Bioinformatics Perspective of Drug Repurposing


Cite item

Full Text

Abstract

Different diseases can be treated with various therapeutic agents. Drug discovery aims to find potential molecules for existing and emerging diseases. However, factors, such as increasing development cost, generic competition due to the patent expiry of several drugs, increase in conservative regulatory policies, and insufficient breakthrough innovations impairs the development of new drugs and the learning productivity of pharmaceutical industries. Drug repurposing is the process of finding new therapeutic applications for already approved, withdrawn from use, abandoned, and experimental drugs. Drug repurposing is another method that may partially overcome the hurdles related to drug discovery and hence appears to be a wise attempt. However, drug repurposing being not a standard regulatory process, leads to administrative concerns and problems. The drug repurposing also requires expensive, high-risk clinical trials to establish the safety and efficacy of the repurposed drug. Recent innovations in the field of bioinformatics can accelerate the new drug repurposing studies by identifying new targets of the existing drugs along with drug candidate screening and refinement. Recent advancements in the field of comprehensive high throughput data in genomics, epigenetics, chromosome architecture, transcriptomic, proteomics, and metabolomics may also contribute to the understanding of molecular mechanisms involved in drug-target interaction. The present review describes the current scenario in the field of drug repurposing along with the application of various bioinformatic tools for the identification of new targets for the existing drug.

