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.

Molecular Comparison of Gases, Liquids, and Solids

Molecular Comparison of Gases, Liquids, and Solids

Particles in a solid are tightly packed together (fixed shape) and often arranged in a regular pattern; in a liquid, they are close together with no regular arrangement (no fixed shape); in a gas, they are far apart with no regular arrangement (no fixed shape). Particles in a solid vibrate about fixed positions (cannot flow) and do not generally move in relation to one another; in a liquid, they move past each other (can flow) but remain in essentially constant contact; in a gas, they move...

Distribution of Molecular Speeds

Distribution of Molecular Speeds

The motion of molecules in a gas is random in magnitude and direction for individual molecules, but a gas of many molecules has a predictable distribution of molecular speeds. This predictable distribution of molecular speeds is known as the Maxwell-Boltzmann distribution. The distribution of molecular speeds in liquids is comparable to that of gases but not identical and can help to understand the phenomenon of the boiling and vapor pressure of a liquid. Consider that a molecule requires a...

Molecular Shapes

Molecular Shapes

Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.
Two regions of electron density in a diatomic...

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Modern Molecular Taxonomy

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...

You might also read

Related Articles

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

Sort by

Same author

Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.

Nature communications·2025

Same author

Author Correction: Computer-assisted quantification of motile and invasive capabilities of cancer cells.

Scientific reports·2025

Same author

Selective inhibition of Mycobacterium tuberculosis GpsI unveils a novel strategy to target the RNA metabolism.

Nucleic acids research·2025

Same author

Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023.

Chemical science·2025

Same author

Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023.

Chemical science·2025

Same author

Discovery of 1,3,4-Oxadiazin-5-One Derivative CJ1-34 as a Partial ATP Synthase Inhibitor for CNS Applications.

Chemistry (Weinheim an der Bergstrasse, Germany)·2025

Same journal

SpaceExpander: An Automated System for Drafting Markush Claims to Expand Chemical Space.

Molecular informatics·2026

Same journal

A Structure-Informed Atlas of Venom-Derived Peptides Reveals the Organization of Chemical Space.

Molecular informatics·2026

Same journal

ConGen: Targeted Molecule Generation Through Contrastive Learning and Latent Optimization.

Molecular informatics·2026

Same journal

Novel Molecules Generation Using Graph Generative Adversarial Networks.

Molecular informatics·2026

Same journal

An Attention-Driven Graph Transformer With Nonlinear Modeling and Neuro-Fuzzy Fusion for High-Order Toxic Molecular Graph Learning.

Molecular informatics·2026

Same journal

Molecular Modeling and Chemoinformatics in Ukraine.

Molecular informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 17, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Graph Kernels for Molecular Similarity.

Matthias Rupp^1,2, Gisbert Schneider³

¹Beilstein Endowed Chair for Cheminformatics, Goethe University, Siesmayerstr. 70, 60323 Frankfurt am Main, Germany. mrupp@mrupp.info.

Molecular Informatics

|July 28, 2016

Summary

This summary is machine-generated.

Graph kernels offer powerful molecular similarity measures for cheminformatics tasks like virtual screening. This review covers graph kernel types, their strengths, weaknesses, and applications in machine learning.

Keywords:

Graph kernels Machine learning Molecular similarity Structure graph

More Related Videos

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Related Experiment Videos

Last Updated: Mar 17, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Published on: March 12, 2020

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Area of Science:

Cheminformatics
Machine Learning
Graph Theory

Background:

Molecular similarity is crucial for drug discovery and chemical research.
Graph kernels provide a formal mathematical framework for comparing molecular structures.
Their positive semi-definite property enables integration with kernel-based machine learning algorithms.

Purpose of the Study:

To review major graph kernel types for molecular similarity.
To discuss the advantages and limitations of different graph kernel approaches.
To highlight successful applications of graph kernels in cheminformatics.

Main Methods:

Review of graph kernels based on random walks.
Analysis of subgraph-based graph kernels.
Examination of graph kernels utilizing optimal assignments.
Discussion of kernel properties and suitability for machine learning.

Main Results:

Graph kernels are versatile tools for quantitative structure-property relationships and virtual screening.
Different kernel types offer distinct advantages for specific cheminformatics problems.
Kernel-based methods enhance the predictive power of machine learning models.

Conclusions:

Graph kernels are essential for advanced cheminformatics applications.
Understanding different kernel types is key to selecting appropriate similarity measures.
Graph kernels facilitate the development of robust predictive models in drug discovery.