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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

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Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
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Machine-Readable Structural Information Is Essential for Natural Products Research.

Karin Steffen1, Nicholas H Oberlies2, Antonis Rokas1

  • 1Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee 37235, United States.

Journal of Natural Products
|October 20, 2025
PubMed
Summary

Publishing machine-readable text descriptors for new natural products can improve data sharing. This simple solution addresses challenges in accessing secondary metabolite big data, promoting scientific collaboration and reuse.

Keywords:
InChIInChIKeyMOLfileSMILESchemical structurescheminformaticschemoinformaticsdata accessibilitydigitization

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Area of Science:

  • Natural Products Chemistry
  • Cheminformatics
  • Scientific Data Management

Background:

  • Structural drawings of secondary metabolites contain valuable chemical information.
  • Current static formats limit the sharing and reuse of this crucial natural products data.
  • The growing need for comprehensive natural products 'big data' in research is unmet by existing data formats.

Purpose of the Study:

  • To discuss the challenges associated with sharing and accessing secondary metabolite structure data.
  • To propose a straightforward solution for enhancing the accessibility and reusability of this data.
  • To advocate for the adoption of machine-readable descriptors in natural products research.

Main Methods:

  • Analysis of current limitations in sharing chemical structure information.
  • Proposal of publishing machine-readable text descriptors for new secondary metabolites.
  • Discussion of implementation feasibility for authors and publishers.

Main Results:

  • Identified significant barriers in current methods of sharing secondary metabolite structural data.
  • Proposed machine-readable text descriptors as a practical solution to overcome these barriers.
  • Highlighted the potential for increased value derived from natural products research through improved data accessibility.

Conclusions:

  • Implementing machine-readable descriptors for new secondary metabolites is a simple and effective solution.
  • This approach aligns with the scientific norms of data sharing and promotes the 'big data' paradigm.
  • Adoption of this method will enhance the value and impact of research into the chemistry of Nature.