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International Conference on Artificial Neural Networks

ICANN 2018: Artificial Neural Networks and Machine Learning – ICANN 2018 pp 335–345Cite as

Machine Learning to Predict Toxicity of Compounds

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11139))

Abstract

Toxicology studies are subject to several concerns, and they raise the importance of an early detection of the potential for toxicity of chemical compounds which is currently evaluated through in vitro assays assessing their bioactivity, or using costly and ethically questionable in vivo tests on animals. Thus we investigate the prediction of the bioactivity of chemical compounds from their physico-chemical structure, and propose that it be automated using machine learning (ML) techniques based on data from in vitro assessment of several hundred chemical compounds. We provide the results of tests with this approach using several ML techniques, using both a restricted dataset and a larger one. Since the available empirical data is unbalanced, we also use data augmentation techniques to improve the classification accuracy, and present the resulting improvements.

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Author information

Authors and Affiliations

  1. University Côte d’Azur, I3S Laboratory, UMR CNRS 7271, CS 40121, 06903, Sophia Antipolis Cedex, France

    Ingrid Grenet, Jean-Paul Comet & Erol Gelenbe

  2. Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Imperial College, London, UK

    Yonghua Yin & Erol Gelenbe

Authors
  1. Ingrid Grenet
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  2. Yonghua Yin
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  3. Jean-Paul Comet
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  4. Erol Gelenbe
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Corresponding author

Correspondence to Ingrid Grenet .

Editor information

Editors and Affiliations

  1. Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. CITEC Bielefeld University, Bielefeld, Germany

    Barbara Hammer

  4. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  5. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

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Grenet, I., Yin, Y., Comet, JP., Gelenbe, E. (2018). Machine Learning to Predict Toxicity of Compounds. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_33

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  • DOI: https://doi.org/10.1007/978-3-030-01418-6_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01417-9

  • Online ISBN: 978-3-030-01418-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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