Potential Drug-disease Associations Research Articles

Drug repositioning by collaborative learning based on graph convolutional inductive network

Motivation:Computational drug repositioning is a vital path to improve efficiency of drug discovery, which aims to find potential Drug–Disease Associations (DDAs) to develop new effects of the existing drugs. Many approaches detected novel DDAs from heterogenous network which integrates similar drugs, similar diseases and the known DDAs. However, sparsity of the known DDAs and intrinsic synergic relations on representations of drugs and diseases in the heterogenous network are still the main challenges for DDAs prediction. Results:To address the problems, a novel drug repositioning approach is proposed here. Firstly, a drug similar network is constructed by Gaussian similarity kernel fusion of multisource drug similarities. Likewise, a disease similar network is generated by the same strategy. Secondly, the known DDAs network is extended by a bi-random walk algorithm from the above-mentioned similar networks. Meanwhile, representations of drugs and diseases are learned from their similar networks through graph convolutions and then a DDAs network is induced from the representations. Finally, to discover latent DDAs, the inductive DDAs network is refined iteratively by collaborating with the extended known DDAs network. Comprehensive experimental results show that our method outperforms several state-of-the-art methods for predicting DDAs on indicators including precision, recall, F1-score, MCC, ROC and AUPR. Moreover, case studies suggest that our method is highly effective in practices. The success of our method may be attributed to three aspects: (1) reliable similar relationships of drugs and diseases; (2) enhanced connectivity of the heterogenous network; (3) reasonable collaborative induction on DDAs network. Our method is freely available at https://github.com/BioMLab/DRCLN.

Drug repositioning via Multi-view Representation Learning with Heterogeneous Graph Neural Network.

Exploring simple and efficient computational methods for drug repositioning has emerged as a popular and compelling topic in the realm of comprehensive drug development. The crux of this technology lies in identifying potential drug-disease associations, which can effectively mitigate the burdens caused by the exorbitant costs and lengthy periods of conventional drugs development. However, current computational drug repositioning methods face challenges in accurately predicting drug-disease associations. These challenges include only considering drugs and diseases to construct a heterogeneous graph without including other biological nodes associated with the disease or drug for a more comprehensive heterogeneous graph, as well as not fully utilizing the local structure of heterogeneous graphs and rich semantic features. To address these problems, we propose a Multi-view Representation Learning method (MRLHGNN) with Heterogeneous Graph Neural Network for drug repositioning. This method is based on a collection of data from multiple biological entities associated with drugs or diseases. It consists of a view-specific feature aggregation module with meta-paths and auto multi-view fusion encoder. To better utilize local structural and semantic information from specific views in heterogeneous graph, MRLHGNN employs a feature aggregation model with variable-length meta-paths to expand the local receptive field. Additionally, it utilizes a transformerbased semantic aggregation module to aggregate semantic features across different view-specific graphs. Finally, potential drug-disease associations are obtained through a multi-view fusion decoder with an attention mechanism. Cross-validation experiments demonstrate the effectiveness and interpretability of the MRLHGNN in comparison to nine state-of-the-art approaches. Case studies further reveal that MRLHGNN can serve as a powerful tool for drug repositioning.

Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction

BackgroundIdentification of potential drug–disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs and diseases, current research still faces challenges in the process of mining potential relationships between drugs and diseases using heterogeneous network data.ResultsIn this study, we propose a learning framework for fusing Graph Transformer Networks and multi-aggregate graph convolutional network to learn efficient heterogenous information graph representations for drug–disease association prediction, termed WMAGT. This method extensively harnesses the capabilities of a robust graph transformer, effectively modeling the local and global interactions of nodes by integrating a graph convolutional network and a graph transformer with self-attention mechanisms in its encoder. We first integrate drug–drug, drug–disease, and disease–disease networks to construct heterogeneous information graph. Multi-aggregate graph convolutional network and graph transformer are then used in conjunction with neural collaborative filtering module to integrate information from different domains into highly effective feature representation.ConclusionsRigorous cross-validation, ablation studies examined the robustness and effectiveness of the proposed method. Experimental results demonstrate that WMAGT outperforms other state-of-the-art methods in accurate drug–disease association prediction, which is beneficial for drug repositioning and drug safety research.

