Skip to main content
SpringerLink
Log in
Book cover

Artificial Neural Networks pp 167–184Cite as

Data Integration Using Advances in Machine Learning in Drug Discovery and Molecular Biology

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2190))

Abstract

While the term artificial intelligence and the concept of deep learning are not new, recent advances in high-performance computing, the availability of large annotated data sets required for training, and novel frameworks for implementing deep neural networks have led to an unprecedented acceleration of the field of molecular (network) biology and pharmacogenomics. The need to align biological data to innovative machine learning has stimulated developments in both data integration (fusion) and knowledge representation, in the form of heterogeneous, multiplex, and biological networks or graphs. In this chapter we briefly introduce several popular neural network architectures used in deep learning, namely, the fully connected deep neural network, recurrent neural network, convolutional neural network, and the autoencoder. Deep learning predictors, classifiers, and generators utilized in modern feature extraction may well assist interpretability and thus imbue AI tools with increased explication, potentially adding insights and advancements in novel chemistry and biology discovery.

The capability of learning representations from structures directly without using any predefined structure descriptor is an important feature distinguishing deep learning from other machine learning methods and makes the traditional feature selection and reduction procedures unnecessary. In this chapter we briefly show how these technologies are applied for data integration (fusion) and analysis in drug discovery research covering these areas: (1) application of convolutional neural networks to predict ligand–protein interactions; (2) application of deep learning in compound property and activity prediction; (3) de novo design through deep learning. We also: (1) discuss some aspects of future development of deep learning in drug discovery/chemistry; (2) provide references to published information; (3) provide recently advocated recommendations on using artificial intelligence and deep learning in -omics research and drug discovery.

This is a preview of subscription content, 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   109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
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

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Tang B, Pan Z, Yin K et al (2019) Recent advances of deep learning in bioinformatics and computational biology. Front Genet 10:214. https://doi.org/10.3389/fgene.2019.00214

    Article  PubMed  PubMed Central  Google Scholar 

  2. Zhavoronkov A (2018) Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry. Mol Pharm 15(10):4311–4313. https://doi.org/10.1021/acs.molpharmaceut.8b00930

    Article  CAS  PubMed  Google Scholar 

  3. Angermueller C, Parnamaa T, Parts L et al (2016) Deep learning for computational biology. Mol Syst Biol 12(7):878. https://doi.org/10.15252/msb.20156651

    Article  PubMed  PubMed Central  Google Scholar 

  4. 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 

  5. Pinu FR, Goldansaz SA, Jaine J (2019) Translational metabolomics: current challenges and future opportunities. Meta 9(6). https://doi.org/10.3390/metabo9060108

  6. Pinu FR, Beale DJ, Paten AM et al (2019) Systems biology and multi-omics integration: viewpoints from the metabolomics research community. Meta 9(4). https://doi.org/10.3390/metabo9040076

  7. Zitnik M, Nguyen F, Wang B et al (2019) Machine learning for integrating data in biology and medicine: principles, practice, and opportunities. Inf Fusion 50:71–91. https://doi.org/10.1016/j.inffus.2018.09.012

    Article  PubMed  Google Scholar 

  8. Gonen M (2012) Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization. Bioinformatics 28(18):2304–2310. https://doi.org/10.1093/bioinformatics/bts360

    Article  CAS  PubMed  Google Scholar 

  9. Zitnik M, Zupan B (2015) Gene network inference by fusing data from diverse distributions. Bioinformatics 31(12):i230–i239. https://doi.org/10.1093/bioinformatics/btv258

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Nelson W, Zitnik M, Wang B et al (2019) To embed or not: network embedding as a paradigm in computational biology. Front Genet 10(381):381. https://doi.org/10.3389/fgene.2019.00381

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. McGillivray P, Clarke D, Meyerson W et al (2018) Network analysis as a grand unifier in biomedical data science. Ann Rev Biomed Data Sci 1(1):153–180. https://doi.org/10.1146/annurev-biodatasci-080917-013444

    Article  Google Scholar 

  12. Min S, Lee B, Yoon S (2017) Deep learning in bioinformatics. Brief Bioinform 18(5):851–869. https://doi.org/10.1093/bib/bbw068

    Article  PubMed  Google Scholar 

  13. 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 

  14. Ching T, Himmelstein DS, Beaulieu-Jones BK et al (2018) Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 15(141). https://doi.org/10.1098/rsif.2017.0387

