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

Molecular Models02:00

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.
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
VSEPR Theory02:37

VSEPR Theory

Valence shell electron-pair repulsion theory (VSEPR theory) enables us to predict the molecular structure around a central atom from an examination of the number of bonds and lone electron pairs in its Lewis structure. The VSEPR model assumes that electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between these electron pairs by maximizing the distance between them. The electrons in the valence shell of a central atom form either bonding...
Newman Projections02:06

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 conformers.
Cell Diagrams and IUPAC Conventions01:21

Cell Diagrams and IUPAC Conventions

Electrochemical cell notation is a standardized symbolic representation that communicates the structure and reaction pathway of galvanic and electrolytic cells. This notation plays a critical role in describing redox reactions and electrochemical cell configurations without the need for detailed diagrams.In electrochemical cell notation, a single vertical line “|” denotes a phase boundary, such as between a solid electrode and an aqueous solution. A double vertical line “||” represents a salt...
Fischer Projections02:18

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

PegaPlus─Interactive Machine Learning by Human Observation for Efficient Clustering and Analysis of Structure-Activity Data.

Journal of chemical information and modeling·2026
Same author

Enabling Automatic Generation of Protein-Ligand Complex Data Sets with Atomistic Detail.

Journal of chemical information and modeling·2026
Same author

Guiding Similarity Search in Chemical Fragment Spaces with Weighted Fingerprints.

Journal of chemical information and modeling·2026
Same author

ActivityFinder: Toward the Fully Automatic Integration of Structural and Binding Affinity Data.

Journal of chemical information and modeling·2026
Same author

A bottom-up approach to find lead compounds in expansive chemical spaces.

Communications chemistry·2025
Same author

Correction: SAVI Space-combinatorial encoding of the billion-size synthetically accessible virtual inventory.

Scientific data·2025
Same journal

tmGNN-XAI: An Explainable Graph Neural Network Tool for Predicting Electronic Properties of Transition Metal Complexes from SMILES.

Journal of chemical information and modeling·2026
Same journal

QSAR in the Browser: An Interactive Cheminformatics Web Application.

Journal of chemical information and modeling·2026
Same journal

FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.

Journal of chemical information and modeling·2026
Same journal

Derisking Affinity Optimization for Macrocycles and Cyclic Peptides: High-Precision Free Energy Simulations across Five Diverse Targets.

Journal of chemical information and modeling·2026
Same journal

An End-User Audit of Reproducibility, Data Leakage, and Overfitting of the Top-Ranked ADMET Prediction Models in TDC Leaderboards.

Journal of chemical information and modeling·2026
Same journal

PFASGroups: An Open-Source Framework for Automated Identification, Structural Classification, and Prioritization of Per- and Polyfluoroalkyl Substances.

Journal of chemical information and modeling·2026
See all related articles

Related Experiment Video

Updated: Jun 9, 2026

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

From structure diagrams to visual chemical patterns.

Karen Schomburg1, Hans-Christian Ehrlich, Katrin Stierand

  • 1Research Group for Computational Molecular Design, Center for Bioinformatics, University of Hamburg, Hamburg, Germany.

Journal of Chemical Information and Modeling
|August 28, 2010
PubMed
Summary
This summary is machine-generated.

Chemists can now visualize chemical patterns with a new graphical concept, enhancing readability and accessibility. This innovation simplifies the formulation and understanding of complex molecular features for broader scientific use.

More Related Videos

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Related Experiment Videos

Last Updated: Jun 9, 2026

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Chemical visualization

Background:

  • Chemical structures are typically communicated using 2D diagrams, which are preferred over systematic names.
  • Standardized visualization methods for chemical patterns, crucial for filtering molecules by properties, have been lacking.
  • Existing linear pattern languages (e.g., SMARTS) are difficult for chemists without computational expertise to interpret.

Purpose of the Study:

  • To introduce a novel visualization concept for chemical patterns.
  • To extend the standard structure diagram concept to include property descriptions and logical combinations of chemical features.
  • To develop a tool for converting text-based chemical patterns into an easily understandable visual format.

Main Methods:

  • Developed a new visualization concept for chemical patterns, building upon existing 2D structure diagram standards.
  • Created the SMARTSviewer tool to translate chemical patterns encoded in SMARTS (SMILES Arbitrary Target Specification) strings into visual representations.
  • Applied the visualization concept and tool to recent chemical publications across various fields.

Main Results:

  • The SMARTSviewer generates graphical depictions of chemical patterns, offering a clear overview of features, variations, and similarities.
  • The visual representation eliminates the need to decode complex, linear SMARTS expressions.
  • Demonstrated the broad applicability of the graphical chemical pattern language in diverse chemical research areas.

Conclusions:

  • The new visualization concept significantly improves the human readability and accessibility of chemical patterns.
  • The SMARTSviewer facilitates systematic pattern formulation for chemists, regardless of their computational background.
  • Graphical chemical pattern languages offer a powerful and versatile approach for representing and communicating molecular features in cheminformatics and beyond.