Computational methods for drug design and discovery: focus on China
Authors: Mingyue Zheng, Xian Liu, Yuan Xu, Honglin Li, Cheng Luo, Hualiang Jiang
Journal: Trends in Pharmacological Sciences (2013)

•The leading computational techniques for drug design and discovery are reviewed.•Successful applications of computational techniques are provided.•A novel drug–target binding kinetics calculation approach is introduced. In the past decades, China's computational drug design and discovery research has experienced fast development through various novel methodologies. Application of these methods spans a wide range, from drug target identification to hit discovery and lead optimization. In this review, we firstly provide an overview of China's status in this field and briefly analyze the possible reasons for this rapid advancement. The methodology development is then outlined. For each selected method, a short background precedes an assessment of the method with respect to the needs of drug discovery, and, in particular, work from China is highlighted. Furthermore, several successful applications of these methods are illustrated. Finally, we conclude with a discussion of current major challenges and future directions of the field. In the past decades, China's computational drug design and discovery research has experienced fast development through various novel methodologies. Application of these methods spans a wide range, from drug target identification to hit discovery and lead optimization. In this review, we firstly provide an overview of China's status in this field and briefly analyze the possible reasons for this rapid advancement. The methodology development is then outlined. For each selected method, a short background precedes an assessment of the method with respect to the needs of drug discovery, and, in particular, work from China is highlighted. Furthermore, several successful applications of these methods are illustrated. Finally, we conclude with a discussion of current major challenges and future directions of the field. Since its emergence at the end of the 1970s, computational drug discovery and design (CDDD) has experienced a rapid increase in development, to which China has made significant contributions. With consistent financial support from the government and tremendous research effort, the overall research level of China in this field continues to advance. This trend can be partially revealed by a brief analysis of the research output over the past few years. Taking the structure-based drug design (SBDD) as an example, the publication of scientific papers from China during 2006 to 2010 ranked fifth (citation ranking is seventh), only behind the USA, the UK, Germany, France, and Italy. Among Asian countries, China ranked the top in both the number of publications and citations [1Jiang H.L. Structure-based drug design.in: Han Y. Liang W.P. Chemistry During 2001–2010: China in the World. National Natural Science Foundation of China, 2011: 132-136Google Scholar]. Currently, more than 80 universities and research institutions in China have established drug design research departments or centers and over 100 domestic research groups are engaged in drug design. The main reason for the rapid advancement of research should be ascribed to the major breakthrough in methodology and software development. Currently, many technologies have been developed to boost the efficiency of the drug discovery process. Researchers in China, following the recent trend of scientific advances, have brought us a wealth of innovations, especially in the development of CDDD approaches. In this review, we first summarize leading computational techniques (Table 1) that include target prediction and drug repositioning approaches, docking and scoring algorithms, virtual screening (VS) and lead optimization techniques, as well as new absorption, distribution, metabolism, excretion, and toxicity (ADME/T) related frameworks, with a focus on how China has helped to drive new efforts in this field. In the second section, we show how these approaches have been successfully applied in drug discovery. In the final section, we discuss up-to-date research as the final symphony – a successful case of binding kinetics simulation, which is a paradigm of drug target binding kinetics calculation and is expected to inspire future research.Table 1Summary of the highlighted computational techniques for drug design and discoveryNameMethodFeaturesRefsDrug target predictionTarFisDockReverse docking• An associated large database for potential drug targets (PDTD)• User-friendly web sever6Li H.L. et al.TarFisDock: a web server for identifying drug targets with docking approach.Nucleic Acids Res. 2006; 34: W219-W224Crossref PubMed Scopus (370) Google Scholar, 8Gao Z.T. et al.PDTD: a web-accessible protein database for drug target identification.BMC Bioinformatics. 