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DeepTox: Toxicity Prediction using Deep Learning
Open accessAbstract
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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“Machine learning has also been implemented to assess the toxicity of molecules, for instance, using DeepTox, a DL-based model for evaluating the toxic effects of compounds based on a dataset containing many drug molecules [19].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [19]
In the citing paper
“When working on new test sets, CNN usually predicts with high accuracy [88].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [88]
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“Using CNN to automatically construct abstract chemical fragments, the deep learning model showed very high performance in toxicity prediction based on high-throughput data, with an average area under the curve (AUC) of 0.846 [88].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [88]
In the citing paper
“After extensive data training, CNN can identify those substructure or molecular fragments that might make a molecule toxic.”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [88]
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Metadata
- Authors
- Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S.
- Affiliations
- Johannes Kepler University of Linz (AT)
- Corresponding author
- Hochreiter, Sepp
- Venue
- Frontiers in Environmental Science
- Publisher
- Frontiers Media SA
- Published
- 2016-02-02
- Volume
- 3
- ISSN
- 2296-665X (ISSN-L)
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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