支持向量机在中药肾毒性研究中的应用

Application of support vector machine approach in studying nephron toxicity of Chinese medicinal materials

计算了111个肾毒化合物和90个无肾毒化合物的6 122个分子描述符,采用Cfs Subset Eval评价和Best First-D1-N5搜索相结合的方法筛选特征描述符子集。用支持向量机(SVM)构建化合物肾毒性判别模型。收集通过造成肾小管损坏致肾毒性的39个化合物,与39个无肾毒化合物组成数据集,用相同方法构建化合物肾小管损坏判别模型。2个模型的准确率、灵敏度、特异性、马修斯相关系数均在70%以上。以22个有肾毒的中药成分作为外部验证集,进一步评价肾毒性判别模型,准确率达72.73%。用肾小管损坏模型进一步判别肾毒性模型筛查结果为阳性的中药成分,其中10个中药成分可能通过损害肾小管致肾脏毒性,6个已有文献佐证。实验结果表明本研究构建的2个模型具有一定的准确性,有助于开展中药肾毒性成分筛查工作,并为进一步研究肾毒性机制提供新思路。

On the basis of web databases,111 compounds with nephrotoxicity and 90 compounds without nephrotoxicity were collected as data set of nephrotoxicity discrimination model,39 compounds with tubular necrosis and 39 compounds without tubular necrosiswere collected as data set of tubular necrosis discrimination model. The 6 122 molecular descriptors,including physicochemical,charge distribution and geometrical descriptors were calculated to characterize the molecular structure of the above-mentioned compounds. Cfs Subset Eval valuation method and Best First-D1-N5 searching method were used to select molecular descriptors. Two models with high accuracy were built based on the support vector machine（ SVM） approach,respectively. Accuracy,sensitivity,specificity and matthew＇s correlation coefficient of the two models were all above 70%. By using 22 nephrotoxicity compounds of Chinese medicine,the nephrotoxicity discrimination model was further verified with an accuracy of 72. 73%. Using the tubular necrosis discrimination model,10 potential compounds which can cause tubular necrosis were screened from the positive results of nephrotoxicity discrimination model,6 of them have been verified by literatures. The results demonstrated that the discrimination models can be applied to screen nephrotoxic compounds from Chinese medicinal materials,and they also offer a new research idea for the further studies on the mechanism of nephrotoxicity.