The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Validated QSAR Prediction of OH Tropospheric Degradation of VOCs: Splitting into Training−Test Sets and Consensus Modeling
Abstract
The rate constant for hydroxyl radical tropospheric degradation of 460 heterogeneous organic compounds is predicted by QSAR modeling. The applied Multiple Linear Regression is based on a variety of theoretical molecular descriptors, selected by the Genetic Algorithms-Variable Subset Selection (GA-VSS) procedure. The models were validated for predictivity by both internal and external validation. For the external validation two splitting approaches, D-optimal Experimental Design and Kohonen Artificial Neural Networks (K-ANN), were applied to the original data set to compare the two methodologies. We emphasize that external validation is the only way to establish a reliable QSAR model for predictive purposes. Predicted data by consensus modeling from different models are also proposed.
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Cites
Handbook of Molecular Descriptors
Todeschini, R., & Consonni, V. (2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613106 · Cited by 4,107
Golbraikh, A., & Tropsha, A. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00123-1 · Cited by 3,735
Rational selection of training and test sets for the development of validated QSAR models
Golbraikh, A., Shen, M., Xiao, Z., Xiao, Y.-D., Lee, K.-H., & Tropsha, A. (2003) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1025386326946 · Cited by 712
Cited by
Netzeva, T. I., Worth, A. P., Aldenberg, T., Benigni, R., Cronin, M. T. D., Gramatica, P., Jaworska, J. S., Kahn, S., Klopman, G., Marchant, C. A., et al. (2005) · Alternatives to Laboratory Animals · doi:10.1177/026119290503300209 · Cited by 836
Self-citationComments on the Definition of the Q2 Parameter for QSAR Validation
Consonni, V., Ballabio, D., & Todeschini, R. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900115y · Cited by 585
How 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
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
Good practices in LIBS analysis: Review and advices
El Haddad, J., Canioni, L., & Bousquet, B. (2014) · Spectrochimica Acta Part B: Atomic Spectroscopy · doi:10.1016/j.sab.2014.08.039 · Cited by 307
Principles of QSAR Modeling: Comments and Suggestions From Personal Experience
Gramatica, P. (2020) · International Journal of Quantitative Structure-Property Relationships · doi:10.4018/ijqspr.20200701.oa1 · Cited by 258
Self-citationStatistical external validation and consensus modeling: A QSPR case study for Koc prediction
Gramatica, P., Giani, E., & Papa, E. (2007) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2006.06.005 · Cited by 258
Self-citationEarly ADME/T Predictions: Toy or Tool?
Tetko, I. V., & Oprea, T. I. (2008) · Chemoinformatics Approaches to Virtual Screening · doi:10.1039/9781847558879-00240 · Cited by 5
Metadata
- Authors
- Gramatica, P., Pilutti, P., & Papa, E.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 2004-08-03
- Volume
- 44
- Issue
- 5
- Pages
- 1794-1802
- ISSN
- 0095-2338 (ISSN-L)
- OpenAlex
- W2095138748
- Cited by
- 209
- References
- 3
- FWCI
- 13.61
- Type
- journal article
- Language
- en
- Open access
- closed
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