Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit

Abstract

Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association information of drugs and diseases. However, they tended to construct shallow prediction models to predict drug-associated diseases, which make deeply integrating the information difficult. Further, path information between drugs and diseases is important auxiliary information for association prediction, while it is not deeply integrated. We present a deep learning-based method, CGARDP, for predicting drug-related candidate disease indications. CGARDP establishes a feature matrix by exploiting a variety of biological premises related to drugs and diseases. A novel model based on convolutional neural network (CNN) and gated recurrent unit (GRU) is constructed to learn the local and path representations for a drug-disease pair. The CNN-based framework on the left of the model learns the local representation of the drug-disease pair from their feature matrix. As the different paths have discriminative contributions to the drug-disease association prediction, we construct an attention mechanism at the path level to learn the informative paths. In the right part, a GRU-based framework learns the path representation based on path information between the drug and the disease. Cross-validation results indicate that CGARDP performs better than several state-of-the-art methods. Further, CGARDP retrieves more real drug-disease associations in the top part of the prediction result that are of concern to biologists. Case studies on five drugs demonstrate that CGARDP can discover potential drug-related disease indications.

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

“…Various specially designed AI/ML models have been proposed for detecting novel drug indications. Here, we classify the ML applications for drug repositioning into the following three categories: (i) Similarity‐based methods that employ different types of classifiers like logistic regression, 305,306 SVM, 307–309 RF, 310,311 KNN, 312 and CNN, 313 (ii) feature vector‐based methods that utilize supervised 314–318 and semisupervised 319–321 learning algorithms, and (iii) network‐based methods that mainly use semisupervised learning algorithms (e.g., Laplacian regularized least square, 322–324 label propagation, 325 random walk, 326 and RF 310 ). We provide an in‐depth discussion of these three classes of AI‐based drug repositioning applications in the Supporting Information.…”

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

“…Learned features were then decoded to predict repositioning candidates. Xuan et al [272] presented a novel model that was based on CNN to capture local representation from feature matrices and GRU, to learn path representation from drug-disease paths. The model outperformed other previous studies.…”

An Integrative Network Science and Artificial Intelligence Drug Repurposing Approach for Muscle Atrophy in Spaceflight Microgravity

“…If the input data is in the form of sequences, then Recurrent Neural Networks (RNN) are trained with the time-stamped data and used for prediction of drugs ( Wang et al, 2020 ). Hybrid models that combine the power of Convolutional Neural Networks (CNN) and RNN have been used for drug repurposing ( Xuan et al, 2019 ; Jarada et al, 2020 ). Gene protein and protein–protein interactions are generally depicted in the form of a graph, which have led to identifying disease networks and network medicine approaches for drug repurposing ( Gysi et al, 2020 ).…”