1. Brief Bioinform. 2016 Jul;17(4):696-712. doi: 10.1093/bib/bbv066. Epub 2015 Aug 17. Drug-target interaction prediction: databases, web servers and computational models. Chen X, Yan CC, Zhang X, Zhang X, Dai F, Yin J, Zhang Y. Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug-target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com. DOI: 10.1093/bib/bbv066 PMID: 26283676 [Indexed for MEDLINE] 3. Nucleic Acids Res. 2016 Jan 4;44(D1):D1069-74. doi: 10.1093/nar/gkv1230. Epub 2015 Nov 17. Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Yang H(1), Qin C(2), Li YH(3), Tao L(2), Zhou J(3), Yu CY(3), Xu F(4), Chen Z(5), Zhu F(6), Chen YZ(7). Author information: (1)Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543, Singapore Innovative Drug Research Centre and College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, P. R. China. (2)Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543, Singapore. (3)Innovative Drug Research Centre and College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, P. R. China. (4)College of Pharmacy, State Key Laboratory of Medicinal Chemical Biology and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin 300071, P. R. China. (5)Zhejiang Key Laboratory of Gastro-intestinal Pathophysiology, Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang Chinese Medical University, No. 54 Youdian Road, Hangzhou 310006, China. (6)Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543, Singapore Innovative Drug Research Centre and College of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, P. R. China zhufeng@cqu.edu.cn. (7)Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, 117543, Singapore csccyz@nus.edu.sg. Extensive drug discovery efforts have yielded many approved and candidate drugs targeting various targets in different biological pathways. Several freely accessible databases provide the drug, target and drug-targeted pathway information for facilitating drug discovery efforts, but there is an insufficient coverage of the clinical trial drugs and the drug-targeted pathways. Here, we describe an update of the Therapeutic Target Database (TTD) previously featured in NAR. The updated contents include: (i) significantly increased coverage of the clinical trial targets and drugs (1.6 and 2.3 times of the previous release, respectively), (ii) cross-links of most TTD target and drug entries to the corresponding pathway entries of KEGG, MetaCyc/BioCyc, NetPath, PANTHER pathway, Pathway Interaction Database (PID), PathWhiz, Reactome and WikiPathways, (iii) the convenient access of the multiple targets and drugs cross-linked to each of these pathway entries and (iv) the recently emerged approved and investigative drugs. This update makes TTD a more useful resource to complement other databases for facilitating the drug discovery efforts. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gkv1230 PMCID: PMC4702870 PMID: 26578601 [Indexed for MEDLINE] 6. Annu Rev Pharmacol Toxicol. 2014;54:9-26. doi: 10.1146/annurev-pharmtox-011613-135943. Epub 2013 Aug 30. The druggable genome: Evaluation of drug targets in clinical trials suggests major shifts in molecular class and indication. Rask-Andersen M(1), Masuram S, Schiöth HB. Author information: (1)Department of Neuroscience and Uppsala Biomedical Center, Uppsala University, 75124 Uppsala, Sweden; email: helgi.schioth@neuro.uu.se. The largest innovations within pharmaceutical development come through new compounds that have unique and novel modes of action. These innovations commonly involve expanding the protein space targeted by pharmaceutical agents. At present, information about drugs and drug targets is available online via public databases such as DrugBank and the Therapeutic Targets Database. However, this information is biased, understandably so, toward established drugs and drug-target interactions. To gain a better overview of the drug-targeted portion of the human proteome and the directions of current drug development, we developed a data set of clinical trial drug-target interactions based on CenterWatch's Drugs in Clinical Trials Database, one of the largest databases of its kind. Our curation identified 475 potentially novel clinical trial drug targets. This review aims to identify trends in drug development based on the potentially novel targets currently being explored in clinical trials. DOI: 10.1146/annurev-pharmtox-011613-135943 PMID: 24016212 [Indexed for MEDLINE] 9. AAPS J. 2017 Sep;19(5):1264-1275. doi: 10.1208/s12248-017-0092-6. Epub 2017 Jun 2. Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review. Cheng T(1), Hao M(1), Takeda T(1), Bryant SH(1), Wang Y(2). Author information: (1)National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA. (2)National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA. ywang@ncbi.nlm.nih.gov. The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We also summarize popular public data resources and online tools for DTI prediction. It is found that various types of data were employed including properties of chemical structures, drug therapeutic effects and side effects, drug-target binding, drug-drug interactions, bioactivity data of drug molecules across multiple biological targets, and drug-induced gene expressions. More often, the heterogeneous data were integrated to offer better performance. However, challenges remain such as handling data imbalance, incorporating negative samples and quantitative bioactivity data, as well as maintaining cross-links among different data sources, which are essential for large-scale and automated information integration. DOI: 10.1208/s12248-017-0092-6 PMID: 28577120 [Indexed for MEDLINE] 10. J Chem Inf Model. 