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

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.6K
VSEPR Theory for Determination of Electron Pair Geometries
34.6K
Molecular Models02:00

Molecular Models

39.0K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
39.0K
Molecular Geometry and Dipole Moments02:36

Molecular Geometry and Dipole Moments

13.2K
The VSEPR theory can be used to determine the electron pair geometries and molecular structures as follows:
13.2K
Molecular Orbital Theory I02:35

Molecular Orbital Theory I

32.4K
Overview of Molecular Orbital Theory
32.4K
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.3K
Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
2.3K
Conjugate Addition to α,β-Unsaturated Carbonyl Compounds01:09

Conjugate Addition to α,β-Unsaturated Carbonyl Compounds

4.4K
α,β-Unsaturated carbonyl compounds are molecules bearing a carbonyl and alkene functionality in conjugation with each other. The conjugation in the molecule leads to three resonance structures. The hybrid form exhibits two probable electrophilic sites: the carbonyl carbon and the β carbon.
4.4K

You might also read

Related Articles

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

Sort by
Same author

End-to-End Mandarin Speech Reconstruction Based on Ultrasound Tongue Images Using Deep Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025
Same author

Hybrid Quantum Deep Learning With Superpixel Encoding for Earth Observation Data Classification.

IEEE transactions on neural networks and learning systems·2025
Same author

Comparison of different approaches applied for surgical correction of partial anomalous pulmonary venous connection.

Cardiology in the young·2025
Same author

Enhanced heart sound anomaly detection via WCOS: a semi-supervised framework integrating wavelet, autoencoder and SVM.

Frontiers in neuroinformatics·2025
Same author

Inhibition of soluble epoxide hydrolase ameliorates cerebral blood flow autoregulation and cognition in alzheimer's disease and diabetes-related dementia rat models.

GeroScience·2025
Same author

Developing Soluplus®-Based Microparticle Amorphous Solid Dispersions with High Drug Loading for Enhanced Celecoxib Dissolution via Electrospraying.

AAPS PharmSciTech·2025

Related Experiment Video

Updated: Aug 12, 2025

Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions
11:21

Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

Published on: January 20, 2022

3.5K

Combining Group-Contribution Concept and Graph Neural Networks Toward Interpretable Molecular Property Models.

Adem R N Aouichaoui1, Fan Fan1, Seyed Soheil Mansouri1

  • 1Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs. LyngbyDK-2800, Denmark.

Journal of Chemical Information and Modeling
|January 30, 2023
PubMed
Summary
This summary is machine-generated.

New interpretable graph neural network (GNN) models, attentive group-contribution (AGC) and GroupGAT, improve predictions for chemical properties. These models offer transparency by highlighting key molecular substructures, enhancing drug discovery.

More Related Videos

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

445

Related Experiment Videos

Last Updated: Aug 12, 2025

Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions
11:21

Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

Published on: January 20, 2022

3.5K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

445

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Machine learning in chemistry

Background:

  • Quantitative structure-property relationships (QSPRs) are crucial for accelerating compound discovery but often lack generalizability and accuracy.
  • Existing machine learning and deep learning models for QSPRs frequently suffer from a lack of transparency and interpretability.
  • Traditional group contribution (GC) methods provide interpretability but may have limitations in predictive power.

Purpose of the Study:

  • To develop novel, interpretable graph neural network (GNN) models for predicting physicochemical properties of organic compounds.
  • To integrate the fundamental concept of group contributions (GC) into GNN architectures for enhanced interpretability.
  • To improve the accuracy and generalizability of QSPR models while maintaining transparency.

Main Methods:

  • Development of two interpretable GNN models: attentive group-contribution (AGC) and group-contribution-based graph attention (GroupGAT).
  • Integration of group contribution (GC) principles within the GNN framework.
  • Utilization of attention mechanisms to highlight substructures with the highest attention weights in molecular representations.

Main Results:

  • The proposed AGC and GroupGAT models demonstrated superior performance compared to classical GC models.
  • The developed GNN models outperformed other existing GNN approaches for predicting aqueous solubility, melting point, and various enthalpies (formation, combustion, fusion).
  • The interpretability feature successfully highlighted key molecular substructures relevant to specific properties, consistent with semiempirical GC models.

Conclusions:

  • The developed interpretable GNN models offer a powerful and transparent approach for QSPR modeling.
  • Integrating GC concepts into GNNs enhances model interpretability by identifying critical molecular substructures.
  • These models represent a significant advancement in predicting chemical properties accurately and understandably, aiding in rational compound design.