxicity data is essential for regulatory and safety assessments.
  • Existing Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) models have limitations in predictive accuracy and cross-validation.
  • There is a continuous need for improved modeling strategies to efficiently fill toxicity data gaps.

Purpose of the Study:

  • To develop an improved q-RASAR workflow, termed ARKA-RASAR, by integrating the Arithmetic Residuals in K-groups Analysis (ARKA) framework.
  • To enhance the accuracy and reliability of toxicity predictions for commercial chemicals.
  • To provide a user-friendly tool for computing novel descriptors and developing robust predictive models.

Main Methods:

  • Developed the ARKA-RASAR workflow, incorporating QSAR descriptors and ARKA descriptors to identify similarity among chemical congeners.
  • Utilized five diverse toxicity datasets for model development and comparison with existing QSAR and q-RASAR models.
  • Employed a Java-based tool, Multiclass ARKA-v1.0, for computing multiclass ARKA descriptors and developed hybrid ARKA-RASAR models.

Main Results:

  • ARKA-RASAR models demonstrated superior performance compared to traditional QSAR and q-RASAR models, as validated by Sum of Ranking Differences (SRD) analysis.
  • The workflow showed robust training, testing, and cross-validation statistics, with significant differences confirmed by least significant difference procedures.
  • External validation on pesticide metabolites for acute fish toxicity prediction yielded encouraging and accurate results.

Conclusions:

  • The ARKA-RASAR modeling framework offers a significant advancement in predicting chemical toxicity, addressing limitations of previous methods.
  • The developed workflow is straightforward, reproducible, and transferable, facilitating its adoption in environmental toxicity assessments.
  • ARKA-RASAR presents a promising approach for generating highly robust and predictive models to fill critical gaps in environmental toxicity data.