Interpretable Machine Learning for Structure–Activity Relationship Modeling of Cyclooxygenase-1 Inhibitors
An interpretable computational framework is developed for modeling COX-1 inhibitor activity and characterizing prioritized compounds at the molecular level by integrating QSAR modeling, explainable artificial intelligence, molecular docking, molecular dynamics, and MM/GBSA calculations to investigate COX-1 inhibitors.