Skip to main content
SpringerLink
Log in

Mining Chemogenomic Spaces for Prediction of Drug–Target Interactions

  • Protocol
  • First Online:
Computational Drug Discovery and Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2714))

  • 1525 Accesses

Abstract

The pipeline of drug discovery consists of a number of processes; drug–target interaction determination is one of the salient steps among them. Computational prediction of drug–target interactions can facilitate in reducing the search space of experimental wet lab-based verifications steps, thus considerably reducing time and other resources dedicated to the drug discovery pipeline. While machine learning-based methods are more widespread for drug–target interaction prediction, network-centric methods are also evolving. In this chapter, we focus on the process of the drug–target interaction prediction from the perspective of using machine learning algorithms and the various stages involved for developing an accurate predictor.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
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

Similar content being viewed by others

References

  1. Hughes J, Rees S, Kalindjian S, Philpott K (2011) Principles of early drug discovery. Br J Pharmacol 162(6):1239–1249. https://doi.org/10.1111/j.1476-5381.2010.01127.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Xu L, Ru X, Song R (2021) Application of machine learning for drug–target interaction prediction. Front Genet 12:680117. https://doi.org/10.3389/fgene.2021.680117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Sachdev K, Gupta MK (2019) A comprehensive review of feature based methods for drug target interaction prediction. J Biomed Inform 93:103159. https://doi.org/10.1016/j.jbi.2019.103159

    Article  PubMed  Google Scholar 

  4. Anusuya S, Kesherwani M, Priya VK, Vimala A, Shanmugam G, Velmurugan D, Gromiha MM (2018) Drug-target interactions: prediction methods and applications. Curr Protein Pept Sci 19(6):537–561. https://doi.org/10.2174/1389203718666161108091609

    Article  CAS  PubMed  Google Scholar 

  5. Bagherian M, Sabeti E, Wang K, Sartor MA, Nikolovska-Coleska Z, Najarian K (2020) Machine learning approaches and databases for prediction of drug–target interaction: a survey paper. Brief Bioinform 22(1):247–269. https://doi.org/10.1093/bib/bbz157

    Article  PubMed Central  Google Scholar 

  6. Peng Y, Wang J, Wu Z, Zheng L, Wang B, Liu G, Li W, Tang Y (2022) MPSM-DTI: prediction of drug–target interaction via machine learning based on the chemical structure and protein sequence. Digital Discovery 1(2):115–126. https://doi.org/10.1039/d1dd00011j

    Article  Google Scholar 

  7. Ezzat A, Wu M, Li X-L, Kwoh C-K (2016) Drug-target interaction prediction via class imbalance-aware ensemble learning. BMC Bioinformatics 17(19):509. https://doi.org/10.1186/s12859-016-1377-y

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wang L, You Z-H, Yan X, Liu G, Zhang W (2018) RFDT: a rotation forest-based predictor for predicting drug-target interactions using drug structure and protein sequence information. Curr Protein Pept Sci 19:445–454. https://doi.org/10.2174/1389203718666161114111656

    Article  CAS  PubMed  Google Scholar 

  9. Xiao X, Min J-L, Wang P, Chou K-C (2013) iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS One 8(8):e72234

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Pliakos K, Vens C (2020) Drug-target interaction prediction with tree-ensemble learning and output space reconstruction. BMC Bioinformatics 21(1):49. https://doi.org/10.1186/s12859-020-3379-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Shi H, Liu S, Chen J, Li X, Ma Q, Yu B (2019) Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure. Genomics 111(6):1839–1852. https://doi.org/10.1016/j.ygeno.2018.12.007

    Article  CAS  PubMed  Google Scholar 

  12. Pan J, Li L-P, You Z-H, Yu C-Q, Ren Z-H, Chen Y (2021) Prediction of drug–target interactions by combining dual-tree complex wavelet transform with ensemble learning method. Molecules 26(17):5359

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Xuan P, Sun C, Zhang T, Ye Y, Shen T, Dong Y (2019) Gradient boosting decision tree-based method for predicting interactions between target genes and drugs. Front Genet 10:459. https://doi.org/10.3389/fgene.2019.00459

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chu Y, Shan X, Chen T, Jiang M, Wang Y, Wang Q, Salahub DR, Xiong Y, Wei D-Q (2020) DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method. Brief Bioinform 22(3):bbaa205. https://doi.org/10.1093/bib/bbaa205

    Article  Google Scholar 

  15. Wu Z, Li W, Liu G, Tang Y (2018) Network-based methods for prediction of drug-target interactions. Front Pharmacol 9:1134. https://doi.org/10.3389/fphar.2018.01134

