Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

By Feixiong Cheng, Chuang Liu, Jing Jiang, Weiqiang Lu, Weihua Li, Guixia Liu, Wei‐Xing Zhou, Jin Huang, Yun Tang. PLoS Comput. Biol., 2012

759 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/205eb581c213d2ba4674d995c68edb6972a8c95d

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Citations in the corpus

Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, methodology): “We implemented NN and NBI, according to [27] and [60], respectively.”
Network-based machine learning and graph theory algorithms for precision oncology (2017, background): “The paper 68 predicted drug – target relations for drug repositioning based on a network of three types of relations: drug – drug structural similarity, target – target sequence similarity and drug – target relations from DrugBank.”
Network-based in silico drug efficacy screening (2016, neutral): “The increasing interest in reusing existing drugs for novel therapies has recently given rise to various approaches that aim to identify candidate drugs with similar characteristics to known drugs used in a disease 7,8,19–20 .”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “A method for factorizing the interaction score matrix in terms of kernel matrices (similarity matrices), which can be used as DTI predictors for new drugs and protein KBMF2K [194]. inference methods and uses only DT bipartite network topology similarity [195].”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “…network- or machine learningbased clustering (Hopkins et al., 2006; Hopkins, 2008; Chen et al., 2009e; Xiong et al., 2010; Yang et al., 2010; Hu et al., 2011; Takigawa et al., 2011; Yabuuchi et al., 2011; Cheng et al., 2012a, 2012b; Lee et al., 2012c; Nacher & Schwartz, 2012; Yu et al., 2012).”
Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory (2016, methodology): “Many works on identifying repositioned drugs have been based on various computational methods, such as mapping gene expression profiles using drug response profiles [10–14], the use of side-effect-based similarities [15–17], heterogeneous network clustering [18], and the graph-based inference method”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, methodology): “egorized into four groups: (1) methods that predict new uses for existing drugs on the basis of protein target interaction networks [307, 308, 309, 310, 272], (2) methods that make predictions by analyzing gene expression activation following various drug treatment regimes [311, 312], (3) methods th”
Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction (2016, methodology): “developed three supervised inference methods for DTI prediction based on the complex network theory [16].”