A Survey on the Computational Approaches to Identify Drug Targets in the Postgenomic Era

Dai, Yan-Fen; Zhao, Xing‐Ming

doi:10.1155/2015/239654

Cited by 61 publications

(52 citation statements)

References 57 publications

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“…We compare MGRDMF with some recent Matrix factorization based techniques proposed for DTI prediction under all the above mentioned croos-validation settings. These methods are very recent in the field and have already been compared against older methods [1]. Apart from the baselines (CMF [58] and GRMF [24]), the comparison of MGRDMF has also been done against a variant of GRMF proposed by us: Multi-GRMF, to observe how the incorporation of multi-graph regularization affects its performance.…”

Section: Discussionmentioning

confidence: 99%

“…Conventionally, this was done through time-taking and expensive wet-lab experiments. In recent times, the introduction of computational techniques for prediction of interaction probability [1][2][3][4] has paved the way for appropriate and effective alternatives which could help avoid costly candidate failures. These methods take some existing experimentally valid interactions which are publicly available in databases like STITCH [5], ChEMBL [6], KEGG DRUG [7], DrugBank [8] and SuperTarget [9] to predict the interaction probability of unknown drug-target pairs.…”

Section: Introductionmentioning

confidence: 99%

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Drug-Target Interaction prediction using Multi-Graph Regularized Deep Matrix Factorization

Mongia

Majumdar

2019

Preprint

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Drug discovery is an important field in the pharmaceutical industry with one of its crucial chemogenomic process being drug-target interaction prediction. This interaction determination is expensive and laborious, which brings the need for alternative computational approaches which could help reduce the search space for biological experiments. This paper proposes a novel framework for drug-target interaction (DTI) prediction: Multi-Graph Regularized Deep Matrix Factorization (MGRDMF). The proposed method, motivated by the success of deep learning, finds a low-rank solution which is structured by the proximities of drugs and targets (drug similarities and target similarities) using deep matrix factorization. Deep matrix factorization is capable of learning deep representations of drugs and targets for interaction prediction. It is an established fact that drug and target similarities incorporation preserves the local geometries of the data in original space and learns the data manifold better. However, there is no literature on which the type of similarity matrix (apart from the standard biological chemical structure similarity for drugs and genomic sequence similarity for targets) could best help in DTI prediction. Therefore, we attempt to take into account various types of similarities between drugs/targets as multiple graph Laplacian regularization terms which take into account the neighborhood information between drugs/targets. This is the first work which has leveraged multiple similarity/neighborhood information into the deep learning framework for drug-target interaction prediction. The cross-validation results on four benchmark data sets validate the efficacy of the proposed algorithm by outperforming shallow state-of-the-art computational methods on the grounds of AUPR and AUC.September 18, 2019 1/17 interaction not only assists drug discovery but also affects other fields such as drug repositioning, drug resistance and side-effect prediction [10]. As an example, Drug repositioning [11,12] (using an existing drug for new indications) can grant polypharmacology (multi-target effect) to a drug. Gleevec (imatinib mesylate) is one of the many such examples which was successfully repositioned. Earlier, it was known to interact only with the Bcr-Abl fusion gene which is indicative of leukemia. However, later discoveries showing that it also interacts with PDGF and KIT, repositioned it for the treatment of gastrointestinal stromal tumors [13,14]. The methods available for prediction of DTI can be divided into the following three broad categories: Ligand-based approaches, Docking based approaches, and Chemogenomic approaches. Ligand-based approaches predict interactions by deploying the similarity between the ligands of target proteins [15]. The idea is that molecules with similar structure/property would bind similar proteins [16]. But, the reliability of results might get compromised due to limited information about known ligands per protein. Docking-based approaches use the three-dimensional structure of both ...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Drug-Target Interaction prediction using Multi-Graph Regularized Deep Matrix Factorization

Mongia

Majumdar

2019

Preprint

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“…The living organism's genetic map, medication history, and other traits could effect on phenotypic outcomes of a drug. So, we could not conclude that a similar phenotype corresponds to the same mode of action [17], [7]. Finally, it should be noticed that side effect information could be confused by a patient's medication history, genotype, and other hidden factors [17].…”

mentioning

confidence: 89%

Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning

Shahreza

Ghadiri

Mousavi

et al. 2017

Journal of Biomedical Informatics

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Drug repositioning offers an effective solution to drug discovery, saving both time and resources by finding new indications for existing drugs. Typically, a drug takes effect via its protein targets in the cell. As a result, it is necessary for drug development studies to conduct an investigation into the interrelationships of drugs, protein targets, and diseases. Although previous studies have made a strong case for the effectiveness of integrative network-based methods for predicting these interrelationships, little progress has been achieved in this regard within drug repositioning research. Moreover, the interactions of new drugs and targets (lacking any known targets and drugs, respectively) cannot be accurately predicted by most established methods. In this paper, we propose a novel semi-supervised heterogeneous label propagation algorithm named Heter-LP, which applies both local and global network features for data integration. To predict drug-target, disease-target, and drug-disease associations, we use information about drugs, diseases, and targets as collected from multiple sources at different levels. Our algorithm integrates these various types of data into a heterogeneous network and implements a label propagation algorithm to find new interactions. Statistical analyses of 10-fold cross-validation results and experimental analyses support the effectiveness of the proposed algorithm.

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“…[4a, 5] Therefore, phenotypic drug discoveryb ased on pharmacologically active natural products remains ap owerful strategy forr apidly evaluating the relevant chemical landscape in search of novel therapeutic mechanisms of action. [6] However,l ead identification and guided chemical optimization based on ad efined molecular mechanism of action remains at ime-consuming process.…”

Section: Introductionmentioning

confidence: 99%

Combined Scaffold Evaluation and Systems‐Level Transcriptome‐Based Analysis for Accelerated Lead Optimization Reveals Ribosomal Targeting Spirooxindole Cyclopropanes

et al. 2019

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With evolutionary drug resistance impacting efforts to treat disease, the need for small molecules that exhibit novel molecular mechanisms of action is paramount. In this study, we combined scaffold‐directed synthesis with a hybrid experimental and transcriptome analysis to identify bis‐spirooxindole cyclopropanes that inhibit cancer cell proliferation through disruption of ribosomal function. These findings demonstrate the value of an integrated, biologically inspired synthesis and assay strategy for the accelerated identification of first‐in‐class cancer therapeutic candidates.

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A Survey on the Computational Approaches to Identify Drug Targets in the Postgenomic Era

Cited by 61 publications

References 57 publications

Drug-Target Interaction prediction using Multi-Graph Regularized Deep Matrix Factorization

Drug-Target Interaction prediction using Multi-Graph Regularized Deep Matrix Factorization

Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning

Combined Scaffold Evaluation and Systems‐Level Transcriptome‐Based Analysis for Accelerated Lead Optimization Reveals Ribosomal Targeting Spirooxindole Cyclopropanes

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