Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference
Feixiong Cheng,Chuang Liu,Jing Jiang,Weiqiang Lu,Weihua Li,Guixia Liu,Wei-Xing Zhou,Jin Huang,Yun Tang +8 more
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TLDR
Three supervised inference methods were developed here to predict DTI and used for drug repositioning and indicated that these methods could be powerful tools in prediction of DTIs and drugRepositioning.Abstract:
Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.read more
Citations
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Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2
Yadi Zhou,Yuan Hou,Jiayu Shen,Yin Huang,William R. Martin,Feixiong Cheng,Feixiong Cheng,Feixiong Cheng +7 more
TL;DR: This study presents an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV–host interactome and drug targets in the human protein–protein interaction network.
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Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review
Peter Csermely,Tamas Korcsmaros,Tamas Korcsmaros,Huba Kiss,Gábor London,Ruth Nussinov,Ruth Nussinov +6 more
TL;DR: It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates and an optimized protocol of network-aided drug development is suggested, and a list of systems-level hallmarks of drug quality is provided.
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Drug–target interaction prediction: databases, web servers and computational models
TL;DR: In this review, databases and web servers involved in drug-target identification and drug discovery are summarized, and some state-of-the-art computational models for drug- target interactions prediction, including network-based method, machine learning- based method and so on are introduced.
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Deep-Learning-Based Drug-Target Interaction Prediction.
TL;DR: To accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, a deep-learning-based algorithmic framework named DeepDTIs is developed that reaches or outperforms other state-of-the-art methods.
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Network-based in silico drug efficacy screening.
TL;DR: This work introduces a drug-disease proximity measure that quantifies the interplay between drugs targets and diseases, and indicates that the therapeutic effect of drugs is localized in a small network neighborhood of the disease genes.
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