About the authors

Binita Patel

Department of Life Sciences, School of Sciences, Gujarat University

Email: info@benthamscience.net

Brijesh Gelat

Department of Zoology, BMTC, HG and WBC, School of Sciences, Gujarat University

Email: info@benthamscience.net

Mehul Soni

Department of Bioinformatics, School of Sciences, Gujarat University

Email: info@benthamscience.net

Pooja Rathaur

Department of Life Sciences, School of Sciences, Gujarat University

Email: info@benthamscience.net

Kaid SR

Department of Zoology, BMTC, HG and WBC, School of Sciences, Gujarat University

Author for correspondence.
Email: info@benthamscience.net

References

  1. Lee HM, Kim Y. Drug repurposing is a new opportunity for developing drugs against neuropsychiatric disorders. Schizophr Res Treatment 2016; 2016: 1-12. doi: 10.1155/2016/6378137 PMID: 27073698
  2. Kim TW. Drug repositioning approaches for the discovery of new therapeutics for alzheimer’s disease, neurotherapeutics. Neurotherapeutics 2015; 12(1): 132-42. doi: 10.1007/s13311-014-0325-7
  3. Mehndiratta MM, Wadhai S, Tyagi B, Gulati N, Sinha M. Drug repositioning Int J Epilepsy 2016; 3(2): 091-4. doi: 10.1016/j.ijep.2016.09.002
  4. Padhy BM, Gupta YK. Drug repositioning: Re-investigating existing drugs for new therapeutic indications. J Postgrad Med 2011; 57(2): 153-60. doi: 10.4103/0022-3859.81870 PMID: 21654146
  5. Reaume AG. Drug repurposing through nonhypothesis driven phenotypic screening. Drug Discov Today Ther Strateg 2011; 8(3-4): 85-8. doi: 10.1016/j.ddstr.2011.09.007
  6. Rastogi SC, Rastogi P, Mendiratta N. Mendiratta, Bioinformatics Methods And Applications: Genomics Proteomics And Drug Discovery 3Rd. 2008. https://books.google.co.in/books?hl=en&lr=&id=H-hnEAAAQBAJ&oi=fnd&pg=PP1&dq=bioinformatics&ots=On10Ww3-tZ&sig=FZKLDUkS25Nm-FLfglM4IVRhkSo
  7. Barratt MJ, Frail DE. Drug Repositioning: Bringing New Life to Shelved Assets and Existing Drugs. John Wiley and Sons 2012. doi: 10.1002/9781118274408
  8. Ashburn TT, Thor KB. Drug repositioning: Identifying and developing new uses for existing drugs. Nat Rev Drug Discov 2004; 3(8): 673-83. doi: 10.1038/nrd1468 PMID: 15286734
  9. Dudley JT, Deshpande T, Butte AJ. Exploiting drug-disease relationships for computational drug repositioning. Brief Bioinform 2011; 12(4): 303-11. doi: 10.1093/bib/bbr013 PMID: 21690101
  10. Jafari RM, Sheibani M, Nezamoleslami S, Shayesteh S, Jand Y, Dehpour AR. Drug repositioning: A review. J Iran Med Counc 2018; 1: 7-10.
  11. Wang YW, He SJ, Feng X, et al. Metformin: A review of its potential indications. Drug Des Devel Ther 2017; 11: 2421-9. doi: 10.2147/DDDT.S141675 PMID: 28860713
  12. Gallagher EJ, LeRoith D. Diabetes, cancer, and metformin: Connections of metabolism and cell proliferation. Ann N Y Acad Sci 2011; 1243(1): 54-68. doi: 10.1111/j.1749-6632.2011.06285.x PMID: 22211893
  13. Hirsch HA, Iliopoulos D, Struhl K. Metformin inhibits the inflammatory response associated with cellular transformation and cancer stem cell growth. Proc Natl Acad Sci 2013; 110(3): 972-7. doi: 10.1073/pnas.1221055110 PMID: 23277563
  14. Chong CR, Xu J, Lu J, Bhat S, Sullivan DJ Jr, Liu JO. Inhibition of angiogenesis by the antifungal drug itraconazole. ACS Chem Biol 2007; 2(4): 263-70. doi: 10.1021/cb600362d PMID: 17432820
  15. Kim J, Tang JY, Gong R, et al. Itraconazole, a commonly used antifungal that inhibits Hedgehog pathway activity and cancer growth. Cancer Cell 2010; 17(4): 388-99. doi: 10.1016/j.ccr.2010.02.027 PMID: 20385363
  16. Guastella AJ, Dadds MR, Lovibond PF, Mitchell P, Richardson R. A randomized controlled trial of the effect of d-cycloserine on exposure therapy for spider fear. J Psychiatr Res 2007; 41(6): 466-71. doi: 10.1016/j.jpsychires.2006.05.006 PMID: 16828803
  17. Na ES, De Jesús-Cortés H, Martinez-Rivera A, et al. D-cycloserine improves synaptic transmission in an animal mode of Rett syndrome. PLoS One 2017; 12(8): e0183026. doi: 10.1371/journal.pone.0183026 PMID: 29370281
  18. Aronskyy I, Masoudi-Sobhanzadeh Y, Cappuccio A, Zaslavsky E. Advances in the computational landscape for repurposed drugs against COVID-19. Drug Discov Today 2021; 26(12): 2800-15. doi: 10.1016/j.drudis.2021.07.026 PMID: 34339864
  19. Smith DP, Oechsle O, Rawling MJ, Savory E, Lacoste AMB, Richardson PJ. Expert-augmented computational drug repurposing identified baricitinib as a treatment for COVID-19. Front Pharmacol 2021; 12: 709856. doi: 10.3389/fphar.2021.709856 PMID: 34393789
  20. Brueggeman L, Sturgeon ML, Martin RM, et al. Drug repositioning in epilepsy reveals novel antiseizure candidates. Ann Clin Transl Neurol 2019; 6(2): 295-309. doi: 10.1002/acn3.703 PMID: 30847362
  21. Sun W, Sanderson PE, Zheng W. Drug combination therapy increases successful drug repositioning. Drug Discov Today 2016; 21(7): 1189-95. doi: 10.1016/j.drudis.2016.05.015 PMID: 27240777
  22. Cha Y, Erez T, Reynolds IJ, et al. Drug repurposing from the perspective of pharmaceutical companies. Br J Pharmacol 2018; 175(2): 168-80. doi: 10.1111/bph.13798 PMID: 28369768
  23. Li X, Qin G, Yang Q, Chen L, Xie L. Biomolecular network-based synergistic drug combination discovery. BioMed Res Int 2016; 2016: 1-11. doi: 10.1155/2016/8518945 PMID: 27891522
  24. Chou TC. Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies. Pharmacol Rev 2006; 58(3): 621-81. doi: 10.1124/pr.58.3.10 PMID: 16968952
  25. Chen D, Liu X, Yang Y, Yang H, Lu P. Systematic synergy modeling: Understanding drug synergy from a systems biology perspective. BMC Syst Biol 2015; 9(1): 56. doi: 10.1186/s12918-015-0202-y PMID: 26377814
  26. Zhang N, Fu JN, Chou TC. Synergistic combination of microtubule targeting anticancer fludelone with cytoprotective panaxytriol derived from panax ginseng against MX-1 cells in vitro: Experimental design and data analysis using the combination index method. Am J Cancer Res 2016; 6: 97-104. PMID: 4759401