2008; 9: 104Crossref PubMed Scopus (267) Google ScholarPharmMapperPharmacophore mapping• Auto-generation of 3D structure for small molecules• User-friendly web sever• Fast (fit for batch processing)12Liu X.F. et al.PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach.Nucleic Acids Res. 2010; 38: W609-W614Crossref PubMed Scopus (668) Google ScholarmiRTarCLIPSequence analysis• miRNA target prediction using CLIP sequencing data• User-friendly web sever20Chou C.H. et al.A computational approach for identifying microRNA–target interactions using high-throughput CLIP and PAR-CLIP sequencing.BMC Genomics. 2013; 14: S2PubMed Google ScholarDrug repositioningSTITCHSearch tool for interactions of chemicals• Explore known and predict interactions of chemicals and proteins• User-friendly web sever• Visual presentation of results in network29Kuhn M. et al.STITCH: interaction networks of chemicals and proteins.Nucleic Acids Res. 2008; 36: D684-D688Crossref PubMed Scopus (710) Google Scholar, 30Kuhn M. et al.STITCH 3: zooming in on protein–chemical interactions.Nucleic Acids Res. 2012; 40: D876-D880Crossref PubMed Scopus (246) Google ScholarDrug repositioning by Li et al.Cross-docking• Large-scale docking to predict novel drug–target interactions31Li Y.Y. et al.A computational approach to finding novel targets for existing drugs.PLoS Comput. Biol. 2011; 9: e1002139Crossref Scopus (107) Google ScholarSEALigand-based similarity• Compare targets by the similarity of the ligands that bind to them• User-friendly web sever• 3D structure of drug molecule is not required32Keiser M.J. et al.Relating protein pharmacology by ligand chemistry.Nat. Biotechnol. 2007; 25: 197-206Crossref PubMed Scopus (1635) Google Scholar, 33Keiser M.J. et al.Predicting new molecular targets for known drugs.Nature. 2009; 462: 175-181Crossref PubMed Scopus (1371) Google ScholarNBI, EWNBI, and NWNBINetwork-based inference• Interaction of novel protein or chemical can be predicted• 3D structure of drug molecule is not required34Cheng F.X. et al.Prediction of drug–target interactions and drug repositioning via network-based inference.PLoS Comput. Biol. 2012; 8: e1002503Crossref PubMed Scopus (676) Google Scholar, 35Cheng F.X. et al.Prediction of chemical–protein interactions network with weighted network-based inference method.PLoS ONE. 2012; 7: e41064Crossref PubMed Scopus (95) Google ScholarProtein–ligand interactionGAsDock and MOSFOMDocking• Fast (time is in linear scale with the number of the rotatable bonds of ligand)39Li H.L. et al.GAsDock: a new approach for rapid flexible docking based on an improved multi-population genetic algorithm.Bioorg. Med. Chem. 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Chem. 2006; 49: 534-553Crossref PubMed Scopus (1588) Google ScholarIPMFKnowledge-based scoring function• 'Enriched' knowledge base by incorporating protein–ligand binding affinity data• Improved accuracy for protein–ligand binding affinity prediction45Shen Q.C. et al.Knowledge-based scoring functions in drug design: 2. Can the knowledge base be enriched?.J. Chem. Inf. Model. 2011; 51: 386-397Crossref PubMed Scopus (29) Google ScholarVirtual screening and lead optimizationPocket v.2Structure-based pharmacophore modeling• Derive pharmacophore models based on a given receptor or complex structure• Insensitive to minor conformational changes of protein upon binding of different ligands50Chen J. Lai L.H. Pocket v.2: further developments on receptor-based pharmacophore modeling.J. Chem. Inf. Model. 2006; 46: 2684-2691Crossref PubMed Scopus (118) Google ScholarSHAFTS3D similarity calculation• Fit for large-scale virtual screening• Single or multiple bioactive compounds as searching 'templates'• User-friendly web sever52Liu X.F. et al.SHAFTS: a hybrid approach for 3D molecular similarity calculation. 1. Method and assessment of virtual screening.J. Chem. Inf. Model. 2011; 51: 2372-2385Crossref PubMed Scopus (150) Google Scholar, 80Lu W.Q. et al.SHAFTS: a hybrid approach for 3D molecular similarity calculation. 2. Prospective case study in the discovery of diverse p90 ribosomal S6 protein kinase 2 inhibitors to suppress cell migration.J. Med. Chem. 2011; 54: 3564-3574Crossref PubMed Scopus (79) Google ScholarLigBuilderDe novo drug design• Detect and score potential binding sites of a protein• Build ligand molecule according to receptor-based pharmacophore• Analyze synthesis accessibility of designed molecule53Yuan Y.X. et al.LigBuilder 2: a practical de novo drug design approach.J. Chem. Inf. Model. 