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

Functional Groups02:45

Functional Groups

51.0K
51.0K
Functional Groups02:45

Functional Groups

25.2K
25.2K
Functional Groups02:45

Functional Groups

91.3K
Functional groups are a group of atoms with characteristic properties, which when linked to the carbon skeleton of a molecule, alter the properties of that molecule. For example, the presence of certain functional groups on a molecule will make them hydrophilic, whereas others will make them hydrophobic. These functional groups are an indispensable part of organic chemistry and important components of biological molecules, such as carbohydrates, proteins, lipids, and nucleic acids. Each...
91.3K
Introduction to Functional Groups02:08

Introduction to Functional Groups

38.2K

Functional groups are group of atoms with specific chemical properties that occur within organic molecules and sometimes denoted as “R”. Functional groups are found along the carbon backbone of macromolecules can form chains or rings of carbon atoms. Functional groups can “functionalize” a compound by enabling it to adopt different physical and chemical properties.  
Types of common functional groups
The table below summarizes some of the major functional groups in organic chemistry....
38.2K
Overview of Advanced Functional Groups02:22

Overview of Advanced Functional Groups

31.0K

Functional groups are groups of atoms with specific chemical properties that occur within organic molecules and are sometimes denoted as “R”. Functional groups can “functionalize” a compound by enabling it to adopt different physical and chemical properties.
Types of Advanced Functional Groups
The table below summarizes some of the major functional groups in organic chemistry.
31.0K
Overview of Functional Groups01:19

Overview of Functional Groups

17.7K
Functional groups are a group of atoms with characteristic properties, which when linked to the carbon skeleton of a molecule, alter the properties of that molecule. For example, certain functional groups will make a molecule hydrophilic, whereas others will make them hydrophobic. These functional groups are an indispensable part of organic chemistry and important components of biological molecules, such as carbohydrates, proteins, lipids, and nucleic acids. Each functional group is a unique...
17.7K

You might also read

Related Articles

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

Sort by
Same author

Drug response profile-based machine learning enables strategic cell line and compound selection for drug development.

Bioinformatics (Oxford, England)·2026
Same author

Investigating uncharacterised genes in Saccharomyces cerevisiae using robot scientists.

Scientific reports·2026
Same author

A case study of the application of AI to early stage drug discovery.

Scientific reports·2025
Same author

An Investigation of TDA1 Deficiency in Saccharomyces cerevisiae During Diauxic Growth.

Yeast (Chichester, England)·2025
Same author

Establishing predictive machine learning models for drug responses in patient derived cell culture.

NPJ precision oncology·2025
Same author

Improved Machine Learning Predictions of EC50s Using Uncertainty Estimation from Dose-Response Data.

Journal of chemical information and modeling·2025
Same journal

ZHMolTopoRPI: A Commutative Algebra-Driven Deep Learning Framework for Robust RNA-Protein Interaction Prediction.

Journal of chemical information and modeling·2026
Same journal

PP-MAPS: Dynamic Pharmacophore Signatures of Protein-Peptide Interfaces from Molecular Dynamics Trajectories.

Journal of chemical information and modeling·2026
Same journal

Evaluating Molecular Representations for Predicting Cyclodextrin-PFAS Binding Energy with Machine Learning: Domain Transfer and Data Limitations.

Journal of chemical information and modeling·2026
Same journal

Foldify: Web Application for Protein Structure Prediction.

Journal of chemical information and modeling·2026
Same journal

Identification of Noncovalent Small-Molecules from Virtual Screening Toward the Development of Potential KRAS Inhibitors.

Journal of chemical information and modeling·2026
Same journal

LLM-Assembled Multiscale Cascades for High-Throughput Screening: The Case of Thermoelectric Materials.

Journal of chemical information and modeling·2026
See all related articles
  1. Home
  3. Compressing Chemistry Reveals Functional Groups.
  1. Home
  3. Compressing Chemistry Reveals Functional Groups.

Related Experiment Video

Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.9K

Compressing Chemistry Reveals Functional Groups.

Ruben Sharma1, Ross D King1,2

  • 1Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.

Journal of Chemical Information and Modeling
|March 18, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new computational learning algorithm to identify chemical substructures that effectively explain molecular functions. The discovered patterns improve predictions of biological activity, outperforming traditional methods.

More Related Videos

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.8K
A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System
10:27

A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System

Published on: June 12, 2019

9.2K

Related Experiment Videos

Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.9K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.8K
A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System
10:27

A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System

Published on: June 12, 2019

9.2K

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Machine learning

Background:

  • Traditional chemical functional groups are widely used but their explanatory utility lacks formal assessment.
  • Computational learning theory suggests data compression can reveal good explanations.

Purpose of the Study:

  • To formally assess the utility of traditional chemical functional groups.
  • To develop an unsupervised learning algorithm for discovering explanatory molecular substructures.
  • To improve bioactivity prediction models.

Main Methods:

  • An unsupervised learning algorithm based on the Minimum Message Length (MML) principle was developed.
  • The algorithm searched for substructures that compress approximately three million biologically relevant molecules.
  • The algorithm was applied to 24 bioactivity prediction datasets to discover dataset-specific functional groups.

    • Main Results:

    • Discovered substructures include known functional groups and novel, larger patterns with specific functions.
    • Dataset-specific functional groups were identified.
    • Fingerprints from these groups significantly outperformed standard representations (e.g., MACCS, Morgan) in bioactivity regression tasks.

    Conclusions:

    • The MML-based approach effectively identifies meaningful chemical substructures.
    • Data-driven functional groups offer superior performance in predictive modeling for bioactivity.
    • This method provides a powerful tool for chemical explanation and drug discovery.