Skip to content
2000
image of Repositioning Drugs: A Computational Approach

Abstract

Computational drug repositioning has emerged as an efficient approach to discovering new indications for existing drugs, offering lower risk and cost compared to traditional drug discovery methods. Various computational approaches have been developed, including target-based, gene-expression-based, phenome-based, and multi-omics-based methods. Recent advancements leverage diverse data sources, such as biomedical databases and online health-related information. Techniques incorporating drug structure and target information have shown promising results in predicting new drug indications. Despite significant progress, challenges remain, including data noise reduction, method ensemble, negative sample selection, and data sparseness. Overall, computational drug repositioning continues to be a valuable tool in drug discovery and development.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cdrr/10.2174/0125899775365699250408091801
2025-04-21
2025-08-13

  • From This Site

    /content/journals/cdrr/10.2174/0125899775365699250408091801
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Huang G. Li J. Wang P. Li W. A review of computational drug repositioning approaches. Comb. Chem. High Throughput Screen. 2018 20 10 831 838 10.2174/1386207321666171221112835 29268682
    [Google Scholar]
  2. Jourdan J.P. Bureau R. Rochais C. Dallemagne P. Drug repositioning: A brief overview. J. Pharm. Pharmacol. 2020 72 9 1145 1151 10.1111/jphp.13273 32301512
    [Google Scholar]
  3. Nosengo N. New tricks for old drugs: Faced with skyrocketing costs for developing new drugs, researchers are looking at ways to repurpose older ones--and even some that failed in initial trials. Nature 2016 534 7607 314 317 10.1038/534314a 27306171
    [Google Scholar]
  4. Drug Discov World 2014 Available from: line.com/drug-discovery/p274232-therapeutic-drug-repurposingrepositioning-and-rescue-part-i:-overview. html
  5. Cavalla D. Scientific and commercial value of drug repurposing. Drug Repositioning CRC Press Boca Raton, FL, USA 2017 3 22 10.4324/9781315373669‑1
    [Google Scholar]
  6. Novac N. Challenges and opportunities of drug repositioning. Trends Pharmacol. Sci. 2013 34 5 267 272 10.1016/j.tips.2013.03.004 23582281
    [Google Scholar]
  7. Sahu N.U. Kharkar P.S. Computational drug repositioning: A lateral approach to traditional drug discovery? Curr. Top. Med. Chem. 2016 16 19 2069 2077 10.2174/1568026616666160216153249 26881717
    [Google Scholar]
  8. 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 1912 10.1093/bioinformatics/bty013 29365057
    [Google Scholar]
  9. Vogrinc D. Kunej T. Drug repositioning: Computational approaches and research examples classified according to the evidence level. Discoveries 2017 5 2 e75 10.15190/d.2017.5 32309593
    [Google Scholar]
  10. Luo H. Li M. Yang M. Wu F.X. Li Y. Wang J. Biomedical data and computational models for drug repositioning: A comprehensive review. Brief. Bioinform. 2021 22 2 1604 1619 10.1093/bib/bbz176 32043521
    [Google Scholar]
  11. Napolitano F. Zhao Y. Moreira V.M. Tagliaferri R. Kere J. D’Amato M. Greco D. Drug repositioning: A machine-learning approach through data integration. J. Cheminform. 2013 5 1 30 10.1186/1758‑2946‑5‑30 23800010
    [Google Scholar]
  12. Li J. Lu Z. A new method for computational drug repositioning using drug pairwise similarity. 2012 IEEE International Conference on Bioinformatics and Biomedicine Philadelphia, PA, USA, 04-07 October 2012, pp. 1-4 10.1109/BIBM.2012.6392722
