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Machine learning to predict toxicity of compounds

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Title: Machine learning to predict toxicity of compounds
Authors: Grenet, I
Yin, Y
Comet, J-P
Gelenbe, E
Item Type: Conference Paper
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.
Editors: Kurkova, V
Manolopoulos, Y
Hammer, B
Iliadis, L
Maglogiannis, I
Issue Date: 27-Sep-2018
Date of Acceptance: 27-Sep-2018
URI: http://hdl.handle.net/10044/1/69204
DOI: https://doi.org/10.1007/978-3-030-01418-6_33
ISBN: 9783030014179
ISSN: 0302-9743
Publisher: Springer
Start Page: 335
End Page: 345
Journal / Book Title: Artificial Neural Networks and Machine Learning – ICANN 2018
Copyright Statement: © Springer Nature Switzerland AG 2018. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
Sponsor/Funder: EU H2020 Framework Programme for Research and Innovation
Conference Name: 27th International Conference on Artificial Neural Networks (ICANN)
Keywords: Science & Technology
Computer Science, Artificial Intelligence
Computer Science
Machine learning
Data augmentation
08 Information and Computing Sciences
Artificial Intelligence & Image Processing
Publication Status: Published
Start Date: 2018-10-04
Finish Date: 2018-10-07
Conference Place: Rhodes, Greece
Online Publication Date: 2018-09-27
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering