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Related Concept Videos

Functional Groups02:45

Functional Groups

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...
Functional Groups02:45

Functional Groups

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...
Overview of Advanced Functional Groups02:22

Overview of Advanced Functional Groups


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.
Functional Groups02:45

Functional Groups

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...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and the...

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A Protocol for Computer-Based Protein Structure and Function Prediction
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RGReco: a unified framework for automated R-group recognition in chemical publications.

Yuanjie Xiang1, Yanghong Luo1, Renshuang Liu1

  • 1School of Informatics, Hunan University of Chinese Medicine, Changsha, 410208, Hunan, PR China.

Journal of Cheminformatics
|March 11, 2026
PubMed
Summary

Researchers developed RGReco, a novel framework for extracting R-group information from chemical literature. This AI tool combines deep learning and chemical rules to efficiently parse diverse R-group data from text and images.

Keywords:
Chemical rulesData-driven artificial intelligenceDeep learningMultistage pipelineR-group recognitionRGReco

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Area of Science:

  • Medicinal Chemistry
  • Artificial Intelligence
  • cheminformatics

Background:

  • R-group information is vital for data-driven AI in medicinal chemistry.
  • Manual integration of R-group data from publications is inefficient due to varied formats.
  • Existing automated R-group recognition tools are underdeveloped.

Purpose of the Study:

  • To present RGReco, a novel framework for automated R-group information parsing.
  • To develop a new process for recognizing substituent structures and parsing related text.
  • To improve the efficiency and accuracy of chemical data extraction from scientific literature.

Main Methods:

  • RGReco combines deep learning algorithms with chemical rules.
  • A multistage pipeline is employed to parse R-group information from both images and text.
  • A new method for recognizing substituent structures and parsing associated text was developed.

Main Results:

  • RGReco achieved a precision of 86.4%, recall of 79.7%, and F1 score of 82.9% on a custom dataset.
  • The framework effectively handles diverse R-group image representations found in real-world literature.
  • Demonstrated significant improvement over existing methods for R-group data extraction.

Conclusions:

  • RGReco offers a precise and comprehensive solution for parsing R-group information.
  • The framework accelerates the extraction of crucial chemical data for AI research.
  • RGReco provides a valuable technological tool for medicinal chemists and AI researchers.