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A virtual screening model for COX-2 inhibitors using SVM and k-means clustering method

Cyclooxygenase is the key enzyme of the synthesis of prostaglandins, which serve as mediators of inflammation and are involved in carcinogenesis. Cyclooxygenase inhibitors (NSAIDs) are shown to be efficient in controlling inflammation and are proposed to have chemotherapeutic effects against varies types of cancer. Among the inhibitors of the two isoforms of cyclooxygenases COX-1 and COX-2, COX-2 inhibitors are of great interest among researchers because it is directly involved in carcinogenesis and selective COX-2 inhibitors have less gastrointestinal side effects than non-selective NSAIDs. In this work a virtual screening model for COX-2 inhibitors is developed using support vector machines method. Known COX-2 inhibitors from obtained from MDDR and recent literature were used as positive samples, and a clustering method was applied to build the negative dataset from pubchem and MDDR. 105 descriptors are used to describe the datasets. Different sigma values for the rbf kernel function were investigated and 0.6 is determined to be the best value for final output. The result model has 95.55% sensitivity and 99.9% specificity on our validation using a clustered dataset. The screening using this model to the pubchem and MDDR databases revealed 41845 and 5010 potential inhibitors, respectively.