Skip to main content
SpringerLink
Log in

Text Mining for Drug Discovery

  • Protocol
  • First Online:
Bioinformatics and Drug Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1939))

Abstract

Recent advances in technology have led to the exponential growth of scientific literature in biomedical sciences. This rapid increase in information has surpassed the threshold for manual curation efforts, necessitating the use of text mining approaches in the field of life sciences. One such application of text mining is in fostering in silico drug discovery such as drug target screening, pharmacogenomics, adverse drug event detection, etc. This chapter serves as an introduction to the applications of various text mining approaches in drug discovery. It is divided into two parts with the first half as an overview of text mining in the biosciences. The second half of the chapter reviews strategies and methods for four unique applications of text mining in drug discovery.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Reichert JM (2003) Trends in development and approval times for new therapeutics in the United States. Nat Rev Drug Discov 2(9):695–702. https://doi.org/10.1038/nrd1178

    Google Scholar 

  2. Woodcock J, Woosley R (2008) The FDA critical path initiative and its influence on new drug development. Annu Rev Med 59:1–12. https://doi.org/10.1146/annurev.med.59.090506.155819

    Google Scholar 

  3. Claus BL, Underwood DJ (2002) Discovery informatics: its evolving role in drug discovery. Drug Discov Today 7(18):957–966

    Google Scholar 

  4. Percha B, Garten Y, Altman RB (2012) Discovery and explanation of drug-drug interactions via text mining. Pac Symp Biocomput:410–421

    Google Scholar 

  5. Huang CC, Lu Z (2016) Discovering biomedical semantic relations in PubMed queries for information retrieval and database curation. Database (Oxford) 2016. https://doi.org/10.1093/database/baw025

  6. Kraus M, Niedermeier J, Jankrift M, Tietbohl S, Stachewicz T, Folkerts H, Uflacker M, Neves M (2017) Olelo: a web application for intuitive exploration of biomedical literature. Nucleic Acids Res. https://doi.org/10.1093/nar/gkx363

  7. Bodenreider O (2004) The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res 32(Database issue):D267–D270. https://doi.org/10.1093/nar/gkh061

    Google Scholar 

  8. Mattingly CJ, Colby GT, Forrest JN, Boyer JL (2003) The comparative Toxicogenomics database (CTD). Environ Health Perspect 111(6):793–795

    Google Scholar 

  9. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34(Database issue):D668–D672. https://doi.org/10.1093/nar/gkj067

    Google Scholar 

  10. Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J, Yu B, Zhang J, Bryant SH (2016) PubChem substance and compound databases. Nucleic Acids Res 44(D1):D1202–D1213. https://doi.org/10.1093/nar/gkv951

    Google Scholar 

  11. Nelson SJ, Zeng K, Kilbourne J, Powell T, Moore R (2011) Normalized names for clinical drugs: RxNorm at 6 years. J Am Med Inform Assoc 18(4):441–448. https://doi.org/10.1136/amiajnl-2011-000116

    Google Scholar 

  12. Krallinger M, Rabal O, Lourenco A, Oyarzabal J, Valencia A (2017) Information retrieval and text mining technologies for chemistry. Chem Rev 117(12):7673–7761. https://doi.org/10.1021/acs.chemrev.6b00851

    Google Scholar 

  13. Leaman R, Gonzalez G (2008) BANNER: an executable survey of advances in biomedical named entity recognition. Pac Symp Biocomput:652–663

    Google Scholar 

  14. Leaman R, Islamaj Dogan R, Lu Z (2013) DNorm: disease name normalization with pairwise learning to rank. Bioinformatics 29(22):2909–2917. https://doi.org/10.1093/bioinformatics/btt474

    Google Scholar 

  15. Leaman R, Lu Z (2016) TaggerOne: joint named entity recognition and normalization with semi-Markov models. Bioinformatics 32(18):2839–2846. https://doi.org/10.1093/bioinformatics/btw343

