Toxicity Prediction Using Pre-trained Autoencoder

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

Research output: Contribution to conferencePaperpeer-review

3 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 - 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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