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

You might also read

Related Articles

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

Sort by
Same author

In Vitro Evaluation of Periodontal Fibroblast Response to Bioinspired Porous Channel-Embedded Zirconia Surfaces.

Journal of biomedical materials research. Part A·2026
Same author

Special Issue: Biomaterials for Dental and Orthopedic Applications.

International journal of molecular sciences·2026
Same author

A Scorecard for Information Synthesis in Multiple Experimental Conditions: Application to Bacterial Biofilm Matrix Transcriptomics.

Current microbiology·2025
Same author

Tellurium-Doped Silanised Bioactive Glass-Chitosan Hydrogels: A Dual Action for Antimicrobial and Osteoconductive Platforms.

Polymers·2025
Same author

Reconstruction of a Chronic Quadriceps Tendon Rupture in an Elderly Polio Patient.

Biomedicines·2025
Same author

Correction: Nascimben et al. Extracellular Vesicle Protein Expression in Doped Bioactive Glasses: Further Insights Applying Anomaly Detection. <i>Int. J. Mol. Sci.</i> 2024, <i>25</i>, 3560.

International journal of molecular sciences·2025

Related Experiment Video

Updated: Aug 10, 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

14.0K

Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework.

Mauro Nascimben1,2, Lia Rimondini1

  • 1Department of Health Sciences, Center on Autoimmune and Allergic Diseases CAAD, Università del Piemonte Orientale, 28100 Novara, Italy.

Molecules (Basel, Switzerland)
|February 11, 2023
PubMed
Summary

Spiking neural networks show promise for predicting molecular toxicity, offering energy-efficient alternatives for chemoinformatics tasks. These biologically inspired models achieve high accuracy, comparable to existing methods.

Keywords:
in silico toxicity predictionmachine learningmolecular fingerprintsspiking neural networks

More Related Videos

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

2.8K
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

1.4K

Related Experiment Videos

Last Updated: Aug 10, 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

14.0K
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

2.8K
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

1.4K

Area of Science:

  • Computational chemistry
  • Artificial intelligence
  • Toxicology

Background:

  • Spiking neural networks (SNNs) are biologically inspired machine learning models.
  • SNNs offer potential for energy-efficient computation on specialized hardware.
  • Quantitative structure-activity relationship (QSAR) analysis is crucial for predicting molecular properties like toxicity.

Purpose of the Study:

  • To evaluate the efficacy of SNNs in QSAR analysis for predicting molecular toxicity.
  • To explore SNNs as an alternative to traditional computational methods in chemoinformatics.

Main Methods:

  • Utilized multiple public-domain compound databases.
  • Applied SNNs to molecular fingerprints of varying lengths.
  • Conducted hyperparameter analysis for SNN optimization.

Main Results:

  • SNNs achieved prediction accuracies comparable to established high-quality QSAR frameworks.
  • Demonstrated the performance of SNNs across different molecular fingerprint representations.
  • Identified optimal hyperparameters for SNN application in toxicity prediction.

Conclusions:

  • SNNs present a viable and accurate approach for molecular toxicity prediction.
  • This research suggests SNNs as a potential energy-efficient alternative for chemoinformatics.
  • Findings may drive innovation in computational toxicology and drug discovery.