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

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32 Scopus citations

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.

Original languageEnglish
Article number54
JournalAAPS Journal
Volume20
Issue number3
DOIs
StatePublished - May 1 2018

Bibliographical note

Funding Information:
Funding Information 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).

Publisher Copyright:
© 2018, American Association of Pharmaceutical Scientists.

Keywords

  • blood–brain barrier permeability
  • fingerprint
  • modeling
  • molecular descriptor
  • physical property

ASJC Scopus subject areas

  • Pharmaceutical Science

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