GCNGAT: Drug–disease association prediction based on graph convolution neural network and graph attention network

Predicting drug–disease associations can contribute to discovering new therapeutic potentials of drugs, and providing important association information for new drug research and development. Many existing drug–disease association prediction methods have not distinguished relevant background information for the same drug targeted to different diseases. Therefore, this paper proposes a drug–disease association prediction model based on graph convolutional network and graph attention network (GCNGAT) to reposition marketed drugs under the distinguishment of background information. Firstly, in order to obtain initial drug–disease information, a drug–disease heterogeneous graph structure is constructed based on all known drug–disease associations. Secondly, based on the heterogeneous graph structure, the corresponding subgraphs of each group of drug–disease association pairs are extracted to distinguish different background information for the same drug from different diseases. Finally, a model combining Graph neural network with global Average pooling (GnnAp) is designed to predict potential drug–disease associations by learning drug–disease interaction feature representations. The experimental results show that adding subgraph extraction can effectively improve the prediction performance of the model, and the graph representation learning module can fully extract the deep features of drug–disease. Using the 5-fold cross-validation, the proposed model (GCNGAT) achieves AUC (Area Under the receiver operating characteristic Curve) values of 0.9182 and 0.9417 on the PREDICT dataset and CDataset dataset, respectively. Compared with other predictors on the same dataset (PREDICT dataset), GCNGAT outperforms the existing best-performing model (PSGCN), with a 1.58% increase in the AUC value. It is anticipated that this model can provide experimental reference for drug repositioning and further promote the drug research and development process.

Prediction of drug-disease associations based on reinforcement symmetric metric learning and graph convolution network.

Accurately identifying novel indications for drugs is crucial in drug research and discovery. Traditional drug discovery is costly and time-consuming. Computational drug repositioning can provide an effective strategy for discovering potential drug-disease associations. However, the known experimentally verified drug-disease associations is relatively sparse, which may affect the prediction performance of the computational drug repositioning methods. Moreover, while the existing drug-disease prediction method based on metric learning algorithm has achieved better performance, it simply learns features of drugs and diseases only from the drug-centered perspective, and cannot comprehensively model the latent features of drugs and diseases. In this study, we propose a novel drug repositioning method named RSML-GCN, which applies graph convolutional network and reinforcement symmetric metric learning to predict potential drug-disease associations. RSML-GCN first constructs a drug-disease heterogeneous network by integrating the association and feature information of drugs and diseases. Then, the graph convolutional network (GCN) is applied to complement the drug-disease association information. Finally, reinforcement symmetric metric learning with adaptive margin is designed to learn the latent vector representation of drugs and diseases. Based on the learned latent vector representation, the novel drug-disease associations can be identified by the metric function. Comprehensive experiments on benchmark datasets demonstrated the superior prediction performance of RSML-GCN for drug repositioning.

Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction.

Predicting potential drug-disease associations (RDAs) plays a pivotal role in elucidating therapeutic strategies for diseases and facilitating drug repositioning, making it of paramount importance. However, existing methods are constrained and rely heavily on limited domain-specific knowledge, impeding their ability to effectively predict candidate associations between drugs and diseases. Moreover, the simplistic definition of unknown information pertaining to drug-disease relationships as negative samples presents inherent limitations. To overcome these challenges, we introduce a novel hierarchical negative sampling-based graph contrastive model, termed HSGCLRDA, which aims to forecast latent associations between drugs and diseases. In this study, HSGCLRDA integrates the association information as well as similarity between drugs, diseases and proteins. Meanwhile, the model constructs a drug-disease-protein heterogeneous network. Subsequently, employing a hierarchical structural sampling technique, we establish reliable negative drug-disease samples utilizing PageRank algorithms. Utilizing meta-path aggregation within the heterogeneous network, we derive low-dimensional representations for drugs and diseases, thereby constructing global and local feature graphs that capture their interactions comprehensively. To obtain representation information, we adopt a self-supervised graph contrastive approach that leverages graph convolutional networks (GCNs) and second-order GCNs to extract feature graph information. Furthermore, we integrate a contrastive cost function derived from the cross-entropy cost function, facilitating holistic model optimization. Experimental results obtained from benchmark datasets not only showcase the superior performance of HSGCLRDA compared to various baseline methods in predicting RDAs but also emphasize its practical utility in identifying novel potential diseases associated with existing drugs through meticulous case studies.

Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks

Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.

SLGCN: Structure-enhanced line graph convolutional network for predicting drug–disease associations

Drug repositioning is a rapidly growing strategy in drug discovery, as the time and cost needed are considerably less compared to developing new drugs. In addition to traditional wet experiments, designing effective computational methods to discover potential drug–disease associations is an attractive shortcut in drug repositioning. Most current methods based on graph neural networks ignore the heterophily of the constructed drug–disease network, resulting in inefficient predictions. In this paper, a novel structure-enhanced line graph convolutional network (SLGCN) is proposed to learn comprehensive representations of drug–disease pairs, incorporating structural information to conduct heterophily. First, line graphs centered around drug–disease pairs are extracted. This process turns the association prediction task into a node classification problem, which better displays the learning ability of SLGCN. Then, in message aggregation, a relation matrix is proposed to mark the structural importance of neighboring nodes. In this way, messages from nodes with lower structural importance can be assigned small weights. Unlike vanilla GCN, which adds self-loops to average ego representations and aggregated messages, an update gate is proposed to integrate biology information contained in ego representations with topology information contained in aggregated messages. Extensive experiments show that SLGCN achieves better performance than other advanced methods among the two datasets.

NetPro: Neighborhood Interaction-based Drug Repositioning via Label Propagation.

Drug repositioning is an important approach for predicting new disease indications of the existing drugs in drug discovery. A great progress has been achieved in drug repositioning. However, effectively utilizing the localized neighborhood interaction features of drug and disease in drug-disease associations remains challenging. This paper proposes a neighborhood interaction-based method called NetPro for drug repositioning via label propagation. In NetPro, we first formulate the known drug-disease associations, various disease and drug similarities from different perspectives to construct drug-drug and disease-disease networks. Meanwhile we employ the nearest neighbors and their interactions in the constructed networks to devise a new approach for computing drug similarity and disease similarity. To implement the prediction of new drugs or diseases, a preprocessing step is applied to renew the known drug-disease associations using our calculated drug and disease similarities. We then employ a label propagation model to predict drug-disease associations by the drug and disease linear neighborhood similarities derived from the renewed drug-disease associations. The experimental results on three benchmark datasets show that NetPro can effectively identify potential drug-disease associations and achieve better prediction performance than the existing methods. Case studies further demonstrate that NetPro is capable of predicting promising candidate disease indications for drugs.

Predicting Potential Drug-Disease Associations Based on Hypergraph Learning with Subgraph Matching.

The search for potential drug-disease associations (DDA) can speed up drug development cycles, reduce costly wasted resources, and accelerate disease treatment by repurposing existing drugs that can control further disease progression. As technologies such as deep learning continue to mature, many researchers tend to use emerging technologies to predict potential DDA. The performance of DDA prediction is still challenging and there is some space for improvement due to issues such as the small number of existing associations and possible noise in the data. To better predict DDA, we propose a computational approach based on hypergraph learning with subgraph matching (HGDDA). In particular, HGDDA first extracts feature subgraph information in the validated drug-disease association network and proposes a negative sampling strategy based on similarity network to reduce the data imbalance. Second, the hypergraph Unet module is used by extracting Finally, the potential DDA is predicted by designing a hypergraph combination module to convolution and pooling the two constructed hypergraphs separately, and calculating the difference information between the subgraphs using cosine similarity for node matching. The performance of HGDDA is verified under two standard datasets by 10-fold cross-validation (10-CV), and the results outperform existing drug-disease prediction methods. In addition, to validate the overall utility of the model, the top 10 drugs for the specific disease are predicted through the case study and validated using the CTD database.

