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Structural and Electronic Features-Integrated Machine Learning Framework for High-Throughput Prediction of Organic

Zonghao Liu1, Shuang Wu2, Ce-Hui Mo2

  • 1Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, School of Energy and Environment, Southeast University, Nanjing 210096, China.

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This summary is machine-generated.

This study introduces a machine learning model to predict organic pollutant reactivity towards sulfate radicals. The model uses quantum chemical and molecular descriptors for accurate, high-throughput predictions in advanced oxidation processes.

Keywords:
high-throughput predictionmachine learningmolecular descriptororganic pollutantsq-RASAR model

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Area of Science:

  • Environmental Chemistry
  • Computational Chemistry
  • Chemical Engineering

Background:

  • Predicting organic pollutant reactivity is vital for advanced oxidation processes (AOPs).
  • Structural complexity and diversity of pollutants challenge current predictive models.
  • Developing interpretable, high-throughput frameworks is essential for efficient AOP design.

Purpose of the Study:

  • To develop a machine learning (ML) framework for predicting organic pollutant reactivity towards sulfate radicals.
  • To integrate quantum chemical and molecular fingerprint descriptors for enhanced prediction accuracy.
  • To establish quantitative structure-activity relationships (QSAR) and quantitative read-across structure-activity relationship (QR-SAR) models.

Main Methods:

  • Utilized a machine learning approach combining RDKit and conceptual density functional theory (CDFT) descriptors.
  • Identified key structure-activity relationships including E_HOMO(N), electron-donating capacity, ring structures, branching, and molecular surface areas.
  • Developed a quantitative read-across structure-activity relationship model incorporating intermolecular similarity.

Main Results:

  • Achieved a 2.1-fold increase in applicability domain (AD) compared to traditional QSAR, covering 74.3% across 12 pollutant classes.
  • Identified quantitative reactivity thresholds for key structure-activity features.
  • Experimental validation demonstrated strong predictive performance with R^2 = 0.811.

Conclusions:

  • The developed ML framework offers a transparent and high-throughput method for predicting organic pollutant reactivity.
  • This approach facilitates the rational design of AOPs tailored to specific pollutant profiles.
  • The integrated descriptor approach enhances predictive power and applicability domain for environmental remediation strategies.