  27. Ianevski A, Giri AK, Aittokallio T. SynergyFinder 2.0: Visual analytics of multi-drug combination synergies. Nucleic Acids Res 2020; 48(W1): W488-93. doi: 10.1093/nar/gkaa216 PMID: 32246720
  28. Di Veroli GY, Fornari C, Wang D, et al. Combenefit: An interactive platform for the analysis and visualization of drug combinations. Bioinformatics 2016; 32(18): 2866-8. doi: 10.1093/bioinformatics/btw230 PMID: 27153664
  29. Lewis R, Guha R, Korcsmaros T, Bender A. Synergy Maps: Exploring compound combinations using network-based visualization. J Cheminform 2015; 7(1): 36. doi: 10.1186/s13321-015-0090-6 PMID: 26236402
  30. Xu M, Zhao X, Wang J, et al. DFFNDDS: Prediction of synergistic drug combinations with dual feature fusion networks. J Cheminform 2023; 15(1): 33. doi: 10.1186/s13321-023-00690-3 PMID: 36927504
  31. Zhang M, Lee S, Yao B, Xiao G, Xu L, Xie Y. DIGREM: An integrated web-based platform for detecting effective multi-drug combinations. Bioinformatics 2019; 35(10): 1792-4. doi: 10.1093/bioinformatics/bty860 PMID: 30295728
  32. Brogi S. Computational approaches for drug discovery. Molecules 2019; 24(17): 3061. doi: 10.3390/molecules24173061 PMID: 31443558
  33. Manzoni C, Kia DA, Vandrovcova J, et al. Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief Bioinform 2018; 19(2): 286-302. doi: 10.1093/bib/bbw114 PMID: 27881428
  34. Badkas A, De Landtsheer S, Sauter T. Topological network measures for drug repositioning. Brief Bioinform 2021; 22(4): bbaa357. doi: 10.1093/bib/bbaa357 PMID: 33348366
  35. Chen H, Cheng F, Li J. iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding. PLOS Comput Biol 2020; 16(7): e1008040. doi: 10.1371/journal.pcbi.1008040 PMID: 32667925
  36. Vogrinc D, Kunej T. Drug repositioning: Computational approaches and research examples classified according to the evidence level. Discoveries 2017; 5(2): e75. doi: 10.15190/d.2017.5 PMID: 32309593
  37. Aparoy P, Kumar Reddy K, Reddanna P. Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors. Curr Med Chem 2012; 19(22): 3763-78. doi: 10.2174/092986712801661112 PMID: 22680930
  38. March-Vila E, Pinzi L, Sturm N, et al. On the integration of in silico drug design methods for drug repurposing. Front Pharmacol 2017; 8: 298. doi: 10.3389/fphar.2017.00298 PMID: 28588497
  39. Zhan P, Yu B, Ouyang L. Drug repurposing: An effective strategy to accelerate contemporary drug discovery. Drug Discov Today 2022; 27(7): 1785-8. doi: 10.1016/j.drudis.2022.05.026 PMID: 35661705
  40. Sharma VP. Drug repositioning: A faster path to drug discovery. Adv Pharmacoepidemiol Drug Saf 2012; 1(6) doi: 10.4172/2167-1052.1000e117
  41. Ozsoy MG, Özyer T, Polat F, Alhajj R. Realizing drug repositioning by adapting a recommendation system to handle the process. BMC Bioinformatics 2018; 19(1): 136. doi: 10.1186/s12859-018-2142-1 PMID: 29649971
  42. Brown AS, Patel CJ. A standard database for drug repositioning. Sci Data 2017; 4(1): 170029. doi: 10.1038/sdata.2017.29 PMID: 28291243
  43. Brown AS, Kong SW, Kohane IS, Patel CJ. ksRepo: A generalized platform for computational drug repositioning. BMC Bioinformatics 2016; 17(1): 78. doi: 10.1186/s12859-016-0931-y PMID: 26860211
  44. Ferreira L, dos Santos R, Oliva G, Andricopulo A. Molecular docking and structure-based drug design strategies. Molecules 2015; 20(7): 13384-421. doi: 10.3390/molecules200713384 PMID: 26205061
  45. Hu Y, Bajorath J. Extending the activity cliff concept: structural categorization of activity cliffs and systematic identification of different types of cliffs in the ChEMBL database. J Chem Inf Model 2012; 52(7): 1806-11. doi: 10.1021/ci300274c PMID: 22758389
  46. Gohlke BO, Overkamp T, Richter A, et al. 2D and 3D similarity landscape analysis identifies PARP as a novel off-target for the drug Vatalanib. BMC Bioinformatics 2015; 16(1): 308. doi: 10.1186/s12859-015-0730-x PMID: 26403354
  47. Wang Z, Liang L, Yin Z, Lin J. Improving chemical similarity ensemble approach in target prediction. J Cheminform 2016; 8(1): 20. doi: 10.1186/s13321-016-0130-x PMID: 27110288
  48. Khashan R, Zheng W, Tropsha A. Scoring protein interaction decoys using exposed residues (SPIDER): A novel multibody interaction scoring function based on frequent geometric patterns of interfacial residues. Proteins 2012; 80(9): 2207-17. doi: 10.1002/prot.24110 PMID: 22581643
  49. Geppetti P, Veldhuis NA, Lieu T, Bunnett NW. G protein-coupled receptors: Dynamic machines for signaling pain and itch. Neuron 2015; 88(4): 635-49. doi: 10.1016/j.neuron.2015.11.001 PMID: 26590341
  50. Jacobson KA. New paradigms in GPCR drug discovery. Biochem Pharmacol 2015; 98(4): 541-55. doi: 10.1016/j.bcp.2015.08.085 PMID: 26265138
  51. Sharma AK, Kapoor VK, Kaur G. Herb–drug interactions: A mechanistic approach. Drug Chem Toxicol 2022; 45(2): 594-603. doi: 10.1080/01480545.2020.1738454 PMID: 32160796
  52. Nicholls A, McGaughey GB, Sheridan RP, et al. Molecular shape and medicinal chemistry: A perspective. J Med Chem 2010; 53(10): 3862-86. doi: 10.1021/jm900818s PMID: 20158188
  53. Méndez-Lucio O, Tran J, Medina-Franco JL, Meurice N, Muller M. Toward drug repurposing in epigenetics: olsalazine as a hypomethylating compound active in a cellular context. ChemMedChem 2014; 9(3): 560-5. doi: 10.1002/cmdc.201300555 PMID: 24482360
  54. Pérez-Nueno VI, Venkatraman V, Mavridis L, Ritchie DW. Detecting drug promiscuity using Gaussian ensemble screening. J Chem Inf Model 2012; 52(8): 1948-61. doi: 10.1021/ci3000979 PMID: 22747187
  55. Pérez-Nueno VI, Karaboga AS, Souchet M, Ritchie DW. GES polypharmacology fingerprints: A novel approach for drug repositioning. J Chem Inf Model 2014; 54(3): 720-34. doi: 10.1021/ci4006723 PMID: 24494653
  56. Jenkins JL, Glick M, Davies JW. A 3D similarity method for scaffold hopping from known drugs or natural ligands to new chemotypes. J Med Chem 2004; 47(25): 6144-59. doi: 10.1021/jm049654z PMID: 15566286
  57. Yera ER, Cleves AE, Jain AN. Chemical structural novelty: On-targets and off-targets. J Med Chem 2011; 54(19): 6771-85. doi: 10.1021/jm200666a PMID: 21916467