REDDA: Integrating multiple biological relations to heterogeneous graph neural network for drug-disease association prediction

Computational drug repositioning is an effective way to find new indications for existing drugs, thus can accelerate drug development and reduce experimental costs. Recently, various deep learning-based repurposing methods have been established to identify the potential drug-disease associations (DDA). However, effective utilization of the relations of biological entities to capture the biological interactions to enhance the drug-disease association prediction is still challenging. To resolve the above problem, we proposed a heterogeneous graph neural network called REDDA (Relations-Enhanced Drug-Disease Association prediction). Assembled with three attention mechanisms, REDDA can sequentially learn drug/disease representations by a general heterogeneous graph convolutional network-based node embedding block, a topological subnet embedding block, a graph attention block, and a layer attention block. Performance comparisons on our proposed benchmark dataset show that REDDA outperforms 8 advanced drug-disease association prediction methods, achieving relative improvements of 0.76% on the area under the receiver operating characteristic curve (AUC) score and 13.92% on the precision-recall curve (AUPR) score compared to the suboptimal method. On the other benchmark dataset, REDDA also obtains relative improvements of 2.48% on the AUC score and 4.93% on the AUPR score. Specifically, case studies also indicate that REDDA can give valid predictions for the discovery of -new indications for drugs and new therapies for diseases. The overall results provide an inspiring potential for REDDA in the in silico drug development. The proposed benchmark dataset and source code are available in https://github.com/gu-yaowen/REDDA.

GCMM: graph convolution network based on multimodal attention mechanism for drug repurposing

BackgroundThe main focus of in silico drug repurposing, which is a promising area for using artificial intelligence in drug discovery, is the prediction of drug–disease relationships. Although many computational models have been proposed recently, it is still difficult to reliably predict drug–disease associations from a variety of sources of data.ResultsIn order to identify potential drug–disease associations, this paper introduces a novel end-to-end model called Graph convolution network based on a multimodal attention mechanism (GCMM). In particular, GCMM incorporates known drug–disease relations, drug–drug chemical similarity, drug–drug therapeutic similarity, disease–disease semantic similarity, and disease–disease target-based similarity into a heterogeneous network. A Graph Convolution Network encoder is used to learn how diseases and drugs are embedded in various perspectives. Additionally, GCMM can enhance performance by applying a multimodal attention layer to assign various levels of value to various features and the inputting of multi-source information.Conclusion5 fold cross-validation evaluations show that the GCMM outperforms four recently proposed deep-learning models on the majority of the criteria. It shows that GCMM can predict drug–disease relationships reliably and suggests improvement in the desired metrics. Hyper-parameter analysis and exploratory ablation experiments are also provided to demonstrate the necessity of each module of the model and the highest possible level of prediction performance. Additionally, a case study on Alzheimer’s disease (AD). Four of the five medications indicated by GCMM to have the highest potential correlation coefficient with AD have been demonstrated through literature or experimental research, demonstrating the viability of GCMM. All of these results imply that GCMM can provide a strong and effective tool for drug development and repositioning.

Pathway Analysis of Patients with Severe Acute Respiratory Syndrome.

Coronaviruses are emerging threats for human health, as demonstrated by the ongoing coronavirus disease 2019 (COVID-19) pandemic that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 is closely related to SARS-CoV-1, which was the cause of the 2002-2004 SARS outbreak, but SARS-CoV-1 has been the subject of a relatively limited number of studies. Understanding the potential pathways and molecular targets of SARS-CoV-1 will contribute to current drug repurposing strategies by helping to predict potential drug-disease associations. A microarray dataset, GSE1739, of 10 SARS patients and 4 healthy controls was downloaded from NCBI's GEO repository, and differential expression was identified using NCBI's GEO2R software. Pathway and enrichment analysis of the differentially expressed genes was carried out using Ingenuity Pathway Analysis and Gene Set Enrichment Analysis, respectively. Our findings show that the drugs dexamethasone, filgrastim, interferon alfacon-1, and levodopa were among the most significant upstream regulators of differential gene expression in SARS patients, while neutrophil degranulation was the most significantly enriched pathway. An enhanced understanding of the pathways and molecular targets of SARS-CoV-1 in humans will contribute to current and future drug repurposing strategies, which are an essential tool to combat rapidly emerging health threats.

Heterogeneous multi-scale neighbor topologies enhanced drug-disease association prediction.

