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Mining Structural Information from Gas Chromatography-Electron-Impact Ionization-Mass Spectrometry Data for

Yasuyuki Zushi1,2

  • 1Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology, 16-1 Onogawa, Tsukuba 305-8569, Japan.

Journal of Xenobiotics
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PubMed
Summary
This summary is machine-generated.

Quantitative structure-activity relationship (QSAR) prediction using mass spectral data offers a powerful alternative to traditional methods. This approach effectively predicts chemical properties and toxicities for unknown compounds, enhancing chemical safety assessments.

Keywords:
analytical-descriptor-based modelgas chromatography-electron-impact ionization-mass spectrometry (GC-EI-MS)mass spectraquantitative structure–activity relationship (QSAR)t-distributed stochastic neighbor embedding (t-SNE)unknown chemicals

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Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Toxicology

Background:

  • Quantitative structure-activity relationship (QSAR) models traditionally rely on molecular descriptors.
  • Machine learning advances enable QSAR prediction using analytical signals, such as mass spectra, bypassing the need for complete structural elucidation.
  • Interpreting the complex relationship between mass spectra and chemical structure remains a challenge for developing advanced QSAR methods.

Purpose of the Study:

  • To determine if gas chromatography-electron-impact ionization-mass spectrometry (GC-EI-MS) data provide sufficient structural information for QSAR prediction.
  • To compare the predictive performance of QSAR models based on analytical signals versus traditional molecular descriptors.
  • To evaluate the utility of analytical-signal-based QSAR for predicting physicochemical properties and toxicities of unknown compounds.

Main Methods:

  • Developed QSAR prediction models using machine learning with full scan mass spectral data as input.
  • Compared analytical signal-based QSAR with traditional QSAR using four molecular descriptors: ECFP6, CDK topological descriptor, MACCS key, and PubChem fingerprint.
  • Evaluated predictive performance for molecular weight, log Ko-w, boiling point, melting point, water solubility, and oral toxicities in rats and mice.

Main Results:

  • Both analytical and molecular descriptors captured structural information, albeit differently, with comparable predictive performance.
  • The analytical-descriptor-based QSAR approach successfully predicted physicochemical properties and toxicities for structurally unknown chemicals.
  • This analytical-descriptor-based approach extended predictive capabilities beyond the scope of molecular-descriptor-based methods.

Conclusions:

  • GC-EI-MS data contain meaningful structural information that can be leveraged for QSAR prediction.
  • QSAR models based on analytical signals are valuable for assessing unknown chemicals, offering a complementary approach to traditional methods.
  • The analytical-signal-based QSAR approach shows significant promise for broader applications in chemical safety and discovery.