IUPHAR review - Data-driven computational drug repurposing approaches for opioid use disorder.

Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD.

Self-prediction of relations in GO facilitates its quality auditing.

As applications of the gene ontology (GO) increase rapidly in the biomedical field, quality auditing of it is becoming more and more important. Existing auditing methods are mostly based on rules, observed patterns or hypotheses. In this study, we propose a machine-learning-based framework for GO to audit itself: we first predict the IS-A relations among concepts in GO, then use differences between predicted results and existing relations to uncover potential errors. Specifically, we transfer the taxonomy of GO 2020 January release into a dataset with concept pairs as items and relations between them as labels(pairs with no direct IS-A relation are labeled as ndrs). To fully obtain the representation of each pair, we integrate the embeddings for the concept name, concept definition, as well as concept node in a substring-based topological graph. We divide the dataset into 10 parts, and rotate over all the parts by choosing one part as the testing set and the remaining as the training set each time. After 10 rotations, the prediction model predicted 4,640 existing IS-A pairs as ndrs. In the GO 2022 March release, 340 of these predictions were validated, demonstrating significance with a p-value of 1.60e-46 when compared to the results of randomly selected pairs. On the other hand, the model predicted 2,840 out of 17,079 selected ndrs in GO to be IS-A's relations. After deleting those that caused redundancies and circles, 924 predicted IS-A's relations remained. Among 200 pairs randomly selected, 30 were validated as missing IS-A's by domain experts. In conclusion, this study investigates a novel way of auditing biomedical ontologies by predicting the relations in it, which was shown to be useful for discovering potential errors.

Heterogeneous information network and its application to human health and disease.

The molecular components with the functional interdependencies in human cell form complicated biological network. Diseases are mostly caused by the perturbations of the composite of the interaction multi-biomolecules, rather than an abnormality of a single biomolecule. Furthermore, new biological functions and processes could be revealed by discovering novel biological entity relationships. Hence, more and more biologists focus on studying the complex biological system instead of the individual biological components. The emergence of heterogeneous information network (HIN) offers a promising way to systematically explore complicated and heterogeneous relationships between various molecules for apparently distinct phenotypes. In this review, we first present the basic definition of HIN and the biological system considered as a complex HIN. Then, we discuss the topological properties of HIN and how these can be applied to detect network motif and functional module. Afterwards, methodologies of discovering relationships between disease and biomolecule are presented. Useful insights on how HIN aids in drug development and explores human interactome are provided. Finally, we analyze the challenges and opportunities for uncovering combinatorial patterns among pharmacogenomics and cell-type detection based on single-cell genomic data.

IDDkin: network-based influence deep diffusion model for enhancing prediction of kinase inhibitors.

MOTIVATION: Protein kinases have been the focus of drug discovery research for many years because they play a causal role in many human diseases. Understanding the binding profile of kinase inhibitors is a prerequisite for drug discovery, and traditional methods of predicting kinase inhibitors are time-consuming and inefficient. Calculation-based predictive methods provide a relatively low-cost and high-efficiency approach to the rapid development and effective understanding of the binding profile of kinase inhibitors. Particularly, the continuous improvement of network pharmacology methods provides unprecedented opportunities for drug discovery, network-based computational methods could be employed to aggregate the effective information from heterogeneous sources, which have become a new way for predicting the binding profile of kinase inhibitors. RESULTS: In this study, we proposed a network-based influence deep diffusion model, named IDDkin, for enhancing the prediction of kinase inhibitors. IDDkin uses deep graph convolutional networks, graph attention networks and adaptive weighting methods to diffuse the effective information of heterogeneous networks. The updated kinase and compound representations are used to predict potential compound-kinase pairs. The experimental results show that the performance of IDDkin is superior to the comparison methods, including the state-of-the-art kinase inhibitor prediction method and the classic model widely used in relationship prediction. In experiments conducted to verify its generalizability and in case studies, the IDDkin model also shows excellent performance. All of these results demonstrate the powerful predictive ability of the IDDkin model in the field of kinase inhibitors. AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/CS-BIO/IDDkin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records.

Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Computational methods have been developed to identifying combination pharmacotherapy by analyzing large amounts of biomedical data. Existing computational approaches are often underpowered due to their reliance on our limited understanding of disease mechanisms. On the other hand, observable phenotypic inter-relationships among thousands of diseases often reflect their underlying shared genetic and molecular underpinnings, therefore can offer unique opportunities to design computational models to discover novel combinational therapies by automatically transferring knowledge among phenotypically related diseases. We developed a novel phenome-driven drug discovery system, named TuSDC, which leverages knowledge of existing drug combinations, disease comorbidities, and disease treatments of thousands of disease and drug entities extracted from over 31.5 million biomedical research articles using natural language processing techniques. TuSDC predicts combination pharmacotherapy by extracting representations of diseases and drugs using tensor factorization approaches. In external validation, TuSDC achieved an average precision of 0.77 for top ranked candidates, outperforming a state of art mechanism-based method for discovering drug combinations in treating hypertension. We evaluated top ranked anti-hypertension drug combinations using electronic health records of 84.7 million unique patients and showed that a novel drug combination hydrochlorothiazide-digoxin was associated with significantly lower hazards of subsequent hypertension as compared to the monotherapy hydrochlorothiazide alone (HR: 0.769, 95% CI [0.732, 0.807]) and digoxin alone (0.857, 95% CI [0.785, 0.936]). Data-driven informatics analyses reveal that the renin-angiotensin system is involved in the synergistical interactions of hydrochlorothiazide and digoxin on regulating hypertension. The prediction model's code with PyTorch version 1.5 is available at http://nlp.case.edu/public/data/TuSDC/.

KG-Predict: A knowledge graph computational framework for drug repurposing.

The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data has offered unprecedented opportunities for drug discovery including drug repurposing. Various knowledge graph-based methods have been developed to integrate and analyze complex and heterogeneous data sources to find new therapeutic applications for existing drugs. However, existing methods have limitations in modeling and capturing context-sensitive inter-relationships among tens of thousands of biomedical entities. In this paper, we developed KG-Predict: a knowledge graph computational framework for drug repurposing. We first integrated multiple types of entities and relations from various genotypic and phenotypic databases to construct a knowledge graph termed GP-KG. GP-KG was composed of 1,246,726 associations between 61,146 entities. KG-Predict then aggregated the heterogeneous topological and semantic information from GP-KG to learn low-dimensional representations of entities and relations, and further utilized these representations to infer new drug-disease interactions. In cross-validation experiments, KG-Predict achieved high performances [AUROC (the area under receiver operating characteristic) = 0.981, AUPR (the area under precision-recall) = 0.409 and MRR (the mean reciprocal rank) = 0.261], outperforming other state-of-art graph embedding methods. We applied KG-Predict in identifying novel repositioned candidate drugs for Alzheimer's disease (AD) and showed that KG-Predict prioritized both FDA-approved and active clinical trial anti-AD drugs among the top (AUROC = 0.868 and AUPR = 0.364).

An in-silico method with graph-based multi-label learning for large-scale prediction of circRNA-disease associations.

Circular RNAs (circRNAs) have been proved to be implicated in various pathological processes and play vital roles in tumors. Increasing evidence has shown that circRNAs can serve as an important class of regulators, which have great potential to become a new type of biomarkers for tumor diagnosis and treatment. However, their biological functions remain largely unknown, and it is costly and tremendously laborious to investigate the molecular mechanisms of circRNAs in human diseases based on conventional wet-lab experiments. The emergence and rapid growth of genomics data sources has provided new opportunities for us to decipher the underlying relationships between circRNAs and diseases by computational models. Therefore, it is appealing to develop powerful computational models to discover potential disease-associated circRNAs. Here, we develop an in-silico method with graph-based multi-label learning for large-scale of prediction potential circRNA-disease associations and discovery of those most promising disease circRNAs. By fully exploiting different characteristics of circRNA space and disease space and maintaining the data local geometric structures, the graph regularization and mixed-norm constraint terms are also incorporated into the model to help to make prediction. Results and case studies show that the proposed method outperforms other models and could effectively infer potential associations with high accuracy.

Multiview Joint Learning-Based Method for Identifying Small-Molecule-Associated MiRNAs by Integrating Pharmacological, Genomics, and Network Knowledge.