  58. Lin X, Li X, Lin X. A review on applications of computational methods in drug screening and design. Mol 2020; 25: 1375. doi: 10.3390/molecules25061375
  59. Lionta E, Spyrou G, Vassilatis D, Cournia Z. Structure-based virtual screening for drug discovery: Principles, applications and recent advances. Curr Top Med Chem 2014; 14(16): 1923-38. doi: 10.2174/1568026614666140929124445 PMID: 25262799
  60. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat Rev Drug Discov 2004; 3(11): 935-49. doi: 10.1038/nrd1549 PMID: 15520816
  61. Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: A powerful approach for structure-based drug discovery. Curr Computeraided Drug Des 2011; 7(2): 146-57. doi: 10.2174/157340911795677602 PMID: 21534921
  62. Kinnings SL, Liu N, Buchmeier N, Tonge PJ, Xie L, Bourne PE. Drug discovery using chemical systems biology: Repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLOS Comput Biol 2009; 5(7): e1000423. doi: 10.1371/journal.pcbi.1000423 PMID: 19578428
  63. Sgobba M, Caporuscio F, Anighoro A, Portioli C, Rastelli G. Application of a post-docking procedure based on MM-PBSA and MM-GBSA on single and multiple protein conformations. Eur J Med Chem 2012; 58: 431-40. doi: 10.1016/j.ejmech.2012.10.024 PMID: 23153814
  64. Nacev BA, Grassi P, Dell A, Haslam SM, Liu JO. The antifungal drug itraconazole inhibits vascular endothelial growth factor receptor 2 (VEGFR2) glycosylation, trafficking, and signaling in endothelial cells. J Biol Chem 2011; 286(51): 44045-56. doi: 10.1074/jbc.M111.278754 PMID: 22025615
  65. Choudhury C, Murugan N, Today UP-DD. Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods. Elsevier 2022. Available from: https://www.sciencedirect.com/science/article/pii/S135964462200112X (accessed November 17, 2022).
  66. Wang X, Shen Y, Wang S, et al. PharmMapper 2017 update: A web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res 2017; 45(W1): W356-60. doi: 10.1093/nar/gkx374 PMID: 28472422
  67. Meslamani J, Li J, Sutter J, Stevens A, Bertrand HO, Rognan D. Protein-ligand-based pharmacophores: Generation and utility assessment in computational ligand profiling. J Chem Inf Model 2012; 52(4): 943-55. doi: 10.1021/ci300083r PMID: 22480372
  68. Zhang Y, Xhaard H, Ghemtio L. Predictive classification models and targets identification for betulin derivatives as Leishmania donovani inhibitors. J Cheminform 2018; 10(1): 40. doi: 10.1186/s13321-018-0291-x PMID: 30120601
  69. Voet A, Qing X, Lee XY, et al. Pharmacophore modeling: Advances, limitations, and current utility in drug discovery. J Receptor Ligand Channel Res 2014; 7: 81-92. doi: 10.2147/JRLCR.S46843
  70. Lee M, Kim D. Large-scale reverse docking profiles and their applications. BMC Bioinformatics 2012; 13 (Suppl. 17): S6. doi: 10.1186/1471-2105-13-S17-S6
  71. Huang H, Zhang G, Zhou Y, et al. Reverse screening methods to search for the protein targets of chemopreventive compounds. Front Chem 2018; 6: 138. doi: 10.3389/fchem.2018.00138 PMID: 29868550
  72. Lee A, Lee K, Kim D. Using reverse docking for target identification and its applications for drug discovery. Expert Opin Drug Discov 2016; 11(7): 707-15. doi: 10.1080/17460441.2016.1190706 PMID: 27186904
  73. Zhao J, Yang P, Li F, et al. Therapeutic effects of astragaloside IV on myocardial injuries: multi-target identification and network analysis. PLoS One 2012; 7(9): e44938. doi: 10.1371/journal.pone.0044938 PMID: 23028693
  74. Cheng F, Liu C, Jiang J, et al. Prediction of drug-target interactions and drug repositioning via network-based inference. PLOS Comput Biol 2012; 8(5): e1002503. doi: 10.1371/journal.pcbi.1002503 PMID: 22589709
  75. Li H, Gao Z, Kang L, et al. TarFisDock: A web server for identifying drug targets with docking approach Nucleic Acids Res 2006; 34(Web Server): W219-24. doi: 10.1093/nar/gkl114 PMID: 16844997
  76. Gao Z, Li H, Zhang H, et al. PDTD: A web-accessible protein database for drug target identification. BMC Bioinformatics 2008; 9(1): 104. doi: 10.1186/1471-2105-9-104 PMID: 18282303
  77. Xu X, Huang M, Zou X. Docking-based inverse virtual screening: Methods, applications, and challenges. Biophys Rep 2018; 4(1): 1-16. doi: 10.1007/s41048-017-0045-8 PMID: 29577065
  78. Scrima M, Lauro G, Grimaldi M, et al. Structural evidence of N6-isopentenyladenosine as a new ligand of farnesyl pyrophosphate synthase. J Med Chem 2014; 57(18): 7798-803. doi: 10.1021/jm500869x PMID: 25184810
  79. Schomburg KT, Bietz S, Briem H, Henzler AM, Urbaczek S, Rarey M. Facing the challenges of structure-based target prediction by inverse virtual screening. J Chem Inf Model 2014; 54(6): 1676-86. doi: 10.1021/ci500130e PMID: 24851945
  80. Ellingson SR, Dakshanamurthy S, Brown M, Smith JC, Baudry J. Accelerating virtual high-throughput ligand docking: Current technology and case study on a petascale supercomputerConcurr Comput Pract Exp. John Wiley and Sons Ltd 2014; pp. 1268-77. doi: 10.1002/cpe.3070
  81. Badar MS, Shamsi S, Ahmed J, Alam MA. Molecular dynamics simulations: Concept. Methods, and Applications. Springer Nature 2022; pp. 131-51. doi: 10.1007/978-3-030-94651-7_7
  82. De Vivo M, Masetti M, Bottegoni G, Cavalli A. Role of molecular dynamics and related methods in drug discovery. J Med Chem 2016; 59(9): 4035-61. doi: 10.1021/acs.jmedchem.5b01684 PMID: 26807648
  83. Durrant JD, McCammon JA. Molecular dynamics simulations and drug discovery. BMC Biol 2011; 9(1): 71. doi: 10.1186/1741-7007-9-71 PMID: 22035460
  84. Xue H, Li J, Xie H, Wang Y. Review of drug repositioning approaches and resources. Int J Biol Sci 2018; 14(10): 1232-44. doi: 10.7150/ijbs.24612 PMID: 30123072
  85. Cheng F, Desai RJ, Handy DE, et al. Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat Commun 2018; 9(1): 2691. doi: 10.1038/s41467-018-05116-5 PMID: 30002366
  86. Sutherland JJ, Webster YW, Willy JA, et al. Toxicogenomic module associations with pathogenesis: A network-based approach to understanding drug toxicity. Pharmacogenomics J 2018; 18(3): 377-90. doi: 10.1038/tpj.2017.17 PMID: 28440344