Identifying new uses of approved drugs is an effective way to reduce the time and cost of drug development. Recent computational approaches for predicting drug-disease associations have integrated multi-sourced data on drugs and diseases. However, neighboring topologies of various scales in multiple heterogeneous drug-disease networks have yet to be exploited and fully integrated. We propose a novel method for drug-disease association prediction, called MGPred, used to encode and learn multi-scale neighboring topologies of drug and disease nodes and pairwise attributes from heterogeneous networks. First, we constructed three heterogeneous networks based on multiple kinds of drug similarities. Each network comprises drug and disease nodes and edges created based on node-wise similarities and associations that reflect specific topological structures. We also propose an embedding mechanism to formulate topologies that cover different ranges of neighbors. To encode the embeddings and derive multi-scale neighboring topology representations of drug and disease nodes, we propose a module based on graph convolutional autoencoders with shared parameters for each heterogeneous network. We also propose scale-level attention to obtain an adaptive fusion of informative topological representations at different scales. Finally, a learning module based on a convolutional neural network with various receptive fields is proposed to learn multi-view attribute representations of a pair of drug and disease nodes. Comprehensive experiment results demonstrate that MGPred outperforms other state-of-the-art methods in comparison to drug-related disease prediction, and the recall rates for the top-ranked candidates and case studies on five drugs further demonstrate the ability of MGPred to retrieve potential drug-disease associations.

Drug–disease associations prediction via Multiple Kernel-based Dual Graph Regularized Least Squares

Predicting associations in drug–disease network provides effective information for the drug repositioning. Therefore, it is an important task to develop an effective drug–disease association prediction method. In this paper, we propose a model, called the Multiple Kernel-based Dual Graph Regularized Least Squares Model (MKDGRLS), to predict potential drug–disease associations. First, we calculate the multiple kernels of drug and disease spaces respectively. Moreover, we utilize multiple kernels and related Laplacian regularization terms to construct MKDGRLS. Finally, we solve the object function of MKDGRLS by the Alternating Least squares algorithm (ALSA), for predicting drug–disease associations. Simultaneously, we design a variant MKDGRLS-A, which is added one dimension as the bias for compensate the inexact solution of linear systems. Our proposed approach has better prediction performance than existing prediction tools under two types of cross validation on the three real drug–disease association network datasets. The results of the case studies of the two diseases prove that our model can effectively predict new associations. We also test MKDGRLS on six real-world networks and it achieves better performance than other methods. In conclusion, our research work can effectively discover potential drug–disease associations, and can provide effective help for related drug repositioning work.

Predictive intelligence powered attentional stacking matrix factorization algorithm for the computational drug repositioning

Artificial Intelligence (AI) technologies are widely used to study the computational drug repositioning to find potential new uses for marketed drugs, which have a huge impact on drug development. However, due to the sparsity of datasets and the linear limitations of traditional AI or machine learning models lead to the inability of most methods to effectively obtain hidden feature of drugs and diseases To end this, Predictive Intelligence (PI) has been proposed in recent years. Hence, in this work, we propose a PI powered Attentional Stacking Matrix Factorization (PI-ASMF). Firstly, in order to extract the effective latent factor of the drugs or diseases the ASMF model superimposes the drug–disease similarity information and the drug–disease association information into the PI model, which ensures that it can learn the effective latent factor and alleviates the cold start problem to a certain extent. Subsequently, the latent factors of drugs and diseases are element-wise multiplied with their respective attentional vectors to learn the adaptive weights of each feature. Finally, the latent factors of drugs and diseases are fed into the prediction module to generate predictive values of potential drug–disease associations. The AUC and AUPR values of PI-ASMF model on two real datasets are 0.892 and 0.168, 0.913 and 0.25, respectively, which verified its superiority and validity.

Predicting Drug-Disease Association Based on Ensemble Strategy.

Drug repositioning is used to find new uses for existing drugs, effectively shortening the drug research and development cycle and reducing costs and risks. A new model of drug repositioning based on ensemble learning is proposed. This work develops a novel computational drug repositioning approach called CMAF to discover potential drug-disease associations. First, for new drugs and diseases or unknown drug-disease pairs, based on their known neighbor information, an association probability can be obtained by implementing the weighted K nearest known neighbors (WKNKN) method and improving the drug-disease association information. Then, a new drug similarity network and new disease similarity network can be constructed. Three prediction models are applied and ensembled to enable the final association of drug-disease pairs based on improved drug-disease association information and the constructed similarity network. The experimental results demonstrate that the developed approach outperforms recent state-of-the-art prediction models. Case studies further confirm the predictive ability of the proposed method. Our proposed method can effectively improve the prediction results.