2017 Oct 23;57(10):2395-2400. doi: 10.1021/acs.jcim.7b00175. Epub 2017 Oct 12. PhID: An Open-Access Integrated Pharmacology Interactions Database for Drugs, Targets, Diseases, Genes, Side-Effects, and Pathways. Deng Z(1), Tu W(1), Deng Z(1), Hu QN(1)(2). Author information: (1)Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences , Wuhan, 430071, China. (2)Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences , 300308, Tianjin, China. The current network pharmacology study encountered a bottleneck with a lot of public data scattered in different databases. There is a lack of an open-access and consolidated platform that integrates this information for systemic research. To address this issue, we have developed PhID, an integrated pharmacology database which integrates >400 000 pharmacology elements (drug, target, disease, gene, side-effect, and pathway) and >200 000 element interactions in branches of public databases. PhID has three major applications: (1) assisting scientists searching through the overwhelming amount of pharmacology element interaction data by names, public IDs, molecule structures, or molecular substructures; (2) helping visualizing pharmacology elements and their interactions with a web-based network graph; and (3) providing prediction of drug-target interactions through two modules: PreDPI-ki and FIM, by which users can predict drug-target interactions of PhID entities or some drug-target pairs of their own interest. To get a systems-level understanding of drug action and disease complexity, PhID as a network pharmacology tool was established from the perspective of data layer, visualization layer, and prediction model layer to present information untapped by current databases. DOI: 10.1021/acs.jcim.7b00175 PMID: 28906116 [Indexed for MEDLINE] 14. Brief Bioinform. 2016 Nov;17(6):1070-1080. Epub 2015 Oct 21. Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database. Barneh F, Jafari M, Mirzaie M. Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com. DOI: 10.1093/bib/bbv094 PMID: 26490381 [Indexed for MEDLINE] 21. BMC Syst Biol. 2015;9 Suppl 4:S2. doi: 10.1186/1752-0509-9-S4-S2. Epub 2015 Jun 11. A weighted and integrated drug-target interactome: drug repurposing for schizophrenia as a use case. Huang LC, Soysal E, Zheng W, Zhao Z, Xu H, Sun J. BACKGROUND: Computational pharmacology can uniquely address some issues in the process of drug development by providing a macroscopic view and a deeper understanding of drug action. Specifically, network-assisted approach is promising for the inference of drug repurposing. However, the drug-target associations coming from different sources and various assays have much noise, leading to an inflation of the inference errors. To reduce the inference errors, it is necessary and critical to create a comprehensive and weighted data set of drug-target associations. RESULTS: In this study, we created a weighted and integrated drug-target interactome (WinDTome) to provide a comprehensive resource of drug-target associations for computational pharmacology. We first collected drug-target interactions from six commonly used drug-target centered data sources including DrugBank, KEGG, TTD, MATADOR, PDSP K(i) Database, and BindingDB. Then, we employed the record linkage method to normalize drugs and targets to the unique identifiers by utilizing the public data sources including PubChem, Entrez Gene, and UniProt. To assess the reliability of the drug-target associations, we assigned two scores (Score_S and Score_R) to each drug-target association based on their data sources and publication references. Consequently, the WinDTome contains 546,196 drug-target associations among 303,018 compounds and 4,113 genes. To assess the application of the WinDTome, we designed a network-based approach for drug repurposing using mental disorder schizophrenia (SCZ) as a case. Starting from 41 known SCZ drugs and their targets, we inferred a total of 264 potential SCZ drugs through the associations of drug-target with Score_S higher than two in WinDTome and human protein-protein interactions. Among the 264 SCZ-related drugs, 39 drugs have been investigated in clinical trials for SCZ treatment and 74 drugs for the treatment of other mental disorders, respectively. Compared with the results using other Score_S cutoff values, single data source, or the data from STITCH, the inference of 264 SCZ-related drugs had the highest performance. CONCLUSIONS: The WinDTome generated in this study contains comprehensive drug-target associations with confidence scores. Its application to the SCZ drug repurposing demonstrated that the WinDTome is promising to serve as a useful resource for drug repurposing. DOI: 10.1186/1752-0509-9-S4-S2 PMCID: PMC4474536 PMID: 26100720 [Indexed for MEDLINE] 26. Database (Oxford). 2018 Jan 1;2018:1-13. doi: 10.1093/database/bay083. Drug Target Commons 2.0: a community platform for systematic analysis of drug-target interaction profiles. Tanoli Z(1), Alam Z(1), Vähä-Koskela M(1), Ravikumar B(1), Malyutina A(1), Jaiswal A(1), Tang J(1)(2), Wennerberg K(1)(3), Aittokallio T(1)(2). Author information: (1)Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. (2)Department of Mathematics and Statistics, University of Turku, Turku, Finland. (3)Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark. Drug Target Commons (DTC) is a web platform (database with user interface) for community-driven bioactivity data integration and standardization for comprehensive mapping, reuse and analysis of compound-target interaction profiles. End users can search, upload, edit, annotate and export expert-curated bioactivity data for further analysis, using an application programmable interface, database dump or tab-delimited text download options. To guide chemical biology and drug-repurposing applications, DTC version 2.0 includes updated clinical development information for the compounds and target gene-disease associations, as well as cancer-type indications for mutant protein targets, which are critical for precision oncology developments. DOI: 10.1093/database/bay083 PMCID: PMC6146131 PMID: 30219839 [Indexed for MEDLINE] 48. Curr Drug Metab. 