Toxicity Prediction Using Pre-trained Autoencoder

Mykola Galushka, Browne Fiona, Maurice Mulvenna, RR Bond, G Lightbody

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

Toxicology in the 21st Century (Tox21) is a collaborative initiative whose purpose is to investigate and develop efficient testing approaches to predict the impact chemical compounds have on Humans. In this paper we investigate how a pre-trained auto-encoder can be used to build classifiers capable of predicting the toxicity property of chemical compounds. Using a Deep Learning approach, we performed experiments to deter-mine if chemical compound fingerprints can be used to predict active and inactive compounds based on simplified molecular-input line-entry system (SMILES) in twelve selected assays. We conducted these experiments using data from ChEMBL and Tox21 to investigate how the latent layer produced by an auto-encoder can be used to train a classifier. All experimental results are compared against the winning teams of the Tox21 challenge, where positives and limitations of the proposed approaches are discussed.
Original languageEnglish
Pages299
Number of pages304
Publication statusPublished - 3 Dec 2018
EventIEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Madrid, Spain
Duration: 3 Dec 20186 Dec 2018
http://orienta.ugr.es/bibm2018/

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine (BIBM)
CountrySpain
CityMadrid
Period3/12/186/12/18
Internet address

Keywords

  • Chemical Compounds
  • Autoencoder

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  • Cite this

    Galushka, M., Fiona, B., Mulvenna, M., Bond, RR., & Lightbody, G. (2018). Toxicity Prediction Using Pre-trained Autoencoder. 299. Paper presented at IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain. https://ieeexplore.ieee.org/document/8621421