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Improved Prediction of Blood–Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints

  • Research Article
  • Theme: Better Drugs for Better Life: Drug Discovery and Development Colloquium 2017
  • Published:
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Abstract

Blood–brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

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Acknowledgments

The authors acknowledge the Computer Center at the University of Kentucky for supercomputing time on a Dell Supercomputer Cluster consisting of 388 nodes or 4816 processors.

Funding

This work was supported in part by the National Science Foundation (NSF grant CHE-1111761) and the National Institutes of Health (NIH grants UH2/UH3 DA041115, R01 DA035552, R01 DA032910, R01 DA013930, R01 DA025100, and UL1TR001998).

Author information

Authors and Affiliations

  1. Center for Pharmaceutical Innovation and Research, University of Kentucky, 789 South Limestone Street, Lexington, Kentucky, 40536, USA

    Yaxia Yuan, Fang Zheng & Chang-Guo Zhan

  2. Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, Kentucky, 40536, USA

    Yaxia Yuan, Fang Zheng & Chang-Guo Zhan

  3. Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, Kentucky, 40536, USA

    Yaxia Yuan, Fang Zheng & Chang-Guo Zhan

Authors
  1. Yaxia Yuan
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  2. Fang Zheng
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Correspondence to Chang-Guo Zhan.

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The authors declare that they have no conflict of interest.

Additional information

Guest Editors: Shraddha Thakkar and Cesar M. Compadre

Electronic Supplementary Material

Additional data (Tables S1 and S2) for the accuracy comparison associated with the RS method.

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Yuan, Y., Zheng, F. & Zhan, CG. Improved Prediction of Blood–Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints. AAPS J 20, 54 (2018). https://doi.org/10.1208/s12248-018-0215-8

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  • DOI: https://doi.org/10.1208/s12248-018-0215-8

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