2018 Sep 24. doi: 10.2174/1389200219666180925091851. [Epub ahead of print] A Review of Recent Advances and Research on Drug Target Identification Methods. Hu Y(1), Zhao T(1), Zhang N(1), Zhang Y(2), Cheng L(3). Author information: (1)School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001. China. (2)Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin 150088. China. (3)College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081. China. From a therapeutic viewpoint, understanding how drugs bind and regulate the functions of their target proteins to protect against disease is crucial. The identification of drug targets plays a significant role in drug discovery and in studying the mechanisms of diseases. The development of methods to identify drug targets has become a popular issue in this field of research. Originally, scientists used biological experiments to identify drug targets. However, recently, an increasing number of scientists use computational methods to identify drug targets. Although thousands of drug targets are estimated to exist, only hundreds of these potential targets have been verified. Therefore, in this paper, we systematically review the recent work on identifying drug targets. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org. DOI: 10.2174/1389200219666180925091851 PMID: 30251599 50. Brief Bioinform. 2018 Dec 18. doi: 10.1093/bib/bby119. [Epub ahead of print] Interactive visual analysis of drug-target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing. Tanoli Z(1), Alam Z(1), Ianevski A(1)(2), Wennerberg K(3), Vähä-Koskela M(1), Aittokallio T(1)(2)(4). Author information: (1)Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland. (2)Helsinki Institute for Information Technology, Aalto University, Espoo, Finland. (3)University of Copenhagen, Biotech Research & Innovation Centre (BRIC). (4)Department of Mathematics and Statistics, University of Turku, Turku, Finland. Knowledge of the full target space of drugs (or drug-like compounds) provides important insights into the potential therapeutic use of the agents to modulate or avoid their various on- and off-targets in drug discovery and precision medicine. However, there is a lack of consolidated databases and associated data exploration tools that allow for systematic profiling of drug target-binding potencies of both approved and investigational agents using a network-centric approach. We recently initiated a community-driven platform, Drug Target Commons (DTC), which is an open-data crowdsourcing platform designed to improve the management, reproducibility and extended use of compound-target bioactivity data for drug discovery and repurposing, as well as target identification applications. In this work, we demonstrate an integrated use of the rich bioactivity data from DTC and related drug databases using Drug Target Profiler (DTP), an open-source software and web tool for interactive exploration of drug-target interaction networks. DTP was designed for network-centric modeling of mode-of-action of multi-targeting anticancer compounds, especially for precision oncology applications. DTP enables users to construct an interaction network based on integrated bioactivity data across selected chemical compounds and their protein targets, further customizable using various visualization and filtering options, as well as cross-links to several drug and protein databases to provide comprehensive information of the network nodes and interactions. We demonstrate here the operation of the DTP tool and its unique features by several use cases related to both drug discovery and drug repurposing applications, using examples of anticancer drugs with shared target profiles. DTP is freely accessible at http://drugtargetprofiler.fimm.fi/. DOI: 10.1093/bib/bby119 PMID: 30566623 64. PLoS One. 2015 May 7;10(5):e0126492. doi: 10.1371/journal.pone.0126492. eCollection 2015. Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System. Chen L(1), Chu C(2), Lu J(3), Kong X(4), Huang T(4), Cai YD(5). Author information: (1)College of Life Science, Shanghai University, Shanghai, People's Republic of China; College of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of China. (2)Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China. (3)Department of Medicinal Chemistry, School of Pharmacy, Yantai University, Shandong, Yantai, People's Republic of China. (4)Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China. (5)College of Life Science, Shanghai University, Shanghai, People's Republic of China. Drug-target interaction (DTI) is a key aspect in pharmaceutical research. With the ever-increasing new drug data resources, computational approaches have emerged as powerful and labor-saving tools in predicting new DTIs. However, so far, most of these predictions have been based on structural similarities rather than biological relevance. In this study, we proposed for the first time a "GO and KEGG enrichment score" method to represent a certain category of drug molecules by further classification and interpretation of the DTI database. A benchmark dataset