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Fakhraei S, Huang B, Raschid L, Getoor L (2014) Network-based drug-target interaction prediction with probabilistic soft logic. IEEE/ACM Trans Comput Biol Bioinform 11(5):775–787. https://doi.org/10.1109/tcbb.2014.2325031

    Article  PubMed  Google Scholar 

  17. Ye Q, Hsieh C-Y, Yang Z, Kang Y, Chen J, Cao D, He S, Hou T (2021) A unified drug–target interaction prediction framework based on knowledge graph and recommendation system. Nat Commun 12(1):6775. https://doi.org/10.1038/s41467-021-27137-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M (2008) Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13):i232–i240. https://doi.org/10.1093/bioinformatics/btn162

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wang Y, Zeng J (2013) Predicting drug-target interactions using restricted Boltzmann machines. Bioinformatics 29:i126–i134. https://doi.org/10.1093/bioinformatics/btt234

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Cao D-S, Zhang L-X, Tan G-S, Xiang Z, Zeng W-B, Xu Q-S, Chen AF (2014) Computational prediction of drug target interactions using chemical, biological, and network features. Mol Inform 33(10):669–681. https://doi.org/10.1002/minf.201400009

    Article  CAS  PubMed  Google Scholar 

  21. Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M (2007) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36(suppl_1):D901–D906. https://doi.org/10.1093/nar/gkm958

    Article  CAS  Google Scholar 

  22. Chen X, Ji ZL, Chen YZ (2002) TTD: therapeutic target database. Nucleic Acids Res 30(1):412–415. https://doi.org/10.1093/nar/30.1.412

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Gao Z, Li H, Zhang H, Liu X, Kang L, Luo X, Zhu W, Chen K, Wang X, Jiang H (2008) PDTD: a web-accessible protein database for drug target identification. BMC Bioinformatics 9(1):104. https://doi.org/10.1186/1471-2105-9-104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE (2020) PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res 49(D1):D1388–D1395. https://doi.org/10.1093/nar/gkaa971

    Article  CAS  PubMed Central  Google Scholar 

  25. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2011) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40(D1):D1100–D1107. https://doi.org/10.1093/nar/gkr777

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Irwin JJ, Shoichet BK (2005) ZINC − a free database of commercially available compounds for virtual screening. J Chem Inf Model 45(1):177–182. https://doi.org/10.1021/ci049714+

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Irwin JJ, Tang KG, Young J, Dandarchuluun C, Wong BR, Khurelbaatar M, Moroz YS, Mayfield J, Sayle RA (2020) ZINC20—a free Ultralarge-scale chemical database for ligand discovery. J Chem Inf Model 60(12):6065–6073. https://doi.org/10.1021/acs.jcim.0c00675

    Article  CAS  PubMed Central  Google Scholar 

  28. Mohanraj K, Karthikeyan BS, Vivek-Ananth RP, Chand RPB, Aparna SR, Mangalapandi P, Samal A (2018) IMPPAT: a curated database of Indian medicinal plants, phytochemistry and therapeutics. Sci Rep 8(1):4329. https://doi.org/10.1038/s41598-018-22631-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J (2015) BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 44(D1):D1045–D1053. https://doi.org/10.1093/nar/gkv1072

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Tang J, Szwajda A, Shakyawar S, Xu T, Hintsanen P, Wennerberg K, Aittokallio T (2014) Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. J Chem Inf Model 54(3):735–743. https://doi.org/10.1021/ci400709d

    Article  CAS  PubMed  Google Scholar 

  31. Davis MI, Hunt JP, Herrgard S, Ciceri P, Wodicka LM, Pallares G, Hocker M, Treiber DK, Zarrinkar PP (2011) Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol 29(11):1046–1051. https://doi.org/10.1038/nbt.1990

    Article  CAS  PubMed  Google Scholar 

  32. Jo T, Japkowicz N (2004) Class imbalances versus small disjuncts. SIGKDD Explor Newsl 6(1):40–49. https://doi.org/10.1145/1007730.1007737

    Article  Google Scholar 

  33. Wei Q, Dunbrack RL Jr (2013) The role of balanced training and testing data sets for binary classifiers in bioinformatics. PLoS One 8(7):e67863

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Nath A, Karthikeyan S (2017) Enhanced prediction and characterization of CDK inhibitors using optimal class distribution. Interdisciplinary Sciences: Computational Life Sciences 9(2):292–303. https://doi.org/10.1007/s12539-016-0151-1

    Article  CAS  PubMed  Google Scholar 

  35. Mohammed R, Rawashdeh J, Abdullah M Machine learning with oversampling and Undersampling techniques: overview study and experimental results. In: 2020 11th international conference on information and communication systems (ICICS), 7–9 April 2020 2020, pp 243–248. https://doi.org/10.1109/icics49469.2020.239556

