Cited by 42Computational Drug Discovery MethodsAnimal testing and alternativesMachine Learning in Materials Science

Estimation of acute oral toxicity in rat using local lazy learning

Open access
Lu, Jing, Peng, Jianlong, Wang, Jinan, Shen, Qiancheng, Bi, Yi, Gong, Likun, Zheng, Mingyue, Luo, Xiaomin +3 more·2014·Journal of Cheminformatics

Abstract

BACKGROUND: Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, LD50, is frequently used as a general indicator of a substance's acute toxicity, and there is a high demand on developing non-animal-based prediction of LD50. Unfortunately, it is difficult to accurately predict compound LD50 using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes. RESULTS: In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop LD50 prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients R(2) of 0.712 on a test set containing 2,896 compounds. CONCLUSION: Encouraged by the promising results, we expect that our consensus LLL model of LD50 would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus.

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 · 10.1186/1758-2946-6-26

Cites

24 of 24 references verified0 citation errors · 0 not checkable

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.

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?

  • Lu et al. used k-NN combined linear regression model to predict acute oral toxicity in rats and achieved an R square value of 0.712, using local chemical structure represented by molecular fingerprints.

    “in 2011, Lu et al. used k-NN combined linear regression model to predict acute oral toxicity in rats and achieved a R square value of 0.712, for which they utilized the local chemical structure that was represented by molecular fingerprints [31].”

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

Flagged first, then by citations

Research works printed but not linked: 1

  1. European Parliament and of council Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No. 793/93 and Commission Regulation (EC) No. 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC 2006 Official Journal of the European Union 396 1 849

Cited by

Sorted by citations

Authors
Lu, J., Peng, J., Wang, J., Shen, Q., Bi, Y., Gong, L., Zheng, M., Luo, X., Zhu, W., Jiang, H., & Chen, K.
Affiliations
  • Yantai University (CN)
  • Chinese Academy of Sciences (CN)
  • East China University of Science and Technology (CN)
Corresponding author
  • Zheng, Mingyue
  • Luo, Xiaomin
Venue
Journal of Cheminformatics
Published
2014-05-16
Volume
6
Issue
1
Pages
26-26
ISSN
1758-2946 (ISSN-L)
OpenAlex
W2160344370
Cited by
42
References
24
Citations by year
2026: 1 · 2025: 1 · 2024: 4 · 2023: 1 · 2022: 6 · 2021: 3 · 2020: 5 · 2019: 5 · 2018: 6 · 2017: 2 · 2016: 5 · 2015: 3
FWCI
3.44
Type
journal article
Language
en
Open access
Locations
  • Journal of Cheminformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
National Natural Science Foundation of China (2013ZX09507001, 81001399, 81220108025, 2012ZX09301-001-002, 2012AA020308)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
acute oral toxicity, k-nearest neighbors, QSAR, applicability domain, consensus model, structure-toxicity relationship, rat 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