Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Hybridization of Atomic Orbitals II

Hybridization of Atomic Orbitals II

sp3d and sp3d 2 Hybridization

Molecular Models

Molecular Models

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.

Hybridization of Atomic Orbitals I

Hybridization of Atomic Orbitals I

The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...

Valence Bond Theory and Hybridized Orbitals

Valence Bond Theory and Hybridized Orbitals

According to valence bond theory, a covalent bond results when: (1) an orbital on one atom overlaps an orbital on a second atom, and (2) the single electrons in each orbital combine to form an electron pair. The strength of a covalent bond depends on the extent of overlap of the orbitals involved. Maximum overlap is possible when the orbitals overlap on a direct line between the two nuclei.
A σ bond (single bond in a Lewis structure) is a covalent bond in which the electron density is...

Molecular Orbital Theory II

Molecular Orbital Theory II

Molecular Orbital Energy Diagrams

You might also read

Related Articles

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

Sort by

Same author

Multi-AOP: a lightweight multi-view deep learning framework for antioxidant peptide discovery.

Bioresources and bioprocessing·2026

Same author

Enhancing kinase-inhibitor activity and selectivity prediction through contrastive learning.

Nature communications·2025

Same author

Integrating AI and genomics: predictive CNN models for schizophrenia phenotypes.

Journal of integrative bioinformatics·2025

Same author

Predicting progression of mild cognitive impairment patients through four distinctive subgroups obtained by unsupervised learning algorithms.

Journal of Alzheimer's disease : JAD·2025

Same author

ABIET: An explainable transformer for identifying functional groups in biological active molecules.

Computers in biology and medicine·2025

Same author

Combining graph neural networks and transformers for few-shot nuclear receptor binding activity prediction.

Journal of cheminformatics·2024

Search research articles

Related Experiment Video

Updated: Jun 18, 2025

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

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

Published on: October 13, 2023

A Hybrid GNN Approach for Improved Molecular Property Prediction.

Pedro Quesado¹, Luis H M Torres¹, Bernardete Ribeiro¹

¹Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, Univ Coimbra, Coimbra, Portugal.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|July 31, 2024

Summary

This summary is machine-generated.

This study introduces a novel hybrid deep learning approach using graph neural networks (GNNs) for accurate molecular property prediction in drug discovery. The hybrid GNN model significantly improves upon existing methods, accelerating the identification of potential therapeutic compounds.

Keywords:

Deep Learning Drug discovery Graph Neural Networks Molecular Graph Molecular property prediction

More Related Videos

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

Related Experiment Videos

Last Updated: Jun 18, 2025

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

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

Published on: October 13, 2023

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

Area of Science:

Computational Chemistry
Cheminformatics
Artificial Intelligence in Drug Discovery

Background:

Drug discovery is essential for improving human health but faces challenges with time-consuming and resource-intensive experimental methods.
Deep learning (DL), particularly graph neural networks (GNNs), offers a powerful alternative for identifying drug candidates by analyzing molecular data patterns.
Current GNN frameworks have limitations, with individual models excelling in specific tasks but lacking generalizability.

Purpose of the Study:

To develop a hybrid graph neural network (GNN) approach that integrates multiple GNN frameworks to enhance molecular property prediction accuracy.
To combine the strengths of different GNN methods and mitigate their individual limitations in the context of drug discovery.
To provide a more robust and accurate computational tool for identifying potential therapeutic compounds.

Main Methods:

A multi-layered hybrid GNN architecture was designed, integrating diverse graph-based methods.
The architecture computes graph embeddings by aggregating information from multiple GNN frameworks.
Extensive experiments were conducted on multiple benchmark datasets to validate the proposed approach.

Main Results:

The proposed hybrid GNN approach demonstrated significantly superior performance compared to state-of-the-art graph-based models.
The model accurately predicts molecular properties, aiding in the identification of promising drug candidates.
The results highlight the effectiveness of integrating multiple GNNs for improved molecular representation learning.

Conclusions:

The hybrid GNN approach offers a significant advancement in molecular property prediction for drug discovery.
This method effectively overcomes the limitations of individual GNN frameworks, leading to enhanced accuracy.
The developed approach provides a valuable tool for accelerating the identification of novel therapeutic compounds.