    [Google Scholar]
  13. March-Vila E. Pinzi L. Sturm N. Tinivella A. Engkvist O. Chen H. Rastelli G. On the integration of in silico drug design methods for drug repurposing. Front. Pharmacol. 2017 8 298 10.3389/fphar.2017.00298 28588497
    [Google Scholar]
  14. Yella J.K. Yaddanapudi S. Wang Y. Jegga A.G. Changing trends in computational drug repositioning. Pharmaceuticals 2018 11 2 57 10.3390/ph11020057 29874824
    [Google Scholar]
  15. Scannell J.W. Blanckley A. Boldon H. Warrington B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov. 2012 11 3 191 200 10.1038/nrd3681 22378269
    [Google Scholar]
  16. Michienzi A. Tobarran N. Hieger M.A. Extended release acetaminophen overdose with delayed peak concentrations. Am. J. Ther. 2022 29 6 e655 e656 10.1097/MJT.0000000000001329 33491964
    [Google Scholar]
  17. Gautret P. Lagier J.C. Parola P. Hoang V.T. Meddeb L. Mailhe M. Doudier B. Courjon J. Giordanengo V. Vieira V.E. Tissot Dupont H. Honoré S. Colson P. Chabrière E. La Scola B. Rolain J.M. Brouqui P. Raoult D. RETRACTED: Hydroxychloroquine and azithromycin as a treatment of COVID-19: Results of an open-label non-randomized clinical trial. Int. J. Antimicrob. Agents 2020 56 1 105949 10.1016/j.ijantimicag.2020.105949 32205204
    [Google Scholar]
  18. Beigel J.H. Tomashek K.M. Dodd L.E. Mehta A.K. Zingman B.S. Kalil A.C. Hohmann E. Chu H.Y. Luetkemeyer A. Kline S. Lopez de Castilla D. Finberg R.W. Dierberg K. Tapson V. Hsieh L. Patterson T.F. Paredes R. Sweeney D.A. Short W.R. Touloumi G. Lye D.C. Ohmagari N. Oh M. Ruiz-Palacios G.M. Benfield T. Fätkenheuer G. Kortepeter M.G. Atmar R.L. Creech C.B. Lundgren J. Babiker A.G. Pett S. Neaton J.D. Burgess T.H. Bonnett T. Green M. Makowski M. Osinusi A. Nayak S. Lane H.C. Remdesivir for the treatment of Covid-19—preliminary report. N. Engl. J. Med. 2020 383 19 1813 1826 10.1056/NEJMoa2007764 32445440
    [Google Scholar]
  19. De Caterina R. Aimo A. Ridker P.M. Aspirin therapy for primary prevention: The case for continuing prescribing to patients at high cardiovascular risk—A review. Thromb. Haemost. 2020 120 2 199 206 10.1055/s‑0039‑3400294 31887781
    [Google Scholar]
  20. Tardif J.C. Bouabdallaoui N. L’Allier P.L. Gaudet D. Shah B. Pillinger M.H. Lopez-Sendon J. da Luz P. Verret L. Audet S. Dupuis J. Denault A. Pelletier M. Tessier P.A. Samson S. Fortin D. Tardif J.D. Busseuil D. Goulet E. Lacoste C. Dubois A. Joshi A.Y. Waters D.D. Hsue P. Lepor N.E. Lesage F. Sainturet N. Roy-Clavel E. Bassevitch Z. Orfanos A. Stamatescu G. Grégoire J.C. Busque L. Lavallée C. Hétu P.O. Paquette J.S. Deftereos S.G. Levesque S. Cossette M. Nozza A. Chabot-Blanchet M. Dubé M.P. Guertin M.C. Boivin G. Colchicine for community-treated patients with COVID-19 (COLCORONA): A phase 3, randomised, double-blinded, adaptive, placebo-controlled, multicentre trial. Lancet Respir. Med. 2021 9 8 924 932 10.1016/S2213‑2600(21)00222‑8 34051877
    [Google Scholar]
  21. Liu Y. Yan L.M. Wan L. Xiang T.X. Le A. Liu J.M. Peiris M. Poon L.L.M. Zhang W. Viral dynamics in mild and severe cases of COVID-19. Lancet Infect. Dis. 2020 20 6 656 657 10.1016/S1473‑3099(20)30232‑2 32199493
    [Google Scholar]
  22. Saengboonmee C. Sanlung T. Wongkham S. Repurposing metformin for cancer treatment: A great challenge of a promising drug. Anticancer Res. 2021 41 12 5913 5918 10.21873/anticanres.15410 34848445