Link Prediction Only With Interaction Data and its Application on Drug Repositioning.

To assist drug development, many computational methods have been proposed to identify potential drug-disease treatment associations before wet experiments. Based on the assumption that similar drugs may treat similar diseases, most methods calculate the similarities of drugs and diseases by using various chemical or biological features. However, since these features may be unknown or hard to collect, such methods will not work in the face of incomplete data. Besides, due to the lack of validated negative samples in the drug-disease associations data, most methods have no choice but to simply select some unlabeled samples as negative ones, which may introduce noises and decrease the reliability of prediction. Herein, we propose a new method (TS-SVD) which only uses those known drug-protein, disease-protein and drug-disease interactions to predict the potential drug-disease associations. In a constructed drug-protein-disease heterogeneous network, assuming that drugs/diseases relating to some common proteins or diseases/drugs may be similar, we get the common neighbors count matrix of drugs/diseases, then convert it to a topological similarity matrix. After that, we get low dimensional embedding representations of drug-disease pairs by using topological features and singular value decomposition. Finally, a Random Forest classifier is trained to do the prediction. To train a more reasonable model, we select out some reliable negative samples based on the k -step neighbors relationships between drugs and diseases. Compared with some state-of-the-art methods, we use less information but achieve better or comparable performance. Meanwhile, our strategy for selecting reliable negative samples can improve the performances of these methods. Case studies have further shown the practicality of our method in discovering novel drug-disease associations.

Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network.

Identifying drug-disease associations is helpful for not only predicting new drug indications and recognizing lead compounds, but also preventing, diagnosing, treating diseases. Traditional experimental methods are time consuming, laborious and expensive. Therefore, it is urgent to develop computational method for predicting potential drug-disease associations on a large scale. Herein, a novel method was proposed to identify drug-disease associations based on the deep learning technique. Molecular structure and clinical symptom information were used to characterize drugs and diseases. Then, a novel two-dimensional matrix was constructed and mapped to a gray-scale image for representing drug-disease association. Finally, deep convolution neural network was introduced to build model for identifying potential drug-disease associations. The performance of current method was evaluated based on the training set and test set, and accuracies of 89.90 and 86.51% were obtained. Prediction ability for recognizing new drug indications, lead compounds and true drug-disease associations was also investigated and verified by performing various experiments. Additionally, 3,620,516 potential drug-disease associations were identified and some of them were further validated through docking modeling. It is anticipated that the proposed method may be a powerful large scale virtual screening tool for drug research and development. The source code of MATLAB is freely available on request from the authors.

Overlap matrix completion for predicting drug-associated indications.

Identification of potential drug-associated indications is critical for either approved or novel drugs in drug repositioning. Current computational methods based on drug similarity and disease similarity have been developed to predict drug-disease associations. When more reliable drug- or disease-related information becomes available and is integrated, the prediction precision can be continuously improved. However, it is a challenging problem to effectively incorporate multiple types of prior information, representing different characteristics of drugs and diseases, to identify promising drug-disease associations. In this study, we propose an overlap matrix completion (OMC) for bilayer networks (OMC2) and tri-layer networks (OMC3) to predict potential drug-associated indications, respectively. OMC is able to efficiently exploit the underlying low-rank structures of the drug-disease association matrices. In OMC2, first of all, we construct one bilayer network from drug-side aspect and one from disease-side aspect, and then obtain their corresponding block adjacency matrices. We then propose the OMC2 algorithm to fill out the values of the missing entries in these two adjacency matrices, and predict the scores of unknown drug-disease pairs. Moreover, we further extend OMC2 to OMC3 to handle tri-layer networks. Computational experiments on various datasets indicate that our OMC methods can effectively predict the potential drug-disease associations. Compared with the other state-of-the-art approaches, our methods yield higher prediction accuracy in 10-fold cross-validation and de novo experiments. In addition, case studies also confirm the effectiveness of our methods in identifying promising indications for existing drugs in practical applications.