  87. Wu Z, Wang Y, Chen L. Network-based drug repositioning. Mol Biosyst 2013; 9(6): 1268-81. doi: 10.1039/c3mb25382a PMID: 23493874
  88. Kuchaiev O, Stevanović A, Hayes W, Pržulj N. GraphCrunch 2: Software tool for network modeling, alignment and clustering. BMC Bioinformatics 2011; 12(1): 24. doi: 10.1186/1471-2105-12-24 PMID: 21244715
  89. Sander J, Ester M, Kriegel HP, Xu X. Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications. Data Min Knowl Discov 1998; 2(2): 169-94. doi: 10.1023/A:1009745219419
  90. Crawford J, Milenković T. ClueNet: Clustering a temporal network based on topological similarity rather than denseness. PLoS One 2018; 13(5): e0195993. doi: 10.1371/journal.pone.0195993 PMID: 29738568
  91. Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods 2012; 9(5): 471-2. doi: 10.1038/nmeth.1938 PMID: 22426491
  92. Yu L, Huang J, Ma Z, Zhang J, Zou Y, Gao L. Inferring drugdisease associations based on known protein complexes. BMC Med Genomics 2015; 8(S2)(2): S2. doi: 10.1186/1755-8794-8-S2-S2 PMID: 26044949
  93. Wu C, Gudivada RC, Aronow BJ, Jegga AG. Computational drug repositioning through heterogeneous network clustering. BMC Syst Biol 2013; 7(S5)(5): S6. doi: 10.1186/1752-0509-7-S5-S6 PMID: 24564976
  94. Köhler S, Bauer S, Horn D, Robinson PN. Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 2008; 82(4): 949-58. doi: 10.1016/j.ajhg.2008.02.013 PMID: 18371930
  95. Rao N, Poojari T, Poojary C, Sande R, Sawant S. Drug Repurposing: A shortcut to new biological entities. Pharm Chem J 2022; 56(9): 1203-14. doi: 10.1007/s11094-022-02778-w PMID: 36531825
  96. Adie EA, Adams RR, Evans KL, Porteous DJ, Pickard BS. Speeding disease gene discovery by sequence based candidate prioritization. BMC Bioinformatics 2005; 6(1): 55. doi: 10.1186/1471-2105-6-55 PMID: 15766383
  97. Vanunu O, Magger O, Ruppin E, Shlomi T, Sharan R. Associating genes and protein complexes with disease via network propagation. PLOS Comput Biol 2010; 6(1): e1000641. doi: 10.1371/journal.pcbi.1000641 PMID: 20090828
  98. Martínez V, Navarro C, Cano C, Fajardo W, Blanco A. DrugNet: Network-based drug–disease prioritization by integrating heterogeneous data. Artif Intell Med 2015; 63(1): 41-9. doi: 10.1016/j.artmed.2014.11.003 PMID: 25704113
  99. Emig D, Ivliev A, Pustovalova O, et al. Drug target prediction and repositioning using an integrated network-based approach. PLoS One 2013; 8(4): e60618. doi: 10.1371/journal.pone.0060618 PMID: 23593264
  100. Chen X, Yan CC, Zhang X, et al. Drug–target interaction prediction: Databases, web servers and computational models. Brief Bioinform 2016; 17(4): 696-712. doi: 10.1093/bib/bbv066 PMID: 26283676
  101. Zenke Y, Yoh K, Matsumoto S, et al. Clinical impact of gastric acid-suppressing medication use on the efficacy of erlotinib and gefitinib in patients with advanced non–small-cell lung cancer harboring EGFR mutations. Clin Lung Cancer 2016; 17(5): 412-8. doi: 10.1016/j.cllc.2016.01.006 PMID: 26944770
  102. Jarada TN, Rokne JG, Alhajj R. A review of computational drug repositioning: Strategies, approaches, opportunities, challenges, and directions. J Cheminform 2020; 12(1): 46. doi: 10.1186/s13321-020-00450-7 PMID: 33431024
  103. Xia X. Bioinformatics and drug discovery. Curr Top Med Chem 2017; 17(15): 1709-26. doi: 10.2174/1568026617666161116143440 PMID: 27848897
  104. Buniello A, MacArthur JAL, Cerezo M, et al. The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 2019; 47(D1): D1005-12. doi: 10.1093/nar/gky1120 PMID: 30445434
  105. Shaffer JR, Feingold E, Marazita ML. Genome-wide association studies: Prospects and challenges for oral health. J Dent Res 2012; 91(7): 637-41. doi: 10.1177/0022034512446968 PMID: 22562461
  106. Irham LM, Adikusuma W, Perwitasari DA, et al. The use of genomic variants to drive drug repurposing for chronic hepatitis B. Biochem Biophys Rep 2022; 31: 101307. doi: 10.1016/j.bbrep.2022.101307 PMID: 35832745
  107. Lau A, So HC. Turning genome-wide association study findings into opportunities for drug repositioning. Comput Struct Biotechnol J 2020; 18: 1639-50. doi: 10.1016/j.csbj.2020.06.015 PMID: 32670504
  108. Bhat GR, Sethi I, Rah B, Kumar R, Afroze D. Innovative in silico approaches for characterization of genes and proteins. Front Genet 2022; 13: 865182. doi: 10.3389/fgene.2022.865182 PMID: 35664302
  109. Diogo D, Tian C, et al. henome-wide association studies across large population cohorts support drug target validation, Nature. Com. (n.d.). Available from: https://www.nature.com/articles/s41467–018-06540-3 (accessed August 7, 2023).
  110. Cronin RM, Field JR, Bradford Y, et al. Phenome-wide association studies demonstrating pleiotropy of genetic variants within FTO with and without adjustment for body mass index. Front Genet 2014; 5: 250. doi: 10.3389/fgene.2014.00250 PMID: 25177340
  111. Lippmann C, Kringel D, Ultsch A, Lötsch J. Computational functional genomics-based approaches in analgesic drug discovery and repurposing. Pharmacogenomics 2018; 19(9): 783-97. doi: 10.2217/pgs-2018-0036 PMID: 29792109
  112. Hu L, Chen H, Zhang X, et al. https://academic.oup.com/jrr/article-abstract/61/6/842/5899185 n.d.
  113. Jiang H, Huang Y. An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network. BMC Bioinformatics 2022; 23(1): 9. doi: 10.1186/s12859-021-04553-2 PMID: 34983364
  114. Zhao BW, Hu L, You ZH, Wang L, Su XR. HINGRL: Predicting drug–disease associations with graph representation learning on heterogeneous information networks. Brief Bioinform 2022; 23(1): bbab515. doi: 10.1093/bib/bbab515 PMID: 34891172
  115. Tanoli Z, Vähä-Koskela M, Aittokallio T. Artificial intelligence, machine learning, and drug repurposing in cancer. Expert Opin Drug Discov 2021; 16(9): 977-89. doi: 10.1080/17460441.2021.1883585 PMID: 33543671
  116. Zheng M, Liu X, Xu Y, Li H, Luo C, Jiang H. Computational methods for drug design and discovery: Focus on China. Trends Pharmacol Sci 2013; 34(10): 549-59. doi: 10.1016/j.tips.2013.08.004 PMID: 24035675
