A GCN-based drug discovery approach to reduce the side effects of anti-inflammatory drugs in adolescent sports and maintain athletic performance

Drug discovery is a complex and multidisciplinary process aimed at identifying new medicines to treat diseases, driven by advances in biotechnology and computational methods. To address the challenges associated with the use of anti-inflammatory drugs in adolescent sports, we have developed a novel approach leveraging graph convolutional networks (GCN) for drug discovery. Our GCN-based method, named GCN-Med, integrates diverse data sources, including molecular structures, biological activity profiles, and known side-effect information, to predict new drug candidates with reduced side effects. Through the construction of a comprehensive dataset and careful tuning of the training parameters, we observed a significant improvement in the model's ability to accurately predict the side effects of anti-inflammatory drugs, as evidenced by decreasing Mean Absolute Error (MAE) and Mean Squared Error (MSE) over the course of training. Comparative analysis with alternative models, such as Random Forest and traditional Graph Neural Networks (GNNs), further highlighted the superiority of GCN-Med in capturing the complex relationships within the drug discovery dataset. Visualizations of the predicted versus actual side effect values for ten drugs also confirmed the robustness of GCN-Med in forecasting the side effects of anti-inflammatory drugs. This GCN-based approach offers a promising avenue for developing safer and more effective treatment options to maintain athletic performance while reducing the side effects of anti-inflammatory drugs in adolescent sports.