Further exploring rm2 metrics for validation of QSPR models

Ojha, Probir Kumar; Mitra, Indrajit; Das, Rudra Narayan; Roy, Kunal

doi:10.1016/j.chemolab.2011.03.011

Cited by 490 publications

(250 citation statements)

References 125 publications

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“…There are also blocked attributes (correlation weight is equal Table 2. Criteria of predictability [19,20] for models calculated with the CORAL software.…”

Section: Resultsmentioning

confidence: 99%

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Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical

et al. 2012

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The rate constants (K OH ) of reactions between 78 organic aromatic pollutants and hydroxyl radical were examined. Simplified molecular input line entry system was used as representation of the molecular structure of the pollutants. Quantitative structure-property relationships was developed using CORAL software (http://www.insilico.eu/CORAL) for four random splits of the data into the subtraining, calibration, and test sets. The obtained results reveal good predictive potential of the applied approach: correlation coefficients (r 2 ) for the test sets of the four random splits are 0.75, 0.91, 0.84, and 0.80. Using the Monte Carlo method CORAL software generated the optimal descriptors for one-variable models. The reproducibility of each model was tested performing three runs of the Monte Carlo optimization. The current data were compared to previous results and discussed.

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“…There are also blocked attributes (correlation weight is equal Table 2. Criteria of predictability [19,20] for models calculated with the CORAL software.…”

Section: Resultsmentioning

confidence: 99%

“…Table 2 contains statistical criteria of the predictability of the models. [18,19] One can see (Table 2) that the predictability for all splits is quite good. Figure 2 displays graphical representation of the models developed for four splits.…”

Section: Introductionmentioning

confidence: 90%

Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical

et al. 2012

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“…All parameters listed below Eq. (2) and employed to verified model's fitting, robustness and predictive abilities (calculated by means of quality measurement summarised in Table 1, [41][42][43][44][45][46]) meet the required criteria and, therefore, also proves the model's quality. The Yscrambling procedure (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Table 1 Quality measures for QSAR models [41][42][43][44][45][46] Goodness of fit Robustness Predictive ability…”

Section: Resultsmentioning

confidence: 99%

Molecular features of thymidine analogues governing the activity of human thymidine kinase

et al. 2018

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Modified uridines seem to be able to work as hypoxic tumour cell radiosensitisers. Before they sensitise cells to ionising radiation, they have to be incorporated into the genomic DNA and the latter process has to be preceded by the phosphorylation of the modified uridine, which in human cells is executed by human thymidine kinase 1 (hTK1). In the current study, we present the quantitative structure-activity relationship (QSAR) model allowing to identify and understand the molecular features of nucleoside derivatives governing the hTK1 kinase activity. The developed model meets all requirements of a reliable QSAR model and is based on only two molecular properties: the shape of the nucleoside determined by atom substitutions and the ability of the molecule to intermolecular interactions with the enzyme. These results have important implications for the rational designing of new hTK1 substrates and should significantly reduce the time and cost of studies on new radiosensitisers.

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“…The Q 2 value takes care of the statistical significance of the model since it is calculated by using the LOO predicted values of the training set compounds that are generated during each leave-one-out (LOO) cross validated cycle. Furthermore, the 2 m r metrics 38 were calculated for describing the performed internal (LOO) validation. Equations (1) and (2) give the formulae for computing the 2 m r metrics.…”

Section: Validation Of the Qspr Modelsmentioning

confidence: 99%

QSPR Modeling of Odor Threshold of Aliphatic Alcohols Using Extended Topochemical Atom (ETA) Indices

Pal¹,

Mitra²,

Roy³

2014

Croat. Chem. Acta

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Abstract. The present work establishes a quantitative structure-property relationship (QSPR) between topochemical features and odor threshold (OT) of aliphatic alcohols. A data set of 53 aliphatic alcohols was chosen for the analysis employing different chemometric techniques, among which, genetic function approximation with spline option (GFA-spline) showed the most acceptable results in terms of internal and external validation metric values. The extended topochemical atom (ETA) indices, developed by the present authors' group, were considered as descriptors for model development. Additionally, selected non-ETA descriptors were also tried for model development. It was observed that the models with ETA indices significantly surpass the predictive ability of the models developed using other descriptors. The final model suggests that molecular branching and electronic parameters significantly influence the odor potency of the molecules. Additionally, increased lipophilicity and reduced electronegativity increase the odorant property. The model thus developed may effectively be used for prediction of odor threshold of any untested aliphatic alcohols. (doi: 10.5562/cca2284)

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Further exploring rm2 metrics for validation of QSPR models

Cited by 490 publications

References 125 publications

Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical

Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical

Molecular features of thymidine analogues governing the activity of human thymidine kinase

QSPR Modeling of Odor Threshold of Aliphatic Alcohols Using Extended Topochemical Atom (ETA) Indices

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