The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Exploring the impact of size of training sets for the development of predictive QSAR models
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Citations
Cites
SNEDECOR, G. W. (1939) · Soil Science · doi:10.1097/00010694-193902000-00006 · Cited by 19,359
Everitt, B. J., Landau, S., & Leese, M. (1974) · Cited by 9,168
Principles of QSAR models validation: internal and external
Gramatica, P. (2007) · QSAR & Combinatorial Science · doi:10.1002/qsar.200610151 · Cited by 2,208
Eriksson, L., Jaworska, J., Worth, A. P., Cronin, M. T. D., McDowell, R. M., & Gramatica, P. (2003) · Environmental Health Perspectives · doi:10.1289/ehp.5758 · Cited by 1,325
Darlington, R. B. (1990) · Cited by 986
3D QSAR in drug design : theory, methods and applications
Kubinyi, H. (2000) · Medical Entomology and Zoology · Cited by 858
Assessing Model Fit by Cross-Validation
Hawkins, D. M., Basak, S. C., & Mills, D. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci025626i · Cited by 757
Chemometric methods in molecular design
Waterbeemd, H. V. D. (1995) · Medical Entomology and Zoology · Cited by 561
Gasteiger, J., & Zupan, J. (1993) · Angewandte Chemie International Edition in English · doi:10.1002/anie.199305031 · Cited by 503
Golbraikh, A., & Tropsha, A. (2002) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1020869118689 · Cited by 500
Cited by
Further exploring r metrics for validation of QSPR models
Ojha, P. K., Mitra, I., Das, R. N., & Roy, K. (2011) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2011.03.011 · Cited by 554
Self-citationHow not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)
Dearden, J. C., Cronin, M. T. D., & Kaiser, K. L. E. (2009) · SAR and QSAR in Environmental Research · doi:10.1080/10629360902949567 · Cited by 468
Roy, K., Chakraborty, P., Mitra, I., Ojha, P. K., Kar, S., & Das, R. N. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23231 · Cited by 466
Self-citationBig data and artificial intelligence modeling for drug discovery
Zhu, H. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010919-023324 · Cited by 455
Gajewicz, A. (2017) · Nanoscale · doi:10.1039/c7nr02211e · Cited by 71
Metadata
- Authors
- Roy, P. P., Leonard, J. T., & Roy, K.
- Venue
- Chemometrics and Intelligent Laboratory Systems
- Publisher
- Elsevier BV
- Published
- 2008-01
- Volume
- 90
- Issue
- 1
- Pages
- 31-42
- ISSN
- 0169-7439 (ISSN-L)
- OpenAlex
- W1996656176
- Cited by
- 306
- References
- 34
- FWCI
- 16.37
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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- CoChat. (2026). Citation check: Exploring the impact of size of training sets for the development of predictive QSAR models. https://app.cochat.ai/papers/10.1016/j.chemolab.2007.07.004
- The paper
- Roy, P. P., Leonard, J. T., & Roy, K. (2008). Exploring the impact of size of training sets for the development of predictive QSAR models. Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/j.chemolab.2007.07.004
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.
