Cited by 1,081Computational Drug Discovery MethodsMachine Learning in Materials ScienceAnimal testing and alternatives

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DeepTox: Toxicity Prediction using Deep Learning

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Mayr, Andreas, Klambauer, Günter, Unterthiner, Thomas, Hochreiter, Sepp·2016·Frontiers in Environmental Science

DOI 10.3389/fenvs.2015.00080

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Abstract

The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 different toxic effects by specifically designed assays. We participated in this challenge to assess the performance of Deep Learning in computational toxicity prediction. Deep Learning has already revolutionized image processing, speech recognition, and language understanding but has not yet been applied to computational toxicity. Deep Learning is founded on novel algorithms and architectures for artificial neural networks together with the recent availability of very fast computers and massive datasets. It discovers multiple levels of distributed representations of the input, with higher levels representing more abstract concepts. We hypothesized that the construction of a hierarchy of chemical features gives Deep Learning the edge over other toxicity prediction methods. Furthermore, Deep Learning naturally enables multi-task learning, that is, learning of all toxic effects in one neural network and thereby learning of highly informative chemical features. In order to utilize Deep Learning for toxicity prediction, we have developed the DeepTox pipeline. First, DeepTox normalizes the chemical representations of the compounds. Then it computes a large number of chemical descriptors that are used as input to machine learning methods. In its next step, DeepTox trains models, evaluates them, and combines the best of them to ensembles. Finally, DeepTox predicts the toxicity of new compounds. In the Tox21 Data Challenge, DeepTox had the highest performance of all computational methods winning the grand challenge, the nuclear receptor panel, the stress response panel, and six single assays (teams “Bioinf@JKU”). We found that Deep Learning excelled in toxicity prediction and outperformed many other computational approaches like naive Bayes, support vector machines, and random forests.

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Authors: Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S.
Affiliations: Johannes Kepler University of Linz (AT)
Corresponding author: Hochreiter, Sepp
ORCID: Mayr, Andreas
Klambauer, Günter
Unterthiner, Thomas
Hochreiter, Sepp
Venue: Frontiers in Environmental Science
Publisher: Frontiers Media SA
Published: 2016-02-02
Volume: 3
ISSN: 2296-665X (ISSN-L)
DOI: 10.3389/fenvs.2015.00080
OpenAlex: W2189911347
Cited by: 1,081
References: 89
Citations by year: 2026: 148 · 2025: 124 · 2024: 126 · 2023: 124 · 2022: 95 · 2021: 143 · 2020: 98 · 2019: 108 · 2018: 79 · 2017: 24 · 2016: 9
FWCI: 67.46
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: Frontiers in Environmental Science (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: ChemBioBridge
European Commission
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
Keywords: toxicity prediction, deep learning, multi-task learning, molecular descriptors, ensemble learning, environmental chemicals, drug toxicity

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DeepTox: Toxicity Prediction using Deep Learning

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