  15. Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616–1637. https://doi.org/10.1109/tkde.2018.2807452

    Article  Google Scholar 

  16. Chen H, Engkvist O, Wang Y et al (2018) The rise of deep learning in drug discovery. Drug Discov Today 23(6):1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039

    Article  PubMed  Google Scholar 

  17. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647

    Article  CAS  PubMed  Google Scholar 

  18. Zeng K, Yu J, Wang R et al (2017) Coupled deep autoencoder for single image super-resolution. IEEE Trans Cybern 47(1):27–37. https://doi.org/10.1109/TCYB.2015.2501373

    Article  PubMed  Google Scholar 

  19. Yang W, Liu Q, Wang S et al (2018) Down image recognition based on deep convolutional neural network. Informat Process Agricult 5(2):246–252. https://doi.org/10.1016/j.inpa.2018.01.004

    Article  Google Scholar 

  20. Smith JS, Roitberg AE, Isayev O (2018) Transforming computational drug discovery with machine learning and AI. ACS Med Chem Lett 9(11):1065–1069. https://doi.org/10.1021/acsmedchemlett.8b00437

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Vamathevan J, Clark D, Czodrowski P et al (2019) Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 18(6):463–477. https://doi.org/10.1038/s41573-019-0024-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hochreiter S, Klambauer G, Rarey M (2018) Machine learning in drug discovery. J Chem Inf Model 58(9):1723–1724. https://doi.org/10.1021/acs.jcim.8b00478

    Article  CAS  PubMed  Google Scholar 

  23. Rifaioglu AS, Atas H, Martin MJ et al (2018) Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform. https://doi.org/10.1093/bib/bby061

  24. Mak KK, Pichika MR (2019) Artificial intelligence in drug development: present status and future prospects. Drug Discov Today 24(3):773–780. https://doi.org/10.1016/j.drudis.2018.11.014

    Article  PubMed  Google Scholar 

  25. Goh GB, Hodas NO, Vishnu A (2017) Deep learning for computational chemistry. J Comput Chem 38(16):1291–1307. https://doi.org/10.1002/jcc.24764

    Article  CAS  PubMed  Google Scholar 

  26. Pagadala NS, Syed K, Tuszynski J (2017) Software for molecular docking: a review. Biophys Rev 9(2):91–102. https://doi.org/10.1007/s12551-016-0247-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ragoza M, Hochuli J, Idrobo E et al (2017) Protein-ligand scoring with convolutional neural networks. J Chem Inf Model 57(4):942–957. https://doi.org/10.1021/acs.jcim.6b00740

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Pereira JC, Caffarena ER, Dos Santos CN (2016) Boosting Docking-Based Virtual Screening with Deep Learning. J Chem Inf Model 56(12):2495–2506. https://doi.org/10.1021/acs.jcim.6b00355

    Article  CAS  PubMed  Google Scholar 

  29. Paladino A, Marchetti F, Rinaldi S et al (2017) Protein design: from computer models to artificial intelligence. Wiley Inter Rev Comput Mol Sci 7(5):e1318. https://doi.org/10.1002/wcms.1318

    Article  CAS  Google Scholar 

  30. Yoshiki K, Shinji H, Hitoshi G (2016) Molecular activity prediction using deep learning software library. 2016 international conference on advanced informatics: concepts, theory and application (ICAICTA):1–6

    Google Scholar 

  31. Rodrigues T, Bernardes GJL (2019) Machine learning for target discovery in drug development. Curr Opin Chem Biol 56:16–22. https://doi.org/10.1016/j.cbpa.2019.10.003

    Article  CAS  PubMed  Google Scholar 

  32. Lusci A, Pollastri G, Baldi P (2013) Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J Chem Inf Model 53(7):1563–1575. https://doi.org/10.1021/ci400187y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J et al (2015) Convolutional networks on graphs for learning molecular fingerprints. In: Paper presented at the 29th annual conference on neural information processing systems, NIPS 2015, Montreal, Canada, 7 through 12 December 2015

    Google Scholar 

  34. Putin E, Asadulaev A, Ivanenkov Y et al (2018) Reinforced adversarial neural computer for de novo molecular design. J Chem Inf Model 58(6):1194–1204. https://doi.org/10.1021/acs.jcim.7b00690