    [Google Scholar]
  23. Lokhande A.S. Devarajan P.V. A review on possible mechanistic insights of Nitazoxanide for repurposing in COVID-19. Eur. J. Pharmacol. 2021 891 173748 10.1016/j.ejphar.2020.173748 33227285
    [Google Scholar]
  24. Kastritis E. Dimopoulos M.A. Thalidomide in the treatment of multiple myeloma. Best Pract. Res. Clin. Haematol. 2007 20 4 681 699 10.1016/j.beha.2007.09.001 18070713
    [Google Scholar]
  25. Malek A.E. Granwehr B.P. Kontoyiannis D.P. Doxycycline as a potential partner of COVID-19 therapies. IDCases 2020 21 e00864 10.1016/j.idcr.2020.e00864 32566483
    [Google Scholar]
  26. Ashburn T.T. Thor K.B. Drug repositioning: Identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 2004 3 8 673 683 10.1038/nrd1468 15286734
    [Google Scholar]
  27. Li Q Cheng T Wang Y Bryant SH PubChem as a public resource for drug discovery. Drug Discov Today 2010 15 23-24 1052 1057 10.1016/j.drudis.2010.10.003
    [Google Scholar]
  28. Gaulton A. Hersey A. Nowotka M. Bento A.P. Chambers J. Mendez D. Mutowo P. Atkinson F. Bellis L.J. Cibrián-Uhalte E. Davies M. Dedman N. Karlsson A. Magariños M.P. Overington J.P. Papadatos G. Smit I. Leach A.R. The ChEMBL database in 2017. Nucleic Acids Res. 2017 45 D1 D945 D954 10.1093/nar/gkw1074 27899562
    [Google Scholar]
  29. Wishart D.S. Feunang Y.D. Guo A.C. Lo E.J. Marcu A. Grant J.R. Sajed T. Johnson D. Li C. Sayeeda Z. Assempour N. Iynkkaran I. Liu Y. Maciejewski A. Gale N. Wilson A. Chin L. Cummings R. Le D. Pon A. Knox C. Wilson M. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 2018 46 D1 D1074 D1082 10.1093/nar/gkx1037 29126136
    [Google Scholar]
  30. Zhao C. Dai X. Li Y. Guo Q. Zhang J. Zhang X. Wang L. EK-DRD: A comprehensive database for drug repositioning inspired by experimental knowledge. J. Chem. Inf. Model. 2019 59 9 3619 3624 10.1021/acs.jcim.9b00365 31433187
    [Google Scholar]
  31. Davis A.P. Grondin C.J. Johnson R.J. Sciaky D. King B.L. McMorran R. Wiegers J. Wiegers T.C. Mattingly C.J. The comparative toxicogenomics database: Update 2017. Nucleic Acids Res. 2017 45 D1 D972 D978 10.1093/nar/gkw838 27651457
    [Google Scholar]
  32. Bateman A. Martin M-J. Orchard S. Magrane M. Agivetova R. Ahmad S. Alpi E. Bowler-Barnett E.H. Britto R. Bursteinas B. Bye-A-Jee H. Coetzee R. Cukura A. Da Silva A. Denny P. Dogan T. Ebenezer T.G. Fan J. Castro L.G. Garmiri P. Georghiou G. Gonzales L. Hatton-Ellis E. Hussein A. Ignatchenko A. Insana G. Ishtiaq R. Jokinen P. Joshi V. Jyothi D. Lock A. Lopez R. Luciani A. Luo J. Lussi Y. MacDougall A. Madeira F. Mahmoudy M. Menchi M. Mishra A. Moulang K. Nightingale A. Oliveira C.S. Pundir S. Qi G. Raj S. Rice D. Lopez M.R. Saidi R. Sampson J. Sawford T. Speretta E. Turner E. Tyagi N. Vasudev P. Volynkin V. Warner K. Watkins X. Zaru R. Zellner H. Bridge A. Poux S. Redaschi N. Aimo L. Argoud-Puy G. Auchincloss A. Axelsen K. Bansal P. Baratin D. Blatter M-C. Bolleman J. Boutet E. Breuza L. Casals-Casas C. de Castro E. Echioukh K.C. Coudert E. Cuche B. Doche M. Dornevil D. Estreicher A. Famiglietti M.L. Feuermann M. Gasteiger E. Gehant S. Gerritsen V. Gos A. Gruaz-Gumowski N. Hinz U. Hulo C. Hyka-Nouspikel N. Jungo F. Keller G. Kerhornou A. Lara V. Le Mercier P. Lieberherr D. Lombardot T. Martin X. Masson P. Morgat A. Neto T.B. Paesano S. Pedruzzi I. Pilbout S. Pourcel L. Pozzato M. Pruess M. Rivoire C. Sigrist C. Sonesson K. Stutz A. Sundaram S. Tognolli M. Verbregue L. Wu C.H. Arighi C.N. Arminski L. Chen C. Chen Y. Garavelli J.S. Huang H. Laiho K. McGarvey P. Natale D.A. Ross K. Vinayaka C.R. Wang Q. Wang Y. Yeh L-S. Zhang J. Ruch P. Teodoro D. UniProt: The universal protein knowledgebase in 2021. Nucleic Acids Res. 2021 49 D1 D480 D489 10.1093/nar/gkaa1100 33237286
