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

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.