Similarity‐based machine learning support vector machine predictor of drug‐drug interactions with improved accuracies. (18th December 2018)

• Record Type: Journal Article
• Authors: Song, Dalong; Chen, Yao; Min, Qian; Sun, Qingrong; Ye, Kai; Zhou, Changjiang; Yuan, Shengyue; Sun, Zhaolin; Liao, Jun
• Is Part Of: Journal of clinical pharmacy and therapeutics. Volume 44:Number 2(2019)
• Page Start: 268
• Page End: 275
• Publication Date: 2018-12-18
• Subjects: drug‐drug interactions -- machine learning -- pairwise kernel -- similarity‐based model -- support vector machines; Clinical pharmacology -- Periodicals; Chemotherapy -- Periodicals; 615
• DOI: 10.1111/jcpt.12786
• Languages: English
• ISSNs: 0269-4727

Abstract

Summary: What is known and objective: 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. Method: 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. Result: 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. Conclusion: 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. Abstract : Five similarities of molecular structure, 3D, IPF, target and ADE were extracted from databases like DrugBank and SIDER. Vectors of these similarities were put in a pairwise kernel method of SVM for classification of interactions of unknow drug‐drug pairs.