Abstract
Drug repositioning is a method of systematically identifying potential molecular targets that known drugs may act on. Compared with traditional methods, drug repositioning has been extensively studied due to the development of multi-omics technology and system biology methods. Because of its biological network properties, it is possible to apply machine learning related algorithms for prediction. Based on various heterogeneous network model, this paper proposes a method named THNCDF for predicting drug–target interactions. Various heterogeneous networks are integrated to build a tripartite network, and similarity calculation methods are used to obtain similarity matrix. Then, the cascade deep forest method is used to make prediction. Results indicate that THNCDF outperforms the previously reported methods based on the 10-fold cross-validation on the benchmark data sets proposed by Y. Yamanishi. The area under Precision Recall curve (AUPR) value on the Enzyme, GPCR, Ion Channel, and Nuclear Receptor data sets is 0.988, 0.980, 0.938, and 0.906 separately. The experimental results well illustrate the feasibility of this method.
Highlights
In the past few decades, investment in drug research and development has grown rapidly, but most drugs have failed in the first phase of clinical trials
The main contributions of this paper are summarized as follows: We study various calculation methods based on the tripartite heterogeneous network, and adopt the Gaussian kernel between each layer, and the Tanimoto’s coefficient is used in the drug layer to calculate the chemical structure similarity matrix
In order to evaluate the performance of our method, we mainly introduce drug–target interaction (DTI) prediction results compared with baseline methods on the benchmark data sets that are proposed by Y
Summary
In the past few decades, investment in drug research and development has grown rapidly, but most drugs have failed in the first phase of clinical trials. It normally costs billions of dollars and consumes 10 years for any drug to be put on the market completely (Roessler et al, 2021). Drug repositioning has a wide prospect and provides evidence for further drug discovery, whose purpose is to determine potential therapeutic targets for existing drugs, thereby saving time and minimizing risks of conventional drug development (Stein et al, 2021). Despite that potential drug indications can be directly detected by target or cell screening of thousands of drugs in synthetic databases, there are still hurdles to massively relocate drugs owing to the needs of collecting existing drugs, specialized equipment, and screening tests (Turanli et al, 2018)
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