consisting of 2,015 drugs that are assigned to nine categories ((1) G protein-coupled receptors, (2) cytokine receptors, (3) nuclear receptors, (4) ion channels, (5) transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens and (9) pathogens) was constructed by collecting data from KEGG. We analyzed each category and each drug for its contribution in GO terms and KEGG pathways using the popular feature selection "minimum redundancy maximum relevance (mRMR)" method, and key GO terms and KEGG pathways were extracted. Our analysis revealed the top enriched GO terms and KEGG pathways of each drug category, which were highly enriched in the literature and clinical trials. Our results provide for the first time the biological relevance among drugs, targets and biological functions, which serves as a new basis for future DTI predictions. DOI: 10.1371/journal.pone.0126492 PMCID: PMC4423955 PMID: 25951454 [Indexed for MEDLINE] 90. BMC Bioinformatics. 2014 Mar 11;15:68. doi: 10.1186/1471-2105-15-68. Drug2Gene: an exhaustive resource to explore effectively the drug-target relation network. Roider HG, Pavlova N, Kirov I, Slavov S, Slavov T, Uzunov Z, Weiss B(1). Author information: (1)Bayer Pharma AG, Müllerstr 178, 13342 Berlin, Germany. bertram.weiss@bayer.com. BACKGROUND: Information about drug-target relations is at the heart of drug discovery. There are now dozens of databases providing drug-target interaction data with varying scope, and focus. Therefore, and due to the large chemical space, the overlap of the different data sets is surprisingly small. As searching through these sources manually is cumbersome, time-consuming and error-prone, integrating all the data is highly desirable. Despite a few attempts, integration has been hampered by the diversity of descriptions of compounds, and by the fact that the reported activity values, coming from different data sets, are not always directly comparable due to usage of different metrics or data formats. DESCRIPTION: We have built Drug2Gene, a knowledge base, which combines the compound/drug-gene/protein information from 19 publicly available databases. A key feature is our rigorous unification and standardization process which makes the data truly comparable on a large scale, allowing for the first time effective data mining in such a large knowledge corpus. As of version 3.2, Drug2Gene contains 4,372,290 unified relations between compounds and their targets most of which include reported bioactivity data. We extend this set with putative (i.e. homology-inferred) relations where sufficient sequence homology between proteins suggests they may bind to similar compounds. Drug2Gene provides powerful search functionalities, very flexible export procedures, and a user-friendly web interface. CONCLUSIONS: Drug2Gene v3.2 has become a mature and comprehensive knowledge base providing unified, standardized drug-target related information gathered from publicly available data sources. It can be used to integrate proprietary data sets with publicly available data sets. Its main goal is to be a 'one-stop shop' to identify tool compounds targeting a given gene product or for finding all known targets of a drug. Drug2Gene with its integrated data set of public compound-target relations is freely accessible without restrictions at http://www.drug2gene.com. DOI: 10.1186/1471-2105-15-68 PMCID: PMC4234465 PMID: 24618344 [Indexed for MEDLINE] 97. Nucleic Acids Res. 2012 Jan;40(Database issue):D1113-7. doi: 10.1093/nar/gkr912. Epub 2011 Nov 8. SuperTarget goes quantitative: update on drug-target interactions. Hecker N(1), Ahmed J, von Eichborn J, Dunkel M, Macha K, Eckert A, Gilson MK, Bourne PE, Preissner R. Author information: (1)Structural Bioinformatics Group, Institute for Physiology, Charité-University Medicine Berlin, Lindenberger Weg 80, 13125 Berlin, Germany. There are at least two good reasons for the on-going interest in drug-target interactions: first, drug-effects can only be fully understood by considering a complex network of interactions to multiple targets (so-called off-target effects) including metabolic and signaling pathways; second, it is crucial to consider drug-target-pathway relations for the identification of novel targets for drug development. To address this on-going need, we have developed a web-based data warehouse named SuperTarget, which integrates drug-related information associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. At present, the updated database contains >6000 target proteins, which are annotated with >330,000 relations to 196,000 compounds (including approved drugs); the vast majority of interactions include binding affinities and pointers to the respective literature sources. The user interface provides tools for drug screening and target similarity inclusion. A query interface enables the user to pose complex queries, for example, to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target proteins within a certain affinity range. SuperTarget is available at http://bioinformatics.charite.de/supertarget. DOI: 10.1093/nar/gkr912 PMCID: PMC3245174 PMID: 22067455 [Indexed for MEDLINE] 121. Nucleic Acids Res. 2018 Jan 4;46(D1):D1074-D1082. doi: 10.1093/nar/gkx1037. DrugBank 5.0: a major update to the DrugBank database for 2018. Wishart DS(1)(2)(3)(4), Feunang YD(1), Guo AC(1), Lo EJ(1), Marcu A(1), Grant JR(1), Sajed T(2), Johnson D(1), Li C(1), Sayeeda Z(1), Assempour N(1), Iynkkaran I(1)(4), Liu Y(2), Maciejewski A(1), Gale N(5), Wilson A(5), Chin L(5), Cummings R(5), Le D(5), Pon A(1)(5), Knox C(1)(5), Wilson M(1)(5). Author information: (1)Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada. (2)Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada. (3)Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2N8, Canada. (4)Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2R3, Canada. (5)OMx Personal Health Analytics, Inc., 301-10359 104 St NW, Edmonton, AB T5J 1B9, Canada. DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year's update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gkx1037 PMCID: PMC5753335 PMID: 29126136 128. Nucleic Acids Res. 2017 Jan 4;45(D1):D932-D939. doi: 10.1093/nar/gkw993. Epub 2016 Oct 26. DrugCentral: online drug compendium. Ursu O(1), Holmes J(1), Knockel J(2), Bologa CG(1), Yang JJ(1), Mathias SL(1), Nelson SJ(1), Oprea TI(3). Author information: (1)Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA. (2)Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA. (3)Translational Informatics Division, Department of Internal Medicine, The University of New Mexico Health Science Center, Albuquerque, NM 87131, USA toprea@salud.unm.edu. DrugCentral (http://drugcentral.org) is an open-access online drug compendium. DrugCentral integrates structure, bioactivity, regulatory, pharmacologic actions and indications for active pharmaceutical ingredients approved by FDA and other regulatory agencies. Monitoring of regulatory agencies for new drugs approvals ensures the resource is up-to-date. DrugCentral integrates content for active ingredients with pharmaceutical formulations, indexing drugs and drug label annotations, complementing similar resources available online. Its complementarity with other online resources is facilitated by cross referencing to external resources. At the molecular level, DrugCentral bridges drug-target interactions with pharmacological action and indications. The integration with FDA drug labels enables text mining applications for drug adverse events and clinical trial information. Chemical structure overlap between DrugCentral and five online drug resources, and the overlap between DrugCentral FDA-approved drugs and their presence in four different chemical collections, are discussed. DrugCentral can be accessed via the web application or downloaded in relational database format. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. DOI: 10.1093/nar/gkw993 PMCID: PMC5210665 PMID: 27789690 [Indexed for MEDLINE] 142. J Biomed Semantics. 2017 Jun 6;8(1):20. doi: 10.1186/s13326-017-0131-3. Literature evidence in open targets - a target validation platform. Kafkas Ş(1)(2), Dunham I(3)(4), McEntyre J(3)(4). Author information: (1)European Molecular Biology Laboratory (EMBL-EBI), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK. kafkas@ebi.ac.uk. (2)Open Targets, Wellcome Genome Campus, Hinxton, CB10 1SD, UK. kafkas@ebi.ac.uk. (3)European Molecular Biology Laboratory (EMBL-EBI), European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, CB10 1SD, UK. (4)Open Targets, Wellcome Genome Campus, Hinxton, CB10 1SD, UK. BACKGROUND: We present the Europe PMC literature component of Open Targets - a target validation platform that integrates various evidence to aid drug target identification and validation. The component identifies target-disease associations in documents and ranks the documents based on their confidence from the Europe PMC literature database, by using rules utilising expert-provided heuristic information. The confidence score of a given document represents how valuable the document is in the scope of target validation for a given target-disease association by taking into account the credibility of the association based on the properties of the text. The component serves the platform regularly with the up-to-date data since December, 2015. RESULTS: Currently, there are a total number of 1168365 distinct target-disease associations text mined from >26 million PubMed abstracts and >1.2 million Open Access full text articles. Our comparative analyses on the current available evidence data in the platform revealed that 850179 of these associations are exclusively identified by literature mining. CONCLUSIONS: This component helps the platform's users by providing the most relevant literature hits for a given target and disease. The text mining evidence along with the other types of evidence can be explored visually through https://www.targetvalidation.org and all the evidence data is available for download in json format from https://www.targetvalidation.org/downloads/data . DOI: 10.1186/s13326-017-0131-3 PMCID: PMC5461726 PMID: 28587637 [Indexed for MEDLINE] 155. Brief Bioinform. 2018 Jul 31. doi: 10.1093/bib/bby061. [Epub ahead of print] Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Rifaioglu AS(1)(2), Atas H(3), Martin MJ(4), Cetin-Atalay R(1), Atalay V(1), Dogan T(5). Author information: (1)Department of Computer Engineering, Middle East Technical University, Ankara, Turkey. (2)Department of Computer Engineering, İskenderun Technical University, Hatay, Turkey. (3)Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey. (4)European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, Hinxton CB10 1SD, UK. (5)Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey and European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, Hinxton CB10 1SD, UK. The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance.The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods. DOI: 10.1093/bib/bby061 PMID: 30084866 190. Curr Protoc Bioinformatics. 