Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data

By Wen Zhang, Yanlin Chen, Feng Liu, Fei Luo, Gang Tian, Xiaohong Li. 2017

315 citations recorded by the source corpus.

Drug-drug interactions (DDIs) are one of the major concerns in drug discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions in the entire lifecycle of drugs, and are important for the drug safety surveillance. Since many DDIs are not detected or observed in clinical trials, this work is aimed to predict unobserved or undetected DDIs. In this paper, we collect a variety of drug data that may influence drug-drug interactions, i.e., drug substructure data, drug target data, drug enzyme data, drug transporter data, drug pathway data, drug indication data, drug side effect data, drug off side effect data and known drug-drug interactions. We adopt three representative methods: the neighbor recommender method, the random walk method and the matrix perturbation method to build prediction models based on different data. Thus, we evaluate the usefulness of different information sources for the DDI prediction. Further, we present flexible frames of integrating different models with suitable ensemble rules, including weighted average ensemble rule and classifier ensemble rule, and develop ensemble models to achieve better performances. The experiments demonstrate that different data sources provide diverse information, and the DDI network based on known DDIs is one of most important information for DDI prediction. The ensemble methods can produce better performances than individual methods, and outperform existing state-of-the-art methods. The datasets and source codes are available at https://github.com/zw9977129/drug-drug-interaction/.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Interpreting Classifiers through Attribute Interactions in Datasets (2017, background): “Two difficult problems involving interactions concern drug-drug interactions in pharmacovigilance (e.g., Zhang et al., 2017; Cheng & Zhao, 2014) and investigating interactions between single nucleotide polymorphisms (SNPs) in bioinformatics (e.g., Lunetta et al., 2004; Moore et al., 2010).”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, methodology): “The methods aggregate similarity measures through clustering or label propagation in order to identify potential drug-drug interactions [299, 300, 301].”