Query: Drug target interaction       Data integration

title: DeepPurpose: a deep learning library for drug target interaction prediction
abstract: Accurate prediction of drug target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy to use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state of the art performance of DeepPurpose on several benchmark datasets. Availability and implementation https:/github.com/kexinhuang12345/DeepPurpose. Supplementary information Supplementary data are available at Bioinformatics online.

title: ML DTI: Mutual Learning Mechanism for Interpretable Drug Target Interaction Prediction.
abstract: Deep learning (DL) provides opportunities for the identification of drug target interactions (DTIs) The challenges of applying DL lie primarily with the lack of interpretability. Also, most of the existing DL based methods formulate the drug and target encoder as two independent modules without considering the relationship between them. In this study, we propose a mutual learning mechanism to bridge the gap between the two encoders. We formulated the DTI problem from a global perspective by inserting mutual learning layers between the two encoders. The mutual learning layer was achieved by multihead attention and position aware attention. The neural attention mechanism also provides effective visualization, which makes it easier to analyze a model. We evaluated our approach using three benchmark kinase data sets under different experimental settings and compared the proposed method to three baseline models. We found that the four methods yielded similar results in the random split setting (training and test sets share common drugs and targets) while the proposed method increases the predictive performance significantly in the orphan target and orphan drug split setting (training and test sets share only targets or drugs) The experimental results demonstrated that the proposed method improved the generalization and interpretation capability of DTI modeling.

title: Cascade Deep Forest With Heterogeneous Similarity Measures for Drug Target Interaction Prediction
abstract: Drug repositioning is a method of systematically identifying potential molecular targets that known drugs may act on. Compared with traditional methods, drug repositioning has been extensively studied due to the development of multi omics technology and system biology methods. Because of its biological network properties, it is possible to apply machine learning related algorithms for prediction. Based on various heterogeneous network model, this paper proposes a method named THNCDF for predicting drug target interactions. Various heterogeneous networks are integrated to build a tripartite network, and similarity calculation methods are used to obtain similarity matrix. Then, the cascade deep forest method is used to make prediction. Results indicate that THNCDF outperforms the previously reported methods based on the 10 fold cross validation on the benchmark data sets proposed by Y. Yamanishi. The area under Precision Recall curve (AUPR) value on the Enzyme, GPCR, Ion Channel, and Nuclear Receptor data sets is 0.988, 0.980, 0.938, and 0.906 separately. The experimental results well illustrate the feasibility of this method.

title: Drug Target Interaction Prediction Based on Multi Similarity Fusion and Sparse Dual Graph Regularized Matrix Factorization
abstract: Drug target interactions (DTIs) prediction plays a vital role in drug discovery and design. Current studies typically use only standard drug similarity and target similarity, but the influence of known interactions has not been taken into account. In this paper, we propose an ensembled computational approach called multi similarity fusion and sparse dual graph regularized matrix factorization (MSDGRMF) for DTIs prediction. Specifically, different similarities are integrated to mine more useful information from the known interactions. The dual graph regularized matrix factorization is used to predict the DTIs, in which the manifold learning is used for the low dimensional representation of the drugs and targets data. In addition, not all the information of drug pairs and target pairs is useful. Thus, the useless information is discarded by sparse process. The proposed MSDGRMF is evaluated and compared on some benchmark datasets. Comparison results show that the MSDGRMF is better than some state of the art approaches. More importantly, the proposed method can contribute to predicting potential DTIs.

title: KG DTI: a knowledge graph based deep learning method for drug target interaction predictions and Alzheimer's disease drug repositions
abstract: Drug repositioning, which recommends approved drugs to potential targets by predicting drug target interactions (DTIs) can save the cost and shorten the period of drug development. In this work, we propose a novel knowledge graph based deep learning method, named KG DTI, for DTIs predictions. Specifically, a knowledge graph of 29,607 positive drug target pairs is constructed by DistMult embedding strategy. A Conv Conv module is proposed to extract features of drug target pairs (DTPs) which is followed by a fully connected neural network for DTIs calculation. Data experiments are conducted on randomly chosen 11,840 positive and negative samples. It is obtained that KG DTI achieves average ACC by 88.0% F1 Score by 87.7% AUROC by 94.3% and AUPR by 95% in five fold cross validation. In practice, KG DTI is applied to reposition drugs to Alzheimer's disease (AD) by Apolipoprotein E target. As results, it is found that seven of the top ten recommended drugs have been used in clinic practice or with literature supported useful to AD. Ligand target docking results show that the top one recommended drug can dock with Apolipoprotein E significantly, which gives vital hints in repositioning potential drug to AD treatment.

