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Clark, D. E. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 2. Prediction of blood–brain barrier penetration. J. Pharm. Sci. 1999, 88, 815–821.

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Clark, D. E. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 2. Prediction of blood–brain barrier penetration. J. Pharm. Sci. 1999, 88, 815–821.

QDB archive DOI: 10.15152/QDB.199   DOWNLOAD

QsarDB content

Property logBB: The ratio of brain−blood partitioning as logBB

Compounds: 70 | Models: 2 | Predictions: 6

Eq.6: Model with PSA and ClogP (DEC-I)

Regression model (regression)

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Name Type n

R2

σ

Training set training 55 0.787 0.347
Test set 1 external validation 7 0.193 0.553
Test set 2 external validation 6 0.943 0.226
Eq.7: Model with PSA and MlogP (DEC-II)

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 55 0.767 0.363
Test set 1 external validation 7 0.169 0.561
Test set 2 external validation 6 0.952 0.284

Citing

When using this data, please cite the original article and this QDB archive:

  • Clark, D. E. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 2. Prediction of blood–brain barrier penetration. J. Pharm. Sci. 1999, 88, 815–821. http://dx.doi.org/10.1021/js980402t

  • Piir, G.; Sild, S. QDB archive #199. QsarDB repository, 2017. http://dx.doi.org/10.15152/QDB.199

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Title: Clark, D. E. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 2. Prediction of blood–brain barrier penetration. J. Pharm. Sci. 1999, 88, 815–821.
Abstract: This paper describes the derivation of a simple QSAR model for the prediction of log BB from a set of 55 diverse organic compounds. The model contains two variables: polar surface area (PSA) and calculated logP, both of which can be rapidly computed. It therefore permits the prediction of log BB for large compound sets, such as virtual combinatorial libraries. The performance of this QSAR on two test sets taken from the literature is illustrated and compared with results from other reported computational approaches to log BB prediction.
URI: http://hdl.handle.net/10967/199
http://dx.doi.org/10.15152/QDB.199
Date: 2017-05-18


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