2011; 51: 1083-1091Crossref PubMed Scopus (19) Google ScholarLD1.0Target-focused library construction• Comprehensive consideration of binding affinity, drug-likeness, and ADME/T properties54Chen G. et al.Focused combinatorial library design based on structural diversity, druglikeness and binding affinity score.J. Comb. Chem. 2005; 7: 398-406Crossref PubMed Scopus (66) Google ScholarAutoT&TDe novo drug design• Fast (conformation searching is not required)• Fragment database is not required• Fit for fragment-based drug design55Li Y. et al.Automatic tailoring and transplanting: a practical method that makes virtual screening more useful.J. Chem. Inf. Model. 2011; 51: 1474-1491Crossref PubMed Scopus (11) Google ScholariScreenWeb service for TCM-related design• Perform virtual screening and de novo drug design online• User-friendly web sever56Chen C.Y.C. TCM Database@Taiwan: The world's largest traditional Chinese medicine database for drug screening in silico.PLoS ONE. 2011; 6: e15939Crossref PubMed Scopus (683) Google Scholar, 57Tsai T.Y. et al.iScreen: world's first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan.J. Comput. Aided Mol. Des. 2011; 25: 525-531Crossref PubMed Scopus (131) Google ScholarADME/T properties predictionSOMEVizWeb service for SOM prediction• Predict reaction-specific CYP450-mediated SOMs• User-friendly web server63Zheng M.Y. et al.Site of metabolism prediction for six biotransformations mediated by cytochromes P450.Bioinformatics. 2009; 25: 1251-1258Crossref PubMed Scopus (80) Google Scholar, 64Shen Q.C. et al.SOMEViz: a web service for site of metabolism estimating and visualizing.Protein Pept. Lett. 2012; 19: 905-909Crossref PubMed Scopus (4) Google ScholarRS-WebPredictorWeb service for SOM prediction• Predict isozyme-specific CYP450-mediated SOMs• User-friendly web sever• 3D structure of ligand is not required• Fast65Zaretzki J. et al.RS-WebPredictor: a server for predicting CYP-mediated sites of metabolism on drug-like molecules.Bioinformatics. 2013; 29: 497-498Crossref PubMed Scopus (57) Google ScholarhERG prediction by Wang et al.Ligand-based SAR analysis• Structural patterns favorable or unfavorable for hERG potassium channel blockage are highlighted• 3D structure of ligand is not required69Wang S. et al.ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage.Mol. Pharm. 2012; 9: 996-1010Crossref PubMed Scopus (140) Google ScholarhERG prediction by Di Martino et al.Docking protocol• Explain the structure–activity relationship for congeneric chemicals70Di Martino G.P. et al.An automated docking protocol for hERG channel blockers.J. Chem. Inf. Model. 2013; 53: 159-175Crossref PubMed Scopus (16) Google ScholarPKKBWeb service for ADME/T property searching• High quality ADME/T data for drug molecules72Cao D.Y. et al.ADMET evaluation in drug discovery. 11. PharmacoKinetics Knowledge Base (PKKB): a comprehensive database of pharmacokinetic and toxic properties for drugs.J. Chem. Inf. Model. 2012; 52: 1132-1137Crossref PubMed Scopus (65) Google ScholarAdmetSARWeb service for ADME/T property searching• A user-friendly web server• Various ADME/T-related databases• Predefined SAR models allowing users to predict for chemicals not in database73Cheng F.X. et al.admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties.J. Chem. Inf. Model. 2012; 52: 3099-3105Crossref PubMed Scopus (1396) Google ScholarMetaADEDBOnline web service for ADE property searching• A user-friendly web server• Predefined SAR models allowing users to predict for chemicals not in database74Cheng F.X. et al.Adverse drug events: database construction and in silico prediction.J. Chem. Inf. Model. 2013; 53: 744-752Crossref PubMed Scopus (108) Google Scholar, 75Cheng F.X. et al.Prediction of polypharmacological profiles of drugs by the integration of chemical, side effects and therapeutic space.J. Chem. Inf. Model. 2013; 53: 753-762Crossref PubMed Scopus (86) Google Scholar Open table in a new tab Identifying targets is the first key step in the drug discovery pipeline. As of 2006, there were only 324 identified molecular targets for FDA-approved drugs [2Overington J.P. et al.How many drug targets are there?.Nat. Rev. Drug Discov. 2006; 5: 993-996Crossref PubMed Scopus (2923) Google Scholar]. Although this total figure is under debate, it is significantly lower than the number of proteins unveiled by the completion of human and numerous pathogen genomes, of which many can be potentially druggable [3Hopkins A.L. Groom C.R. The druggable genome.Nat. Rev. Drug Discov. 