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Improving machine learning in early drug discovery

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Abstract

The high cost for new medicines is hindering their development and machine learning is therefore being used to avoid carrying out physical experiments. Here, we present a comparison between three different machine learning approaches in a classification setting where learning and prediction follow a teaching schedule to mimic the drug discovery process. The approaches are standard SVM classification, SVM based multi-kernel classification and SVM classification based on learning using privileged information. Our two main conclusions are derived using experimental in-vitro data and compound structure descriptors. The in-vitro data is assumed to i) be completely absent in the standard SVM setting, ii) be available at all times when applying multi-kernel learning, or iii) be available as privileged information during training only. The structure descriptors are always available. One conclusion is that multi-kernel learning has higher odds than standard SVM in producing higher accuracy. The second is that learning using privileged information does not have higher odds than the standard SVM, although it may improve accuracy when the training sets are small.

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References

  1. Agresti, A.: Categorical Data Analysis. John Wiley & Sons, Inc., Hooken (2001)

    MATH  Google Scholar 

  2. Arrowsmith, J., Miller, P.: Trial Watch: Phase II and Phase III attrition rates 2011–2012. Nat. Publ. Group 12(8), 569–569 (2013)

    Google Scholar 

  3. Ballard, P., Brassil, P., Bui, K.H., Dolgos, H., Petersson, C., Tunek, A., Webborn, P.J.H.: The right compound in the right assay at the right time: an integrated discovery DMPK strategy. Drug Metab. Rev. 44(3), 224–252 (2012)

    Article  Google Scholar 

  4. Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm

    Article  Google Scholar 

  5. Cook, D., Brown, D., Alexander, R., March, R., Morgan, P., Satterthwaite, G., Pangalos, M.N.: Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat. Publ. Group 13(6), 419–431 (2014)

    Google Scholar 

  6. Costello, J.C., Heiser, L.M., Georgii, E., Nen, M.G.O., Menden, M.P., Wang, N.J., Bansal, M., Ammadud din, M., Hintsanen, P., Khan, S.A., Mpindi, J.P., Kallioniemi, O., Honkela, A., Aittokallio, T., Wennerberg, K., Collins, J.J., Gallahan, D., Singer, D., Saez-Rodriguez, J., Kaski, S., Gray, J.W., Stolovitzky, G.: A community effort to assess and improve drug sensitivity prediction algorithms. Nat. Biotechnol. 32, 1202–1212 (2014)

  7. DiMasi, J.A.: Cost of Developing a New Drug. Tech. Rep. R&D Cost Study Briefing, Tufts Center for the Study of Drug Development, Boston, MA (2014)

  8. Eckert, H., Bajorath, J.: Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov. Today 12(5-6), 225–233 (2007)

    Article  Google Scholar 

  9. Eklund, M., Norinder, U., Boyer, S., Carlsson, L.: The application of conformal prediction to the drug discovery process. Annals of Mathematics and Artificial Intelligence pp. 1–16. doi:10.1007/s10472-013-9378-2 (2013)

  10. Eklund, M., Norinder, U., Boyer, S., Carlsson, L.: The application of conformal prediction to the drug discovery process. Ann. Math. Artif. Intell. 74(1), 117–132 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  11. Faulon, J.L., Churchwell, C.J., Visco, D.P.: The Signature Molecular Descriptor. 2. Enumerating Molecules from Their Extended Valence Sequences. J. Chem. Inf. Comput. Sci. 43(3), 721–734 (2003)

    Article  Google Scholar 

  12. Faulon, J.L., Visco, D.P., Pophale, R.S.: The Signature Molecular Descriptor. 1. Using Extended Valence Sequences in QSAR and QSPR Studies. J. Chem. Inf. Comput. Sci. 43(3), 707–720 (2003)

    Article  Google Scholar 

  13. Gönen, M.: Alpaydın, E.: Multiple kernel learning algorithms. J. Mach. Learn. Res. 12(Jul), 2211–2268 (2011)

    MathSciNet  MATH  Google Scholar 

  14. Helal, K.Y., Maciejewski, M., Gregori-Puigjané, E., Glick, M., Wassermann, A.M.: Public Domain HTS Fingerprints: Design and Evaluation of Compound Bioactivity Profiles from PubChem’s Bioassay Repository. Journal of Chemical Information and Modeling p. acs.jcim.5b00498. doi:10.1021/acs.jcim.5b00498 (2016)

  15. Herper, M.: The Truly Staggering Cost Of Inventing New Drugs. Forbes (2012)

  16. Lapin, M., Hein, M., Schiele, B.: Learning using privileged information: SVM+ and weighted SVM. Neural Netw. 53, 95–108 (2014)

    Article  MATH  Google Scholar 

  17. Li, W., Dai, D., Tan, M., Xu, D., Van Gool, L.: Fast algorithms for linear and kernel SVM+ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2258–2266 (2016)

  18. Liang, L., Cherkassky, V.: Connection between svm+ and multi-task learning 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 2048–2054. IEEE (2008)

  19. Liu, R., Schyman, P., Wallqvist, A.: Critically assessing the predictive power of qsar models for human liver microsomal stability. J. Chem. Inf. Model. 55(8), 1566–1575 (2015)

    Article  Google Scholar 

  20. Mayr, A., Klambauer, G., Unterthiner, T., Hochreiter, S.: DeepTox: Toxicity Prediction using Deep Learning. Front. Environ. Sci. 3, 24–15 (2016)