The emergence of a large amount of pharmacological, genomic, and network knowledge data provides new challenges and opportunities for drug discovery and development. Identification of real small-molecule drug (SM)-miRNA associations is not only important in the development of effective drug repositioning but also crucial in providing a better understanding of the mechanisms by which small-molecule drugs achieve the purpose of treating diseases by regulating miRNA expression. However, challenges remain in accurately determining potential associations between small molecules and miRNAs using information from multiomics data. In this study, we adopted a novel framework called SMAJL to improve the prediction of small molecule-miRNA associations with joint learning. First, we use enhancing matrix completions to obtain the network knowledge of small molecule-miRNA associations. Then, we extract the information of small-molecule fingerprints and miRNA sequences into feature vectors to obtain small-molecule structure and miRNA sequence information. Finally, we incorporate small-molecule structure information, miRNA sequence data, and heterogeneous network knowledge into a joint learning model based on a Restricted Boltzmann Machine (RBM) to predict association scores. To validate the effectiveness of our method, the SMAJL model is compared with four state-of-the-art methods in terms of 5-fold cross-validation. The results demonstrate that the AUC and AUPRC of the SMAJL are obviously superior to those of other comparison methods. The SMAJL model also achieved great results in terms of robustness and case studies, further demonstrating its strong predictive power.

Identification of Small Molecule-miRNA Associations with Graph Regularization Techniques in Heterogeneous Networks.

MicroRNAs (miRNAs) are significant regulators of post-transcriptional levels and have been confirmed to be targeted by small molecule (SM) drugs. It is a novel insight to treat human diseases and accelerate drug discovery by targeting miRNA with small molecules. Computational approaches for discovering novel small molecule-miRNA associations by integrating more heterogeneous network information provide a new idea for the multiple node association prediction between small molecule-miRNA and small molecule-disease associations at a system level. In this study, we proposed a new computational model based on graph regularization techniques in heterogeneous networks, called identification of small molecule-miRNA associations with graph regularization techniques (SMMARTs), to discover potential small molecule-miRNA associations. The novelty of the model lies in the fact that the association score of a small molecule-miRNA pair is calculated by an iterative method in heterogeneous networks that incorporates small molecule-disease associations and miRNA-disease associations. The experimental results indicate that SMMART has better performance than several state-of-the-art methods in inferring small molecule-miRNA associations. Case studies further illustrate the effectiveness of SMMART for small molecule-miRNA association prediction.

Incorporating Multisource Knowledge To Predict Drug Synergy Based on Graph Co-regularization.

Drug combinations may reduce toxicity and increase therapeutic efficacy, offering a promising strategy to conquer multiple complex diseases. However, due to large-scale combinatorial space, it remains challenging to identify effective combinations. Although many computational methods have focused on predicting drug synergy to reduce combinatorial space, they fail to effectively consider multiple sources of important knowledge. Thus, it is necessary to propose a computational method that can exploit useful information to predict drug synergy. Here, we developed a computational method to predict drug synergy based on graph co-regularization, named DSGCR. By incorporating drug-target network patterns, pharmacological patterns, and prior knowledge of drug combinations, DSGCR performs predictions of synergistic drug combinations. Compared to several existing methods, DSGCR achieves superior performance in predicting drug synergy in terms of various metrics via cross-validation. Additionally, we analyzed the importance of various sources of drug knowledge concerning three DSGCR's scenarios. Finally, the potential of DSGCR to score drug synergy was confirmed by three predicted synergistic drug combinations.

Potential circRNA-disease association prediction using DeepWalk and network consistency projection.

A growing body of experimental studies have reported that circular RNAs (circRNAs) are of interest in pathogenicity mechanism research and are becoming new diagnostic biomarkers. As experimental techniques for identifying disease-circRNA interactions are costly and laborious, some computational predictors have been advanced on the basis of the integration of biological features about circRNAs and diseases. However, the existing circRNA-disease relationships are not well exploited. To solve this issue, a novel method named DeepWalk and network consistency projection for circRNA-disease association prediction (DWNCPCDA) is proposed. Specifically, our method first reveals features of nodes learned by the deep learning method DeepWalk based on known circRNA-disease associations to calculate circRNA-circRNA similarity and disease-disease similarity, and then these two similarity networks are further employed to feed to the network consistency projection method to predict unobserved circRNA-disease interactions. As a result, DWNCPCDA shows high-accuracy performances for disease-circRNA interaction prediction: an AUC of 0.9647 with leave-one-out cross validation and an average AUC of 0.9599 with five-fold cross validation. We further perform case studies to prioritize latent circRNAs related to complex human diseases. Overall, this proposed method is able to provide a promising solution for disease-circRNA interaction prediction, and is capable of enhancing existing similarity-based prediction methods.

MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation.

MiRNAs are a class of small non-coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time-consuming, a large number of computational models have been developed to effectively predict reliable disease-related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA-disease associations and then utilizes the label propagation algorithm to reliably predict disease-related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA-disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA-disease association prediction.

A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations.

MOTIVATION: MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational approaches are biased towards known miRNA-disease associations, which is inappropriate for those new diseases or miRNAs without any known association information. RESULTS: In this study, we propose a new method with graph regularized non-negative matrix factorization in heterogeneous omics data, called GRNMF, to discover potential associations between miRNAs and diseases, especially for new diseases and miRNAs or those diseases and miRNAs with sparse known associations. First, we integrate the disease semantic information and miRNA functional information to estimate disease similarity and miRNA similarity, respectively. Considering that there is no available interaction observed for new diseases or miRNAs, a preprocessing step is developed to construct the interaction score profiles that will assist in prediction. Next, a graph regularized non-negative matrix factorization framework is utilized to simultaneously identify potential associations for all diseases. The results indicated that our proposed method can effectively prioritize disease-associated miRNAs with higher accuracy compared with other recent approaches. Moreover, case studies also demonstrated the effectiveness of GRNMF to infer unknown miRNA-disease associations for those novel diseases and miRNAs. AVAILABILITY AND IMPLEMENTATION: The code of GRNMF is freely available at https://github.com/XIAO-HN/GRNMF/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SNMDA: A novel method for predicting microRNA-disease associations based on sparse neighbourhood.

miRNAs are a class of small noncoding RNAs that are associated with a variety of complex biological processes. Increasing studies have shown that miRNAs have close relationships with many human diseases. The prediction of the associations between miRNAs and diseases has thus become a hot topic. Although traditional experimental methods are reliable, they could only identify a limited number of associations as they are time-consuming and expensive. Consequently, great efforts have been made to effectively predict reliable disease-related miRNAs based on computational methods. In this study, we present a novel approach to predict the potential microRNA-disease associations based on sparse neighbourhood. Specifically, our method takes advantage of the sparsity of the miRNA-disease association network and integrates the sparse information into the current similarity matrices for both miRNAs and diseases. To demonstrate the utility of our method, we applied global LOOCV, local LOOCV and five-fold cross-validation to evaluate our method, respectively. The corresponding AUCs are 0.936, 0.882 and 0.934. Three types of case studies on five common diseases further confirm the performance of our method in predicting unknown miRNA-disease associations. Overall, results show that SNMDA can predict the potential associations between miRNAs and diseases effectively.

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods.

Recently, increasing studies have shown that miRNAs are involved in the development and progression of various complex diseases. Consequently, predicting potential miRNA-disease associations makes an important contribution to understanding the pathogenesis of diseases, developing new drugs as well as designing individualized diagnostic and therapeutic approaches for different human diseases. Nonetheless, the inherent noise and incompleteness in the existing biological datasets have limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on global linear neighborhoods (GLNMDA). Specifically, our method obtains a new miRNA/disease similarity matrix by linearly reconstructing each miRNA/disease according to the known experimentally verified miRNA-disease associations. We then adopt label propagation to infer the potential associations between miRNAs and diseases. As a result, GLNMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.867 and 0.929, respectively) and 5-fold cross validation (average AUC of 0.926). Case studies on five common human diseases further confirmed the utility of our method in discovering latent miRNA-disease pairs. Taken together, GLNMDA could serve as a reliable computational tool for miRNA-disease association prediction.

Human disease MiRNA inference by combining target information based on heterogeneous manifolds.

