The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Application of Random Forest Approach to QSAR Prediction of Aquatic Toxicity
Abstract
This work is devoted to the application of the random forest approach to QSAR analysis of aquatic toxicity of chemical compounds tested on Tetrahymena pyriformis. The simplex representation of the molecular structure approach implemented in HiT QSAR Software was used for descriptors generation on a two-dimensional level. Adequate models based on simplex descriptors and the RF statistical approach were obtained on a modeling set of 644 compounds. Model predictivity was validated on two external test sets of 339 and 110 compounds. The high impact of lipophilicity and polarizability of investigated compounds on toxicity was determined. It was shown that RF models were tolerant for insertion of irrelevant descriptors as well as for randomization of some part of toxicity values that were representing a "noise". The fast procedure of optimization of the number of trees in the random forest has been proposed. The discussed RF model had comparable or better statistical characteristics than the corresponding PLS or KNN models.
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
Golbraikh, A., & Tropsha, A. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00123-1 · Cited by 3,735
Cited by
DeepTox: toxicity prediction using deep learning
Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S. (2018) · Chemical Science · doi:10.1039/c8sc00148k · Cited by 584
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-citationInterpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future
Polishchuk, P. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00274 · Cited by 236
Self-citationWu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193
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
Self-citationWang, Y., Guo, Y., Kuang, Q., Pu, X., Ji, Y., Zhang, Z., & Li, M. (2015) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-014-9827-y · Cited by 69
Per Aspera Ad Astra : Application of Simplex Qsar Approach in Antiviral Research
Muratov, E. N., Artemenko, A. G., Varlamova, E. V., Polischuk, P. G., Lozitsky, V. P., Fedchuk, A. S., Lozitska, R. L., Gridina, T. L., Koroleva, L. S., Sil’nikov, V. N., et al. (2010) · Future Medicinal Chemistry · doi:10.4155/fmc.10.194 · Cited by 64
Self-citation
Metadata
- Authors
- Polishchuk, P. G., Muratov, E. N., Artemenko, A. G., Kolumbin, O. G., Muratov, N. N., & Kuz’min, V. E.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2009-10-27
- Volume
- 49
- Issue
- 11
- Pages
- 2481-2488
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2010524461
- Cited by
- 182
- References
- 1
- FWCI
- 7.08
- 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
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.
