{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T00:40:00Z","timestamp":1773535200656,"version":"3.50.1"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T00:00:00Z","timestamp":1603411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Korea government","award":["NRF-2019M3E5D4065682"],"award-info":[{"award-number":["NRF-2019M3E5D4065682"]}]},{"name":"Korea government","award":["NRF-2018R1A5A1025077"],"award-info":[{"award-number":["NRF-2018R1A5A1025077"]}]},{"name":"Korea Chemical Bank of Korea Research Institute of Chemical Technology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Identification of blood\u2013brain barrier (BBB) permeability of a compound is a major challenge in neurotherapeutic drug discovery. Conventional approaches for BBB permeability measurement are expensive, time-consuming and labor-intensive. BBB permeability is associated with diverse chemical properties of compounds. However, BBB permeability prediction models have been developed using small datasets and limited features, which are usually not practical due to their low coverage of chemical diversity of compounds. Aim of this study is to develop a BBB permeability prediction model using a large dataset for practical applications. This model can be used for facilitated compound screening in the early stage of brain drug discovery.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>A dataset of 7162 compounds with BBB permeability (5453 BBB+ and 1709 BBB-) was compiled from the literature, where BBB+ and BBB- denote BBB-permeable and non-permeable compounds, respectively. We trained a machine learning model based on Light Gradient Boosting Machine (LightGBM) algorithm and achieved an overall accuracy of 89%, an area under the curve (AUC) of 0.93, specificity of 0.77 and sensitivity of 0.93, when 10-fold cross-validation was performed. The model was further evaluated using 74 central nerve system compounds (39 BBB+ and 35 BBB-) obtained from the literature and showed an accuracy of 90%, sensitivity of 0.85 and specificity of 0.94. Our model outperforms over existing BBB permeability prediction models.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availabilityand implementation<\/jats:title>\n                  <jats:p>The prediction server is available at http:\/\/ssbio.cau.ac.kr\/software\/bbb.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa918","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T12:19:12Z","timestamp":1602677952000},"page":"1135-1139","source":"Crossref","is-referenced-by-count":161,"title":["LightBBB: computational prediction model of blood\u2013brain-barrier penetration based on LightGBM"],"prefix":"10.1093","volume":"37","author":[{"given":"Bilal","family":"Shaker","sequence":"first","affiliation":[{"name":"84 Heukseok-ro, Dongjak-gu, Department of Biomedical Engineering, Chung-Ang University , Seoul 06974, Republic of Korea"}]},{"given":"Myeong-Sang","family":"Yu","sequence":"additional","affiliation":[{"name":"84 Heukseok-ro, Dongjak-gu, Department of Biomedical Engineering, Chung-Ang University , Seoul 06974, Republic of Korea"}]},{"given":"Jin Sook","family":"Song","sequence":"additional","affiliation":[{"name":"Convergence Drug Research Center, Korea Research Institute of Chemical Technology , Daejeon 34114, Republic of Korea"}]},{"given":"Sunjoo","family":"Ahn","sequence":"additional","affiliation":[{"name":"Convergence Drug Research Center, Korea Research Institute of Chemical Technology , Daejeon 34114, Republic of Korea"}]},{"given":"Jae Yong","family":"Ryu","sequence":"additional","affiliation":[{"name":"Convergence Drug Research Center, Korea Research Institute of Chemical Technology , Daejeon 34114, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4242-0818","authenticated-orcid":false,"given":"Kwang-Seok","family":"Oh","sequence":"additional","affiliation":[{"name":"Convergence Drug Research Center, Korea Research Institute of Chemical Technology , Daejeon 34114, Republic of Korea"}]},{"given":"Dokyun","family":"Na","sequence":"additional","affiliation":[{"name":"84 Heukseok-ro, Dongjak-gu, Department of Biomedical Engineering, Chung-Ang University , Seoul 06974, Republic of Korea"}]}],"member":"286","published-online":{"date-parts":[[2020,10,23]]},"reference":[{"key":"2023051612053591800_btaa918-B1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/nrn1824","article-title":"Astrocyte\u2013endothelial interactions at the blood\u2013brain barrier","volume":"7","author":"Abbott","year":"2006","journal-title":"Nat. Rev. Neurosci"},{"key":"2023051612053591800_btaa918-B2","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.ejmech.2003.12.004","article-title":"The factors that influence permeation across the blood\u2013brain barrier","volume":"39","author":"Abraham","year":"2004","journal-title":"Eur. J. Med. Chem"},{"key":"2023051612053591800_btaa918-B3","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1021\/ci034205d","article-title":"Blood\u2013brain barrier permeation models: discriminating between potential CNS and non-CNS drugs including P-glycoprotein substrates","volume":"44","author":"Adenot","year":"2004","journal-title":"J. Chem. Inf. Comput. Sci"},{"key":"2023051612053591800_btaa918-B4","doi-asserted-by":"crossref","first-page":"e00050","DOI":"10.1002\/prp2.50","article-title":"Characterization of a novel brain barrier ex vivo insect-based P-glycoprotein screening model","volume":"2","author":"Andersson","year":"2014","journal-title":"Pharmacol. Res. Perspect"},{"key":"2023051612053591800_btaa918-B5","first-page":"16","article-title":"Efficient way of web development using python and flask","volume":"6","author":"Aslam","year":"2015","journal-title":"Int. J. Adv. Res. Comput. Sci"},{"key":"2023051612053591800_btaa918-B6","first-page":"1","article-title":"A simple method to predict blood\u2013brain barrier permeability of drug-like compounds using classification