2018 Mar;61(1):1.34.1-1.34.46. doi: 10.1002/cpbi.46. Accessing Expert-Curated Pharmacological Data in the IUPHAR/BPS Guide to PHARMACOLOGY. Sharman JL(1), Harding SD(1), Southan C(1), Faccenda E(1), Pawson AJ(1), Davies JA(1); NC-IUPHAR(1). Author information: (1)Deanery of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom. The IUPHAR/BPS Guide to PHARMACOLOGY is an expert-curated, open-access database of information on drug targets and the substances that act on them. This unit describes the procedures for searching and downloading ligand-target binding data and for finding detailed annotations and the most relevant literature. The database includes concise overviews of the properties of 1,700 data-supported human drug targets and related proteins, divided into families, and 9,000 small molecule and peptide experimental ligands and approved drugs that bind to those targets. More detailed descriptions of pharmacology, function, and pathophysiology are provided for a subset of important targets. The information is reviewed regularly by expert subcommittees of the IUPHAR Committee on Receptor Nomenclature and Drug Classification. A new immunopharmacology portal has recently been added, drawing together data on immunological targets, ligands, cell types, processes and diseases. The data are available for download and can be accessed computationally via Web services. © 2018 by John Wiley & Sons, Inc. © 2018 John Wiley & Sons, Inc. DOI: 10.1002/cpbi.46 PMID: 30040201 210. Adv Protein Chem Struct Biol. 2018;111:263-282. doi: 10.1016/bs.apcsb.2017.09.002. Epub 2017 Nov 6. Human Interactomics: Comparative Analysis of Different Protein Interaction Resources and Construction of a Cancer Protein-Drug Bipartite Network. De Las Rivas J(1), Alonso-López D(2), Arroyo MM(3). Author information: (1)Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), Salamanca, Spain. Electronic address: jrivas@usal.es. (2)Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), Salamanca, Spain. (3)Bioinformatics and Functional Genomics Group, Cancer Research Center (CiC-IMBCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), Salamanca, Spain; Pontifical Catholic University of Puerto Rico (PCUPR), Ponce, Puerto Rico. Unraveling the protein interaction wiring that occurs in human cells as a scaffold of biological processes requires the identification of all elements that constitute such molecular interaction networks. Proteome-wide experimental studies and bioinformatic comprehensive efforts have provided reliable and updated compendiums of the human protein interactome. In this work, we present a current view of available databases of human protein-protein interactions (PPIs) that allow building protein interaction networks. We also investigate human proteins as targets of specific drugs to analyze how chemicals interact with different target proteins, placing also the study in a network relational space. Hence, we undertake a description of several major drug-target resources to provide a present perspective of the associations between human proteins and specific chemicals. The identification of molecular targets for specific drugs is a critical step to improve disease therapy. As different diseases have different biomolecular scenarios, we addressed the identification of drug-targeted genes focusing our investigations on cancer and cancer genes. So, a description of resources that provide curated compendiums of human cancer genes is presented. Cancer is a complex disease where multiple genetic changes rewire cellular networks during carcinogenesis. This indicates that cancer drug therapy needs the implementation of network-driven studies to reveal multiplex interactions between cancer genes and drugs. To make progress in this direction, in the last part of this work we provide a bipartite network of cancer genes and their drugs shown in a graph landscape that disclose the existence of specific drug-target modules. © 2018 Elsevier Inc. All rights reserved. DOI: 10.1016/bs.apcsb.2017.09.002 PMID: 29459035 273. Nucleic Acids Res. 2019 Jan 8;47(D1):D590-D595. doi: 10.1093/nar/gky962. New approach for understanding genome variations in KEGG. Kanehisa M(1), Sato Y(2), Furumichi M(1), Morishima K(1), Tanabe M(1). Author information: (1)Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. (2)Social ICT Solutions Department, Fujitsu Kyushu Systems Ltd., Hakata-ku, Fukuoka 812-0007, Japan. KEGG (Kyoto Encyclopedia of Genes and Genomes; https://www.kegg.jp/ or https://www.genome.jp/kegg/) is a reference knowledge base for biological interpretation of genome sequences and other high-throughput data. It is an integrated database consisting of three generic categories of systems information, genomic information and chemical information, and an additional human-specific category of health information. KEGG pathway maps, BRITE hierarchies and KEGG modules have been developed as generic molecular networks with KEGG Orthology nodes of functional orthologs so that KEGG pathway mapping and other procedures can be applied to any cellular organism. Unfortunately, however, this generic approach was inadequate for knowledge representation in the health information category, where variations of human genomes, especially disease-related variations, had to be considered. Thus, we have introduced a new approach where human gene variants are explicitly incorporated into what we call 'network variants' in the recently released KEGG NETWORK database. This allows accumulation of knowledge about disease-related perturbed molecular networks caused not only by gene variants, but also by viruses and other pathogens, environmental factors and drugs. We expect that KEGG NETWORK will become another reference knowledge base for the basic understanding of disease mechanisms and practical use in clinical sequencing and drug development. DOI: 10.1093/nar/gky962 PMCID: PMC6324070 PMID: 30321428 274. BMC Bioinformatics. 