The DOI matches its Crossref record.
Prediction of the Acute Toxicity (96-h LC50) of Organic Compounds to the Fathead Minnow (Pimephales promelas) Using a Group Contribution Method
Abstract
A group contribution method has been developed to correlate the acute toxicity (96-h LC50) to the fathead minnow (Pimephales promelas) for 397 organic chemicals. Multilinear regression and computational neural networks (CNNs) were used for model building. The models were able to achieve a fairly good correlation of the data (r2 > 0.9). The linear model, which included four specific interaction terms, provided a rapid means of predicting the toxicity of a compound. The CNN model was able to yield virtually the same predictions with or without the four interaction terms that were included in the multilinear model.
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.
Integrity signals
Registry
Cites
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.
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
Cited by
Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?
Martin, T. M., Harten, P., Young, D. M., Muratov, E. N., Golbraikh, A., Zhu, H., & Tropsha, A. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300338w · Cited by 321
Self-citationQuantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks
Wu, K., & Wei, G.-W. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00558 · Cited by 160
Metadata
- Authors
- Martin, T. M., & Young, D. M.
- Venue
- Chemical Research in Toxicology
- Publisher
- American Chemical Society (ACS)
- Published
- 2001-09-11
- Volume
- 14
- Issue
- 10
- Pages
- 1378-1385
- ISSN
- 0893-228X (ISSN-L)
- 1520-5010
- OpenAlex
- W2070246294
- Cited by
- 174
- References
- 0
- FWCI
- 3.46
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Prediction of the Acute Toxicity (96-h LC50) of Organic Compounds to the Fathead Minnow (Pimephales promelas) Using a Group Contribution Method. https://app.cochat.ai/papers/10.1021/tx0155045
- The paper
- Martin, T. M., & Young, D. M. (2001). Prediction of the Acute Toxicity (96-h LC50) of Organic Compounds to the Fathead Minnow (Pimephales promelas) Using a Group Contribution Method. Chemical Research in Toxicology. https://doi.org/10.1021/tx0155045
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
