Advancing Identification of Transformation Products and Predicting Their Environmental Fate: The Current State of Machine Learning and Artificial Intelligence in Antibiotic Photolysis

The environmental persistence of antibiotic residues in aquatic systems represents a critical global challenge, with photolysis serving as a primary abiotic degradation pathway. Traditional approaches to studying antibiotic photodegradation and transformation product (TP) identification face significant limitations, including complex reaction mechanisms, multiple concurrent pathways, and analytical challenges in characterizing unknown metabolites. The integration of artificial intelligence (AI) and machine learning (ML) technologies has begun to transform this field, offering new capabilities for predicting photodegradation kinetics, elucidating transformation pathways, and identifying novel metabolites. This comprehensive review examines current applications of AI/ML in antibiotic photolysis research, analyzing developments from 2020 to 2025. Key advances include quantitative structure–activity relationship (QSAR) models for photodegradation prediction, deep learning approaches for automated mass spectrometry interpretation, and hybrid computational–experimental frameworks. Machine learning algorithms, particularly Random Forests, support vector machines, and Neural Networks, have demonstrated capabilities in handling multi-dimensional environmental datasets across diverse antibiotic classes, including fluoroquinolones, β-lactams, tetracyclines, and sulfonamides. Despite progress in this field, challenges remain in model interpretability, standardization of datasets, validation protocols, and integration with regulatory frameworks. Future directions include machine-learning-enhanced quantum dynamics for improving mechanistic understanding, real-time AI-guided experimental design, and predictive tools for environmental risk assessment.