Similarity‐based machine learning support vector machine predictor of drug‐drug interactions with improved accuracies.
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. I...
| Publicado en: | Journal of Clinical Pharmacy & Therapeutics Vol. 44; no. 2; pp. 268 - 276 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135059628&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135059628 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02694727 EV4 jtl: Journal of Clinical Pharmacy & Therapeutics issn: 02694727 maglogo: Y pubinfo: dt: Apr2019 vid: 44 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 135059628 135059628 135059628 10.1111/jcpt.12786 135059628 ppf: 268 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Similarity‐based machine learning support vector machine predictor of drug‐drug interactions with improved accuracies. aug: au: Song, Dalong Chen, Yao Min, Qian Sun, Qingrong Ye, Kai Zhou, Changjiang Yuan, Shengyue Sun, Zhaolin Liao, Jun affil: Guizhou University, Guiyang China sug: subj: Machine Learning Methods Drug Interactions Evaluation Human Molecular Structure Validity Models, Structural Fingerprints Adverse Drug Event Validation Studies Pharmacovigilance ab: 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. 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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