2012;13 Suppl 15:S7. doi: 10.1186/1471-2105-13-S15-S7. Epub 2012 Sep 11. IPAD: the Integrated Pathway Analysis Database for Systematic Enrichment Analysis. Zhang F(1), Drabier R. Author information: (1)Department of Academic and Institutional Resources and Technology, University of North Texas Health Science Center, Fort Worth, USA. BACKGROUND: Next-Generation Sequencing (NGS) technologies and Genome-Wide Association Studies (GWAS) generate millions of reads and hundreds of datasets, and there is an urgent need for a better way to accurately interpret and distill such large amounts of data. Extensive pathway and network analysis allow for the discovery of highly significant pathways from a set of disease vs. healthy samples in the NGS and GWAS. Knowledge of activation of these processes will lead to elucidation of the complex biological pathways affected by drug treatment, to patient stratification studies of new and existing drug treatments, and to understanding the underlying anti-cancer drug effects. There are approximately 141 biological human pathway resources as of Jan 2012 according to the Pathguide database. However, most currently available resources do not contain disease, drug or organ specificity information such as disease-pathway, drug-pathway, and organ-pathway associations. Systematically integrating pathway, disease, drug and organ specificity together becomes increasingly crucial for understanding the interrelationships between signaling, metabolic and regulatory pathway, drug action, disease susceptibility, and organ specificity from high-throughput omics data (genomics, transcriptomics, proteomics and metabolomics). RESULTS: We designed the Integrated Pathway Analysis Database for Systematic Enrichment Analysis (IPAD, http://bioinfo.hsc.unt.edu/ipad), defining inter-association between pathway, disease, drug and organ specificity, based on six criteria: 1) comprehensive pathway coverage; 2) gene/protein to pathway/disease/drug/organ association; 3) inter-association between pathway, disease, drug, and organ; 4) multiple and quantitative measurement of enrichment and inter-association; 5) assessment of enrichment and inter-association analysis with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources; and 6) cross-linking of multiple available data sources.IPAD is a comprehensive database covering about 22,498 genes, 25,469 proteins, 1956 pathways, 6704 diseases, 5615 drugs, and 52 organs integrated from databases including the BioCarta, KEGG, NCI-Nature curated, Reactome, CTD, PharmGKB, DrugBank, PharmGKB, and HOMER. The database has a web-based user interface that allows users to perform enrichment analysis from genes/proteins/molecules and inter-association analysis from a pathway, disease, drug, and organ.Moreover, the quality of the database was validated with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources. Two case studies were also presented to demonstrate: 1) self-validation of enrichment analysis and inter-association analysis on brain-specific markers, and 2) identification of previously undiscovered components by the enrichment analysis from a prostate cancer study. CONCLUSIONS: IPAD is a new resource for analyzing, identifying, and validating pathway, disease, drug, organ specificity and their inter-associations. The statistical method we developed for enrichment and similarity measurement and the two criteria we described for setting the threshold parameters can be extended to other enrichment applications. Enriched pathways, diseases, drugs, organs and their inter-associations can be searched, displayed, and downloaded from our online user interface. The current IPAD database can help users address a wide range of biological pathway related, disease susceptibility related, drug target related and organ specificity related questions in human disease studies. DOI: 10.1186/1471-2105-13-S15-S7 PMCID: PMC3439721 PMID: 23046449 [Indexed for MEDLINE] 303. Nucleic Acids Res. 2010 Jan;38(Database issue):D552-6. doi: 10.1093/nar/gkp937. Epub 2009 Nov 6. STITCH 2: an interaction network database for small molecules and proteins. Kuhn M(1), Szklarczyk D, Franceschini A, Campillos M, von Mering C, Jensen LJ, Beyer A, Bork P. Author information: (1)Biotechnology Center, TU Dresden, 01062 Dresden, Germany. Over the last years, the publicly available knowledge on interactions between small molecules and proteins has been steadily increasing. To create a network of interactions, STITCH aims to integrate the data dispersed over the literature and various databases of biological pathways, drug-target relationships and binding affinities. In STITCH 2, the number of relevant interactions is increased by incorporation of BindingDB, PharmGKB and the Comparative Toxicogenomics Database. The resulting network can be explored interactively or used as the basis for large-scale analyses. To facilitate links to other chemical databases, we adopt InChIKeys that allow identification of chemicals with a short, checksum-like string. STITCH 2.0 connects proteins from 630 organisms to over 74,000 different chemicals, including 2200 drugs. STITCH can be accessed at http://stitch.embl.de/. DOI: 10.1093/nar/gkp937 PMCID: PMC2808890 PMID: 19897548 [Indexed for MEDLINE] 407. PLoS One. 2015 Oct 16;10(10):e0139889. doi: 10.1371/journal.pone.0139889. eCollection 2015. Gene-Set Local Hierarchical Clustering (GSLHC)--A Gene Set-Based Approach for Characterizing Bioactive Compounds in Terms of Biological Functional Groups. Chung FH(1), Jin ZH(2), Hsu TT(2), Hsu CL(2), Liu HC(2), Lee HC(3). Author information: (1)Institute of Systems