The emergence of network medicine has provided great insight into the identification of disease-related molecules, which could help with the development of personalized medicine. However, the state-of-the-art methods could neither simultaneously consider target information and the known miRNA-disease associations nor effectively explore novel gene-disease associations as a by-product during the process of inferring disease-related miRNAs. Computational methods incorporating multiple sources of information offer more opportunities to infer disease-related molecules, including miRNAs and genes in heterogeneous networks at a system level. In this study, we developed a novel algorithm, named inference of Disease-related MiRNAs based on Heterogeneous Manifold (DMHM), to accurately and efficiently identify miRNA-disease associations by integrating multi-omics data. Graph-based regularization was utilized to obtain a smooth function on the data manifold, which constitutes the main principle of DMHM. The novelty of this framework lies in the relatedness between diseases and miRNAs, which are measured via heterogeneous manifolds on heterogeneous networks integrating target information. To demonstrate the effectiveness of DMHM, we conducted comprehensive experiments based on HMDD datasets and compared DMHM with six state-of-the-art methods. Experimental results indicated that DMHM significantly outperformed the other six methods under fivefold cross validation and de novo prediction tests. Case studies have further confirmed the practical usefulness of DMHM.

Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity.

Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method's outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases.

Detection of Protein Complexes Based on Penalized Matrix Decomposition in a Sparse Protein⁻Protein Interaction Network.

High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein⁻protein interaction (PPI) networks. In this study, based on penalized matrix decomposition (PMD), a novel method of penalized matrix decomposition for the identification of protein complexes (i.e., PMDpc) was developed to detect protein complexes in the human protein interaction network. This method mainly consists of three steps. First, the adjacent matrix of the protein interaction network is normalized. Second, the normalized matrix is decomposed into three factor matrices. The PMDpc method can detect protein complexes in sparse PPI networks by imposing appropriate constraints on factor matrices. Finally, the results of our method are compared with those of other methods in human PPI network. Experimental results show that our method can not only outperform classical algorithms, such as CFinder, ClusterONE, RRW, HC-PIN, and PCE-FR, but can also achieve an ideal overall performance in terms of a composite score consisting of F-measure, accuracy (ACC), and the maximum matching ratio (MMR).

HKFGCN: A novel multiple kernel fusion framework on graph convolutional network to predict microbe-drug associations.

Accumulating clinical studies have consistently demonstrated that the microbes in the human body closely interact with the human host, actively participating in the regulation of drug effectiveness. Identifying the associations between microbes and drugs can facilitate the development of drug discovery, and microbes have become a new target in antimicrobial drug development. However, the discovery of microbe-drug associations relies on clinical or biological experiments, which are not only time-consuming but also financially burdensome. Thus, the utilization of computational methods to predict microbe-drug associations holds promise for reducing costs and enhancing the efficiency of biological experiments. Here, we introduce a new computational method, called HKFGCN (Heterogeneous information Kernel Fusion Graph Convolution Network), to predict the microbe-drug associations. Instead of extracting feature from a single network in previous studies, HKFGCN separately extracts topological information features from different networks, and further refines them by generating Gaussian kernel features. HKFGCN consists of three main steps. Firstly, we constructed two similarity networks and a microbe-drug association network based on numerous biological data. Second, we employed two types of encoders to extract features from these networks. Next, Gaussian kernel features were obtained from the drug and microbe features at each layer. Finally, we reconstructed the bipartite microbe-drug graph based on the learned representations. Experimental results demonstrate the excellent performance of the HKFGCN model across different datasets using the cross-validation scheme. Additionally, we conduced case studies on human immunodeficiency virus, and the results were corroborated by existing literatures. The prediction model's code is available at https://github.com/roll-of-bubble/HKFGCN.

Deep graph contrastive learning model for drug-drug interaction prediction.

Drug-drug interaction (DDI) is the combined effects of multiple drugs taken together, which can either enhance or reduce each other's efficacy. Thus, drug interaction analysis plays an important role in improving treatment effectiveness and patient safety. It has become a new challenge to use computational methods to accelerate drug interaction time and reduce its cost-effectiveness. The existing methods often do not fully explore the relationship between the structural information and the functional information of drug molecules, resulting in low prediction accuracy for drug interactions, poor generalization, and other issues. In this paper, we propose a novel method, which is a deep graph contrastive learning model for drug-drug interaction prediction (DeepGCL for brevity). DeepGCL incorporates a contrastive learning component to enhance the consistency of information between different views (molecular structure and interaction network), which means that the DeepGCL model predicts drug interactions by integrating molecular structure features and interaction network topology features. Experimental results show that DeepGCL achieves better performance than other methods in all datasets. Moreover, we conducted many experiments to analyze the necessity of each component of the model and the robustness of the model, which also showed promising results. The source code of DeepGCL is freely available at https://github.com/jzysj/DeepGCL.