SAR and QSAR models of cyclooxygenase-1 (COX-1) inhibitors
SAR and QSAR models of cyclooxygenase-1 (COX-1) inhibitors
复制标题
环氧合酶 1 (COX-1) 抑制剂的 SAR 和 QSAR 模型
DOI：
10.1080/1062936x.2018.1513952
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发表时间：
2018-10
影响因子：
3
通讯作者：
Yan Aixia
中科院分区：
文献类型：
--
作者：
Xi Yao;Qin Zijian;Yan Aixia
环氧合酶-1（考克斯-1）是考克斯的一种亚型，是非甾体抗炎药（NSAIDs）的主要作用靶点。因此，开发高效、高选择性的考克斯-1抑制剂具有重要意义。采用支持向量机（SVM）、决策树（DT）和随机森林（RF）方法对1530种环氧合酶-1（考克斯-1）抑制剂建立了12种分类模型。利用MACCS指纹图建立支持向量机的最佳分类模型（模型1A）。训练集和测试集的分类准确率分别为99.67%和97.39%。测试集的马修斯相关系数（MCC）为0.94。利用Kohonen的自组织映射（SOM）方法，将1530个考克斯-1抑制剂按照其不同的骨架结构分为9个亚类。此外，采用多元线性回归（MLR）和支持向量机（SVM）对181个考克斯-1抑制剂的IC 50进行了定量构效关系（QSAR）建模。用支持向量机（SVM）和CORINA Symphony描述子建立最佳QSAR模型（模型5A）。训练集和测试集的相关系数分别为0.93和0.84。本研究中建立的模型可以从作者那里获得。
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.
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