scholarly journals Coupled matrix–matrix and coupled tensor–matrix completion methods for predicting drug–target interactions

2020 ◽  
Author(s):  
Maryam Bagherian ◽  
Renaid B Kim ◽  
Cheng Jiang ◽  
Maureen A Sartor ◽  
Harm Derksen ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Drug Target ◽  
Drug Repositioning ◽  
Matrix Completion ◽  
Area Under The Curve ◽  
Drug Repurposing ◽  
Computational Prediction ◽  
Comprehensive Information ◽  
The Matrix ◽  
Benchmark Datasets

Abstract Predicting the interactions between drugs and targets plays an important role in the process of new drug discovery, drug repurposing (also known as drug repositioning). There is a need to develop novel and efficient prediction approaches in order to avoid the costly and laborious process of determining drug–target interactions (DTIs) based on experiments alone. These computational prediction approaches should be capable of identifying the potential DTIs in a timely manner. Matrix factorization methods have been proven to be the most reliable group of methods. Here, we first propose a matrix factorization-based method termed ‘Coupled Matrix–Matrix Completion’ (CMMC). Next, in order to utilize more comprehensive information provided in different databases and incorporate multiple types of scores for drug–drug similarities and target–target relationship, we then extend CMMC to ‘Coupled Tensor–Matrix Completion’ (CTMC) by considering drug–drug and target–target similarity/interaction tensors. Results: Evaluation on two benchmark datasets, DrugBank and TTD, shows that CTMC outperforms the matrix-factorization-based methods: GRMF, $L_{2,1}$-GRMF, NRLMF and NRLMF$\beta $. Based on the evaluation, CMMC and CTMC outperform the above three methods in term of area under the curve, F1 score, sensitivity and specificity in a considerably shorter run time.

scholarly journals A review on drug repurposing applicable to COVID-19

2020 ◽  
Author(s):  
Serena Dotolo ◽  
Anna Marabotti ◽  
Angelo Facchiano ◽  
Roberto Tagliaferri
Keyword(s):  
Artificial Intelligence ◽  
Drug Target ◽  
Lower Cost ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Chemical Compounds ◽  
The Moment ◽  
New Applications ◽  
Novel Drug ◽  
Small Chemical

Abstract Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

scholarly journals A unified drug–target interaction prediction framework based on knowledge graph and recommendation system

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qing Ye ◽  
Chang-Yu Hsieh ◽  
Ziyi Yang ◽  
Yu Kang ◽  
Jiming Chen ◽  
...  
Keyword(s):  
Drug Target ◽  
Recommendation System ◽  
Drug Repurposing ◽  
Cold Start ◽  
Vital Role ◽  
Knowledge Graph ◽  
Valuable Insight ◽  
Unified Framework ◽  
Benchmark Datasets ◽  
Low Dimensional

AbstractPrediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.

scholarly journals DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246920
Author(s):  
Sk Mazharul Islam ◽  
Sk Md Mosaddek Hossain ◽  
Sumanta Ray
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Drug Repositioning ◽  
Geometric Mean ◽  
Imbalanced Data ◽  
Drug Repurposing ◽  
Search Space ◽  
Classification Model ◽  
Target Interaction ◽  
Rough Approximation

In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

scholarly journals Drug-Target Interaction Prediction Based on Adversarial Bayesian Personalized Ranking

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yihua Ye ◽  
Yuqi Wen ◽  
Zhongnan Zhang ◽  
Song He ◽  
Xiaochen Bo
Keyword(s):  
Partial Order ◽  
Matrix Factorization ◽  
Drug Target ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Learning To Rank ◽  
Prediction Performance ◽  
Target Interaction ◽  
Bayesian Personalized Ranking ◽  
Personalized Ranking

The prediction of drug-target interaction (DTI) is a key step in drug repositioning. In recent years, many studies have tried to use matrix factorization to predict DTI, but they only use known DTIs and ignore the features of drug and target expression profiles, resulting in limited prediction performance. In this study, we propose a new DTI prediction model named AdvB-DTI. Within this model, the features of drug and target expression profiles are associated with Adversarial Bayesian Personalized Ranking through matrix factorization. Firstly, according to the known drug-target relationships, a set of ternary partial order relationships is generated. Next, these partial order relationships are used to train the latent factor matrix of drugs and targets using the Adversarial Bayesian Personalized Ranking method, and the matrix factorization is improved by the features of drug and target expression profiles. Finally, the scores of drug-target pairs are achieved by the inner product of latent factors, and the DTI prediction is performed based on the score ranking. The proposed model effectively takes advantage of the idea of learning to rank to overcome the problem of data sparsity, and perturbation factors are introduced to make the model more robust. Experimental results show that our model could achieve a better DTI prediction performance.

scholarly journals Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Matrix Completion

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Minhui Wang ◽  
Chang Tang ◽  
Jiajia Chen
Keyword(s):  
Drug Target ◽  
Chemical Structure ◽  
Genomic Sequence ◽  
Matrix Completion ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Completion Problem ◽  
Matrix Completion Problem ◽  
The Matrix ◽  
Laplacian Graph

Drug-target interactions play an important role for biomedical drug discovery and development. However, it is expensive and time-consuming to accomplish this task by experimental determination. Therefore, developing computational techniques for drug-target interaction prediction is urgent and has practical significance. In this work, we propose an effective computational model of dual Laplacian graph regularized matrix completion, referred to as DLGRMC briefly, to infer the unknown drug-target interactions. Specifically, DLGRMC transforms the task of drug-target interaction prediction into a matrix completion problem, in which the potential interactions between drugs and targets can be obtained based on the prediction scores after the matrix completion procedure. In DLGRMC, the drug pairwise chemical structure similarities and the target pairwise genomic sequence similarities are fully exploited to serve the matrix completion by using a dual Laplacian graph regularization term; i.e., drugs with similar chemical structure are more likely to have interactions with similar targets and targets with similar genomic sequence similarity are more likely to have interactions with similar drugs. In addition, during the matrix completion process, an indicator matrix with binary values which indicates the indices of the observed drug-target interactions is deployed to preserve the experimental confirmed interactions. Furthermore, we develop an alternative iterative strategy to solve the constrained matrix completion problem based on Augmented Lagrange Multiplier algorithm. We evaluate DLGRMC on five benchmark datasets and the results show that DLGRMC outperforms several state-of-the-art approaches in terms of 10-fold cross validation based AUPR values and PR curves. In addition, case studies also demonstrate that DLGRMC can successfully predict most of the experimental validated drug-target interactions.

scholarly journals Uncovering new drug properties in target-based drug-drug similarity networks

2020 ◽  
Author(s):  
Lucreţia Udrescu ◽  
Paul Bogdan ◽  
Aimée Chiş ◽  
Ioan Ovidiu Sîrbu ◽  
Alexandru Topîrceanu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Azelaic Acid ◽  
Drug Target ◽  
Drug Repositioning ◽  
Traditional Approach ◽  
Test Procedure ◽  
Drug Repurposing ◽  
Biological Targets ◽  
Chemical Structures ◽  
Drug Similarity

ABSTRACTDespite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach – based on knowledge about the chemical structures – cannot fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug-target interactions to infer drug properties. To this end, we define drug similarity based on drug-target interactions and build a weighted Drug-Drug Similarity Network according to the drug-drug similarity relationships. Using an energy-model network layout, we generate drug communities that are associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them as drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. By using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure, based on molecular docking, to further analyze the repurposing of Azelaic acid and Meprobamate.

scholarly journals Scalable Probabilistic Matrix Factorization with Graph-Based Priors

2020 ◽  
Vol 34 (04) ◽  
pp. 5851-5858
Author(s):  
Jonathan Strahl ◽  
Jaakko Peltonen ◽  
Hirsohi Mamitsuka ◽  
Samuel Kaski
Keyword(s):  
Matrix Factorization ◽  
Prediction Accuracy ◽  
Side Information ◽  
Matrix Completion ◽  
Real Data ◽  
Data Matrix ◽  
Laptop Computer ◽  
Completion Problem ◽  
Graphical Lasso ◽  
The Matrix

In matrix factorization, available graph side-information may not be well suited for the matrix completion problem, having edges that disagree with the latent-feature relations learnt from the incomplete data matrix. We show that removing these contested edges improves prediction accuracy and scalability. We identify the contested edges through a highly-efficient graphical lasso approximation. The identification and removal of contested edges adds no computational complexity to state-of-the-art graph-regularized matrix factorization, remaining linear with respect to the number of non-zeros. Computational load even decreases proportional to the number of edges removed. Formulating a probabilistic generative model and using expectation maximization to extend graph-regularised alternating least squares (GRALS) guarantees convergence. Rich simulated experiments illustrate the desired properties of the resulting algorithm. On real data experiments we demonstrate improved prediction accuracy with fewer graph edges (empirical evidence that graph side-information is often inaccurate). A 300 thousand dimensional graph with three million edges (Yahoo music side-information) can be analyzed in under ten minutes on a standard laptop computer demonstrating the efficiency of our graph update.

scholarly journals Uncovering New Drug Properties in Target-Based Drug–Drug Similarity Networks