    Google Scholar 

  16. Swain MC, Cole JM (2016) ChemDataExtractor: a toolkit for automated extraction of chemical information from the scientific literature. J Chem Inf Model 56(10):1894–1904. https://doi.org/10.1021/acs.jcim.6b00207

    Google Scholar 

  17. Leaman R, Wei CH, Lu Z (2015) tmChem: a high performance approach for chemical named entity recognition and normalization. J Chem 7(Suppl 1 Text mining for chemistry and the CHEMDNER track):S3. https://doi.org/10.1186/1758-2946-7-S1-S3

    Google Scholar 

  18. Iyer SV, Harpaz R, LePendu P, Bauer-Mehren A, Shah NH (2014) Mining clinical text for signals of adverse drug-drug interactions. J Am Med Inform Assoc 21(2):353–362. https://doi.org/10.1136/amiajnl-2013-001612

    Google Scholar 

  19. Han X, Kim JJ, Kwoh CK (2016) Active learning for ontological event extraction incorporating named entity recognition and unknown word handling. J Biomed Semantics 7:22. https://doi.org/10.1186/s13326-016-0059-z

    Google Scholar 

  20. Singhal A, Simmons M, Lu Z (2016) Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature. J Am Med Inform Assoc 23(4):766–772. https://doi.org/10.1093/jamia/ocw041

    Google Scholar 

  21. Xu J, Wu Y, Zhang Y, Wang J, Lee HJ, Xu H (2016) CD-REST: a system for extracting chemical-induced disease relation in literature. Database (Oxford) 2016. https://doi.org/10.1093/database/baw036

  22. Sohn S, Kocher JP, Chute CG, Savova GK (2011) Drug side effect extraction from clinical narratives of psychiatry and psychology patients. J Am Med Inform Assoc 18(Suppl 1):i144–i149. https://doi.org/10.1136/amiajnl-2011-000351

    Google Scholar 

  23. Dalleau K, Marzougui Y, Da Silva S, Ringot P, Ndiaye NC, Coulet A (2017) Learning from biomedical linked data to suggest valid pharmacogenes. J Biomed Semantics 8(1):16. https://doi.org/10.1186/s13326-017-0125-1

    Google Scholar 

  24. Singhal A, Leaman R, Catlett N, Lemberger T, McEntyre J, Polson S, Xenarios I, Arighi C, Lu Z (2016) Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges. Database (Oxford) 2016. https://doi.org/10.1093/database/baw161

  25. Jensen PB, Jensen LJ, Brunak S (2012) Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 13(6):395–405. https://doi.org/10.1038/nrg3208

    Google Scholar 

  26. Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG (2016) MIMIC-III, a freely accessible critical care database. Sci Data 3:160035. https://doi.org/10.1038/sdata.2016.35

    Google Scholar 

  27. Fleurence RL, Curtis LH, Califf RM, Platt R, Selby JV, Brown JS (2014) Launching PCORnet, a national patient-centered clinical research network. J Am Med Inform Assoc 21(4):578–582. https://doi.org/10.1136/amiajnl-2014-002747

    Google Scholar 

  28. Dey N, Williams C, Leyland-Jones B, De P (2017) Mutation matters in precision medicine: a future to believe in. Cancer Treat Rev 55:136–149. https://doi.org/10.1016/j.ctrv.2017.03.002

    Google Scholar 

  29. Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Hoover J, Jang W, Katz K, Ovetsky M, Riley G, Sethi A, Tully R, Villamarin-Salomon R, Rubinstein W, Maglott DR (2016) ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res 44(D1):D862–D868. https://doi.org/10.1093/nar/gkv1222

    Google Scholar 

  30. Hamosh A, Scott AF, Amberger J, Valle D, McKusick VA (2000) Online Mendelian inheritance in man (OMIM). Hum Mutat 15(1):57–61. https://doi.org/10.1002/(SICI)1098-1004(200001)15:1<57::AID-HUMU12>3.0.CO;2-G

