Pharmaceutical Research

, 25:1836 | Cite as

New Predictive Models for Blood–Brain Barrier Permeability of Drug-like Molecules

  • Sandhya Kortagere
  • Dmitriy Chekmarev
  • William J. Welsh
  • Sean EkinsEmail author
Research Paper



The goals of the present study were to apply a generalized regression model and support vector machine (SVM) models with Shape Signatures descriptors, to the domain of blood–brain barrier (BBB) modeling.

Materials and Methods

The Shape Signatures method is a novel computational tool that was used to generate molecular descriptors utilized with the SVM classification technique with various BBB datasets. For comparison purposes we have created a generalized linear regression model with eight MOE descriptors and these same descriptors were also used to create SVM models.


The generalized regression model was tested on 100 molecules not in the model and resulted in a correlation r 2 = 0.65. SVM models with MOE descriptors were superior to regression models, while Shape Signatures SVM models were comparable or better than those with MOE descriptors. The best 2D shape signature models had 10-fold cross validation prediction accuracy between 80–83% and leave-20%-out testing prediction accuracy between 80–82% as well as correctly predicting 84% of BBB+ compounds (n = 95) in an external database of drugs.


Our data indicate that Shape Signatures descriptors can be used with SVM and these models may have utility for predicting blood–brain barrier permeation in drug discovery.


blood–brain barrier principal component analysis regression shape signatures support vector machine 



absorption, distribution, metabolism and excretion


blood–brain barrier


central nervous system


molecular electrostatic potential


molecular operating environment


principal component analysis




quantitative structure activity relationship


recursive feature elimination


solvent accessible surface


support vector machine


topological polar surface area


unsupervised forward selection



Support for this work has been provided by the USEPA-funded Environmental Bioinformatics and Computational Toxicology Center (ebCTC), under STAR Grant number GAD R 832721-010. WJW gratefully acknowledges support for this work provided by the Defense Threat Reduction Agency, under contract number HDTRA-BB07TAS020. This work was also funded in part by NIH R21-GM081394 from the National Institute of General Medical Sciences and by NIH Integrated Advanced Information Management Systems (IAIMS) Grant # 2G08LM06230-03A1 from the National Library of Medicine. This work has not been reviewed by and does not represent the opinions of the funding agencies. The authors are sincerely grateful to Randy Zauhar, Ph.D., of the University of the Sciences in Philadelphia, for useful discussions on technical aspects of Shape Signatures.

Supplementary material

11095_2008_9584_MOESM1_ESM.doc (134 kb)
Supplemental Table 1 Details the list of BBB datasets available in literature along with references. (DOC 134 KB)
11095_2008_9584_MOESM2_ESM.xls (67 kb)
Supplemental Table 2 Provides model predictions for the SCUT database and consensus scoring respectively. Both are available online along with the SDF files for the Li, Combined and SCUT datasets. (XLS 67.0 KB)


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Sandhya Kortagere
    • 1
  • Dmitriy Chekmarev
    • 1
  • William J. Welsh
    • 1
  • Sean Ekins
    • 1
    • 2
    • 3
    Email author
  1. 1.Department of Pharmacology and Environmental Bioinformatics and Computational Toxicology Center (ebCTC)University of Medicine & Dentistry of New Jersey (UMDNJ)–Robert Wood Johnson Medical SchoolPiscatawayUSA
  2. 2.Collaborations in ChemistryJenkintownUSA
  3. 3.Department of Pharmaceutical SciencesUniversity of MarylandBaltimoreUSA

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