Computational Model Development of Drug-Target Interaction Prediction: A Review.

Published on May 20, 2019in Current Protein & Peptide Science1.885
· DOI :10.2174/1389203720666190123164310
Qi Zhao1
Estimated H-index: 1
(SAU: Shenyang Aerospace University),
Haifan Yu1
Estimated H-index: 1
(Liaoning University)
+ 2 AuthorsXing Chen7
Estimated H-index: 7
(CUMT: China University of Mining and Technology)
: In the medical field, drug-target interactions are very important for the diagnosis and treatment of diseases, they also can help researchers predict the link between biomolecules in the biological field, such as drug-protein and protein-target correlations. Therefore, the drug-target research is a very popular study in both the biological and medical fields. However, due to the limitations of manual experiments in the laboratory, computational prediction methods for drug-target relationships are increasingly favored by researchers. In this review, we summarize several computational prediction models of the drug-target connections during the past two years, and briefly introduce their advantages and shortcomings. Finally, several further interesting research directions of drug-target interactions are listed.
  • References (22)
  • Citations (1)
#1Anurag Passi (CSIR: Council of Scientific and Industrial Research)H-Index: 4
#2Neeraj Kumar Rajput (CSIR: Council of Scientific and Industrial Research)H-Index: 3
Last. Anshu Bhardwaj (CSIR: Council of Scientific and Industrial Research)H-Index: 10
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Tuberculosis (TB) is the world’s leading infectious killer with 1.8 million deaths in 2015 as reported by WHO. It is therefore imperative that alternate routes of identification of novel anti-TB compounds are explored given the time and costs involved in new drug discovery process. Towards this, we have developed RepTB. This is a unique drug repurposing approach for TB that uses molecular function correlations among known drug-target pairs to predict novel drug-target interactions. In this study...
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#1Rawan S. Olayan (KAUST: King Abdullah University of Science and Technology)H-Index: 1
#2Haitham AshoorH-Index: 8
Last. Vladimir B. Bajic (KAUST: King Abdullah University of Science and Technology)H-Index: 50
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Finding computationally drug–target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear sim...
15 CitationsSource
#1Farshid Rayhan (United International University)H-Index: 4
#2Sajid Ahmed (United International University)H-Index: 5
Last. M. Sohel Rahman (BUET: Bangladesh University of Engineering and Technology)H-Index: 21
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Prediction of new drug-target interactions is critically important as it can lead the researchers to find new uses for old drugs and to disclose their therapeutic profiles or side effects. However, experimental prediction of drug-target interactions is expensive and time-consuming. As a result, computational methods for predictioning new drug-target interactions have gained a tremendous interest in recent times. Here we present iDTI-ESBoost, a prediction model for identification of drug-target i...
31 CitationsSource
#1Ladislav Peska (MTA: Hungarian Academy of Sciences)H-Index: 1
#2Krisztian BuzaH-Index: 14
Last. Jlia Koller (Semmelweis University)H-Index: 1
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Proposing BRDTI method for per-drug ranking of DTIs.Performed comparative evaluation of BRDTI w.r.t. AUC and per-drug nDCG.BRDTI achieved best average results on predicting new targets for existing drugs. Background and objectiveIn silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning app...
16 CitationsSource
#1Magnus D. LynchH-Index: 13
#2C. N. S. Lynch ('KCL': King's College London)H-Index: 1
Last. Frank WattH-Index: 109
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Deep sequencing can detect somatic DNA mutations in tissues permitting inference of clonal relationships. This has been applied to human epidermis, where sun exposure leads to the accumulation of mutations and an increased risk of skin cancer. However, previous studies have yielded conflicting conclusions about the relative importance of positive selection and neutral drift in clonal evolution. Here, we sequenced larger areas of skin than previously, focusing on cancer-prone skin spanning five d...
12 CitationsSource
#1Wen ZhangH-Index: 21
#2Yanlin ChenH-Index: 8
Last. Dingfang LiH-Index: 6
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Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. First...
24 CitationsSource
#1Bence Bolgár (BME: Budapest University of Technology and Economics)H-Index: 5
#2Peter Antal (BME: Budapest University of Technology and Economics)H-Index: 17
Background Computational fusion approaches to drug-target interaction (DTI) prediction, capable of utilizing multiple sources of background knowledge, were reported to achieve superior predictive performance in multiple studies. Other studies showed that specificities of the DTI task, such as weighting the observations and focusing the side information are also vital for reaching top performance.
4 CitationsSource
#1Ali Ezzat (NTU: Nanyang Technological University)H-Index: 5
#2Min Wu (Agency for Science, Technology and Research)H-Index: 15
Last. Kwoh Chee Keong (NTU: Nanyang Technological University)H-Index: 27
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Abstract Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational methods inefficient or occasionally prohibitive. This motivates us to derive a reduced feature set ...
21 CitationsSource
#1Nansu Zong (UCSD: University of California, San Diego)H-Index: 7
#2Hyeoneui Kim (UCSD: University of California, San Diego)H-Index: 17
Last. Olivier Harismendy (UCSD: University of California, San Diego)H-Index: 25
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Motivation: A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction. Results: We propose a similarity-based drug-target...
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#1Ming WenH-Index: 9
#2Zhimin ZhangH-Index: 14
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Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug–target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug–drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represen...
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Cited By1
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Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug repositioning. To reduce heavily experiment cost, booming machine learning has been applied to this field and developed many computational methods, especially binary classification methods. However, there is still much room for improvement in the performance of current methods. Multi-label learning can reduce difficulties faced by binary classification learning with high predictive performance, and has n...
#1Pu Wang (Huda: Hubei University)
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BACKGROUND: G protein-coupled receptors (GPCRs) mediate a variety of important physiological functions, are closely related to many diseases, and constitute the most important target family of modern drugs. Therefore, the research of GPCR analysis and GPCR ligand screening is the hotspot of new drug development. Accurately identifying the GPCR-drug interaction is one of the key steps for designing GPCR-targeted drugs. However, it is prohibitively expensive to experimentally ascertain the interac...
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#2Somayah Albaradie (KAUST: King Abdullah University of Science and Technology)
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#2Aman Chandra Kaushik (Jiangnan University)H-Index: 1
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Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the current DTI prediction approaches suffer from a problem of low precision and high false-positive rate. In this study, we aim to develop a novel DTI prediction method for improving the predictio...
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