    Google Scholar 

  31. Bamford S, Dawson E, Forbes S, Clements J, Pettett R, Dogan A, Flanagan A, Teague J, Futreal PA, Stratton MR, Wooster R (2004) The COSMIC (catalogue of somatic mutations in cancer) database and website. Br J Cancer 91(2):355–358. https://doi.org/10.1038/sj.bjc.6601894

    Google Scholar 

  32. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, Junkins H, McMahon A, Milano A, Morales J, Pendlington ZM, Welter D, Burdett T, Hindorff L, Flicek P, Cunningham F, Parkinson H (2017) The new NHGRI-EBI catalog of published genome-wide association studies (GWAS catalog). Nucleic Acids Res 45(D1):D896–D901. https://doi.org/10.1093/nar/gkw1133

    Google Scholar 

  33. Cheng D, Knox C, Young N, Stothard P, Damaraju S, Wishart DS (2008) PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites. Nucleic Acids Res 36(Web Server issue):W399–W405. https://doi.org/10.1093/nar/gkn296

    Google Scholar 

  34. Rebholz-Schuhmann D, Marcel S, Albert S, Tolle R, Casari G, Kirsch H (2004) Automatic extraction of mutations from Medline and cross-validation with OMIM. Nucleic Acids Res 32(1):135–142. https://doi.org/10.1093/nar/gkh162

    Google Scholar 

  35. Doughty E, Kertesz-Farkas A, Bodenreider O, Thompson G, Adadey A, Peterson T, Kann MG (2011) Toward an automatic method for extracting cancer- and other disease-related point mutations from the biomedical literature. Bioinformatics 27(3):408–415. https://doi.org/10.1093/bioinformatics/btq667

    Google Scholar 

  36. Wei CH, Kao HY, Lu Z (2015) GNormPlus: an integrative approach for tagging genes, gene families, and protein domains. Biomed Res Int 2015:918710. https://doi.org/10.1155/2015/918710

    Google Scholar 

  37. Wei CH, Harris BR, Kao HY, Lu Z (2013) tmVar: a text mining approach for extracting sequence variants in biomedical literature. Bioinformatics 29(11):1433–1439. https://doi.org/10.1093/bioinformatics/btt156

    Google Scholar 

  38. Ravikumar KE, Wagholikar KB, Li D, Kocher JP, Liu H (2015) Text mining facilitates database curation - extraction of mutation-disease associations from bio-medical literature. BMC Bioinformatics 16:185. https://doi.org/10.1186/s12859-015-0609-x

    Google Scholar 

  39. Torii M, Hu Z, Wu CH, Liu H (2009) BioTagger-GM: a gene/protein name recognition system. J Am Med Inform Assoc 16(2):247–255. https://doi.org/10.1197/jamia.M2844

    Google Scholar 

  40. Caporaso JG, Baumgartner WA Jr, Randolph DA, Cohen KB, Hunter L (2007) MutationFinder: a high-performance system for extracting point mutation mentions from text. Bioinformatics 23(14):1862–1865. https://doi.org/10.1093/bioinformatics/btm235

    Google Scholar 

  41. Wei CH, Kao HY, Lu Z (2013) PubTator: a web-based text mining tool for assisting biocuration. Nucleic Acids Res 41(Web Server issue):W518–W522. https://doi.org/10.1093/nar/gkt441

    Google Scholar 

  42. Wermter J, Tomanek K, Hahn U (2009) High-performance gene name normalization with GeNo. Bioinformatics 25(6):815–821. https://doi.org/10.1093/bioinformatics/btp071

    Google Scholar 

  43. Mahmood AS, Wu TJ, Mazumder R, Vijay-Shanker K (2016) DiMeX: a text mining system for mutation-disease association extraction. PLoS One 11(4):e0152725. https://doi.org/10.1371/journal.pone.0152725

