ABSTRACT

Leukotriene A4 hydrolase (LTA4H) is an important anti-inflammatory target which can convert leukotriene A4 (LTA4) into pro-inflammatory substance leukotriene B4 (LTB4). In this paper, we built 18 classification models for 463 LTA4H inhibitors by using support vector machine (SVM), random forest (RF) and K-Nearest Neighbour (KNN). The best classification model (Model 2A) was built from RF and MACCS fingerprints. The prediction accuracy of 88.96% and the Matthews correlation coefficient (MCC) of 0.74 had been achieved on the test set. We also divided the 463 LTA4H inhibitors into six subsets using K-Means. We found that the highly active LTA4H inhibitors mostly contained diphenylmethane or diphenyl ether as the scaffold and pyridine or piperidine as the side chain. In addition, six quantitative structure–activity relationship (QSAR) models for 172 LTA4H inhibitors were built by multiple linear regression (MLR) and SVM. The best QSAR model (Model 6A) was built by using SVM and CORINA Symphony descriptors. The coefficients of determination of the training set and the test set were equal to 0.81 and 0.79, respectively. Classification and QSAR models could be used for subsequent virtual screening, and the obtained fragments that were important for highly active inhibitors would be helpful for designing new LTA4H inhibitors.

摘要

白三烯A4水解酶（LTA4H）是重要的抗炎靶标，其可以将白三烯A4（LTA4）转化为促炎物质白三烯B4（LTB4）。在本文中，我们使用支持向量机（SVM），随机森林（RF）和K最近邻（KNN）为463个LTA4H抑制剂建立了18个分类模型。最佳分类模型（模型2A）是根据RF和MACCS指纹建立的。在测试集上已经实现了88.96％的预测准确性和0.74的Matthews相关系数（MCC）。我们还使用K-Means将463个LTA4H抑制剂分为6个子集。我们发现高活性LTA4H抑制剂主要包含二苯基甲烷或二苯醚作为支架，吡啶或哌啶作为侧链。此外，通过多元线性回归（MLR）和支持向量机（SVM）建立了172种LTA4H抑制剂的六个定量构效关系（QSAR）模型。最佳的QSAR模型（​​模型6A）是通过使用SVM和CORINA Symphony描述符建立的。训练集和测试集的确定系数分别等于0.81和0.79。分类和QSAR模型可用于后续的虚拟筛选，所获得的片段对于高活性抑制剂很重要，将有助于设计新的LTA4H抑制剂。