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

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.

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Graphical Representation of Inequalities

Graphical Representation of Inequalities

The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...

Newman Projections

Newman Projections

Different notations are used to represent the three-dimensional structure of molecules on two-dimensional surfaces. One of the most commonly used representations is the dash-wedge formula. The dashed wedges, solid wedges, and the plane lines indicate the groups situated behind the plane, coming out of the plane, and in the plane, respectively.
The organic molecules rotate across the single bonds leading to numerous temporary three-dimensional structures of varying energy known as...

Fischer Projections

Fischer Projections

Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines. While...

You might also read

Related Articles

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

Sort by

Same author

Rethinking false-negative rate of sentinel node navigation surgery after non-curative endoscopic submucosal dissection: Insights from SENORITA 2 trial.

Chinese journal of cancer research = Chung-kuo yen cheng yen chiu·2026

Same author

Capturing omega sign in the clinical assessment of depression by deep learning.

Translational psychiatry·2026

Same author

AI decodes protein-ligand binding.

Nature chemical biology·2026

Same author

Discovery of a New Scaffold RSK2 Inhibitor With Virtual and Phenotypical Screening.

ChemMedChem·2026

Same author

Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform.

Nature protocols·2026

Same author

EpiMII: Structure-Aware Graph Neural Networks for MHC-II Epitope Generation.

Research (Washington, D.C.)·2026

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

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

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

Published on: June 13, 2025

Unified and explainable molecular representation learning for imperfectly annotated data from the hypergraph view.

Bowen Wang¹, Junyou Li², Donghao Zhou¹

¹Department of Computer Science and Engineering, The Chinese University of Hong Kong, Ma Liu Shui, Hong Kong, China.

Nature Communications

|October 1, 2025

Summary

This summary is machine-generated.

Molecular representation learning (MRL) accelerates drug discovery by predicting chemical properties. OmniMol, a novel framework, enhances MRL explainability and performance by modeling molecules as hypergraphs and incorporating physical symmetry.

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 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

Related Experiment Videos

Last Updated: Jan 16, 2026

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

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

Published on: June 13, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 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

Area of Science:

Computational Chemistry
Drug Discovery
Machine Learning

Background:

Molecular representation learning (MRL) shows potential in drug development.
Dataset imperfections and lack of explainability hinder MRL model design.

Purpose of the Study:

To develop a unified and explainable multi-task MRL framework.
To address challenges in MRL for drug discovery.

Main Methods:

Formulated molecules and properties as a hypergraph, capturing property-property, molecule-property, and molecule-molecule relationships.
Developed OmniMol, integrating a meta-information encoder and task-routed mixture of experts (t-MoE).
Implemented an SE(3)-encoder with equilibrium conformation supervision for physical symmetry and conformational relaxation.

Main Results:

Achieved state-of-the-art performance in chemical property prediction.
Demonstrated top performance in chirality-aware tasks.
Provided explainability for all three key molecular relationships.

Conclusions:

OmniMol offers a unified, explainable, and high-performing framework for molecular representation learning.
The framework effectively captures correlations among properties and physical principles among molecules.
OmniMol shows promise for practical applications in accelerating drug development.