QSPR Modeling of Nonsteroidal Anti-inflammatory Drugs (NSAIDs) Employing Reverse Degree-Based Topological Descriptors

Nonsteroidal anti-inflammatory drugs (NSAIDs) constitute a medication class often utilized to mitigate pain, reduce inflammation, and lower fevers. They are commonly employed to address symptoms such as headaches, dysmenorrhea, sprains, strains, colds, flu, coronavirus, and chronic conditions like arthritis, which entail prolonged discomfort. This study employs reverse degree-based entropy measures in quantitative structure properties relationship (QSPR) analysis to study NSAIDs medications' structure. A MATLAB program aids in computing these descriptors, facilitating the prediction of pharmacological activity. The linear regression model shows a strong relationship between the calculated indices and several physicochemical parameters of NSAIDs drugs. Comparative analysis with quadratic and cubic regression models is presented. It has been found that the reverse third Zagreb entropy, the Reverse Randić entropy and the reverse hyper Zagreb entropy are the best predictors for the considered physicochemical properties. This research enhances the understanding of Lyme medication structures and their pharmacological activity prediction.

QSPR Modeling of Nonsteroidal Anti-inflammatory Drugs (NSAIDs) Employing Reverse Degree-Based Topological Descriptors Keywords:

Reverse degree-based topological indices , Nonsteroidal anti-inflammatory drugs (NSAIDs) , QSAR/QSPR models , Human Health