Structural analysis of topoisomerase IIα catalytic inhibitors exhibited anti-tumor properties to use them in cancer therapeutic procedures. In this study, a quantitative structure-activity relationship (QSAR) modeling including multiple linear regression (MLR) and least squares-support vector machine (LS-SVM) analysis was applied on a series of 46 synthesized xanthone derivatives as topoisomerase IIα inhibitors. It was aimed to predict half-maximal inhibitory activity (IC 50 ) of the compounds against breast cancer cell line HCT15 using the best computational method with the least error prediction. Genetic algorithm multiple linear regression (GA-MLR) explored the functional parameters of the final models. The achieved QSAR models presented a reliable relationship between chemical structure properties as descriptors and the inhibitory activities of compounds. The models were confirmed using Leave one out-cross-validation (LOO-CV) method (for the best linear model including whole dataset R 2 = 0.955, Q 2 = 0.930, RMSE = 0.151, F = 115.593 and for the best non-linear model R 2 = 0.981, Q 2 = 0.971, RMSE = 0.099, F = 258.902). So, it was demonstrated that these models especially non-linear models are predictive models of high quality that can produce appropriate inhibitory activity prediction of the compounds to use them in pharmaceutical industries.

中文翻译：

---

拓扑异构酶 IIα 催化抑制剂对乳腺癌细胞系 HCT15 的抗癌活性的预测 QSAR 模型：GA-MLR 和 LS-SVM 建模

拓扑异构酶 IIα 催化抑制剂的结构分析显示出抗肿瘤特性，可将它们用于癌症治疗程序。在这项研究中，将包括多元线性回归 (MLR) 和最小二乘支持向量机 (LS-SVM) 分析在内的定量构效关系 (QSAR) 建模应用于一系列 46 种合成的呫吨酮衍生物作为拓扑异构酶 IIα 抑制剂。其目的是使用具有最小误差预测的最佳计算方法来预测化合物对乳腺癌细胞系HCT15的半数最大抑制活性(IC 50 )。遗传算法多元线性回归 (GA-MLR) 探索了最终模型的功能参数。所实现的 QSAR 模型显示了作为描述符的化学结构特性与化合物的抑制活性之间的可靠关系。使用留一交叉验证 (LOO-CV) 方法确认模型（对于包括整个数据集的最佳线性模型，R 2 = 0.955，Q 2 = 0.930，RMSE = 0.151，F = 115.593，对于最佳非线性模型 R 2 = 0.981，Q 2 = 0.971，RMSE = 0.099，F = 258.902）。因此，证明这些模型，尤其是非线性模型是高质量的预测模型，可以对化合物进行适当的抑制活性预测，以将其用于制药行业。593 和最佳非线性模型 R 2 = 0.981，Q 2 = 0.971，RMSE = 0.099，F = 258.902）。因此，证明这些模型，尤其是非线性模型是高质量的预测模型，可以对化合物进行适当的抑制活性预测，以将其用于制药行业。593 和最佳非线性模型 R 2 = 0.981，Q 2 = 0.971，RMSE = 0.099，F = 258.902）。因此，证明这些模型尤其是非线性模型是高质量的预测模型，可以对化合物进行适当的抑制活性预测，以将其用于制药行业。