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Mar. Drugs 2019, 17(2), 81; https://doi.org/10.3390/md17020081

Predicting Blood–Brain Barrier Permeability of Marine-Derived Kinase Inhibitors Using Ensemble Classifiers Reveals Potential Hits for Neurodegenerative Disorders

1
CONACYT, Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN, Irapuato, Guanajuato 36824, Mexico
2
Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4072, Australia
3
Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
*
Author to whom correspondence should be addressed.
Received: 25 December 2018 / Revised: 22 January 2019 / Accepted: 24 January 2019 / Published: 29 January 2019
(This article belongs to the Special Issue New Frontiers in Marine-Derived Kinase Modulators)
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Abstract

The recent success of small-molecule kinase inhibitors as anticancer drugs has generated significant interest in their application to other clinical areas, such as disorders of the central nervous system (CNS). However, most kinase inhibitor drug candidates investigated to date have been ineffective at treating CNS disorders, mainly due to poor blood–brain barrier (BBB) permeability. It is, therefore, imperative to evaluate new chemical entities for both kinase inhibition and BBB permeability. Over the last 35 years, marine biodiscovery has yielded 471 natural products reported as kinase inhibitors, yet very few have been evaluated for BBB permeability. In this study, we revisited these marine natural products and predicted their ability to cross the BBB by applying freely available open-source chemoinformatics and machine learning algorithms to a training set of 332 previously reported CNS-penetrant small molecules. We evaluated several regression and classification models, and found that our optimised classifiers (random forest, gradient boosting, and logistic regression) outperformed other models, with overall cross-validated model accuracies of 80%–82% and 78%–80% on external testing. All 3 binary classifiers predicted 13 marine-derived kinase inhibitors with appropriate physicochemical characteristics for BBB permeability. View Full-Text
Keywords: marine natural products; kinase inhibitors; blood–brain barrier permeability; neurological disorders; machine learning; QSPR; RDKit marine natural products; kinase inhibitors; blood–brain barrier permeability; neurological disorders; machine learning; QSPR; RDKit
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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 (CC BY 4.0).

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  • Externally hosted supplementary file 1
    Link: http://github.com/plissonf/BBB-Models
    Description: All scripts, data files and top 3 pickled models are made available on Github repository https://github.com/plissonf/BBB-Models under a MIT license.
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Plisson, F.; Piggott, A.M. Predicting Blood–Brain Barrier Permeability of Marine-Derived Kinase Inhibitors Using Ensemble Classifiers Reveals Potential Hits for Neurodegenerative Disorders. Mar. Drugs 2019, 17, 81.

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