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Related Concept Videos

π Electron Effects on Chemical Shift: Overview01:27

π Electron Effects on Chemical Shift: Overview

An applied magnetic field causes loosely bound π-electrons in organic molecules to circulate, producing a local or induced diamagnetic field over a large spatial volume. As the molecules tumble in solution, the field generated by π-electrons in spherical substituents results in a zero net field. However, the net field generated by π-electrons in non-spherical substituents is not zero. The effect of this induced field depends on the orientation of the molecule with respect to B0, resulting in...

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Related Experiment Video

Updated: Jun 8, 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

ChemBrowser: a flexible framework for mining chemical documents.

Xian Wu1, Li Zhang, Ying Chen

  • 1IBM China Research Lab, Beijing 100193, China. wuxian@cn.ibm.com

Advances in Experimental Medicine and Biology
|September 25, 2010
PubMed
Summary
This summary is machine-generated.

Automated chemical annotation, extracting chemical and biological entities, is crucial for R&D. A flexible framework, ChemBrowser, combines various techniques for effective chemical entity extraction from documents.

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Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
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Published on: March 22, 2019

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Last Updated: Jun 8, 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

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
07:11

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Published on: March 22, 2019

Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Chemistry

Background:

  • Automated extraction of chemical and biological entities and relations from text is vital for biochemical research and development.
  • Text mining and artificial intelligence (AI) technologies offer potential for automated chemical entity extraction, termed Chemical Annotation.
  • Existing techniques include dictionary-based, rule-based, and machine learning approaches, but no single method is universally effective.

Purpose of the Study:

  • To address the challenges of real-world chemical annotation scenarios.
  • To present ChemBrowser, a flexible framework for composing and executing diverse chemical annotation techniques.
  • To demonstrate the adaptability of ChemBrowser for specific tasks, such as extracting chemical names from patents.

Main Methods:

  • Development of ChemBrowser, featuring customizable processing units for annotation.
  • Introduction of a high-level composition language to define annotator workflows.
  • Implementation of an execution engine to translate the composition language into functional annotators.
  • Tailoring an annotator for chemical name extraction from patent documents.

Main Results:

  • ChemBrowser provides a flexible framework for chemical annotation.
  • The combinatorial approach allows for effective composition of different annotation techniques.
  • An annotator tailored for chemical name extraction from patents was successfully created and easily modified.
  • Demonstrated the practical impact and configurability of the ChemBrowser system.

Conclusions:

  • A combinatorial approach using flexible frameworks like ChemBrowser is effective for chemical annotation.
  • ChemBrowser facilitates the creation and customization of chemical annotators for specific needs.
  • The system shows promise for improving the efficiency of biochemical research and development through automated text analysis.