RANDOM FOREST MODELING OF MOLECULAR DESCRIPTORS OF COX-2-TARGETED NON-STEROIDAL ANTI-INFLAMMATORY DRUGS (NSAIDS)

Liza Tybaco Billones¹*, Alex Cerbito Gonzaga¹

ABSTRACT

The discovery of next-generation non-steroidal anti-inflammatory drugs (NSAIDs) remains an active area of research as over a billion people suffer from pain and inflammation. A strategic approach in this endeavor is establishing a quantitative relationship between the anti-inflammatory activity and the molecular descriptors of inhibitors of cyclooxygenase-2 (COX-2) that will streamline and expedite the discovery and the subsequent development of novel NSAIDs devoid of side effects associated with COX-1 inhibition. In this work, Random Forest (RF) technique was implemented to formulate a robust quantitative model that predicts the inhibitory activity of compounds on COX-2. The model established in this work displayed excellent predictive performance on compound classification with 93% accuracy and 0.98 AUC. Upon application to two external sets, 759 newly designed derivatives of COX-2 inhibitors and 188 structurally similar compounds were predicted active; 19 of them were found to be promising leads as COX-2-acting anti-inflammatory drugs. The top 2 hits with the highest probability of being active were also found to have the strongest binding affinity with COX-2 and are superior to the known COX-2 selective inhibitors. The RF model is likewise conservative in identifying compounds as active making it all the more beneficial as it helps avoid costly failures at the later stages of the drug discovery phase.

Keywords: Molecular descriptors, NSAID, COX-2 inhibitors, Random forest, Anti-inflammatory

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