Biology and Bioinformatics, National Central University, Zhongli, 32001, Taiwan; Center for Dynamical Biomarkers and Translational Medicine, National Central University, Zhongli, 32001, Taiwan. (2)Institute of Systems Biology and Bioinformatics, National Central University, Zhongli, 32001, Taiwan. (3)Institute of Systems Biology and Bioinformatics, National Central University, Zhongli, 32001, Taiwan; Center for Dynamical Biomarkers and Translational Medicine, National Central University, Zhongli, 32001, Taiwan; Department of Physics, Chung Yuan Christian University, Zhongli, 32023, Taiwan; Physics Division, National Center for Theoretical Sciences, Hsinchu, 30043, Taiwan. Gene-set-based analysis (GSA), which uses the relative importance of functional gene-sets, or molecular signatures, as units for analysis of genome-wide gene expression data, has exhibited major advantages with respect to greater accuracy, robustness, and biological relevance, over individual gene analysis (IGA), which uses log-ratios of individual genes for analysis. Yet IGA remains the dominant mode of analysis of gene expression data. The Connectivity Map (CMap), an extensive database on genomic profiles of effects of drugs and small molecules and widely used for studies related to repurposed drug discovery, has been mostly employed in IGA mode. Here, we constructed a GSA-based version of CMap, Gene-Set Connectivity Map (GSCMap), in which all the genomic profiles in CMap are converted, using gene-sets from the Molecular Signatures Database, to functional profiles. We showed that GSCMap essentially eliminated cell-type dependence, a weakness of CMap in IGA mode, and yielded significantly better performance on sample clustering and drug-target association. As a first application of GSCMap we constructed the platform Gene-Set Local Hierarchical Clustering (GSLHC) for discovering insights on coordinated actions of biological functions and facilitating classification of heterogeneous subtypes on drug-driven responses. GSLHC was shown to tightly clustered drugs of known similar properties. We used GSLHC to identify the therapeutic properties and putative targets of 18 compounds of previously unknown characteristics listed in CMap, eight of which suggest anti-cancer activities. The GSLHC website http://cloudr.ncu.edu.tw/gslhc/ contains 1,857 local hierarchical clusters accessible by querying 555 of the 1,309 drugs and small molecules listed in CMap. We expect GSCMap and GSLHC to be widely useful in providing new insights in the biological effect of bioactive compounds, in drug repurposing, and in function-based classification of complex diseases. DOI: 10.1371/journal.pone.0139889 PMCID: PMC4652590 PMID: 26473729 [Indexed for MEDLINE] 410. BMC Bioinformatics. 2015;16 Suppl 13:S4. doi: 10.1186/1471-2105-16-S13-S4. Epub 2015 Sep 25. DMAP: a connectivity map database to enable identification of novel drug repositioning candidates. Huang H, Nguyen T, Ibrahim S, Shantharam S, Yue Z, Chen JY. BACKGROUND: Drug repositioning is a cost-efficient and time-saving process to drug development compared to traditional techniques. A systematic method to drug repositioning is to identify candidate drug's gene expression profiles on target disease models and determine how similar these profiles are to approved drugs. Databases such as the CMAP have been developed recently to help with systematic drug repositioning. METHODS: To overcome the limitation of connectivity maps on data coverage, we constructed a comprehensive in silico drug-protein connectivity map called DMAP, which contains directed drug-to-protein effects and effect scores. The drug-to-protein effect scores are compiled from all database entries between the drug and protein have been previously observed and provide a confidence measure on the quality of such drug-to-protein effects. RESULTS: In DMAP, we have compiled the direct effects between 24,121 PubChem Compound ID (CID), which were mapped from 289,571 chemical entities recognized from public literature, and 5,196 reviewed Uniprot proteins. DMAP compiles a total of 438,004 chemical-to-protein effect relationships. Compared to CMAP, DMAP shows an increase of 221 folds in the number of chemicals and 1.92 fold in the number of ATC codes. Furthermore, by overlapping DMAP chemicals with the approved drugs with known indications from the TTD database and literature, we obtained 982 drugs and 622 diseases; meanwhile, we only obtained 394 drugs with known indication from CMAP. To validate the feasibility of applying new DMAP for systematic drug repositioning, we compared the performance of DMAP and the well-known CMAP database on two popular computational techniques: drug-drug-similarity-based method with leave-one-out validation and Kolmogorov-Smirnov scoring based method. In drug-drug-similarity-based method, the drug repositioning prediction using DMAP achieved an Area-Under-Curve (AUC) score of 0.82, compared with that using CMAP, AUC = 0.64. For Kolmogorov-Smirnov scoring based method, with DMAP, we were able to retrieve several drug indications which could not be retrieved using CMAP. DMAP data can be queried using the existing C2MAP server or downloaded freely at: http://bio.informatics.iupui.edu/cmaps CONCLUSIONS: Reliable measurements of how drug affect disease-related proteins are critical to ongoing drug development in the genome medicine era. We demonstrated that DMAP can help drug development professionals assess drug-to-protein relationship data and improve chances of success for systematic drug repositioning efforts. DOI: 10.1186/1471-2105-16-S13-S4 PMCID: PMC4597058 PMID: 26423722 [Indexed for MEDLINE]