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Quantum-Transport Informed Machine Learning for Identifying Tobacco-Induced Regioisomeric DNA Adducts

Dipti Maurya1, Sneha Mittal1, Dyuti Chatterjee1

  • 1Department of Chemistry, Indian Institute of Technology (IIT) Indore, Indore, Madhya Pradesh 453552, India.

Analytical Chemistry
|February 18, 2026
PubMed

Insights

This study introduces a new graphene nanogap platform using machine learning to detect tobacco carcinogen DNA adducts. This technology offers rapid, real-time identification of cancer-causing agents for improved risk assessment.

Area of Science:

  • Nanotechnology
  • Molecular Biology
  • Machine Learning

Background:

  • Tobacco smoke contains genotoxic carcinogens forming DNA adducts, crucial in mutagenesis and carcinogenesis.
  • Regioisomers of DNA adducts present detection challenges due to subtle structural differences.
  • Conventional protein-based nanopores have limitations in precise adduct identification.

Purpose of the Study:

  • To develop an advanced platform for detecting and identifying tobacco carcinogen DNA adducts, including regioisomers.
  • To overcome the limitations of existing nanopore technologies for adduct analysis.
  • To enhance cancer risk assessment through improved biomarker discovery.

Main Methods:

  • Development of a machine learning-empowered graphene nanogap platform.
  • Integration of quantum transport analysis with a semisupervised learning framework.
  • Utilizing transmission spectra and I-V characteristics for electronic fingerprint extraction.
  • Employing a self-training random forest classifier for automated recognition.

Main Results:

  • The platform successfully extracted distinct tunneling signatures for precise adduct identification.
  • High accuracy was achieved in the automated recognition of DNA adducts.
  • Demonstrated rapid and real-time detection capabilities for tobacco carcinogen DNA adducts.

Conclusions:

  • The developed graphene nanogap platform offers a novel solution for detecting complex DNA adducts.
  • This approach advances biomarker discovery for tobacco-related carcinogens.
  • The technology holds significant potential for improving cancer risk assessment and early detection strategies.

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