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In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
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Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow.

José T Moreira-Filho1, Dhruv Ranganath2, Mike Conway3

  • 1National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA. teofilo.moreirafilho@nih.gov.

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Summary

This study introduces a user-friendly chemical grouping workflow in KNIME, simplifying data analysis without coding. It integrates advanced machine learning and interpretation tools for accessible chemical categorization and understanding.

Keywords:
Chemical groupingData visualizationExplainable artificial intelligenceFeature selectionKNIME workflowMachine learningSHapley additive exPlanationsSupervised classificationUnsupervised clustering

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Area of Science:

  • Computational chemistry and cheminformatics
  • Data science and machine learning

Background:

  • Increasing availability of chemical data necessitates advanced analytical techniques.
  • Existing chemical grouping tools often require specialized programming skills or commercial software.

Purpose of the Study:

  • To develop a user-friendly, accessible chemical grouping workflow using KNIME.
  • To integrate molecular descriptor calculation, feature selection, dimensionality reduction, and machine learning for effective chemical categorization.

Main Methods:

  • Implementation of a comprehensive workflow in KNIME, a free, open-source data analytics platform.
  • Integration of supervised and unsupervised machine learning methods with hyperparameter optimization.
  • Inclusion of tools for interpretation, identifying key descriptors, and generating natural language summaries.

Main Results:

  • A novel, comprehensive chemical grouping workflow in KNIME was developed.
  • The workflow enhances accessibility through a graphical interface, eliminating the need for extensive programming skills.
  • Automated processes for descriptor calculation, feature selection, dimensionality reduction, and machine learning were integrated.

Conclusions:

  • The developed KNIME workflow provides an accessible and powerful tool for chemical grouping and data analysis.
  • The integration of interpretation tools and natural language summaries enhances the usability and understanding of chemical group rationales.
  • The workflow demonstrates utility in case studies, such as eye irritation and corrosion datasets.