  117. Mervin LH, Bulusu KC, Kalash L, et al. Orthologue chemical space and its influence on target prediction. Bioinformatics 2018; 34(1): 72-9. doi: 10.1093/bioinformatics/btx525 PMID: 28961699
  118. Cichonska A, Pahikkala T, Szedmak S, et al. Learning with multiple pairwise kernels for drug bioactivity predictionBioinformatics. Oxford: Academic 2018; pp. i509-18. doi: 10.1093/bioinformatics/bty277
  119. Jiang M, Li Z, Zhang S, et al. Drug–target affinity prediction using graph neural network and contact maps. RSC Advances 2020; 10(35): 20701-12. doi: 10.1039/D0RA02297G PMID: 35517730
  120. Beck BR, Shin B, Choi Y, Park S, Kang K. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput Struct Biotechnol J 2020; 18: 784-90. doi: 10.1016/j.csbj.2020.03.025 PMID: 32280433
  121. Ravikumar B, Timonen S, Alam Z, Parri E, Wennerberg K, Aittokallio T. Chemogenomic analysis of the druggable kinome and its application to repositioning and lead identification studies. Cell Chem Biol 2019; 26(11): 1608-1622.e6. doi: 10.1016/j.chembiol.2019.08.007 PMID: 31521622
  122. Gilvary C, Elkhader J, Madhukar N, Henchcliffe C, Goncalves MD, Elemento O. A machine learning and network framework to discover new indications for small molecules. PLOS Comput Biol 2020; 16(8): e1008098. doi: 10.1371/journal.pcbi.1008098 PMID: 32764756
  123. Gönen M, Khan S, Samuel K. Kernelized Bayesian Matrix Factorization. Proceedings of the 30th International Conference on Macline Learning,PMLR 28(3) 364-72.2013;
  124. Güvenç Paltun B, Mamitsuka H, Kaski S. Improving drug response prediction by integrating multiple data sources: Matrix factorization, kernel and network-based approaches. Brief Bioinform 2021; 22(1): 346-59. doi: 10.1093/bib/bbz153 PMID: 31838491
  125. Huang EW, Bhope A, Lim J, Sinha S, Emad A. Tissue-guided LASSO for prediction of clinical drug response using preclinical samples. PLOS Comput Biol 2020; 16(1): e1007607. doi: 10.1371/journal.pcbi.1007607 PMID: 31967990
  126. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today 2021; 26(1): 80-93. doi: 10.1016/j.drudis.2020.10.010 PMID: 33099022
  127. Tang J, Aittokallio T. Network pharmacology strategies toward multi-target anticancer therapies: From computational models to experimental design principles. Curr Pharm Des 2014; 20(1): 23-36.
  128. Branco I, Choupina A. Bioinformatics: New tools and applications in life science and personalized medicine. Appl Microbiol Biotechnol 2021; 105(3): 937-51. doi: 10.1007/s00253-020-11056-2 PMID: 33404829
  129. Low ZY, Farouk IA, Lal SK. Drug repositioning: New approaches and future prospects for life-debilitating diseases and the COVID-19 pandemic outbreak. Viruses 2020; 12(9): 1058. doi: 10.3390/v12091058 PMID: 32972027
  130. Jourdan JP, Bureau R, Rochais C, Dallemagne P. Drug repositioning: A brief overview. J Pharm Pharmacol 2020; 72(9): 1145-51. doi: 10.1111/jphp.13273 PMID: 32301512
  131. Krishnamurthy N, Grimshaw AA, Axson SA, Choe SH, Miller JE. Drug repurposing: A systematic review on root causes, barriers and facilitators. BMC Health Serv Res 2022; 22(1): 970. doi: 10.1186/s12913-022-08272-z PMID: 35906687
  132. Sharma PP, Bansal M, Sethi A, et al. Computational methods directed towards drug repurposing for COVID-19: Advantages and limitations. RSC Advances 2021; 11(57): 36181-98. doi: 10.1039/D1RA05320E PMID: 35492747
  133. Haupt VJ, Schroeder M. Old friends in new guise: Repositioning of known drugs with structural bioinformatics. Brief Bioinform 2011; 12(4): 312-26. doi: 10.1093/bib/bbr011 PMID: 21441562
  134. Shineman DW, Alam J, Anderson M, et al. Overcoming obstacles to repurposing for neurodegenerative disease. Ann Clin Transl Neurol 2014; 1(7): 512-8. doi: 10.1002/acn3.76 PMID: 25356422
  135. de Oliveira EAM, Lang KL. Drug repositioning: Concept, classification, methodology, and importance in rare/orphans and neglected diseases. J Appl Pharm Sci 2018; 8: 157-65. doi: 10.7324/JAPS.2018.8822
  136. Su M. Amitriptyline therapy in chronic pain. Int Arch Clin Pharmacol 2015; 1(1): 1. doi: 10.23937/2572-3987.1510001
  137. Salih NA, van Griensven J, Chappuis F, et al. Liposomal amphotericin B for complicated visceral leishmaniasis (kala-azar) in eastern Sudan: How effective is treatment for this neglected disease? Trop Med Int Health 2014; 19(2): 146-52. doi: 10.1111/tmi.12238 PMID: 24433217
  138. Dalen JE. Aspirin to prevent heart attack and stroke: What’s the right dose? Am J Med 2006; 119(3): 198-202. doi: 10.1016/j.amjmed.2005.11.013 PMID: 16490462
  139. Grandaliano G, Losappio V, Maiorano A. Immunosuppression in kidney transplantation. Kidney Transplant. Challenging Futur 2012; pp. 186-207. doi: 10.2174/978160805144111201010186
  140. Fagien S, Walt JG, Carruthers J, et al. Patient-reported outcomes of bimatoprost for eyelash growth: Results from a randomized, double-masked, vehicle-controlled, parallel-group study. Aesthet Surg J 2013; 33(6): 789-98. doi: 10.1177/1090820X13495887 PMID: 23873891
  141. Pathak A, Kumar S, Kumar M, Dikshit H. Study to assess the role of bromocriptine in treatment of diabetes mellitus. Int J Basic Clin Pharmacol 2016; 423-8. doi: 10.18203/2319-2003.ijbcp20160756
  142. Davis KL, Kaye JA, Masters ET, Iyer S. Real-world outcomes in patients with ALK-positive non-small cell lung cancer treated with crizotinib. Curr Oncol 2018; 25(1): 40-9. doi: 10.3747/co.25.3723 PMID: 29507494
  143. Schade S, Paulus W. D-cycloserine in neuropsychiatric diseases: A systematic review. Int J Neuropsychopharmacol 2016; 19(4): pyv102. doi: 10.1093/ijnp/pyv102 PMID: 26364274
  144. Colombo MD, Cassano N, Bellia G, Vena GA. Cyclosporine regimens in plaque psoriasis: An overview with special emphasis on dose, duration, and old and new treatment approaches. Sci World J 2013; 2013: 11. doi: 10.1155/2013/805705
  145. McMahon CG. Dapoxetine: A new option in the medical management of premature ejaculation. Ther Adv Urol 2012; 4(5): 233-51. doi: 10.1177/1756287212453866 PMID: 23024705
  146. Lynn M, Drake A, Larry MD, Millikan E. The antipruritic effect of 5% doxepin cream. Arch Dermatol 1995; 1403-8. Available from: http://archderm.jamanetwork.com/
  147. Arnold LM. Duloxetine and other antidepressants in the treatment of patients with fibromyalgia. Pain Med 2007; 8(2): S63-74. doi: 10.1111/j.1526-4637.2006.00178.x PMID: 17714117
  148. Zhou H. Clinical pharmacokinetics of etanercept: A fully humanized soluble recombinant tumor necrosis factor receptor fusion protein. J Clin Pharmacol 2005; 45(5): 490-7. doi: 10.1177/0091270004273321 PMID: 15831771
  149. Royce ME, Osman D. Everolimus in the treatment of metastatic breast cancer. Breast Cancer 2015; 9: BCBCR.S29268. doi: 10.4137/BCBCR.S29268 PMID: 26417203
  150. Wallin Å. Minthon, Wattmo C. Galantamine treatment in Alzheimer’s disease: Response and long-term outcome in a routine clinical setting. Neuropsychiatr Dis Treat 2011; 7: 565-76. doi: 10.2147/NDT.S24196 PMID: 22003296
  151. Pauwels B, Korst AEC, Lardon F, Vermorken JB. Combined modality therapy of gemcitabine and radiation. Oncologist 2005; 10(1): 34-51. doi: 10.1634/theoncologist.10-1-34 PMID: 15632251
  152. Clarke AK. Antimalarial drugs in the treatment of rheumatological diseases. Rheumatology 1998; 37(5): 580b. doi: 10.1093/rheumatology/37.5.580b PMID: 9651091
  153. Peters CP, Eshuis EJ, Toxopeüs FM, et al. Adalimumab for Crohn’s disease: Long-term sustained benefit in a population-based cohort of 438 patients. J Crohn’s Colitis 2014; 8(8): 866-75. doi: 10.1016/j.crohns.2014.01.012 PMID: 24491515
  154. Kim BR, Ohn J, Choi CW, Youn SW. Methotrexate in a real-world psoriasis treatment: Is it really a dangerous medication for all? Ann Dermatol 2017; 29(3): 346-8. doi: 10.5021/ad.2017.29.3.346 PMID: 28566915
  155. Glesk I, Xu L, Rand D, Prucnal PR. Wawelenght tunable semiconductor fiber ring laser through electro-optical polarization control. Acta Phys Slovaca 2003; 53: 413-6.
  156. Billes SK, Sinnayah P, Cowley MA. Naltrexone/bupropion for obesity: An investigational combination pharmacotherapy for weight loss. Pharmacol Res 2014; 84: 1-11. doi: 10.1016/j.phrs.2014.04.004 PMID: 24754973
  157. Evidente VGH, Pappert EJ. Botulinum toxin therapy for cervical dystonia: The science of dosing, tremor and other hyperkinetic movements. Tremor Other Hyperkinet Mov 2014; 12(4): 273. doi: 10.7916/D84X56BF
  158. Stiff P, Micallef I, McCarthy P, et al. Treatment with plerixafor in non-Hodgkin’s lymphoma and multiple myeloma patients to increase the number of peripheral blood stem cells when given a mobilizing regimen of G-CSF: Implications for the heavily pretreated patient. Biol Blood Marrow Transplant 2009; 15(2): 249-56. doi: 10.1016/j.bbmt.2008.11.028 PMID: 19167685
  159. Takahashi M, Nishida S, Nakamura M, et al. Restless legs syndrome augmentation among Japanese patients receiving pramipexole therapy: Rate and risk factors in a retrospective study. PLoS One 2017; 12(3): e0173535. doi: 10.1371/journal.pone.0173535 PMID: 28264052
  160. Stacey BR, Emir B, Petersel D, Murphy K. Pregabalin in treatment-refractory fibromyalgia. Open Rheumatol J 2010; 4(1): 35-8. doi: 10.2174/1874312901004010035 PMID: 21270936
  161. Reikvam H, Hovland R, Forthun RB, et al. Disease-stabilizing treatment based on all-trans retinoic acid and valproic acid in acute myeloid leukemia – identification of responders by gene expression profiling of pretreatment leukemic cells. BMC Cancer 2017; 17(1): 630. doi: 10.1186/s12885-017-3620-y PMID: 28877686
  162. Vogel V. Update on raloxifene: Role in reducing the risk of invasive breast cancer in postmenopausal women. Breast Cancer 2011; 3: 127-37. doi: 10.2147/BCTT.S11288 PMID: 24367182
  163. Buch MH, Smolen JS, Betteridge N, et al. Updated consensus statement on the use of rituximab in patients with rheumatoid arthritis. Ann Rheum Dis 2011; 70(6): 909-20. doi: 10.1136/ard.2010.144998 PMID: 21378402
  164. Lüscher Dias T, Schuch V, Beltrão-Braga PCB, et al. Drug repositioning for psychiatric and neurological disorders through a network medicine approach. Transl Psychiatry 2020; 10(1): 141. doi: 10.1038/s41398-020-0827-5 PMID: 32398742
  165. Latif T, Chauhan N, Khan R, Moran A, Usmani SZ. Thalidomide and its analogues in the treatment of multiple myeloma. Exp Hematol Oncol 2012; 1(1): 27. doi: 10.1186/2162-3619-1-27 PMID: 23210501
  166. Pacini C, Iorio F, Gonçalves E, et al. DvD: An R/Cytoscape pipeline for drug repurposing using public repositories of gene expression data. Bioinformatics 2013; 29(1): 132-4. doi: 10.1093/bioinformatics/bts656 PMID: 23129297
  167. Poroikov VV, Filimonov DA, Ihlenfeldt WD, et al. PASS biological activity spectrum predictions in the enhanced open NCI database browser. J Chem Inf Comput Sci 2003; 43(1): 228-36. doi: 10.1021/ci020048r PMID: 12546557
  168. Luo H, Chen J, Shi L, et al. DRAR-CPI: A server for identifying drug repositioning potential and adverse drug reactions via the chemical–protein interactome. Nucleic Acids Res 2011; 39(Web Server issue): (Suppl. 2): W492-8.. doi: 10.1093/nar/gkr299 PMID: 21558322
  169. Setoain J, Franch M, Martínez M, et al. NFFinder: An online bioinformatics tool for searching similar transcriptomics experiments in the context of drug repositioning. Nucleic Acids Res 2015; 43(W1): W193-9. doi: 10.1093/nar/gkv445 PMID: 25940629
  170. Zhong Y, Chen EY, Liu R, et al. Renoprotective effect of combined inhibition of angiotensin-converting enzyme and histone deacetylase. J Am Soc Nephrol 2013; 24(5): 801-11. doi: 10.1681/ASN.2012060590 PMID: 23559582
  171. Peyvandipour A, Saberian N, Shafi A, Donato M, Draghici S. A novel computational approach for drug repurposing using systems biology. Bioinformatics 2018; 34(16): 2817-25. doi: 10.1093/bioinformatics/bty133 PMID: 29534151
  172. Luo H, Zhang P, Cao XH, et al. DPDR-CPI, a server that predicts drug positioning and drug repositioning via chemical-protein interactome. Sci Rep 2016; 6(1): 35996. doi: 10.1038/srep35996 PMID: 27805045
