SAR and QSAR models of cyclooxygenase-1 (COX-1) inhibitors. Issue 10 (3rd October 2018)

• Record Type: Journal Article
• Authors: Xi, Y.; Qin, Z.; Yan, A.
• Is Part Of: SAR and QSAR in environmental research. Volume 29:Issue 10(2018)
• Page Start: 755
• Page End: 784
• Publication Date: 2018-10-03
• Subjects: cyclooxygenase-1 (COX-1) inhibitors -- QSAR -- classification models -- support vector machine -- decision tree -- random forest -- multiple linear regression; Structure-activity relationships (Biochemistry) -- Periodicals; QSAR (Biochemistry) -- Periodicals; 572.4
• DOI: 10.1080/1062936X.2018.1513952
• Languages: English
• ISSNs: 1062-936X

Abstract

ABSTRACT: Cyclooxygenase-1 (COX-1) is one isoform of COX, and it is a main target of nonsteroidal anti-inflammatory drugs (NSAIDs). It is important to develop efficient and selective COX-1 inhibitors. In this work, 12 classification models for 1530 cyclooxygenase-1 (COX-1) inhibitors were built by support vector machine (SVM), decision tree (DT) and random forest (RF) methods. The best classification model (model 1A) was built by SVM with MACCS fingerprints. The classification accuracies for the training and test sets were 99.67% and 97.39%, respectively. The Matthews correlation coefficient (MCC) of the test set was 0.94. We also divided the 1530 COX-1 inhibitors into nine subsets according to their different scaffolds using Kohonen's self-organizing map (SOM). In addition, six quantitative structure–activity relationship (QSAR) models for 181 COX-1 inhibitors whose IC50 were measured by enzyme immunoassay were built by multiple linear regression (MLR) and SVM. The best QSAR model (model 5A) was built by SVM with CORINA Symphony descriptors. The correlation coefficients of the training and test sets are 0.93 and 0.84, respectively. The models built in this study can be obtained from the authors.