Work

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

Year: 2026

Type: article

Abstract: The photocatalytic degradation of antibiotics is effective but may yield transformation products (TPs) that sustain or amplify ecological risks, including antibiotic resistance gene (ARG) induction. This study developed a predictive framework that couples photocatalytic experiments, high-resolution mass spectrometry, density functional theory (DFT) calculations and machine learning (ML) to assess risks of TPs. Using tetracycline as a model compound, we constructed a reaction network over 120 steps and 9 533 reactions, and trained an ML model to rapidly predict Gibbs free energy changes with DFT accuracy. Automatic transition-state searches were integrated to evaluate kinetic accessibility within the network. The generalizability of this approach was validated with pathways of five different antibiotics involving 545 reactions. Furthermore, a multi-dimensional scoring system was developed that integrates diversity, ecotoxicity, biodegradability, and feasibility (DEBF) to prioritize pathways by both reactivity and sustainability. Several hydroxylated, aminated, and amide-ketone TPs were identified as high-risk species with enhanced ARG-binding potential. By bridging molecular energetics with ecological outcomes, this work offers a generalizable, mechanism-anchored, and risk-aware approach for analyzing photocatalytic transformations and deriving design principles for pollutant degradation that balance efficiency with ecological safety.

Source: Angewandte Chemie International Edition

DOI: 10.1002/anie.202520124