The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction
Abstract
The method for QSAR modelling of rat acute toxicity based on the combination of QNA (Quantitative Neighbourhoods of Atoms) descriptors, PASS (Prediction of Activity Spectra for Substances) predictions and self‐consistent regression (SCR) is presented. PASS predicted biological activity profiles are used as independent input variables for QSAR modelling with SCR. QSAR models were developed using LD50 values for compounds tested on rats with four types of administration (oral, intravenous, intraperitoneal, subcutaneous). The proposed method was evaluated on the set of compounds tested for acute rat toxicity with oral administration (7286 compounds) used for testing the known QSAR methods in T.E.S.T. 3.0 program (U.S. EPA). The several other sets of compounds tested for acute rat toxicity by different routes of administration selected from SYMYX MDL Toxicity Database were used too. The method was compared with the results of prediction of acute rodent toxicity for noncongeneric sets obtained by ACD/Labs Inc. The test sets were predicted with regards to the applicability domain. Comparison of accuracy for QSAR models obtained separately using QNA descriptors, PASS predictions, nearest neighbours’ assessment with consensus models clearly demonstrated the benefits of consensus prediction. Free available web‐service for prediction of LD50 values of rat acute toxicity was developed: http://www.pharmaexpert.ru/GUSAR/AcuToxPredict/
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
Cites
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578
Quantitative Structure−Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure
Zhu, H., Martin, T. M., Ye, L., Sedykh, A., Young, D. M., & Tropsha, A. (2009) · Chemical Research in Toxicology · doi:10.1021/tx900189p · Cited by 287
Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis
Zhu, H., Tropsha, A., Fourches, D., Varnek, A., Papa, E., Gramatica, P., Öberg, T., Dao, P., Cherkasov, A., & Tetko, I. V. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700443v · Cited by 287
Poroikov, V. V., Filimonov, D. A., Borodina, Y. V., Lagunin, A. A., & Kos, A. (2000) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci000383k · Cited by 254
Self-citationPASS Biological Activity Spectrum Predictions in the Enhanced Open NCI Database Browser
Poroikov, V. V., Filimonov, D. A., Ihlenfeldt, W.-D., Gloriozova, T. A., Lagunin, A. A., Borodina, Y. V., Stepanchikova, A. V., & Nicklaus, M. C. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci020048r · Cited by 236
Self-citationQNA-based ‘Star Track’ QSAR approach
Filimonov, D. A., Zakharov, A. V., Lagunin, A. A., & Poroikov, V. (2009) · SAR and QSAR in environmental research · doi:10.1080/10629360903438370 · Cited by 100
Self-citationPASS: identification of probable targets and mechanisms of toxicity
Poroikov, V., Filimonov, D. A., Lagunin, A. A., Gloriozova, T. A., & Zakharov, A. V. (2007) · SAR and QSAR in environmental research · doi:10.1080/10629360601054032 · Cited by 94
Self-citationConsensus QSAR Models: Do the Benefits Outweigh the Complexity?
Hewitt, M., Cronin, M., Madden, J. C., Rowe, P., Johnson, C., Obi, A., & Enoch, S. J. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700016d · Cited by 89
Prediction of Biological Activity Spectra via The Internet
Sadym, A., Lagunin, A. A., Filimonov, D. A., & Poroikov, V. (2003) · SAR and QSAR in environmental research · doi:10.1080/10629360310001623935 · Cited by 72
Self-citationSazonovas, A., Japertas, P., & Didžiapetris, R. (2010) · SAR and QSAR in environmental research · doi:10.1080/10629360903568671 · Cited by 54
Cited by
QSAR Modeling: Where have you been? Where are you going to?
Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm4004285 · Cited by 2,164
Universal Approach for Structural Interpretation of QSAR/QSPR Models
Polishchuk, P. G., Kuz'min, V. E., Artemenko, A. G., & Muratov, E. N. (2013) · Molecular Informatics · doi:10.1002/minf.201300029 · Cited by 76
Martin, T. M., Lilavois, C. R., & Barron, M. G. (2017) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2017.1343204 · Cited by 34
Metadata
- Authors
- Lagunin, A., Zakharov, A., Filimonov, D., & Poroikov, V.
- Venue
- Molecular Informatics
- Publisher
- Wiley
- Published
- 2011-03-14
- Volume
- 30
- Issue
- 2-3
- Pages
- 241-250
- ISSN
- 1868-1743 (ISSN-L)
- 1868-1751
- OpenAlex
- W2106421086
- Cited by
- 379
- References
- 14
- FWCI
- 5.38
- 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: QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction. https://app.cochat.ai/papers/10.1002/minf.201000151
- The paper
- Lagunin, A., Zakharov, A., Filimonov, D., & Poroikov, V. (2011). QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction. Molecular Informatics. https://doi.org/10.1002/minf.201000151
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.