    Article  Google Scholar 

  21. Pasupa, K., Hussain, Z., Shawe-Taylor, J., Willett, P.: Drug screening with elastic-net multiple kernel learning 13th IEEE International Conference on BioInformatics and BioEngineering, pp 1–5 (2013). doi:10.1109/BIBE.2013.6701529

  22. Paul, S.M., Mytelka, D.S., Dunwiddie, C.T., Persinger, C.C., Munos, B.H., Lindborg, S.R., Schacht, A.L.: How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nature Reviews Drug Discovery 1–12 (2010)

  23. Pechyony, D., Izmailov, R., Vashist, A., Vapnik, V.: Smo-style algorithms for learning using privileged information DMIN, pp. 235–241 (2010)

  24. Pechyony, D., Vapnik, V.: Fast optimization algorithms for solving svm+. Stat. Learning and Data Science 1 (2011)

  25. Peck, R.W., Lendrem, D.W., Grant, I., Lendrem, B.C., Isaacs, J.D.: Why is it hard to terminate failing projects in pharmaceutical R&D?. Nature Publishing Group, 1–2 (2015)

  26. Petrone, P.M., Simms, B., Nigsch, F., Lounkine, E., Kutchukian, P., Cornett, A., Deng, Z., Davies, J.W., Jenkins, J.L., Glick, M.: Rethinking molecular similarity: Comparing compounds on the basis of biological activity. ACS Chem. Biol. 7(8), 1399–1409 (2012). doi:10.1021/cb3001028

    Article  Google Scholar 

  27. Ribeiro, B., Silva, C., Chen, N., Vieira, A., das Neves, J.C.: Enhanced default risk models with SVM+. Expert Syst. Appl. 39(11), 10,140–10,152 (2012)

    Article  Google Scholar 

  28. Riniker, S., Wang, Y., Jenkins, J.L., Landrum, G.A.: Using information from historical high-throughput screens to predict active compounds. doi:10.1021/ci500190p (2014)

  29. Scannell, J.W., Bosley, J.: When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis. PLoS ONE 11(2), e0147,215–21 (2016)

    Article  Google Scholar 

  30. Serra-Toro, C., Traver, V.J., Pla, F.: Exploring some practical issues of SVM+: Is really privileged information that helps Pattern Recogn. Lett. 42, 40–46 (2014)

    Article  Google Scholar 

  31. Steinbeck, C., Han, Y., Kuhn, S., Horlacher, O., Luttmann, E., Willighagen, E.: The chemistry development kit (cdk) an open-source java library for chemo- and bioinformatics. J. Chem. Inf. Comput. Sci. 43(2), 493–500 (2003). doi:10.1021/ci025584y PMID: 12653513

    Article  Google Scholar 

  32. Vapnik, V.: Learning Using Privileged Information: Similarity Control and Knowledge Transfer (2015)

  33. Vapnik, V., Vashist, A.: A new learning paradigm: Learning using privileged information. Neural Netw. 22(5), 544–557 (2009)

    Article  MATH  Google Scholar 

  34. Vovk, V., Shafer, G., Gammerman, A.: Algorithmic learning in a random world. Springer, New York (2005)

    MATH  Google Scholar 

  35. Wang, Z., Ji, Q.: Classifier learning with hidden information Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4969–4977 (2015)

  36. Waring, M.J., Arrowsmith, J., Leach, A.R., Leeson, P.D., Mandrell, S., Owen, R.M., Pairaudeau, G., Pennie, W.D., Pickett, S.D., Wang, J., Wallace, O., Weir, A.: An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat. Publ. Group 14(7), 475–486 (2015)

    Google Scholar 

  37. Woolf, B.: On estimating the relation between blood group and disease. Ann. Human Genet. 19, 251–253 (1955)

    Article  Google Scholar 

  38. Xu, X., Zhou, J.T., Tsang, I., Qin, Z., Goh, R.S.M., Liu, Y.: Simple and efficient learning using privileged information BeyondLabeler: Human is More Than a Labeler. Workshop of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16), New York City, USA. arXiv:1604.01518(2016)

  39. Yau, E., Petersson, C., Dolgos, H., Peters, S.A.: A comparative evaluation of models to predict human intestinal metabolism from nonclinical data. Biopharmaceutics & Drug Disposition (2017)

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Acknowledgments

AD is supported by the Science Foundation Ireland Industry Fellowship No. 15/IFA/2925.

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Authors and Affiliations

  1. AstraZeneca, Innovative Medicines & Early Development, Quantitative Biology, Discovery Sciences, Cambridge Science Park, Cambridge, CB4 0WG, UK

    Claus Bendtsen

  2. University College Dublin Systems Biology Ireland, Belfiled, Dublin, Republic of Ireland

    Andrea Degasperi

  3. AstraZeneca, Innovative Medicines & Early Development, Predictive Compound ADME & Safety, Drug Safety & Metabolism, Pepparedsleden 1, 431 83, Mölndal, Sweden

    Ernst Ahlberg

  4. AstraZeneca, Innovative Medicines & Early Development, Quantitative Biology, Discovery Sciences, Pepparedsleden 1, 431 83, Mölndal, Sweden

    Lars Carlsson

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  1. Claus Bendtsen
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  2. Andrea Degasperi
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  3. Ernst Ahlberg
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  4. Lars Carlsson
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Correspondence to Claus Bendtsen.

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Bendtsen, C., Degasperi, A., Ahlberg, E. et al. Improving machine learning in early drug discovery. Ann Math Artif Intell 81, 155–166 (2017). https://doi.org/10.1007/s10472-017-9541-2

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Keywords

Mathematics Subject Classification (2010)