Current application of conformal prediction in drug discovery

Two useful applications
  • Ernst AhlbergEmail author
  • Oscar Hammar
  • Claus Bendtsen
  • Lars Carlsson


We present two applications of conformal prediction relevant to drug discovery. The first application is around interpretation of predictions and the second one around the selection of compounds to progress in a drug discovery project setting.


Drug discovery Conformal prediction Interpretation 


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  1. 1.
    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)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Curran, M. E., Splawski, I., Timothy, K. W., Vincen, G., Green, E. D., Keating, M. T.: A molecular basis for cardiac arrhythmia: HERG mutations cause long QT syndrome. Cell 80(5), 795–803 (1995). doi: 10.1016/0092-8674(95)90358-5, CrossRefGoogle Scholar
  4. 4.
    Scannell, J. W., Bosley, J.: When quality beats quantity: decision theory, drug discovery, and the reproducibility crisis. PLoS ONE 11(2), 1–21 (2016). doi: 10.1371/journal.pone.0147215 CrossRefGoogle Scholar
  5. 5.
    Spjuth, O., Eklund, M., Helgee, E. A., Boyer, S., Carlsson, L.: Integrated Decision Support for Assessing Chemical Liabilities. J. Chem. Inf. Model. 51(8), 1840 (2011)CrossRefGoogle Scholar
  6. 6.
    Gramatica, P.: [Online accessed january 26, 2012]. Available from: (2008)
  7. 7.
    Christianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, 1st edn. Cambridge University Press, Cambridge, UK (2004)Google Scholar
  8. 8.
    Breimann, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  9. 9.
    Mathea, M., Klingspohn, W., Baumann, K.: Chemoinformatic classification methods and their applicability domain. Mol. Inf. 35(5), 160–180 (2016). doi: 10.1002/minf.201501019 CrossRefGoogle Scholar
  10. 10.
    Vovk, V., Gammerman, A., Shafer, G.: Algorithmic Learning in a Random World. Springer New York, Inc., Secaucus, NJ, USA (2005)zbMATHGoogle Scholar
  11. 11.
    Wood, D. J., Carlsson, L., Eklund, M., Norinder, U., Stålring, J.: QSAR with experimental and predictive distributions: an information theoretic approach for assessing model quality. J. Comput. Aided Mol. Des. 27(3), 203–219 (2013). doi: 10.1007/s10822-013-9639-5 CrossRefGoogle Scholar
  12. 12.
    Guha, R.: On the interpretation and interpretability of quantitative structure–activity relationship models. J. Comput. Aided Mol. Des. 22(12), 857–871 (2008). doi: 10.1007/s10822-008-9240-5 CrossRefGoogle Scholar
  13. 13.
    Carlsson, L., Ahlberg, E., Boyer, S.: Interpretation of nonlinear QSAR models applied to Ames mutagenicity data. J. Chem. Info. Model. 49(11), 2551–2558 (2009)CrossRefGoogle Scholar
  14. 14.
    Ahlberg, E., Spjuth, O., Hasselgren, C., Carlsson, L.: Interpretation of Conformal Prediction Classification Models, pp 323–334. Springer International Publishing, Cham (2015)Google Scholar

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© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Predictive Compound ADME & Safety, Drug Safety & MetabolismAstraZeneca, Innovative Medicines & Early DevelopmentMölndalSweden
  2. 2.Quantitative Biology, Discovery SciencesAstraZeneca, Innovative Medicines & Early DevelopmentMölndalSweden
  3. 3.Quantitative Biology, Discovery SciencesAstraZeneca, Innovative Medicines & Early DevelopmentCambridgeUK

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