An Explainable 2D-QSAR Machine Learning Approach for Predicting COX-2 Inhibitory Activity Using Molecular Fingerprints

Background/Objectives: Cyclooxygenase-2 (COX-2) is a well-established target in the development of anti-inflammatory drugs due to its central role in mediating inflammation. The identification of novel COX-2 inhibitors remains a key focus in pharmaceutical research. This study aimed to develop a robust and interpretable machine learning framework to predict COX-2 inhibitory activity and support virtual screening efforts. Methods: A curated dataset of 2052 compounds was obtained from the ChEMBL database. Molecular structures were encoded using Morgan fingerprints derived from SMILES representations. Several machine learning algorithms were trained and evaluated, including ensemble-based methods. Model performance was assessed using internal validation and external test sets. Robustness was further evaluated through Y-randomization tests. Model interpretability was investigated using SHAP (SHapley Additive exPlanations) analysis to identify key structural features contributing to activity. Results: Among the evaluated models, ensemble methods demonstrated superior predictive performance, with the Random Forest algorithm providing the most consistent and reliable results across validation and external datasets. Y-randomization confirmed that the model predictions were not due to chance correlations. SHAP analysis revealed that the most influential features corresponded to chemically meaningful substructures aligned with known COX-2 pharmacophore characteristics. The final optimized model was successfully deployed as a publicly accessible web application for real-time prediction using SMILES input. Conclusions: This study demonstrates the effectiveness of explainable machine learning approaches in predicting COX-2 inhibitory activity. The developed framework provides a reliable and interpretable tool for accelerating COX-2 inhibitor discovery and facilitating virtual screening in drug development.