title: TarDict: A RandomForestClassifier based software predicts drug target interaction using SMILES
abstract: The future of therapeutics depends on understanding the interaction between the chemical structure of the drug and the target protein that contributes to the etiology of the disease in order to improve drug discovery. Predicting the target of unknown drugs being investigated from already identified drug data is very important not only for understanding different processes of drug and molecular interactions but also for the development of new drugs. Using machine learning and published drug information we design an easy to use tool that predicts biological target proteins for medical drugs. TarDict is based on a chemical simplified line entry molecular input system called SMILES. It receives SMILES entries and returns a list of possible similar drugs as well as possible drug targets. TarDict uses 20442 drug entries that have well known biological targets to construct a prognostic computational model capable of predicting novel drug targets with an accuracy of 95% We developed a machine learning approach to recommend target proteins to approved drug targets. We have shown that the proposed method is highly predictive on a testing dataset consisting of 4088 targets and 102 manually entered drugs. The proposed computational model is an efficient and cost effective tool for drug target discovery and prioritization. Such novel tool could be used to enhance drug design, predict potential target and identify combination therapy crossroads.

title: DTi2Vec: Drug target interaction prediction using network embedding and ensemble learning
abstract: Drug target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in silico methods to predict potential DTI has become a competitive research niche, with one of its main focuses being improving the prediction accuracy. Using machine learning (ML) models for this task, specifically network based approaches, is effective and has shown great advantages over the other computational methods. However, ML model development involves upstream hand crafted feature extraction and other processes that impact prediction accuracy. Thus, network based representation learning techniques that provide automated feature extraction combined with traditional ML classifiers dealing with downstream link prediction tasks may be better suited paradigms. Here, we present such a method, DTi2Vec, which identifies DTIs using network representation learning and ensemble learning techniques. DTi2Vec constructs the heterogeneous network, and then it automatically generates features for each drug and target using the nodes embedding technique. DTi2Vec demonstrated its ability in drug target link prediction compared to several state of the art network based methods, using four benchmark datasets and large scale data compiled from DrugBank. DTi2Vec showed a statistically significant increase in the prediction performances in terms of AUPR. We verified the "novel" predicted DTIs using several databases and scientific literature. DTi2Vec is a simple yet effective method that provides high DTI prediction performance while being scalable and efficient in computation, translating into a powerful drug repositioning tool.

title: Drug Target Interaction Prediction Based on Adversarial Bayesian Personalized Ranking
abstract: 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.

title: Collaborative Matrix Factorization with Soft Regularization for Drug Target Interaction Prediction
abstract: Identifying the potential drug target interactions (DTI) is critical in drug discovery. The drug target interaction prediction methods based on collaborative filtering have demonstrated attractive prediction performance. However, many corresponding models cannot accurately express the relationship between similarity features and DTI features. In order to rationally represent the correlation, we propose a novel matrix factorization method, so called collaborative matrix factorization with soft regularization (SRCMF) SRCMF improves the prediction performance by combining the drug and the target similarity information with matrix factorization. In contrast to general collaborative matrix factorization, the fundamental idea of SRCMF is to make the similarity features and the potential features of DTI approximate, not identical. Specifically, SRCMF obtains low rank feature representations of drug similarity and target similarity, and then uses a soft regularization term to constrain the approximation between drug (target) similarity features and drug (target) potential features of DTI. To comprehensively evaluate the prediction performance of SRCMF, we conduct cross validation experiments under three different settings. In terms of the area under the precision recall curve (AUPR) SRCMF achieves better prediction results than six state of the art methods. Besides, under different noise levels of similarity data, the prediction performance of SRCMF is much better than that of collaborative matrix factorization. In conclusion, SRCMF is robust leading to performance improvement in drug target interaction prediction.

title: Autoencoder based Drug Target Interaction Prediction by Preserving the Consistency of Chemical Properties and Functions of Drugs.
abstract: Exploring the potential drug target interactions (DTIs) is a key step in drug discovery and repurposing. In recent years, predicting the probable DTIs through computational methods has gradually become a research hot spot. However, most of the previous studies failed to judiciously take into account the consistency between the chemical properties of drug and its functions. The changes of these relationships may lead to a severely negative effect on the prediction of DTIs.  We propose an autoencoder based method, AEFS, under spatial consistency constraints to predict DTIs. A heterogeneous network is established to integrate the information of drugs, proteins and diseases. The original drug features are projected to an embedding (protein) space by a multi layer encoder, and further projected into label (disease) space by a decoder. In this process, the clinical information of drugs is introduced to assist the DTI prediction. By maintaining the distribution of drug correlation in the original feature, embedding and label space, AEFS keeps the consistency between chemical properties and functions of drugs. Experimental comparisons indicate that AEFS is more robust for imbalanced data and of significantly superior performance in DTI prediction. Case studies further confirm its ability to mine the latent drug target interactions. AVAILABILITY The code of AEFS is available at https:/github.com/JackieSun818/AEFS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.