    Google Scholar 

  44. Van Cutsem E, Kohne CH, Hitre E, Zaluski J, Chien CRC, Makhson A, D'Haens G, Pinter T, Lim R, Bodoky G, Roh JK, Folprecht G, Ruff P, Stroh C, Tejpar S, Schlichting M, Nippgen J, Rougier P (2009) Cetuximab and chemotherapy as initial treatment for metastatic colorectal cancer. New Engl J Med 360(14):1408–1417. https://doi.org/10.1056/Nejmoa0805019

    Google Scholar 

  45. Hewett M, Oliver DE, Rubin DL, Easton KL, Stuart JM, Altman RB, Klein TE (2002) PharmGKB: the Pharmacogenetics Knowledge Base. Nucleic Acids Res 30(1):163–165

    Google Scholar 

  46. Maglott D, Ostell J, Pruitt KD, Tatusova T (2011) Entrez gene: gene-centered information at NCBI. Nucleic Acids Res 39:D52–D57. https://doi.org/10.1093/nar/gkq1237

    Google Scholar 

  47. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 29(1):308–311

    Google Scholar 

  48. Pakhomov S, McInnes BT, Lamba J, Liu Y, Melton GB, Ghodke Y, Bhise N, Lamba V, Birnbaum AK (2012) Using PharmGKB to train text mining approaches for identifying potential gene targets for pharmacogenomic studies. J Biomed Inform 45(5):862–869. https://doi.org/10.1016/j.jpi.2012.04.007

    Google Scholar 

  49. Ian H, Witten EF (2011) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco

    Google Scholar 

  50. Xu R, Wang Q (2013) A semi-supervised approach to extract pharmacogenomics-specific drug-gene pairs from biomedical literature for personalized medicine. J Biomed Inform 46(4):585–593. https://doi.org/10.1016/j.jbi.2013.04.001

    Google Scholar 

  51. Hakenberg J, Voronov D, Nguyen VH, Liang S, Anwar S, Lumpkin B, Leaman R, Tari L, Baral C (2012) A SNPshot of PubMed to associate genetic variants with drugs, diseases, and adverse reactions. J Biomed Inform 45(5):842–850. https://doi.org/10.1016/j.jbi.2012.04.006

    Google Scholar 

  52. Chang JT, Altman RB (2004) Extracting and characterizing gene-drug relationships from the literature. Pharmacogenetics 14(9):577–586

    Google Scholar 

  53. Lakiotaki K, Kartsaki E, Kanterakis A, Katsila T, Patrinos GP, Potamias G (2016) ePGA: a web-based information system for translational pharmacogenomics. PLoS One 11(9). ARTN e0162801). https://doi.org/10.1371/journal.pone.0162801

  54. Dalma-Weiszhausz DD, Warrington J, Tanimoto EY, Miyada CG (2006) The affymetrix GeneChip platform: an overview. Methods Enzymol 410:3–28. https://doi.org/10.1016/S0076-6879(06)10001-4

    Google Scholar 

  55. Ding H, Takigawa I, Mamitsuka H, Zhu S (2014) Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Brief Bioinform 15(5):734–747. https://doi.org/10.1093/bib/bbt056

    Google Scholar 

  56. Cheng F, Liu C, Jiang J, Lu W, Li W, Liu G, Zhou W, Huang J, Tang Y (2012) Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol 8(5):e1002503. https://doi.org/10.1371/journal.pcbi.1002503

    Google Scholar 

  57. Chen B, Ding Y, Wild DJ (2012) Assessing drug target association using semantic linked data. PLoS Comput Biol 8(7). ARTN e1002574). https://doi.org/10.1371/journal.pcbi.1002574

  58. Chen B, Dong X, Jiao D, Wang H, Zhu Q, Ding Y, Wild DJ (2010) Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data. BMC Bioinformatics 11:255. https://doi.org/10.1186/1471-2105-11-255

    Google Scholar 

  59. Chen B, Ding Y, Wild DJ (2012) Improving integrative searching of systems chemical biology data using semantic annotation. J Chem 4(1):6. https://doi.org/10.1186/1758-2946-4-6

