On the Misleading Use of  for QSAR Model Comparison

Consonni, Viviana; Todeschini, Roberto; Ballabio, Davide; Grisoni, Francesca

doi:10.1002/minf.201800029

Cited by 39 publications

(35 citation statements)

References 20 publications

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“…The values for various internal validation parameters such as r 2 cv , CCC cv , RMSE cv , MAE cv , CCC cv , and Q 2 LMO vindicate the statistical robustness of the QSAR model. The high values of r 2 ex , Q 2 F 1 , Q 2 F 2 , Q 2 F 3 , and CCC ex confirm the external predictive ability of the model [42][43][44][45][46][47][48].…”

Section: Resultssupporting

confidence: 53%

Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling

Masand¹,

Rastija

Patil

et al. 2020

SAR and QSAR in Environmental Research

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A quantitative structure-activity relationship (QSAR) model was built from a dataset of 54 peptide-type compounds as SARS-CoV inhibitors. The analysis was executed to identify prominent and hidden structural features that govern anti-SARS-CoV activity. The QSAR model was derived from the genetic algorithm-multi-linear regression (GA-MLR) methodology. This resulted in the generation of a statistically robust and highly predictive model. In addition, it satisfied the OECD principles for QSAR validation. The model was validated thoroughly and fulfilled the threshold values of a battery of statistical parameters (e.g. r 2 = 0.87, Q 2 loo = 0.82). The derived model is successful in identifying many atom-pairs as important structural features that govern the anti-SARS-CoV activity of peptide-type compounds. The newly developed model has a good balance of descriptive and statistical approaches. Consequently, the present work is useful for future modifications of peptide-type compounds for SARS-CoV and SARS-CoV-2 activity.

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Section: Resultssupporting

confidence: 53%

Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling

Masand¹,

Rastija

Patil

et al. 2020

SAR and QSAR in Environmental Research

View full text Add to dashboard Cite

show abstract

“…The symbols have their usual meaning, which are available in the supplementary material also. Gramatica, 2011, 2012;Consonni et al, 2009;Consonni et al, 2019;Gramatica, 2013;Gramatica et al, 2012). In short, the developed QSAR models fulfill the recommended threshold values for many internal and external validation parameters.…”

Section: Resultsmentioning

confidence: 82%

Structure features of peptide-type SARS-CoV main protease inhibitors: Quantitative structure activity relationship study

Masand¹,

Akasapu

Gandhi

et al. 2020

Chemometrics and Intelligent Laboratory Systems

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In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80-0.82, Q2loo = 0.74-0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti-SARS-CoV activity. File list (3) download file view on ChemRxiv COvid-19 Manuscript.docx (669.91 KiB) download file view on ChemRxiv Supplementary material-Ki-Manuscript.docx (376.96 KiB) download file view on ChemRxiv Descriptors for submission-Ki.csv (6.95 KiB) Structure features of peptide-type SARS-CoV main protease inhibitors: Quantitative Structure Activity Relationship study

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“…Further external validation of the modified model was performed using

Q_{F 3}^{2}

metric [30, 31] according to the following equation:

\begin{matrix} true \\ right Q_{F 3}^{2} = 1 - \frac{\sum_{i = 1}^{n_{T E S T}} {(y_{i} - {\overset{y}{true}}_{i})}^{2} / n_{T E S T}}{\sum_{j = 1}^{n_{T R}} {(y_{j} - {\overset{y}{true}}_{T R})}^{2} / n_{T R}} = 1 - \frac{R M S E P^{2}}{s_{T R}^{2}} \end{matrix}

where y i is the experimental response of the i th test molecule,

{\hat{y}}_{i}

the corresponding predicted response, y j is the experimental response of the j th training molecule, and

{\bar{y}}_{T R}

is the average response of the training set; n TR and n TEST are the number of training and test molecules, respectively.

Q_{F 3}^{2}

can also be expressed using the ratio of the square of RMSEP over

s_{T R}^{2}

, which is a biased estimate of the training data variance representing the training response distribution around the mean and acts as a scaling factor of the average model error.…”

Section: Methodsmentioning

confidence: 99%

Multivariate image analysis–quantitative structure‐retention relationship study of polychlorinated biphenyls using partial least squares and radial basis function neural networks

Jalili‐Jahani¹,

Fatehi²

2020

J of Separation Science

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Polychlorinated biphenyls belong to a class of hazardous and environmental pollutants. Gas chromatography separation and experimental relative retention time evaluation of these compounds on a poly (94% methyl/5% phenyl) silicone-based capillary non-bonded and cross-linked column are time consuming and expensive. In this study, relative retention times were estimated using two-dimensional images of molecules based on a newly implemented rapid and simple quantitative structure retention relationship methodology. The resulting descriptors were subjected to partial least square and principal component-radial basis function neural networks as linear and nonlinear models, respectively, to attain a statistical explanation of the retention behavior of the molecules. The high numerical values of correlation coefficients and low root mean square errors in the case of the partial least square model, confirm the supremacy of this model as well as the linear dependency of images of molecules to their relative retention times. Evaluation of the best correlation model performed using internal and external tests and its good applicability domain was checked using a distance to the model in the X-Space plot. This study provides a practical and effective method for analytical chemists working with chromatographic platforms to improve predictive confidence of studies that seek to identify unknown molecules or impurities. K E Y W O R D Spartial least squares, polychlorinated biphenyls, quantitative structure retention relationship, radial basis function neural networks, two-dimensional images

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On the Misleading Use of for QSAR Model Comparison

Cited by 39 publications

References 20 publications

Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling

Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling

Structure features of peptide-type SARS-CoV main protease inhibitors: Quantitative structure activity relationship study

Multivariate image analysis–quantitative structure‐retention relationship study of polychlorinated biphenyls using partial least squares and radial basis function neural networks

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