ReviewComputational and structural biotechnology journal2013
Multivariate linear QSPR/QSAR models: Rigorous evaluation of variable selection for PLS.
Review in Computational and structural biotechnology journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Application of in silico methods to predict the acute toxicity of bicyclic organophosphorus compounds as potential chemical weapon.Archives of toxicology · 2025Article
- Unlocking the Potential of Clustering and Classification Approaches: Navigating Supervised and Unsupervised Chemical Similarity.Environmental health perspectives · 2024Article
- Molecular structural modeling and physical characteristics of anti-breast cancer drugs via some novel topological descriptors and regression models.Current research in structural biology · 2024Article
- A new perspective on the modeling and topological characterization of H-Naphtalenic nanosheets with applications.Journal of molecular modeling · 2022Article
- Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase.International journal of molecular sciences · 2019Article
- Prediction and interpretation of the lipophilicity of small peptides.Journal of computer-aided molecular design · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Basic chemometric methods for making empirical regression models for QSPR/QSAR are briefly described from a user's point of view. Emphasis is given to PLS regression, simple variable selection and a careful and cautious evaluation of the performance of PLS models by repeated double cross validation (rdCV). A demonstration example is worked out for QSPR models that predict gas chromatographic retention indices (values between 197 and 504 units) of 209 polycyclic aromatic compounds (PAC) from molecular descriptors generated by Dragon software. Most favorable models were obtained from data sets containing also descriptors from 3D structures with all H-atoms (computed by Corina software), using stepwise variable selection (reducing 2688 descriptors to a subset of 22). The final QSPR model has typical prediction errors for the retention index of ±12 units (95% tolerance interval, for test set objects). Programs and data are provided as supplementary material for the open source R software environment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.