    Google Scholar 

  60. Zong N, Kim H, Ngo V, Harismendy O (2017) Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations. Bioinformatics 33(15):2337–2344. https://doi.org/10.1093/bioinformatics/btx160

    Google Scholar 

  61. Xu R, Wang Q (2014) Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature. J Biomed Inform 51:191–199. https://doi.org/10.1016/j.jbi.2014.05.013

    Google Scholar 

  62. Schriml LM, Arze C, Nadendla S, Chang YW, Mazaitis M, Felix V, Feng G, Kibbe WA (2012) Disease ontology: a backbone for disease semantic integration. Nucleic Acids Res 40(Database issue):D940–D946. https://doi.org/10.1093/nar/gkr972

    Google Scholar 

  63. Brown EG, Wood L, Wood S (1999) The medical dictionary for regulatory activities (MedDRA). Drug Saf 20(2):109–117

    Google Scholar 

  64. Canada A, Capella-Gutierrez S, Rabal O, Oyarzabal J, Valencia A, Krallinger M (2017) LimTox: a web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes. Nucleic Acids Res. https://doi.org/10.1093/nar/gkx462

  65. Iqbal E, Mallah R, Jackson RG, Ball M, Ibrahim ZM, Broadbent M, Dzahini O, Stewart R, Johnston C, Dobson RJ (2015) Identification of adverse drug events from free text electronic patient records and information in a large mental health case register. PLoS One 10(8):e0134208. https://doi.org/10.1371/journal.pone.0134208

    Google Scholar 

  66. Wang G, Jung K, Winnenburg R, Shah NH (2015) A method for systematic discovery of adverse drug events from clinical notes. J Am Med Inform Assoc 22(6):1196–1204. https://doi.org/10.1093/jamia/ocv102

    Google Scholar 

  67. Takarabe M, Kotera M, Nishimura Y, Goto S, Yamanishi Y (2012) Drug target prediction using adverse event report systems: a pharmacogenomic approach. Bioinformatics 28(18):I611–I618. https://doi.org/10.1093/bioinformatics/bts413

    Google Scholar 

Download references

Acknowledgments

This research was supported by the NIH Intramural Research Program, National Library of Medicine, and the NIH Medical Research Scholars Program, a public-private partnership supported jointly by the NIH and generous contributions to the Foundation for the NIH from the Doris Duke Charitable Foundation, the Howard Hughes Medical Institute, the American Association for Dental Research, the Colgate-Palmolive Company, and other private donors. No funds from the Doris Duke Charitable Foundation were used to support research that used animals. This work was also supported by the National Natural Science Foundation of China (Grant No. 81601573), the National Key Research and Development Program of China (Grant No. 2016YFC0901901), the National Population and Health Scientific Data Sharing Program of China, and the Knowledge Centre for Engineering Sciences and Technology (Medical Centre) and the Key Laboratory of Knowledge Technology for Medical Integrative Publishing.

Author information

Author notes

  1. Si Zheng, Shazia Dharssi and Meng Wu contributed equally to this work.

Authors and Affiliations

  1. Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Si Zheng, Meng Wu & Jiao Li

  2. National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA

    Shazia Dharssi & Zhiyong Lu

Authors
  1. Si Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shazia Dharssi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Meng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiyong Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhiyong Lu .

Editor information

Editors and Affiliations

  1. Department of Pathology, University of New Mexico, Albuquerque, NM, USA

    Richard S. Larson

  2. Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA

    Tudor I. Oprea

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Zheng, S., Dharssi, S., Wu, M., Li, J., Lu, Z. (2019). Text Mining for Drug Discovery. In: Larson, R., Oprea, T. (eds) Bioinformatics and Drug Discovery. Methods in Molecular Biology, vol 1939. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9089-4_13

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9089-4_13

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9088-7

  • Online ISBN: 978-1-4939-9089-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation