A Machine Learning-Based Biological Drug–Target Interaction Prediction Method for a Tripartite Heterogeneous Network

Abstract

Drug repositioning is the identification of interactions between drugs and target proteins in pharmaceutical sciences. Traditional large-scale validation through chemical experiments is time-consuming and expensive, while drug repositioning can drastically decrease the cost and duration taken by traditional drug development. With the rapid advancement of high-throughput technologies and the explosion of various biological and medical data, computational drug repositioning methods have been used to systematically identify potential drug–target interactions. Some of them are based on a particular class of machine learning algorithms called kernel methods. In this paper, we propose a new machine learning prediction method combining multiple kernels into a tripartite heterogeneous drug–target–disease interaction spaces in order to integrate multiple sources of biological information simultaneously. This novel network algorithm extends the traditional drug–target interaction bipartite graph to the third disease layer. Meanwhile, Gaussian kernel functions on heterogeneous networks and the regularized least square method of the Kronecker product are used to predict new drug–target interactions. The values of AUPR (area under the precision–recall curve) and AUC (the area under the receiver operating characteristic curve) of the proposed algorithm are significantly improved. Especially, the AUC values are improved to 0.99, 0.99, 0.97, and 0.96 on four benchmark data sets. These experimental results substantiate that the network topology can be used for predicting drug–target interactions.

Cascade Deep Forest With Heterogeneous Similarity Measures for Drug–Target Interaction Prediction

“…To ensure that the features in the network model are distinguishable, the similarity of the medicinal chemical structure is a relatively objective feature ( Zheng and Wu, 2021 ). In particular, the chemical structure of various drugs under the same standard can be obtained through the simplified molecular-input line-entry system (SIMILES), and then converted into 166-bits string of a certain length fingerprint.…”

“…The Gaussian kernel is defined as the unimodal of the Euclidean distance between any two points in the network ( Zheng and Wu, 2021 ). In THNCDF method, the Gaussian kernel is mainly used to calculate the feature of the connection between two layers, like the edge between the drug layer and the target layer or between the target layer and the disease layer.…”

“…According to the above multiple similarity matrices, we construct a kernel containing the spatial information of drugs and targets ( Ding et al, 2018 , 2020a , b ; Zheng and Wu, 2021 ). Since the similarity matrix is not a positive definite matrix, predictions are ultimately required.…”

“…In our experiments, we use the data sets listed in Table 1 to build a tripartite heterogeneous network model. Table 1 shows the exactly biologic data sets we used during the experiments ( Yamanishi et al, 2008 ; Zheng and Wu, 2021 ). Especially, the main resource of the data set for the disease layer is from DisGeNET.…”

“…Benchmark data sets.In this study, we propose THNCDF, a new computational approach for molecular target identification from known drugtarget centered DTI prediction. It utilizes low-dimensional but informative matrix representations of features for both drugs and targets through a cascade deep forest classifier in prediction of DTI(Zheng and Wu, 2021).…”