Forecasting Banned Substances: Leveraging GNN and Explainable AI for Sports Anti-Doping

Ensuring fairness in competitive sports requires robust mechanisms for detecting prohibited substances. Despite established regulations, challenges persist in accurately identifying new and emerging doping agents. This study introduces the use of Graph Neural Network (GNN) and Explainable AI (XAI) to classify substances as prohibited or non-prohibited, based on molecular and pharmacological data. The study utilizes Knowledge Graphs (KG) of heterogeneous type to develop predictive models. Explainability methods like Integrated Gradients and Saliency provide transparency into the models' decisions, ensuring traceability and accountability in classification results. By offering a novel, AI-driven approach to doping detection, this work supports regulatory bodies in making informed decisions and enhances the robustness of anti-doping measures.