  173. Napolitano F, Carrella D, Mandriani B, et al. gene2drug: A computational tool for pathway-based rational drug repositioning. Bioinformatics 2018; 34(9): 1498-505. doi: 10.1093/bioinformatics/btx800 PMID: 29236977
  174. Coelho ED, Arrais JP, Oliveira JL. Computational discovery of putative leads for drug repositioning through drug-target interaction prediction. PLOS Comput Biol 2016; 12(11): e1005219. doi: 10.1371/journal.pcbi.1005219 PMID: 27893735
  175. Zhou H, Gao M, Skolnick J. Comprehensive prediction of drug-protein interactions and side effects for the human proteome. Sci Rep 2015; 5(1): 11090. doi: 10.1038/srep11090 PMID: 26057345
  176. Luo H, Wang J, Li M, et al. Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithmBioinformatics. Oxford University Press 2016; pp. 2664-71. doi: 10.1093/bioinformatics/btw228
  177. Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M. Break down in order to build up: Decomposing small molecules for fragment-based drug design with e MolFrag. J Chem Inf Model 2017; 57(4): 627-31. doi: 10.1021/acs.jcim.6b00596 PMID: 28346786
  178. Jia Z, Liu Y, Guan N, Bo X, Luo Z, Barnes MR. Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery. BMC Genomics 2016; 17(1): 414. doi: 10.1186/s12864-016-2737-8 PMID: 27234029
  179. Yu H, Choo S, Park J, Jung J, Kang Y, Lee D. Prediction of drugs having opposite effects on disease genes in a directed network. BMC Syst Biol 2016; 10(S1) (Suppl. 1): S2. doi: 10.1186/s12918-015-0243-2 PMID: 26818006
  180. Kim J, Yoo M, Kang J, Tan AC. K-Map: Connecting kinases with therapeutics for drug repurposing and development. Hum Genomics 2013; 7(1): 20. doi: 10.1186/1479-7364-7-20 PMID: 24060470
  181. Ferrero E, Agarwal P. Connecting genetics and gene expression data for target prioritisation and drug repositioning. BioData Min 2018; 11(1): 7. doi: 10.1186/s13040-018-0171-y PMID: 29881461
  182. Dai S-X, Chen H, Li W-X, et al. Efficient repositioning of approved drugs as anti-HIV agents using Anti-HIV-Predictor. BioRxiv 2016; 087445. doi: 10.1101/087445
  183. Fu C, Jin G, Gao J, Zhu R, Ballesteros-villagrana E, Wong STC. DrugMap Central: An on-line query and visualization tool to facilitate drug repositioning studies. Bioinformatics 2013; 29(14): 1834-6. doi: 10.1093/bioinformatics/btt279 PMID: 23681121
  184. Gottlieb A, Stein GY, Ruppin E, Sharan R. PREDICT: A method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol 2011; 7(1): 496. doi: 10.1038/msb.2011.26 PMID: 21654673
  185. Luo H, Li M, Wang S, Liu Q, Li Y, Wang J. Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics 2018; 34(11): 1904-12. doi: 10.1093/bioinformatics/bty013 PMID: 29365057
  186. Zu S, Chen T, Li S. Global optimization-based inference of chemogenomic features from drug–target interactions. Bioinformatics 2015; 31(15): 2523-9. doi: 10.1093/bioinformatics/btv181 PMID: 25819672
  187. Zhou X, Wang M, Katsyv I, Irie H, Zhang B. EMUDRA: Ensemble of multiple drug repositioning approaches to improve prediction accuracy. Bioinformatics 2018; 34(18): 3151-9. doi: 10.1093/bioinformatics/bty325 PMID: 29688306
  188. Low YS, Daugherty AC, Schroeder EA, et al. Synergistic drug combinations from electronic health records and gene expression. J Am Med Inform Assoc 2017; 24(3): 565-76. doi: 10.1093/jamia/ocw161 PMID: 27940607
  189. Lim H, Poleksic A, Yao Y, et al. Large-scale off-target identification using fast and accurate dual regularized one-class collaborative filtering and its application to drug repurposing. PLOS Comput Biol 2016; 12(10): e1005135. doi: 10.1371/journal.pcbi.1005135 PMID: 27716836
  190. Chen H, Zhang Z, Peng W. miRDDCR: A miRNA-based method to comprehensively infer drug-disease causal relationships. Sci Rep 2017; 7(1): 15921. doi: 10.1038/s41598-017-15716-8 PMID: 29162848
  191. Louhimo R, Laakso M, Belitskin D, Klefström J, Lehtonen R, Hautaniemi S. Data integration to prioritize drugs using genomics and curated data. BioData Min 2016; 9(1): 21. doi: 10.1186/s13040-016-0097-1 PMID: 27231484
  192. Brown AS, Patel CJ. MeSHDD: Literature-based drug-drug similarity for drug repositioning. J Am Med Inform Assoc 2017; 24(3): 614-8. doi: 10.1093/jamia/ocw142 PMID: 27678460
  193. Shameer K, Johnson KW, Glicksberg BS, et al. Prioritizing small molecule as candidates for drug repositioning using. Mach Learn 2018. doi: 10.1101/331975
  194. Kim S, Thiessen PA, Bolton EE, et al. PubChem substance and compound databases. Nucleic Acids Res 2016; 44(D1): D1202-13. doi: 10.1093/nar/gkv951 PMID: 26400175
  195. Wishart DS, Knox C, Guo AC, et al. DrugBank: A knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 2008; 36(Database issue)(1): D901-6.. doi: 10.1093/nar/gkm958 PMID: 18048412
  196. Dabral S, Khan IA, Pant T, et al. Deciphering the precise target for saroglitazar associated antiangiogenic effect: A computational synergistic approach. ACS Omega 2023; 8(17): 14985-5002. doi: 10.1021/acsomega.2c07570 PMID: 37151537
  197. Sood D, Kumar N, Singh A, Sakharkar MK, Tomar V, Chandra R. Antibacterial and pharmacological evaluation of fluoroquinolones: A chemoinformatics approach. Genomics Inform 2018; 16(3): 44-51. doi: 10.5808/GI.2018.16.3.44 PMID: 30309202
  198. Jukič M, Kores K, Janežič D, Bren U. Repurposing of drugs for SARS-CoV-2 using inverse docking fingerprints. Front Chem 2021; 9: 757826. doi: 10.3389/fchem.2021.757826 PMID: 35028304
  199. Li X, Yu J, Zhang Z, et al. Network bioinformatics analysis provides insight into drug repurposing for COVID-19. Medicine in Drug Discovery 2021; 10: 100090. doi: 10.1016/j.medidd.2021.100090 PMID: 33817623
  200. Lavecchia A, Cerchia C. In silico methods to address polypharmacology: Current status, applications and future perspectives. Drug Discov Today 2016; 21(2): 288-98. doi: 10.1016/j.drudis.2015.12.007 PMID: 26743596

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2024 Bentham Science Publishers