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Mapping Antibiotic Photocatalytic Transformation and Resistance Risks with a DFT-Informed Machine Learning Workflow.

Chen-Chen Zhao1, Sihan Xing1, Cheng Fu1

  • 1State Key Laboratory of Coordination Chemistry, School of Chemistry, Nanjing University, Nanjing, Jiangsu, 210023, P.R. China.

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|January 20, 2026
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Summary
This summary is machine-generated.

Photocatalytic degradation of antibiotics can create risky transformation products (TPs). This study developed a predictive framework using machine learning and DFT to assess TP risks, identifying high-risk species and prioritizing sustainable pollutant degradation.

Keywords:
Environmental risk assessmentPhotocatalysisThermodynamic feasibilityTransformation pathwaysWorkflow‐based prediction

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Area of Science:

  • Environmental Chemistry
  • Computational Chemistry
  • Ecotoxicology

Background:

  • Photocatalytic degradation effectively removes antibiotics but can generate transformation products (TPs) with ecological risks, including antibiotic resistance gene (ARG) induction.
  • Assessing the risks associated with these TPs is crucial for ensuring safe and sustainable water treatment.

Purpose of the Study:

  • To develop a predictive framework for assessing the ecological risks of antibiotic transformation products (TPs) generated during photocatalytic degradation.
  • To integrate experimental data, computational modeling, and machine learning to predict TP properties and prioritize sustainable degradation pathways.

Main Methods:

  • Coupling photocatalytic experiments with high-resolution mass spectrometry, density functional theory (DFT) calculations, and machine learning (ML) models.
  • Constructing extensive reaction networks and employing automatic transition-state searches to evaluate kinetic accessibility.
  • Developing a multi-dimensional scoring system (DEBF) integrating diversity, ecotoxicity, biodegradability, and feasibility to prioritize degradation pathways.

Main Results:

  • A reaction network of over 9,533 reactions was constructed for tetracycline degradation, and an ML model was trained to predict Gibbs free energy changes with DFT accuracy.
  • The framework's generalizability was validated across five different antibiotics, involving 545 reactions.
  • Several hydroxylated, aminated, and amide-ketone TPs were identified as high-risk due to enhanced ARG-binding potential.

Conclusions:

  • The developed framework offers a generalizable, mechanism-anchored, and risk-aware approach for analyzing photocatalytic transformations.
  • This method enables the derivation of design principles for pollutant degradation that balance efficiency with ecological safety.
  • The study highlights the importance of considering TP risks in photocatalytic processes for environmental protection.