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

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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.

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          Most cited references 47

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          Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information

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            Isolation and structure of bryostatin 1

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              Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 2. Prediction of blood-brain barrier penetration.

               Michael Clark (1999)
              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.
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                Author and article information

                Journal
                Mar Drugs
                Mar Drugs
                marinedrugs
                Marine Drugs
                MDPI
                1660-3397
                29 January 2019
                February 2019
                : 17
                : 2
                Affiliations
                [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; andrew.piggott@ 123456mq.edu.au
                [3 ]Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
                Author notes
                [* ]Correspondence: fabien.plisson@ 123456cinvestav.mx ; Tel.: +52-(1)-462-166-3000 (ext. 3036)
                Article
                marinedrugs-17-00081
                10.3390/md17020081
                6410078
                30699889
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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