Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey

Ezzat, Ali; Wu, Min; Li, Xiaoli; Kwoh, Chee Keong

doi:10.1093/bib/bby002

Cited by 201 publications

(162 citation statements)

References 148 publications

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“…X m×n can be expressed as a product of two matrices U m×r and V r×n . The complete matrix factorization problem (1) can be framed as, (2).…”

Section: Matrix Factorizationmentioning

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%

“…In one of the recent studies [2], it was shown that matrix factorization works best for DTI prediction. This method assumes that there are very few (latent) factors which are actually responsible for drug target interactions.…”

Section: Introductionmentioning

confidence: 99%

“…In the training set, positive samples are the experimentally known interactions while the negative ones are either non-interactions or unknown interactions. The second category includes Similarity-based methods, which take into account two similarity matrices corresponding to drug and target similarity, respectively, along with the drug-target interaction matrix.Amongst the recent techniques proposed for DTI prediction, matrix factorization based models depict the most promising results [2]. There has been recent work on deep Matrix Factorization [22] too.…”

mentioning

confidence: 99%

“…Amongst the recent techniques proposed for DTI prediction, matrix factorization based models depict the most promising results [2]. There has been recent work on deep Matrix Factorization [22] too.…”

mentioning

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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“…X m×n can be expressed as a product of two matrices U m×r and V r×n . The complete matrix factorization problem (1) can be framed as, (2).…”

Section: Matrix Factorizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

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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Deep Learning on Site‐Specific Drug Delivery System

Mishra¹,

Mishra²,

Mazumder³

2022

Deep Learning for Targeted Treatments

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A computational approach for predicting drug–target interactions from protein sequence and drug substructure fingerprint information

Liu

You

et al. 2020

Int J Intell Syst

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Identification of drug-target interactions (DTIs) is critical for discovering potential target protein candidates for new drugs. However, traditional experimental methods have limitations in discovering DTIs. They are time-consuming, tedious, and expensive, and often suffer from high false-positive rates and false-negative rates. Therefore, using computational methods to predict DTIs has received extensive attention from many researchers in recent years. To address this issue, in this paper, an effective prediction model is presented which is based on the information of drug molecular structure data and protein sequence data. It performs prediction with the following procedures. First, we transform the sequences of each target into a positionspecific scoring matrix (PSSM), such that the features can retain biological evolutionary information. We then use a feature vector of molecular substructure fingerprints to describe the chemical structure information of the drug compounds. Second, the Legendre moments algorithm is used to extract new features from the PSSM. Finally, a classification algorithm called rotation forest is used to perform prediction, we tested its prediction performance on four

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Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey

Cited by 201 publications

References 148 publications

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

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

Deep Learning on Site‐Specific Drug Delivery System

A computational approach for predicting drug–target interactions from protein sequence and drug substructure fingerprint information

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