  36. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Int Res 16(1):321–357

    Google Scholar 

  37. Han H, Wang W-Y, Mao B-H (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang DS, Zhang XP, Huang GB (eds) Advances in intelligent computing. ICIC 2005, Lecture notes in computer science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_91

    Chapter  Google Scholar 

  38. Nakamura M, Kajiwara Y, Otsuka A, Kimura H (2013) LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling technique for biomedical data. BioData mining 6:16. https://doi.org/10.1186/1756-0381-6-16

    Article  PubMed  PubMed Central  Google Scholar 

  39. Barua S, Islam MM, Yao X, Murase K (2014) MWMOTE--Majority Weighted Minority Oversampling Technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26(2):405–425. https://doi.org/10.1109/tkde.2012.232

    Article  Google Scholar 

  40. Batista G, Prati R, Monard M-C (2004) A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations 6:20–29. https://doi.org/10.1145/1007730.1007735

    Article  Google Scholar 

  41. Yadav A, Sahu R, Nath A (2020) A representation transfer learning approach for enhanced prediction of growth hormone binding proteins. Comput Biol Chem 87:107274. https://doi.org/10.1016/j.compbiolchem.2020.107274

    Article  CAS  PubMed  Google Scholar 

  42. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11(1):137–148. https://doi.org/10.1080/00401706.1969.10490666

    Article  Google Scholar 

  43. Sahu R, Yadav A, Nath A (2021) Estimation of maximum recommended therapeutic dose of anti-retroviral drugs using diversified sampling and varied descriptors. Minerva Biotechnol Biomol Res 33(4):210–218

    Google Scholar 

  44. Jain AK (2008) Data clustering: 50 years beyond K-means. In: Daelemans W, Goethals B, Morik K (eds) Machine learning and knowledge discovery in databases. ECML PKDD 2008. Lecture notes in computer science(), vol 5211. Springer, Berlin, Heidelberg

    Google Scholar 

  45. Nath A, Subbiah K (2016) Unsupervised learning assisted robust prediction of bioluminescent proteins. Comput Biol Med 68:27–36. https://doi.org/10.1016/j.compbiomed.2015.10.013

    Article  CAS  PubMed  Google Scholar 

  46. Chen Z, Zhao P, Li F, Leier A, Marquez-Lago TT, Wang Y, Webb GI, Smith AI, Daly RJ, Chou K-C, Song J (2018) iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics 34(14):2499–2502. https://doi.org/10.1093/bioinformatics/bty140

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wang J, Yang B, Revote J, Leier A, Marquez-Lago TT, Webb G, Song J, Chou K-C, Lithgow T (2017) POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles. Bioinformatics 33(17):2756–2758. https://doi.org/10.1093/bioinformatics/btx302

    Article  CAS  PubMed  Google Scholar 

  48. Mohammadi A, Zahiri J, Mohammadi S, Khodarahmi M, Arab SS (2022) PSSMCOOL: a comprehensive R package for generating evolutionary-based descriptors of protein sequences from PSSM profiles. Biol Methods Protoc 7(1):bpac008. https://doi.org/10.1093/biomethods/bpac008

    Article  CAS  PubMed Central  Google Scholar 

  49. Cao D-S, Xu Q-S, Liang Y-Z (2013) propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics 29(7):960–962. https://doi.org/10.1093/bioinformatics/btt072

    Article  CAS  PubMed  Google Scholar 

  50. Xiao N, Cao D-S, Zhu M-F, Xu Q-S (2015) protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics 31(11):1857–1859. https://doi.org/10.1093/bioinformatics/btv042

    Article  CAS  PubMed  Google Scholar 

  51. Ruiz-Blanco YB, Paz W, Green J, Marrero-Ponce Y (2015) ProtDCal: a program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins. BMC Bioinformatics 16(1):162. https://doi.org/10.1186/s12859-015-0586-0

    Article  PubMed  PubMed Central  Google Scholar 

  52. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. The MIT Press

    Google Scholar 

  53. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539

    Article  CAS  PubMed  Google Scholar 

  54. Emmert-Streib F, Yang Z, Feng H, Tripathi S, Dehmer M (2020) An introductory review of deep learning for prediction models with Big Data. Front Artif Intell 3:4. https://doi.org/10.3389/frai.2020.00004

    Article  PubMed  PubMed Central  Google Scholar 

  55. Vargas R, Mosavi A, Ruiz R (2017) Deep learning: a review. Advances in intelligent systems and computing 5

    Google Scholar 

  56. Huang K, Fu T, Glass LM, Zitnik M, Xiao C, Sun J (2020) DeepPurpose: a deep learning library for drug–target interaction prediction. Bioinformatics 36(22–23):5545–5547. https://doi.org/10.1093/bioinformatics/btaa1005

