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].

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.

Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization

2017 ◽  
Vol 14 (3) ◽  
pp. 646-656 ◽  
Author(s):  
Ali Ezzat ◽  
Peilin Zhao ◽  
Min Wu ◽  
Xiao-Li Li ◽  
Chee-Keong Kwoh
Keyword(s):  
Matrix Factorization ◽  
Drug Target ◽  
Target Interaction ◽  
Interaction Prediction

scholarly journals Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model

Viruses ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1325
Author(s):  
Yoonjung Choi ◽  
Bonggun Shin ◽  
Keunsoo Kang ◽  
Sungsoo Park ◽  
Bo Ram Beck
Keyword(s):  
Deep Learning ◽  
Angiotensin Converting Enzyme ◽  
Drug Target ◽  
Expression Profiles ◽  
Drug Repurposing ◽  
Converting Enzyme ◽  
Angiotensin Converting Enzyme 2 ◽  
Target Interaction ◽  
Approved Drugs ◽  
Fda Approved Drugs

Previously, our group predicted commercially available Food and Drug Administration (FDA) approved drugs that can inhibit each step of the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using a deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI). Unfortunately, additional clinically significant treatment options since the approval of remdesivir are scarce. To overcome the current coronavirus disease 2019 (COVID-19) more efficiently, a treatment strategy that controls not only SARS-CoV-2 replication but also the host entry step should be considered. In this study, we used MT-DTI to predict FDA approved drugs that may have strong affinities for the angiotensin-converting enzyme 2 (ACE2) receptor and the transmembrane protease serine 2 (TMPRSS2) which are essential for viral entry to the host cell. Of the 460 drugs with Kd of less than 100 nM for the ACE2 receptor, 17 drugs overlapped with drugs that inhibit the interaction of ACE2 and SARS-CoV-2 spike reported in the NCATS OpenData portal. Among them, enalaprilat, an ACE inhibitor, showed a Kd value of 1.5 nM against the ACE2. Furthermore, three of the top 30 drugs with strong affinity prediction for the TMPRSS2 are anti-hepatitis C virus (HCV) drugs, including ombitasvir, daclatasvir, and paritaprevir. Notably, of the top 30 drugs, AT1R blocker eprosartan and neuropsychiatric drug lisuride showed similar gene expression profiles to potential TMPRSS2 inhibitors. Collectively, we suggest that drugs predicted to have strong inhibitory potencies to ACE2 and TMPRSS2 through the DTI model should be considered as potential drug repurposing candidates for COVID-19.

scholarly journals Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19

2021 ◽  
Vol 11 ◽  
Author(s):  
Xianfang Tang ◽  
Lijun Cai ◽  
Yajie Meng ◽  
JunLin Xu ◽  
Changcheng Lu ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
De Novo ◽  
Characteristic Curve ◽  
Drug Repurposing ◽  
Factorization Method ◽  
Therapeutic Drugs ◽  
Indicator Matrix ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Non Negative Matrix Factorization

A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug repurposing aims to explore new applications of approved drugs, which can significantly reduce time and cost compared with de novo drug discovery. In this study, we built a virus-drug dataset, which included 34 viruses, 210 drugs, and 437 confirmed related virus-drug pairs from existing literature. Besides, we developed an Indicator Regularized non-negative Matrix Factorization (IRNMF) method, which introduced the indicator matrix and Karush-Kuhn-Tucker condition into the non-negative matrix factorization algorithm. According to the 5-fold cross-validation on the virus-drug dataset, the performance of IRNMF was better than other methods, and its Area Under receiver operating characteristic Curve (AUC) value was 0.8127. Additionally, we analyzed the case on COVID-19 infection, and our results suggested that the IRNMF algorithm could prioritize unknown virus-drug associations.

scholarly journals Comparison of Three Recent Personalization Algorithms

2020 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Matrix Factorization ◽  
Parallel Implementation ◽  
Apache Spark ◽  
Test Set ◽  
Hit Rate ◽  
The Matrix ◽  
Bayesian Personalized Ranking ◽  
Personalized Ranking

<p>Personalization algorithms recommend products to users based on their previous interactions with the system. The products could be books, movies, or products in a retail system. The earliest personalization algorithms were based on factorization of the user-item matrix where each entry in the matrix would correspond to an interaction, or absence of an interaction of the user with the product. In this article, we compare three recently developed personalization algorithms. The three algorithms are Bayesian Personalized Ranking, Taxonomy Discovery for Personalized Recommendations and Multi-Matrix Factorization. We compare the three algorithms on the hit rate @ position 10 on a held out test set on 1 million users and 200 thousand items in the catalog of Target Corporation. We report our findings in table 1. We develop all three algorithms on an Apache Spark parallel implementation.</p>

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 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 learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Jiajie Peng ◽  
Jingyi Li ◽  
Xuequn Shang
Keyword(s):  
Neural Network ◽  
Drug Discovery ◽  
Drug Target ◽  
Deep Neural Network ◽  
Computational Prediction ◽  
Representation Learning ◽  
Feature Representation ◽  
New Drugs ◽  
Target Interaction ◽  
Interaction Prediction

Abstract Background Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the particularity of biochemical experiments, the development of new drugs is not only costly, but also time-consuming. Therefore, the computational prediction of drug target interactions has become an essential way in the process of drug discovery, aiming to greatly reducing the experimental cost and time. Results We propose a learning-based method based on feature representation learning and deep neural network named DTI-CNN to predict the drug-target interactions. We first extract the relevant features of drugs and proteins from heterogeneous networks by using the Jaccard similarity coefficient and restart random walk model. Then, we adopt a denoising autoencoder model to reduce the dimension and identify the essential features. Third, based on the features obtained from last step, we constructed a convolutional neural network model to predict the interaction between drugs and proteins. The evaluation results show that the average AUROC score and AUPR score of DTI-CNN were 0.9416 and 0.9499, which obtains better performance than the other three existing state-of-the-art methods. Conclusions All the experimental results show that the performance of DTI-CNN is better than that of the three existing methods and the proposed method is appropriately designed.

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