Computational strategies for unraveling insights from known inhibitors for further lead optimization: A case study on Celecoxib analogues

Inhibition of Cyclooxygenase-2 (COX-2) represents a well-established and promising strategy in the development of anti-inflammatory drugs, given its pivotal role in mediating inflammation. Selective inhibition of COX-2 over COX-1 is critical for reducing the gastrointestinal side effects commonly associated with traditional nonsteroidal anti-inflammatory drugs (NSAIDs). Celecoxib is a widely recognized selective COX-2 inhibitor that has been extensively studied for its therapeutic efficacy. Its structural analogues differ in their biological activities despite their close structural resemblance. The objective of the present work is to systematically investigate the influence of subtle structural modifications on celecoxib, focusing on its COX-2 inhibitory activity profile. A dataset comprising 375 analogues was curated based on the structural similarity with celecoxib. Molecular descriptors were calculated using RDKit and Mordred, followed by correlation analysis. Weak correlation was observed between the computed molecular descriptors and biological activity. Molecular docking studies were performed using Genetic Optimisation for Ligand Docking (GOLD). Critical interactions were identified by interaction analysis. Further, the Structure-Activity Landscape Index (SALI) analysis aided in identifying the activity cliffs in the dataset. The findings from the interaction analysis and SALI offer a comprehensive understanding of the Structure-Activity relationship of celecoxib analogues and deliver valuable insights for further lead optimization.