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

The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.

DeepTox: Toxicity Prediction using Deep Learning

Open access
Mayr, Andreas, Klambauer, Günter, Unterthiner, Thomas, Hochreiter, Sepp·2016·Frontiers in Environmental Science
DOI 10.3389/fenvs.2015.00080
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fenvs.2015.00080

Cites

89 of 89 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 89 references

Research works printed but not linked: 2

  1. Svetnik, 2003, Random forest: a classification and regression tool for compound classification and QSAR modeling, J. Chem. Inf. Comput. Sci., 43, 1947, 10.1021/ci034160g · DOI
  2. Unterthiner, 2014, Deep learning as an opportunity in virtual screening

Cited by

Sorted by citations

Items 501–600 of 1,000

Related

See all

Metadata

Authors
Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S.
Affiliations
Johannes Kepler University of Linz (AT)
Corresponding author
Hochreiter, Sepp
ORCID
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
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 ›

Are you an author, or do you see a problem with this page? Request a review