Toxicity Prediction Using Pre-trained Autoencoder

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.

Conference

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

Fingerprint

Chemical compounds
Toxicity
Classifiers
Assays
Experiments
Testing

Keywords

  • Chemical Compounds
  • Autoencoder

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.
Galushka, Mykola ; Fiona, Browne ; Mulvenna, Maurice ; Bond, RR ; Lightbody, G. / Toxicity Prediction Using Pre-trained Autoencoder. Paper presented at IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain.304 p.
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title = "Toxicity Prediction Using Pre-trained Autoencoder",
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.",
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Galushka, M, Fiona, B, Mulvenna, M, Bond, RR & Lightbody, G 2018, 'Toxicity Prediction Using Pre-trained Autoencoder' Paper presented at IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, 3/12/18 - 6/12/18, pp. 299.

Toxicity Prediction Using Pre-trained Autoencoder. / Galushka, Mykola; Fiona, Browne; Mulvenna, Maurice; Bond, RR; Lightbody, G.

2018. 299 Paper presented at IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Toxicity Prediction Using Pre-trained Autoencoder

AU - Galushka, Mykola

AU - Fiona, Browne

AU - Mulvenna, Maurice

AU - Bond, RR

AU - Lightbody, G

PY - 2018/12/3

Y1 - 2018/12/3

N2 - 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.

AB - 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.

KW - Chemical Compounds

KW - Autoencoder

M3 - Paper

SP - 299

ER -

Galushka M, Fiona B, Mulvenna M, Bond RR, Lightbody G. Toxicity Prediction Using Pre-trained Autoencoder. 2018. Paper presented at IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain.