Machine Learning for Predicting Degradation of Pharmaceutical Pollutants: Interdisciplinary Integration of Electrochemical Oxidation and AI

The EO-Pharmaceutical-Pollutants project integrates traditional machine learning, optimized XGBoost models, and graph neural networks (GNNs) to build a comprehensive framework for predicting the degradation kinetics of pharmaceutical pollutants during electrochemical oxidation, providing a data-driven scientific tool for environmental governance. This project is accompanied by an academic paper, representing the cutting-edge of the interdisciplinary field of environmental science and AI, emphasizing the use of SHAP methods to enhance model interpretability, aiding in mechanism understanding and process optimization.

The development of modern pharmaceutical industry has led to pharmaceutical pollutants (antibiotics, hormones, etc.) entering water environments through human excretion, medical waste, and pharmaceutical wastewater. These pollutants have biological activity, persistence, and accumulative properties, which are difficult to effectively remove using traditional water treatment processes. As an advanced oxidation process, electrochemical oxidation has become a potential solution due to its high efficiency, environmental friendliness, and no need for chemical additives. However, it is affected by multiple factors such as pollutant molecular structure, electrode materials, and current density. Traditional experimental optimization is time-consuming and labor-intensive, so machine learning technology is urgently needed.

Model Architecture:

1. Traditional machine learning benchmarks: Support Vector Machine (SVM), Random Forest, Gradient Boosting Tree, used as performance references.
2. Optimized XGBoost model: Handles missing values, provides feature importance ranking, uses regularization to prevent overfitting, and accelerates training via parallel processing.
3. Graph Neural Network (GNN): Treats molecules as graph structures (atoms as nodes, chemical bonds as edges), captures topological structures and spatial configurations, and enables end-to-end learning without manually designed descriptors.

Data Processing: Cleans missing/anomalous values, calculates molecular descriptors, standardizes features, splits into training/validation/test sets; molecular representations are of two types: fixed-length feature vectors (traditional ML) and graph structures (GNN).

Interpretability: Uses SHAP methods to analyze feature contributions, revealing key molecular features, parameter sensitivity, and mechanism consistency.

The project dataset contains 355 observation samples (from 31 pharmaceutical compounds), covering multi-dimensional features such as molecular structure descriptors, electrochemical operation parameters, and environmental conditions. The target variables are degradation kinetic parameters (reaction rate constant, half-life). The dataset size is considerable in the field of environmental chemistry, covering multiple types of pharmaceutical molecules, providing a basis for model generalization. Model advantages include the robustness of XGBoost and the accurate capture of molecular structure-activity relationships by GNN.

Contributions to Electrochemical Oxidation Technology: Optimize process parameters, evaluate the treatability of new pollutants, and guide reactor design. Contributions to the Field of Machine Learning: Demonstrate the application potential of GNN in environmental chemistry, multi-modal feature fusion strategies, and practical examples of SHAP interpretability. Environmental Protection Significance: Predict pollutant persistence, support water treatment process selection, and provide scientific basis for environmental policy formulation.

Limitations: Covers only 31 compounds, data comes from specific experimental conditions, and the depth of mechanism explanation is insufficient. Future Directions: Expand the dataset (more drugs and experimental conditions), improve GNN architecture (e.g., Graph Attention Network), multi-task learning (predict degradation and product distribution simultaneously), develop real-time prediction tools, and cross-domain migration to other advanced oxidation processes.

The project is open source. The code structure includes modules for data loading, feature calculation, model definition, training evaluation, and SHAP analysis. Dependent libraries include scikit-learn, XGBoost, PyTorch Geometric, RDKit, SHAP, Pandas/NumPy, etc. Users need to cite relevant academic papers to comply with academic open-source norms.