2002; 1: 727-730Crossref PubMed Scopus (2811) Google Scholar]. Moreover, many drugs elicit their therapeutic activities by modulating multiple targets, but the multi-target interactions are either largely unknown or insufficiently understood in most cases. The use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. One of the computational approaches demonstrated to be efficient and cost effective in target identification is 'reverse' docking [4Chen Y.Z. Zhi D.G. Ligand–protein inverse docking and its potential use in the computer search of protein targets of a small molecule.Proteins. 2001; 43: 217-226Crossref PubMed Scopus (353) Google Scholar, 5Paul N. et al.Recovering the true targets of specific ligands by virtual screening of the Protein Data Bank.Proteins. 2004; 54: 671-680Crossref PubMed Scopus (105) Google Scholar, 6Li H.L. et al.TarFisDock: a web server for identifying drug targets with docking approach.Nucleic Acids Res. 2006; 34: W219-W224Crossref PubMed Scopus (370) Google Scholar]. Molecular docking is a method to predict the predominant binding mode(s) of a ligand with a protein of known 3D structure, which is routinely used in structure-based drug discovery for hit identification (VS) and lead optimization. More details about the method are provided in the section 'Protein–ligand interaction'. Reverse docking, opposite to the conventional process of docking application, for example, in structure-based VS, is to dock a given small molecule into the predefined binding sites of a pool of protein structures. A comparison of these two paradigms is shown in Figure 1. The identified protein 'hit' can then serve as a potential candidate target for experimental validation. Li et al. presented such a computational tool named 'Target Fishing Dock' (TarFisDock) [6Li H.L. et al.TarFisDock: a web server for identifying drug targets with docking approach.Nucleic Acids Res. 2006; 34: W219-W224Crossref PubMed Scopus (370) Google Scholar], which consists of a front-end web interface and a back-end using DOCK [7Ewing T.J.A. et al.DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases.J. Comput. Aided Mol. Des. 2001; 15: 411-428Crossref PubMed Scopus (1134) Google Scholar] for reverse docking. A feature of this work is a companion potential drug target database (PDTD) containing 1207 entries and 841 potential drug targets [8Gao Z.T. et al.PDTD: a web-accessible protein database for drug target identification.BMC Bioinformatics. 2008; 9: 104Crossref PubMed Scopus (267) Google Scholar]. The repository of drug targets can be categorized into 14 types according to their therapeutic areas, constituting a comprehensive and valuable source for identifying potential targets of small molecules. By searching through PDTD, TarFisDock can not only predict potential binding targets for a given small molecule but also provide valuable information about their related diseases, biological functions, and regulating (signaling) pathways. So far, TarFisDock has been used for evaluating over 5000 small molecules by more than 2497 users from over 30 countries and regions. Some of the predicted results have been verified by bioassay and crystallographic studies [9Cai J.H. et al.Peptide deformylase is a potential target for anti-Helicobacter pylori drugs: reverse docking, enzymatic assay, and X-ray crystallography validation.Protein Sci. 2006; 15: 2071-2081Crossref PubMed Scopus (89) Google Scholar, 10Chen Z. et al.Acenaphtho[1,2-b]pyrrole-based selective fibroblast growth factor receptor 1 (FGFR1) inhibitors: design, synthesis, and biological activity.J. Med. Chem. 2011; 54: 3732-3745Crossref PubMed Scopus (39) Google Scholar, 11Jeong C.H. et al.[6]-Gingerol suppresses colon cancer growth by targeting leukotriene A4 hydrolase.Cancer Res. 2009; 69: 5584-5591Crossref PubMed Scopus (167) Google Scholar]. Despite increased efficiency and convenience, the docking-based methods still take a considerable amount of computational resources as a single job may involve executing hundreds or even thousands of molecular docking runs. Moreover, they heavily rely on the available 3D structures of the targets and a priori knowledge about the ligand binding sites. As a result, these methods are not appropriate for assessing a large batch of small molecules. Pharmacophore modeling is another frequently used technique in drug hit discovery, which usually requires less time to screen a ligand than docking. It measures to what extent a query molecule possesses the spatial arrangement of features essential for protein–ligand interaction. Similar to molecular docking, this approach can also be used 'in reverse' to screen a given small molecule against a collection of predefined pharmacophores representing different targets and binding sites. Recently, Liu et al. reported a free web server that hosts a large repertoire of pharmacophores essential for interaction modes between ligand probe and potential targets, entitled PharmMapper [12Liu X.F. et al.PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach.Nucleic Acids Res. 2010; 38: W609-W614Crossref PubMed Scopus (668) Google Scholar]. A total of 7302 pharmacophores were constructed by using a protein structure-based modeling approach [13Wolber G. Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters.J. Chem. Inf. Model. 2005; 45: 160-169Crossref PubMed Scopus (1546) Google Scholar]. Given the query of a small molecule, PharmMapper automatically starts the procedure of reverse mapping against the deposited models and outputs the top ranked hits. Owing to its high throughput ability, PharmMapper can be used for large-scale target screening to profile the regulation genomic network. miRNAs are small non-coding RNA molecules capable of suppressing protein synthesis and regulating genes in many organisms [14Bartel D.P. MicroRNAs: genomics, biogenesis, mechanism, and function.Cell. 2004; 116: 281-297Abstract Full Text Full Text PDF PubMed Scopus (31151) Google Scholar]. This ability makes miRNA a promising technology for future therapeutic development because dysregulation of miRNA function and the changes in miRNA expression level are related to many disorders, including cancers, metabolic and neurodegenerative diseases. 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Wang et al. analyzed RNA-bound and RNA-free Ago forms by performing molecular docking, molecular dynamics simulation, and binding free energy calculation [18Wang Y.H. et al.Mechanism of microRNA–target interaction: molecular dynamics simulations and thermodynamics analysis.PLoS Comput. Biol. 2010; 6: e1000866Crossref PubMed Scopus (74) Google Scholar]. Pang et al. investigated the role of miRNAs in choriocarcinoma, through the combined usage of in silico miRNA target prediction, luciferase functional assays, and western blotting analysis. Their study demonstrated that miR-34a is a tumor suppressive miRNA in choriocarcinoma cells that may be used as a therapeutic target for treating choriocarcinoma [19Pang R.T.K. et al.MicroRNA-34a is a tumor suppressor in choriocarcinoma via regulation of Delta-like1.BMC Cancer. 2013; 13: 25Crossref PubMed Scopus (30) Google Scholar]. Recently, some computational approaches have been developed for the prediction of miRNA targets. Chou et al. reported a systematic approach to identify miRNA–target interactions (MTIs) by mining miRNA–target sites from crosslinking and immunoprecipitation (CLIP) and photoactivatable ribonucleoside-enhanced CLIP (PAR-CLIP) sequencing data, namely miRTarCLIP [20Chou C.H. et al.A computational approach for identifying microRNA–target interactions using high-throughput CLIP and PAR-CLIP sequencing.BMC Genomics. 2013; 14: S2PubMed Google Scholar]. The workflow was integrated into a web-based browser to provide a user-friendly interface and detailed annotations of MTIs. In addition to finding novel drug targets, another practice to boost the productivity of the current drug design process is to find new uses for an existing drug [21Chong C.R. Sullivan D.J. New uses for old drugs.Nature. 2007; 448: 645-646Crossref PubMed Scopus (845) Google Scholar]. This concept is also known as drug repositioning (also known as drug repurposing or reprofiling) and has gained considerable attention recently [22Tobinick E.L. The value of drug repositioning in the current pharmaceutical market.Drug News Perspect. 2009; 22: 119-125Crossref PubMed Scopus (196) Google Scholar, 23Dudley J.T. et al.Exploiting drug–disease relationships for computational drug repositioning.Brief. Bioinform. 2011; 12: 303-311Crossref PubMed Scopus (420) Google Scholar]. Because the starting point in drug repositioning is usually approved drugs with known bioavailability and safety profiles, this approach can significantly reduce the failure risks associated with drug development and potentially facilitate the repositioned compounds to enter clinical phases more rapidly and less costly than new chemical entities [24Boguski M.S. et al.Repurposing with a difference.Science. 2009; 324: 1394-1395Crossref PubMed Scopus (177) Google Scholar]. Given that most of the repositioned drugs currently in the market are discovered by chance [25Sardana D. et al.Drug repositioning for orphan diseases.Brief. Bioinform. 2011; 12: 346-356Crossref PubMed Scopus (187) Google Scholar], it is of great interest to develop computational methods to rationalize and facilitate the process. The knowledge about interactions between proteins and small molecules is essential for developing drug repositioning tools. There are some databases that focus on the biological actions of drugs, for example, DrugBank [26Wishart D.S. et al.DrugBank: a knowledgebase for drugs, drug actions and drug targets.Nucleic Acids Res. 2008; 36: D901-D906Crossref PubMed Scopus (2199) Google Scholar], TTD [27Zhu F. et al.Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery.Nucleic Acids Res. 2012; 40: D1128-D1136Crossref PubMed Scopus (421) Google Scholar], SuperTarget [28Gunther S. et al.SuperTarget and Matador: resources for exploring drug–target relationships.Nucleic Acids Res. 2008; 36: D919-D922Crossref PubMed Scopus (527) Google Scholar], and MATADOR [28Gunther S. et al.SuperTarget and Matador: resources for exploring drug–target relationships.Nucleic Acids Res. 2008; 36: D919-D922Crossref PubMed Scopus (527) Google Scholar]. Recently, Kuhn et al. reported on STITCH (search tool for interactions of chemicals), which integrates information about interactions from metabolic pathways, crystal structures, binding experiments, and drug–target relationships [29Kuhn M. et al.STITCH: interaction networks of chemicals and proteins.Nucleic Acids Res. 2008; 36: D684-D688Crossref PubMed Scopus (710) Google Scholar]. In the latest version, the database contains over 300 000 chemicals and 2.6 million proteins from 1133 organisms [30Kuhn M. et al.STITCH 3: zooming in on protein–chemical interactions.Nucleic Acids Res. 2012; 40: D876-D880Crossref PubMed Scopus (246) Google Scholar]. On the basis of these resources, many computational models have been developed to predict characteristic interactions between the existing drugs and novel molecular targets through target across assessment or ligand–target pathway analysis for prospective repositioning. Li et al. performed a large-scale molecular docking of 4621 small molecule drugs from DrugBank against 252 human protein drug targets, in order to map the drug–target interaction space and find novel interactions. The method emphasizes removing false positive predictions using annotated 'known' interactions, consensus scoring, and ranking thresholds. The utility of the method was verified by predicting novel drug–target interactions. Through a literature search, 31 of the top ranked interactions found corresponding experimental evidence. The method was also used to identify the cancer drug nilotinib as a potent inhibitor of MAPK14, which is a target in inflammatory diseases [31Li Y.Y. et al.A computational approach to finding novel targets for existing drugs.PLoS Comput. Biol. 2011; 9: e1002139Crossref Scopus (107) Google Scholar]. On the basis of the central premise of medicinal chemistry that 'structurally similar molecules have similar biological activity', potential drug targets can be associated with small molecules or known pharmacological properties through chemical similarities. Shoichet and colleagues developed a statistics-based chemoinformatics method named SEA (Similarity Ensemble Approach), which compared targets by the similarity of the ligands that bind to them [32Keiser M.J. et al.Relating protein pharmacology by ligand chemistry.Nat. Biotechnol. 2007; 25: 197-206Crossref PubMed Scopus (1635) Google Scholar]. The approach was used to predict new off-targets for 878 purchasable FDA-approved small molecule drugs and 2787 pharmaceutical compounds. Overall, there were 6928 pairs of drug and ligand sets showing statistical significance. Subsequent pharmacological experiments confirmed 23 new drug–target associations and revealed the cross-activity of some drugs on G-protein-coupled receptors (GPCRs), ion channels, and HIV reverse transcriptase. These results suggested that polypharmacology widely exists, which is probably therapeutically essential for some types of drugs [33Keiser M.J. et al.Predicting new molecular targets for known drugs.Nature. 2009; 462: 175-181Crossref PubMed Scopus (1371) Google Scholar]. Besides classical target- and ligand-based computational methods, in recent years, many drug repositioning approaches based on systems biology have been developed. Cheng et al. proposed an unweighted network-based inference (NBI) method [34Cheng F.X. et al.Prediction of drug–target interactions and drug repositioning via network-based inference.PLoS Comput. Biol. 2012; 8: e1002503Crossref PubMed Scopus (676) Google Scholar], which used the topology similarity of the ligand–target network to prioritize new targets for a given drug, or vice versa. New drug–target interactions for five known dru