    Article  CAS  PubMed Central  Google Scholar 

  57. Tripathi M, Shrivastava S, Karthikeyan S, Sinha D, Nath A (2021) Application of machine learning and molecular modeling in drug discovery and cheminformatics, pp 201–214. https://doi.org/10.1201/9781003126164-10

    Book  Google Scholar 

  58. Cao Y, Charisi A, Cheng L-C, Jiang T, Girke T (2008) ChemmineR: a compound mining framework for R. Bioinformatics 24(15):1733–1734. https://doi.org/10.1093/bioinformatics/btn307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Backman TWH, Cao Y, Girke T (2011) ChemMine tools: an online service for analyzing and clustering small molecules. Nucleic Acids Res 39(suppl 2):W486–W491. https://doi.org/10.1093/nar/gkr320

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Dong J, Cao D-S, Miao H-Y, Liu S, Deng B-C, Yun Y-H, Wang N-N, Lu A-P, Zeng W-B, Chen AF (2015) ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. J Cheminform 7(1):60. https://doi.org/10.1186/s13321-015-0109-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32(7):1466–1474. https://doi.org/10.1002/jcc.21707

    Article  CAS  PubMed  Google Scholar 

  62. Moriwaki H, Tian Y-S, Kawashita N, Takagi T (2018) Mordred: a molecular descriptor calculator. J Cheminform 10(1):4. https://doi.org/10.1186/s13321-018-0258-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003

    Article  PubMed  Google Scholar 

  64. Nath A, Leier A (2020) Improved cytokine–receptor interaction prediction by exploiting the negative sample space. BMC Bioinformatics 21(1):493. https://doi.org/10.1186/s12859-020-03835-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Udell M, Horn C, Zadeh R, Boyd S (2016) Generalized low rank models. Foundations and Trends in Maching Learning 9(1):1–118. https://doi.org/10.1561/2200000055

    Article  Google Scholar 

  66. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11(1):10–18. https://doi.org/10.1145/1656274.1656278

    Article  Google Scholar 

  67. Demšar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, Možina M, Polajnar M, Toplak M, Staric A, Stajdohar M, Umek L, Žagar L, Žbontar J, Žitnik M, Zupan B (2013) Orange: data mining toolbox in Python. J Mach Learn Res 14:2349–2353

    Google Scholar 

  68. Williams G (2009) Rattle: a data mining GUI for R. The R Journal 1:45–55. https://doi.org/10.32614/rj-2009-016

    Article  Google Scholar 

  69. Alcala-Fdez J, Sanchez L, García S, Del Jesus MJ, Ventura S, Garrell J-M, Otero J, Romero C, Bacardit J, Rivas Santos V, Fernández JC, Herrera F (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13:307–318. https://doi.org/10.1007/s00500-008-0323-y

    Article  Google Scholar 

  70. Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2008) KNIME: the Konstanz information miner. In: Data analysis, machine learning and applications. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 319–326

    Chapter  Google Scholar 

  71. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12(2011):2825–2830

    Google Scholar 

  72. Bischl B, Lang M, Kotthoff L, Schiffner J, Richter J, Studerus E, Casalicchio G, Jones ZM (2016) mlr: machine learning in R. J Mach Learn Res 17(1):5938–5942

    Google Scholar 

  73. Liaw A, Wiener M (2001) Classification and regression by RandomForest. Forest 23

    Google Scholar 

  74. Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28(5):1–26. https://doi.org/10.18637/jss.v028.i05

    Article  Google Scholar 

  75. Karatzoglou A, Smola A, Hornik K, Zeileis A (2004) kernlab - An S4 Package for Kernel Methods in R. J Stat Softw 11(9):1–20. https://doi.org/10.18637/jss.v011.i09

    Article  Google Scholar 

  76. Sushko I, Novotarskyi S, Körner R et al (2011) Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information. J Comput Aided Mol Des 25(6):533–554. https://doi.org/10.1007/s10822-011-9440-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/a:1010933404324

    Article  Google Scholar 

  78. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. Paper presented at the proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, California, USA

    Google Scholar 

  79. Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 30(7):1145–1159. https://doi.org/10.1016/S0031-3203(96)00142-2

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, India

    Abhigyan Nath

  2. Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi, India

    Radha Chaube

Authors
  1. Abhigyan Nath
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Radha Chaube
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, Karnataka, India

    Mohini Gore

  2. Department of Botany, Rajaram College Kolhapur, Kolhapur, Maharashtra, India

    Umesh B. Jagtap

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Nath, A., Chaube, R. (2024). Mining Chemogenomic Spaces for Prediction of Drug–Target Interactions. In: Gore, M., Jagtap, U.B. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 2714. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3441-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3441-7_9

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3440-0

  • Online ISBN: 978-1-0716-3441-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation