Toxicity Prediction Using Pre-trained Autoencoder

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

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)


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
Number of pages304
Publication statusPublished (in print/issue) - 3 Dec 2018
EventIEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Madrid, Spain
Duration: 3 Dec 20186 Dec 2018


ConferenceIEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Internet address


  • Chemical Compounds
  • Autoencoder


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