    Article  CAS  PubMed  Google Scholar 

  35. Putin E, Asadulaev A, Vanhaelen Q et al (2018) Adversarial threshold neural computer for molecular de novo design. Mol Pharm 15(10):4386–4397. https://doi.org/10.1021/acs.molpharmaceut.7b01137

    Article  CAS  PubMed  Google Scholar 

  36. Polykovskiy D, Zhebrak A, Vetrov D et al (2018) Entangled conditional adversarial autoencoder for de novo drug discovery. Mol Pharm 15(10):4398–4405. https://doi.org/10.1021/acs.molpharmaceut.8b00839

    Article  CAS  PubMed  Google Scholar 

  37. Segler MHS, Kogej T, Tyrchan C et al (2018) Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci 4(1):120–131. https://doi.org/10.1021/acscentsci.7b00512

    Article  CAS  PubMed  Google Scholar 

  38. Margolin AA, Nemenman I, Basso K et al (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7(1):S7. https://doi.org/10.1186/1471-2105-7-S1-S7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Camacho DM, Collins KM, Powers RK et al (2018) Next-generation machine learning for biological networks. Cell 173(7):1581–1592. https://doi.org/10.1016/j.cell.2018.05.015

    Article  CAS  PubMed  Google Scholar 

  40. De Smet R, Marchal K (2010) Advantages and limitations of current network inference methods. Nat Rev Microbiol 8(10):717–729. https://doi.org/10.1038/nrmicro2419

    Article  CAS  PubMed  Google Scholar 

  41. Lecca P, Priami C (2013) Biological network inference for drug discovery. Drug Discov Today 18(5-6):256–264. https://doi.org/10.1016/j.drudis.2012.11.001

    Article  PubMed  Google Scholar 

  42. Walsh LA, Alvarez MJ, Sabio EY et al (2017) An integrated systems biology approach identifies TRIM25 as a key determinant of breast cancer metastasis. Cell Rep 20(7):1623–1640. https://doi.org/10.1016/j.celrep.2017.07.052

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Duran-Frigola M, Fernández-Torras A, Bertoni M et al (2019) Formatting biological big data for modern machine learning in drug discovery. Wiley Inter Rev Comput Mol Sci 9(6):e1408. https://doi.org/10.1002/wcms.1408

    Article  CAS  Google Scholar 

  44. Crichton G, Guo Y, Pyysalo S et al (2018) Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches. BMC Bioinformatics 19(1):176. https://doi.org/10.1186/s12859-018-2163-9

    Article  PubMed  PubMed Central  Google Scholar 

  45. Bronstein MM, Bruna J, LeCun Y et al (2017) Geometric deep learning: going beyond Euclidean data. IEEE Signal Process Mag 34(4):18–42. https://doi.org/10.1109/MSP.2017.2693418

    Article  Google Scholar 

  46. Hodos RA, Kidd BA, Shameer K et al (2016) In silico methods for drug repurposing and pharmacology. Wiley Interdiscip Rev Syst Biol Med 8(3):186–210. https://doi.org/10.1002/wsbm.1337

    Article  PubMed  PubMed Central  Google Scholar 

  47. Moffat JG, Vincent F, Lee JA et al (2017) Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nat Rev Drug Discov 16(8):531–543. https://doi.org/10.1038/nrd.2017.111

    Article  CAS  PubMed  Google Scholar 

  48. Berger SI, Iyengar R (2009) Network analyses in systems pharmacology. Bioinformatics 25(19):2466–2472. https://doi.org/10.1093/bioinformatics/btp465

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4(11):682–690. https://doi.org/10.1038/nchembio.118

    Article  CAS  PubMed  Google Scholar 

  50. Benson AR, Gleich DF, Leskovec J (2016) Higher-order organization of complex networks. Science 353(6295):163–166. https://doi.org/10.1126/science.aad9029

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Yoon S, Lee D (2019) Meta-path based prioritization of functional drug actions with multi-level biological networks. Sci Rep 9(1):5469. https://doi.org/10.1038/s41598-019-41814-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Montavon G, Samek W, Müller K-R (2018) Methods for interpreting and understanding deep neural networks. Digital Signal Processing 73:1–15. https://doi.org/10.1016/j.dsp.2017.10.011

    Article  Google Scholar 

  53. Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005

    Article  PubMed  Google Scholar 

  54. Mamoshina P, Vieira A, Putin E et al (2016) Applications of deep learning in biomedicine. Mol Pharm 13(5):1445–1454. https://doi.org/10.1021/acs.molpharmaceut.5b00982

    Article  CAS  PubMed  Google Scholar 

  55. Ramsundar B, Liu B, Wu Z et al (2017) Is multitask deep learning practical for pharma? J Chem Inf Model 57(8):2068–2076. https://doi.org/10.1021/acs.jcim.7b00146

    Article  CAS  PubMed  Google Scholar 

  56. Kalinin AA, Higgins GA, Reamaroon N et al (2018) Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics 19(7):629–650. https://doi.org/10.2217/pgs-2018-0008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Aliper A, Plis S, Artemov A et al (2016) Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol Pharm 13(7):2524–2530. https://doi.org/10.1021/acs.molpharmaceut.6b00248

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Putin E, Mamoshina P, Aliper A et al (2016) Deep biomarkers of human aging: application of deep neural networks to biomarker development. Aging (Albany NY) 8(5):1021–1033. https://doi.org/10.18632/aging.100968

    Article  CAS  Google Scholar 

  59. Donner Y, Kazmierczak S, Fortney K (2018) Drug repurposing using deep embeddings of gene expression profiles. Mol Pharm 15(10):4314–4325. https://doi.org/10.1021/acs.molpharmaceut.8b00284

    Article  CAS  PubMed  Google Scholar 

  60. Connectivity Map (CMAP) (2019) Broad Institute. http://www.broadinstitute.org/connectivity-map-cmap. Accessed 1 Nov 2019

  61. NIH LINCS Program (2019) National Institutes of Health (NIH). http://www.lincsproject.org. Accessed 1 Nov 2019

  62. Hudson IL, Leemaqz SY, Shafi D et al (2017) Score function of violations and best cutpoint to identify druggable molecules and associated disease targets. In: Syme G, Hatton MacDonald D, Fulton B et al (eds) MODSIM2017, 22nd international congress on modelling and simulation. Modelling and Simulation Society of Australia and New Zealand, 2017. pp 487–393

    Google Scholar 

  63. Gao XW, Qian Y (2018) Prediction of multidrug-resistant TB from CT pulmonary images based on deep learning techniques. Mol Pharm 15(10):4326–4335. https://doi.org/10.1021/acs.molpharmaceut.7b00875

    Article  CAS  PubMed  Google Scholar 

  64. Li X, Xu Y, Lai L et al (2018) Prediction of human Cytochrome P450 inhibition using a multitask deep autoencoder neural network. Mol Pharm 15(10):4336–4345. https://doi.org/10.1021/acs.molpharmaceut.8b00110

    Article  CAS  PubMed  Google Scholar 

  65. Russo DP, Zorn KM, Clark AM et al (2018) Comparing multiple machine learning algorithms and metrics for estrogen receptor binding prediction. Mol Pharm 15(10):4361–4370. https://doi.org/10.1021/acs.molpharmaceut.8b00546

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Hop P, Allgood B, Yu J (2018) Geometric deep learning autonomously learns chemical features that outperform those engineered by domain experts. Mol Pharm 15(10):4371–4377. https://doi.org/10.1021/acs.molpharmaceut.7b01144

    Article  CAS  PubMed  Google Scholar 

  67. Kuzminykh D, Polykovskiy D, Kadurin A et al (2018) 3D molecular representations based on the wave transform for convolutional neural networks. Mol Pharm 15(10):4378–4385. https://doi.org/10.1021/acs.molpharmaceut.7b01134

    Article  CAS  PubMed  Google Scholar 

  68. Kadurin A, Aliper A, Kazennov A et al (2017) The cornucopia of meaningful leads: applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget 8(7):10883–10890. https://doi.org/10.18632/oncotarget.14073

    Article  PubMed  Google Scholar 

  69. Gomez-Bombarelli R, Wei JN, Duvenaud D et al (2018) Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci 4(2):268–276. https://doi.org/10.1021/acscentsci.7b00572

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Harel S, Radinsky K (2018) Prototype-based compound discovery using deep generative models. Mol Pharm 15(10):4406–4416. https://doi.org/10.1021/acs.molpharmaceut.8b00474

    Article  CAS  PubMed  Google Scholar 

  71. Sanchez-Lengeling B, Aspuru-Guzik A (2018) Inverse molecular design using machine learning: generative models for matter engineering. Science 361(6400):360–365. https://doi.org/10.1126/science.aat2663

    Article  CAS  PubMed  Google Scholar 

  72. Grover A, Leskovec J (2016) node2vec. Paper presented at the proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining—KDD '16

    Google Scholar 

  73. Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk. Paper presented at the proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining—KDD '14

    Google Scholar 

  74. Tang J, Qu M, Wang M et al (2015) Line. Paper presented at the proceedings of the 24th international conference on World Wide Web—WWW '15

    Google Scholar 

  75. Zitnik M, Zupan B (2016) Collective pairwise classification for multi-way analysis of disease and drug data. Paper presented at the biocomputing 2016. 18 Nov 2015

    Google Scholar 

  76. Luo Y, Zhao X, Zhou J et al (2017) A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat Commun 8(1):573. https://doi.org/10.1038/s41467-017-00680-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Wen M, Zhang Z, Niu S et al (2017) Deep-learning-based drug-target interaction prediction. J Proteome Res 16(4):1401–1409. https://doi.org/10.1021/acs.jproteome.6b00618

    Article  CAS  PubMed  Google Scholar 

  78. Lee I, Nam H (2018) Identification of drug-target interaction by a random walk with restart method on an interactome network. BMC Bioinformatics 19(Suppl 8):208. https://doi.org/10.1186/s12859-018-2199-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Gao KY, Fokoue A, Luo H et al (2018) Interpretable drug target prediction using deep neural representation. Paper presented at the proceedings of the twenty-seventh international joint conference on artificial intelligence, 2018/07

    Google Scholar 

  80. Wan F, Hong L, Xiao A et al (2018) NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions. Bioinformatics 35(1):104–111. https://doi.org/10.1093/bioinformatics/bty543

    Article  CAS  Google Scholar 

  81. Ma T, Xiao C, Zhou J et al (2018) Drug similarity integration through attentive multi-view graph auto-encoders. Paper presented at the proceedings of the twenty-seventh international joint conference on artificial intelligence, July 2018.

    Google Scholar 

  82. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv. 1609.02907

    Google Scholar 

  83. Veličković P, Cucurull G, Casanova A et al (2018) Graph attention networks. arXiv 1710.10903v3

    Google Scholar 

  84. Zitnik M, Agrawal M, Leskovec J (2018) Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13):i457–i466. https://doi.org/10.1093/bioinformatics/bty294

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Coley CW, Barzilay R, Green WH et al (2017) Convolutional embedding of attributed molecular graphs for physical property prediction. J Chem Inf Model 57(8):1757–1772. https://doi.org/10.1021/acs.jcim.6b00601

    Article  CAS  PubMed  Google Scholar 

  86. Jin W, Coley C, Barzilay R et al (2017) Predicting organic reaction outcomes with weisfeiler-lehman network. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S (eds) Advances in neural information processing systems 30. Curran Associates, New York, NY, pp 2607–2616

    Google Scholar 

  87. Morris P, DaSilva Y, Clark E et al (2018) Convolutional neural networks for predicting molecular binding affinity to HIV-1 proteins. Paper presented at the proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics—BCB '18

    Google Scholar 

  88. Hamilton WL, Ying R, Leskovec J (2017) Representation learning on graphs: methods and applications. arXiv 1709.05584

    Google Scholar 

Download references

Acknowledgments

The author is most grateful for editorial and graphic design assistance from Dr. Sean A. Hudson (Hudson Software Development, Australia, www.hudson-software.droppages.com).

Author information

Authors and Affiliations

  1. Mathematical Sciences, School of Science, RMIT University, Melbourne, VIC, Australia

    Irene Lena Hudson

Authors
  1. Irene Lena Hudson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Irene Lena Hudson .

Editor information

Editors and Affiliations

  1. Chemistry, Oxford University, Oxford, UK

    Hugh Cartwright

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Hudson, I.L. (2021). Data Integration Using Advances in Machine Learning in Drug Discovery and Molecular Biology. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 2190. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0826-5_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-0826-5_7

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0825-8

  • Online ISBN: 978-1-0716-0826-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation