ToxCast EPA in Vitro to in Vivo Challenge: Insight into the Rank-I Model
Open accessAbstract
The ToxCast EPA challenge was managed by TopCoder in Spring 2014. The goal of the challenge was to develop a model to predict the lowest effect level (LEL) concentration based on in vitro measurements and calculated in silico descriptors. This article summarizes the computational steps used to develop the Rank-I model, which calculated the lowest prediction error for the secret test data set of the challenge. The model was developed using the publicly available Online CHEmical database and Modeling environment (OCHEM), and it is freely available at http://ochem.eu/article/68104 . Surprisingly, this model does not use any in vitro measurements. The logic of the decision steps used to develop the model and the reason to skip inclusion of in vitro measurements is described. We also show that inclusion of in vitro assays would not improve the accuracy of the model.
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.
Integrity signals
Registry
Cites
Statistics
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.
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?
ToxCast Lowest Effect Level prediction challenge: highest prediction accuracy with lowest RMSE of 1.08 log units was achieved using a consensus prediction of Associative Neural Network models.
“The highest prediction accuracy with the lowest RMSE of 1.08 log units was achieved using a consensus prediction of Associative Neural Network87 models developed with several sets of descriptors.92”
From QSAR without borders (2020) · cites this paper as [92]
The top-ranked model in ToxCast was exclusively based on calculated descriptors and was not improved by adding in vitro data as descriptors.
“Although the organizers of the challenge have offered a set of in vitro measurements performed within the ToxCast project, the top-ranked model was exclusively based on the calculated descriptors and was not improved by adding in vitro data as descriptors.92”
From QSAR without borders (2020) · cites this paper as [92]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Handbook of Molecular Descriptors
Todeschini, R., & Consonni, V. (2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613106 · Cited by 4,107 · see citing works
CERAPP: Collaborative Estrogen Receptor Activity Prediction Project
Mansouri, K., Abdelaziz, A., Rybacka, A., Roncaglioni, A., Tropsha, A., Varnek, A., Zakharov, A., Worth, A., Richard, A. M., Grulke, C. M., et al. (2016) · Environmental Health Perspectives · doi:10.1289/ehp.1510267 · Cited by 375 · see citing works
Self-citationA renaissance of neural networks in drug discovery
Baskin, I. I., Winkler, D., & Tetko, I. V. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1201262 · Cited by 235 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
Self-citationA renaissance of neural networks in drug discovery
Baskin, I. I., Winkler, D., & Tetko, I. V. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1201262 · Cited by 235 · see citing works
Self-citationAmorim, A. M. B., Piochi, L. F., Gaspar, A. T., Preto, A. J., Rosário-Ferreira, N., & Moreira, I. S. (2024) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00352 · Cited by 132 · see citing works
Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space
Sosnin, S., Karlov, D., Tetko, I. V., & Fedorov, M. V. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00685 · Cited by 102 · see citing works
“55 We constructed consensus models by averaging of the predictions of top five individual models.”
MethodSelf-citation
- Authors
- Novotarskyi, S., Abdelaziz, A., Sushko, Y., Körner, R., Vogt, J., & Tetko, I. V.
- Affiliations
- eADMET GmbH, Lichtenbergstraße 8, D-85748 Garching, Munich, Germany
- Technical University of Munich (DE)
- Helmholtz Munich (DE)
- Corresponding author
- Tetko, Igor V.
- ORCID
- Tetko, Igor V.
- Venue
- Chemical Research in Toxicology
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-04-27
- Volume
- 29
- Issue
- 5
- Pages
- 768-775
- ISSN
- 0893-228X (ISSN-L)
- 1520-5010
- PMC
- PMC5413193
- OpenAlex
- W2342596260
- Cited by
- 34
- References
- 3
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 3 · 2022: 1 · 2021: 4 · 2020: 5 · 2019: 2 · 2018: 5 · 2017: 2 · 2016: 4
- FWCI
- 3.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Chemical Research in Toxicology (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- University of Regensburg Publication Server (University of Regensburg) (submittedVersion)
- Site cant be reached (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Environmental Toxicology and Ecotoxicology
- Keywords
- in vitro-in vivo extrapolation, prediction error, in vitro assay, computational toxicology
- MeSH terms
- Machine Learning, Dose-Response Relationship, Drug, Models, Theoretical, Neural Networks, Computer, In Vitro Techniques
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
