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Conformal prediction of biological activity of chemical compounds

  • Paolo Toccaceli
  • Ilia Nouretdinov
  • Alexander Gammerman
Open Access
Article
  • 387 Downloads

Abstract

The paper presents an application of Conformal Predictors to a chemoinformatics problem of predicting the biological activities of chemical compounds. The paper addresses some specific challenges in this domain: a large number of compounds (training examples), high-dimensionality of feature space, sparseness and a strong class imbalance. A variant of conformal predictors called Inductive Mondrian Conformal Predictor is applied to deal with these challenges. Results are presented for several non-conformity measures extracted from underlying algorithms and different kernels. A number of performance measures are used in order to demonstrate the flexibility of Inductive Mondrian Conformal Predictors in dealing with such a complex set of data. This approach allowed us to identify the most likely active compounds for a given biological target and present them in a ranking order.

Keywords

Conformal prediction Confidence estimation Chemoinformatics Non-conformity measure 

Mathematics Subject Classification (2010)

68T05 

Notes

Acknowledgments

This project (ExCAPE) has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no. 671555. We are grateful for the help in conducting experiments to the Ministry of Education, Youth and Sports (Czech Republic) that supports the Large Infrastructures for Research, Experimental Development and Innovations project “IT4Innovations National Supercomputing Center – LM2015070”. This work was also supported by EPSRC grant EP/K033344/1 (“Mining the Network Behaviour of Bots”) and by Technology Integrated Health Management (TIHM) project awarded to the School of Mathematics and Information Security at Royal Holloway as part of an initiative by NHS England supported by InnovateUK. We are indebted to Lars Carlsson of Astra Zeneca for providing the data and useful discussions. We are also thankful to Zhiyuan Luo and Vladimir Vovk for many valuable comments and discussions.

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© The Author(s) 2017

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Royal HollowayUniversity of LondonEghamUK

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