    [Google Scholar]
  33. Ashburner M. Ball C.A. Blake J.A. Botstein D. Butler H. Cherry J.M. Davis A.P. Dolinski K. Dwight S.S. Eppig J.T. Harris M.A. Hill D.P. Issel-Tarver L. Kasarskis A. Lewis S. Matese J.C. Richardson J.E. Ringwald M. Rubin G.M. Sherlock G. Gene Ontology: Tool for the unification of biology. Nat. Genet. 2000 25 1 25 29 10.1038/75556 10802651
    [Google Scholar]
  34. Kanehisa M. Furumichi M. Sato Y. Ishiguro-Watanabe M. Tanabe M. KEGG: Integrating viruses and cellular organisms. Nucleic Acids Res. 2021 49 D1 D545 D551 10.1093/nar/gkaa970 33125081
    [Google Scholar]
  35. Jassal B. Matthews L. Viteri G. Gong C. Lorente P. Fabregat A. Sidiropoulos K. Cook J. Gillespie M. Haw R. Loney F. May B. Milacic M. Rothfels K. Sevilla C. Shamovsky V. Shorser S. Varusai T. Weiser J. Wu G. Stein L. Hermjakob H. D’Eustachio P. The reactome pathway knowledgebase. Nucleic Acids Res. 2020 48 D1 D498 D503 31691815
    [Google Scholar]
  36. Piñero J. Bravo À. Queralt-Rosinach N. Gutiérrez-Sacristán A. Deu-Pons J. Centeno E. García-García J. Sanz F. Furlong L.I. DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 2016 gkw943 27924018
    [Google Scholar]
  37. Barrett T. Edgar R. Mining microarray data at NCBI's Gene Expression Omnibus (GEO). Methods Mol Biol 2006 338 175 190
    [Google Scholar]
  38. Solomon B.D. Nguyen A.D. Bear K.A. Wolfsberg T.G. Clinical genomic database. Proc. Natl. Acad. Sci. USA 2013 110 24 9851 9855 10.1073/pnas.1302575110 23696674
    [Google Scholar]
  39. Wang Z Jensen MA Zenklusen JC A practical guide to the cancer genome atlas (TCGA). Methods Mol Biol 2016 1418 111 141
    [Google Scholar]
  40. Kulikova T. Aldebert P. Althorpe N. Baker W. Bates K. Browne P. van den Broek A. Cochrane G. Duggan K. Eberhardt R. Faruque N. Garcia-Pastor M. Harte N. Kanz C. Leinonen R. Lin Q. Lombard V. Lopez R. Mancuso R. McHale M. Nardone F. Silventoinen V. Stoehr P. Stoesser G. Tuli M.A. Tzouvara K. Vaughan R. Wu D. Zhu W. Apweiler R. The EMBL nucleotide sequence database. Nucleic Acids Res. 2004 32 90001 Suppl. 1 27D 30 10.1093/nar/gkh120 14681351
    [Google Scholar]
  41. Jensen L.J. Kuhn M. Stark M. Chaffron S. Creevey C. Muller J. Doerks T. Julien P. Roth A. Simonovic M. Bork P. von Mering C. STRING 8--A global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 2009 37 Database Suppl. 1 D412 D416 10.1093/nar/gkn760 18940858
    [Google Scholar]
  42. Digre A. Lindskog C. The human protein atlas—Integrated omics for single cell mapping of the human proteome. Protein Sci. 2023 32 2 e4562 10.1002/pro.4562 36604173
    [Google Scholar]
  43. Ankeny R.A. Geneticization in MIM/OMIM®? Exploring historic and epistemic drivers of contemporary understandings of genetic disease. J. Med. Philos. 2017 42 4 367 384 10.1093/jmp/jhx013 28641396
    [Google Scholar]
  44. Romão P. Souza Í.C. Silva I. Guimarães V.R. Camargo J.A. dos Santos G.A. Viana N.I. Srougi M. Leite K.R.M. Reis S.T. Pimenta R. Additional activation of the AR gene may be involved in the development of the castration resistance phenotype in prostate cancer. Actas Urol. Esp. 2022 46 2 78 84 10.1016/j.acuroe.2021.10.003 35123885
    [Google Scholar]
  45. Schuffenhauer A. Floersheim P. Acklin P. Jacoby E. Similarity metrics for ligands reflecting the similarity of the target proteins. J. Chem. Inf. Comput. Sci. 2003 43 2 391 405 10.1021/ci025569t 12653501
    [Google Scholar]
  46. DrugRepurposing Online 2025 Available from: http://drugrepurposing.info
  47. Huang Y. Dong D. Zhang W. Wang R. Lin Y.C.D. Zuo H. Huang H.Y. Huang H.D. DrugRepoBank: A comprehensive database and discovery platform for accelerating drug repositioning. Database 2024 2024 baae051 10.1093/database/baae051 38994794
    [Google Scholar]
  48. The Drug Repurposing Hub 2025 Available from: https://repo-hub.broadinstitute.org/repurposing#home
  49. Corsello S.M. Bittker J.A. Liu Z. Gould J. McCarren P. Hirschman J.E. Johnston S.E. Vrcic A. Wong B. Khan M. Asiedu J. Narayan R. Mader C.C. Subramanian A. Golub T.R. The Drug Repurposing Hub: A next-generation drug library and information resource. Nat. Med. 2017 23 4 405 408 10.1038/nm.4306 28388612
    [Google Scholar]
  50. Chiang A.P. Butte A.J. Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin. Pharmacol. Ther. 2009 86 5 507 510 10.1038/clpt.2009.103 19571805
    [Google Scholar]
  51. Bleakley K. Yamanishi Y. Supervised prediction of drug–target interactions using bipartite local models. Bioinformatics 2009 25 18 2397 2403 10.1093/bioinformatics/btp433 19605421
    [Google Scholar]
  52. Wang Y.C. Zhang C.H. Deng N.Y. Wang Y. Kernel-based data fusion improves the drug–protein interaction prediction. Comput. Biol. Chem. 2011 35 6 353 362 10.1016/j.compbiolchem.2011.10.003 22099632
    [Google Scholar]
  53. Kuang Q. Li Y. Wu Y. Li R. Dong Y. Li Y. Xiong Q. Huang Z. Li M. A kernel matrix dimension reduction method for predicting drug-target interaction. Chemom. Intell. Lab. Syst. 2017 162 104 110 10.1016/j.chemolab.2017.01.016
    [Google Scholar]
  54. Verma J.P. Verma J.P. Logistic regression: Developing a model for risk analysis. Data Analysis in Management with SPSS Software 2013 413 442
    [Google Scholar]
  55. Lu K. Logistic regression in biomedical study 2022 International Conference on Biotechnology, Life Science and Medical Engineering (BLSME 2022)
    [Google Scholar]
  56. Zabor EC Reddy CA Tendulkar RD Patil S Logistic regression in clinical studies. Int J Radiat Oncol Biol Phys 2022 112 2 271 277 10.1016/j.ijrobp.2021.08.007
    [Google Scholar]
  57. Cawley G.C. Talbot N.L. Efficient model selection for kernel logistic regression. 2004 10.1109/ICPR.2004.1334249
    [Google Scholar]
  58. Ko Y. Computational drug repositioning: Current progress and challenges. Appl. Sci. 2020 10 15 5076 10.3390/app10155076
    [Google Scholar]
  59. Friesner R.A. Banks J.L. Murphy R.B. Halgren T.A. Klicic J.J. Mainz D.T. Repasky M.P. Knoll E.H. Shelley M. Perry J.K. Shaw D.E. Francis P. Shenkin P.S. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 2004 47 7 1739 1749 10.1021/jm0306430 15027865
    [Google Scholar]
  60. Buckley M. Gjyshi A. Mendoza-Fandiño G. Baskin R. Carvalho R.S. Carvalho M.A. Woods N.T. Monteiro A.N.A. Enhancer scanning to locate regulatory regions in genomic loci. Nat. Protoc. 2016 11 1 46 60 10.1038/nprot.2015.136 26658467
    [Google Scholar]
  61. Li H. Gao Z. Kang L. Zhang H. Yang K. Yu K. Luo X. Zhu W. Chen K. Shen J. Wang X. Jiang H. TarFisDock: A web server for identifying drug targets with docking approach. Nucleic Acids Res. 2006 34 Web Server Suppl. 2 W219 W224 10.1093/nar/gkl114 16844997
    [Google Scholar]
  62. Cheeseright T. Mackey M. Rose S. Vinter A. Molecular field extrema as descriptors of biological activity: Definition and validation. J. Chem. Inf. Model. 2006 46 2 665 676 10.1021/ci050357s 16562997
    [Google Scholar]
  63. Sherman W. Beard H.S. Farid R. Use of an induced fit receptor structure in virtual screening. Chem. Biol. Drug Des. 2006 67 1 83 84 10.1111/j.1747‑0285.2005.00327.x 16492153
    [Google Scholar]
  64. Halgren T. New method for fast and accurate binding-site identification and analysis. Chem. Biol. Drug Des. 2007 69 2 146 148 10.1111/j.1747‑0285.2007.00483.x 17381729
    [Google Scholar]
  65. Tian W. Chen C. Liang J. CASTp 3.0: Computed atlas of surface topography of proteins and beyond. Biophys. J. 2018 114 3 50a 10.1016/j.bpj.2017.11.325
    [Google Scholar]
  66. Skolnick J. Brylinski M. FINDSITE: A combined evolution/structure-based approach to protein function prediction. Brief. Bioinform. 2009 10 4 378 391 10.1093/bib/bbp017 19324930
    [Google Scholar]
  67. Anaxomics, Therapeutic Performance Mapping System (TPMS) 2024 Available from: http://www.anaxomics.com/tpms. php
  68. Cheeseright T.J. Mackey M.D. Melville J.L. Vinter J.G. FieldScreen: Virtual screening using molecular fields. Application to the DUD data set. J. Chem. Inf. Model. 2008 48 11 2108 2117 10.1021/ci800110p 18991371
    [Google Scholar]
  69. Daina A. Michielin O. Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017 7 1 42717 10.1038/srep42717 28256516
    [Google Scholar]
  70. Welter D. MacArthur J. Morales J. Burdett T. Hall P. Junkins H. Klemm A. Flicek P. Manolio T. Hindorff L. Parkinson H. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014 42 D1 D1001 D1006 10.1093/nar/gkt1229 24316577
    [Google Scholar]
  71. Lamb J Crawford ED Peck D Modell JW Blat IC Wrobel MJ Lerner J Brunet JP Subramanian A Ross KN Reich M The connectivity map: Using gene-expression signatures to connect small molecules, genes, and disease. Science 2006 313 5795 1929 1935
    [Google Scholar]
  72. Powers D.M. Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation arXiv:2010.16061 2020
    [Google Scholar]
  73. Bradley A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 1997 30 7 1145 1159 10.1016/S0031‑3203(96)00142‑2
    [Google Scholar]
  74. Davis J. Goadrich M. The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning Pittsburgh, Pennsylvania, USA, 2006 , pp.233–240 10.1145/1143844.1143874
    [Google Scholar]
  75. Christopher DM Prabhakar R Hinrich S Introduction to information retrieval. Cambridge University Press Cambridge, England 2008
    [Google Scholar]
  76. Truchon J.F. Bayly C.I. Evaluating virtual screening methods: Good and bad metrics for the “early recognition” problem. J. Chem. Inf. Model. 2007 47 2 488 508 10.1021/ci600426e 17288412
    [Google Scholar]
  77. Irwin J.J. Shoichet B.K. ZINC--A free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 2005 45 1 177 182 10.1021/ci049714+ 15667143
    [Google Scholar]
/content/journals/cdrr/10.2174/0125899775365699250408091801
Loading
Repositioning Drugs: A Computational Approach
Bentham Science Publishers ; https://doi.org/10.2174/0125899775365699250408091801
/content/journals/cdrr/10.2174/0125899775365699250408091801
/content/journals/cdrr/10.2174/0125899775365699250408091801
Loading

Data & Media loading...


  • Article Type:     Review Article
Keywords: drug discovery and development ; computational approaches ; data sources ; Drug repositioning

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test