Pharmaceutics ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 879 ◽  
Author(s):  
Lucreţia Udrescu ◽  
Paul Bogdan ◽  
Aimée Chiş ◽  
Ioan Ovidiu Sîrbu ◽  
Alexandru Topîrceanu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Azelaic Acid ◽  
Drug Target ◽  
Drug Repositioning ◽  
Traditional Approach ◽  
Test Procedure ◽  
Drug Repurposing ◽  
Biological Targets ◽  
Chemical Structures ◽  
Drug Similarity

Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach—based on knowledge about the chemical structures—can not fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug–target interactions to infer drug properties. To this end, we define drug similarity based on drug–target interactions and build a weighted Drug–Drug Similarity Network according to the drug–drug similarity relationships. Using an energy-model network layout, we generate drug communities associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them potential drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. Using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure based on molecular docking to analyze Azelaic acid and Meprobamate’s repurposing.

scholarly journals Drug-Target Interaction prediction using Multi Graph Regularized Nuclear Norm Minimization

2018 ◽  
Author(s):  
Aanchal Mongia ◽  
Angshul Majumdar
Keyword(s):  
Drug Target ◽  
Matrix Completion ◽  
Graph Laplacian ◽  
Nuclear Norm ◽  
Interaction Matrix ◽  
Multiple Drug ◽  
Nuclear Norm Minimization ◽  
Target Interaction ◽  
Norm Minimization ◽  
Benchmark Datasets

AbstractThe identification of interactions between drugs and target proteins is crucial in pharmaceutical sciences. The experimental validation of interactions in genomic drug discovery is laborious and expensive; hence, there is a need for efficient and accurate in-silico techniques which can predict potential drug-target interactions to narrow down the search space for experimental verification.In this work, we propose a new framework, namely, Multi Graph Regularized Nuclear Norm Minimization, which predicts the interactions between drugs and proteins from three inputs: known drug-target interaction network, similarities over drugs and those over targets. The proposed method focuses on finding a low-rank interaction matrix that is structured by the proximities of drugs and targets encoded by graphs. Previous works on Drug Target Interaction (DTI) prediction have shown that incorporating drug and target similarities helps in learning the data manifold better by preserving the local geometries of the original data. But, there is no clear consensus on which kind and what combination of similarities would best assist the prediction task. Hence, we propose to use various multiple drug-drug similarities and target-target similarities as multiple graph Laplacian (over drugs/targets) regularization terms to capture the proximities exhaustively.Extensive cross-validation experiments on four benchmark datasets using standard evaluation metrics (AUPR and AUC) show that the proposed algorithm improves the predictive performance and outperforms recent state-of-the-art computational methods by a large margin.Author summaryThis work introduces a computational approach, namely Multi-Graph Regularized Nuclear Norm Minimization (MGRNNM), to predict potential interactions between drugs and targets. The novelty of MGRNNM lies in structuring drug-target interactions by multiple proximities of drugs and targets. There have been previous works which have graph regularized Matrix factorization and Matrix completion algorithms to incorporate the standard chemical structure drug similarity and genomic sequence target protein similarity, respectively. We introduce multiple drug-graph laplacian and target-graph laplacian regularization terms to the standard matrix completion framework to predict the missing values in the interaction matrix. The graph Laplacian terms are constructed from various kinds and combinations of similarities over drugs and targets (computed from the interaction matrix itself). In addition to this, we further improve the prediction accuracy by sparsifying the drug and target similarity matrices, respectively. For performance evaluation, we conducted extensive experiments on four benchmark datasets. The experimental results demonstrated that MGRNNM clearly outperforms recent state-of-the-art methods under three different cross-validation settings, in terms of the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPR).

scholarly journals BPR Based Drug Groups and Drug Interaction Extraction

2020 ◽  
Vol 8 (6) ◽  
pp. 5379-5384
Keyword(s):  
Matrix Factorization ◽  
Drug Target ◽  
Drug Repurposing ◽  
Factorization Method ◽  
New Drugs ◽  
Ranking Problem ◽  
Target Interaction ◽  
Interaction Extraction ◽  
Bayesian Personalized Ranking ◽  
Personalized Ranking

The key step of drug discovery is the identification of interaction between drug and target proteins. This isn't just valuable to understand the disease, but also assist to distinguishing antagonistic symptoms of drugs. So, in drug repurposing [3] field the drug-target interaction (DTI) prediction is an essential tool. There are various methods to decipher unknown drug-target interaction [2], this is helped in the area of identifying the lead compound in the drug for a specific disease. In this paper proposes drug-target interaction extraction using Bayesian Personalized Ranking (BPR) method [5]. Here it is also solving the ranking problem by the implementation of matrix factorization method [5]. The proposed procedure can manage the occasion of new drugs and takes compound and hereditary resemblances of meds and targets and target tendency into account. [4].

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