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Int. J. Mol. Sci. 2011, 12(7), 4348-4364; doi:10.3390/ijms12074348
Article

Predictivity Approach for Quantitative Structure-Property Models. Application for Blood-Brain Barrier Permeation of Diverse Drug-Like Compounds

1
 and 2,*
Received: 29 March 2011; in revised form: 9 June 2011 / Accepted: 24 June 2011 / Published: 5 July 2011
(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)
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Abstract: The goal of the present research was to present a predictivity statistical approach applied on structure-based prediction models. The approach was applied to the domain of blood-brain barrier (BBB) permeation of diverse drug-like compounds. For this purpose, 15 statistical parameters and associated 95% confidence intervals computed on a 2 × 2 contingency table were defined as measures of predictivity for binary quantitative structure-property models. The predictivity approach was applied on a set of compounds comprised of 437 diverse molecules, 122 with measured BBB permeability and 315 classified as active or inactive. A training set of 81 compounds (~2/3 of 122 compounds assigned randomly) was used to identify the model and a test set of 41 compounds was used as the internal validation set. The molecular descriptor family on vertices cutting was the computation tool used to generate and calculate structural descriptors for all compounds. The identified model was assessed using the predictivity approach and compared to one model previously reported. The best-identified classification model proved to have an accuracy of 69% in the training set (95%CI [58.53–78.37]) and of 73% in the test set (95%CI [58.32–84.77]). The predictive accuracy obtained on the external set proved to be of 73% (95%CI [67.58–77.39]). The classification model proved to have better abilities in the classification of inactive compounds (specificity of ~74% [59.20–85.15]) compared to abilities in the classification of active compounds (sensitivity of ~64% [48.47–77.70]) in the training and external sets. The overall accuracy of the previously reported model seems not to be statistically significantly better compared to the identified model (~81% [71.45–87.80] in the training set, ~93% [78.12–98.17] in the test set and ~79% [70.19–86.58] in the external set). In conclusion, our predictivity approach allowed us to characterize the model obtained on the investigated set of compounds as well as compare it with a previously reported model. According to the obtained results, the reported model should be chosen if a correct classification of inactive compounds is desired and the previously reported model should be chosen if a correct classification of active compounds is most wanted.
Keywords: in silico prediction; partition-coefficient; blood-brain barrier (BBB); permeation; structure-property relationship (SPR); molecular descriptors family on vertices cutting (MDFV) in silico prediction; partition-coefficient; blood-brain barrier (BBB); permeation; structure-property relationship (SPR); molecular descriptors family on vertices cutting (MDFV)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Bolboacă, S.D.; Jäntschi, L. Predictivity Approach for Quantitative Structure-Property Models. Application for Blood-Brain Barrier Permeation of Diverse Drug-Like Compounds. Int. J. Mol. Sci. 2011, 12, 4348-4364.

AMA Style

Bolboacă SD, Jäntschi L. Predictivity Approach for Quantitative Structure-Property Models. Application for Blood-Brain Barrier Permeation of Diverse Drug-Like Compounds. International Journal of Molecular Sciences. 2011; 12(7):4348-4364.

Chicago/Turabian Style

Bolboacă, Sorana D.; Jäntschi, Lorentz. 2011. "Predictivity Approach for Quantitative Structure-Property Models. Application for Blood-Brain Barrier Permeation of Diverse Drug-Like Compounds." Int. J. Mol. Sci. 12, no. 7: 4348-4364.



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