Song Dalong, Chen Yao, Min Qian, Sun Qingrong, Ye Kai, Zhou Changjiang, Yuan Shengyue, Sun Zhaolin, Liao Jun
Guizhou University, Guiyang, China.
Department of Urology, GuiZhou Provincial People's Hospital, Guiyang, China.
J Clin Pharm Ther. 2019 Apr;44(2):268-275. doi: 10.1111/jcpt.12786. Epub 2018 Dec 18.
Drug-drug interactions (DDI) are frequent causes of adverse clinical drug reactions. Efforts have been directed at the early stage to achieve accurate identification of DDI for drug safety assessments, including the development of in silico predictive methods. In particular, similarity-based in silico methods have been developed to assess DDI with good accuracies, and machine learning methods have been employed to further extend the predictive range of similarity-based approaches. However, the performance of a developed machine learning method is lower than expectations partly because of the use of less diverse DDI training data sets and a less optimal set of similarity measures.
In this work, we developed a machine learning model using support vector machines (SVMs) based on the literature-reported established set of similarity measures and comprehensive training data sets. The established similarity measures include the 2D molecular structure similarity, 3D pharmacophoric similarity, interaction profile fingerprint (IPF) similarity, target similarity and adverse drug effect (ADE) similarity, which were extracted from well-known databases, such as DrugBank and Side Effect Resource (SIDER). A pairwise kernel was constructed for the known and possible drug pairs based on the five established similarity measures and then used as the input vector of the SVM.
The 10-fold cross-validation studies showed a predictive performance of AUROC >0.97, which is significantly improved compared with the AUROC of 0.67 of an analogously developed machine learning model. Our study suggested that a similarity-based SVM prediction is highly useful for identifying DDI.
in silico methods based on multifarious drug similarities have been suggested to be feasible for DDI prediction in various studies. In this way, our pairwise kernel SVM model had better accuracies than some previous works, which can be used as a pharmacovigilance tool to detect potential DDI.
药物相互作用（DDI）是临床药物不良反应的常见原因。早期已致力于实现对DDI的准确识别，以进行药物安全性评估，包括开发计算机预测方法。特别是，已开发出基于相似性的计算机方法来评估DDI，且准确率较高，同时机器学习方法也被用于进一步扩展基于相似性方法的预测范围。然而，已开发的机器学习方法的性能低于预期，部分原因是使用的DDI训练数据集不够多样，以及相似性度量集不够优化。
在这项工作中，我们基于文献报道的既定相似性度量集和综合训练数据集，使用支持向量机（SVM）开发了一种机器学习模型。既定的相似性度量包括二维分子结构相似性、三维药效团相似性、相互作用谱指纹（IPF）相似性、靶点相似性和药物不良反应（ADE）相似性，这些相似性是从知名数据库（如DrugBank和副作用资源库（SIDER））中提取的。基于这五种既定的相似性度量，为已知和可能的药物对构建了成对核，然后将其用作SVM的输入向量。
10折交叉验证研究显示预测性能的受试者工作特征曲线下面积（AUROC）>0.97，与类似开发的机器学习模型的AUROC 0.67相比有显著提高。我们的研究表明，基于相似性的SVM预测对于识别DDI非常有用。
在各种研究中，基于多种药物相似性的计算机方法已被认为对DDI预测是可行的。通过这种方式，我们的成对核SVM模型比一些先前的工作具有更高的准确率，可作为一种药物警戒工具来检测潜在的DDI。