trees","volume":"13","author":"Barigye","year":"2017","journal-title":"Med. Chem"},{"key":"2023051612053591800_btaa918-B7","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1602\/neurorx.2.1.15","article-title":"How to measure drug transport across the blood\u2013brain barrier","volume":"2","author":"Bickel","year":"2005","journal-title":"NeuroRx"},{"key":"2023051612053591800_btaa918-B8","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the ROC curve in the evaluation of machine learning algorithms","volume":"30","author":"Bradley","year":"1997","journal-title":"Pattern Recognit"},{"key":"2023051612053591800_btaa918-B9","first-page":"3099","article-title":"admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties","volume":"52","author":"Cheng","year":"2012","journal-title":"J. Chem. Med"},{"key":"2023051612053591800_btaa918-B10","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1021\/jm990968+","article-title":"Predicting blood\u2212brain barrier permeation from three-dimensional molecular structure","volume":"43","author":"Crivori","year":"2000","journal-title":"J. Med. Chem"},{"key":"2023051612053591800_btaa918-B11","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1021\/acs.chemrestox.5b00396","article-title":"Safety lead optimization and candidate identification: integrating new technologies into decision-making","volume":"29","author":"Dambach","year":"2016","journal-title":"Chem. Res. Toxicol"},{"key":"2023051612053591800_btaa918-B12","doi-asserted-by":"crossref","first-page":"a020412","DOI":"10.1101\/cshperspect.a020412","article-title":"The blood\u2013brain barrier","volume":"7","author":"Daneman","year":"2015","journal-title":"Cold Spring Harb. Perspect. Biol"},{"key":"2023051612053591800_btaa918-B13","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/978-1-4613-0701-3_2","volume-title":"Implications of the Blood\u2013Brain Barrier and Its Manipulation","author":"Davson","year":"1989"},{"key":"2023051612053591800_btaa918-B14","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1089\/10665270260518317","article-title":"Predicting CNS permeability of drug molecules: comparison of neural network and support vector machine algorithms","volume":"9","author":"Doniger","year":"2002","journal-title":"J. Comput. Biol"},{"key":"2023051612053591800_btaa918-B15","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: a gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat"},{"key":"2023051612053591800_btaa918-B16","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1093\/bioinformatics\/btw713","article-title":"Predict drug permeability to blood\u2013brain-barrier from clinical phenotypes: drug side effects and drug indications","volume":"33","author":"Gao","year":"2017","journal-title":"Bioinformatics"},{"key":"2023051612053591800_btaa918-B17","first-page":"286","article-title":"The design and molecular modeling of CNS drugs","volume":"2","author":"George","year":"1999","journal-title":"Curr. Opin. Drug Disc"},{"key":"2023051612053591800_btaa918-B18","doi-asserted-by":"crossref","first-page":"2638","DOI":"10.1021\/ci0600814","article-title":"In silico prediction of blood\u2212brain barrier permeation using the calculated molecular cross-sectional area as main parameter","volume":"46","author":"Gerebtzoff","year":"2006","journal-title":"J. Chem. Inf. Model"},{"key":"2023051612053591800_btaa918-B19","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1002\/qsar.200710019","article-title":"Artificial neural networks in ADMET modeling: prediction of blood\u2013brain barrier permeation","volume":"27","author":"Guerra","year":"2008","journal-title":"QSAR Comb. Sci"},{"key":"2023051612053591800_btaa918-B20","doi-asserted-by":"crossref","first-page":"E10","DOI":"10.3171\/2015.1.FOCUS14767","article-title":"Novel delivery methods bypassing the blood\u2013brain and blood\u2013tumor barriers","volume":"38","author":"Hendricks","year":"2015","journal-title":"Neurosurg. Focus"},{"key":"2023051612053591800_btaa918-B21","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1021\/ci800038f","article-title":"Mold2, molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics","volume":"48","author":"Hong","year":"2008","journal-title":"J. Chem. Inf Model"},{"key":"2023051612053591800_btaa918-B22","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1186\/1471-2105-8-245","article-title":"Artificial neural network models for prediction of intestinal permeability of oligopeptides","volume":"8","author":"Jung","year":"2007","journal-title":"BMC Bioinformatics"},{"key":"2023051612053591800_btaa918-B23","first-page":"3146","article-title":"Lightgbm: a highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural. Inf. Process. Syst"},{"key":"2023051612053591800_btaa918-B24","doi-asserted-by":"crossref","first-page":"1836","DOI":"10.1007\/s11095-008-9584-5","article-title":"New predictive models for blood\u2013brain barrier permeability of drug-like molecules","volume":"25","author":"Kortagere","year":"2008","journal-title":"Pharm. Res"},{"key":"2023051612053591800_btaa918-B25","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1002\/jps.21405","article-title":"Ionization-specific prediction of blood\u2013brain permeability","volume":"98","author":"Lanevskij","year":"2009","journal-title":"J. Pharm. Sci"},{"key":"2023051612053591800_btaa918-B26","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.ddtec.2004.11.007","article-title":"Lead-and drug-like compounds: the rule-of-five revolution","volume":"1","author":"Lipinski","year":"2004","journal-title":"Drug Discov. Today Technol"},{"key":"2023051612053591800_btaa918-B27","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1021\/ci980411n","article-title":"Prediction of the brain\u2212blood distribution of a large set of drugs from structurally derived descriptors using partial least-squares (PLS) modeling","volume":"39","author":"Luco","year":"1999","journal-title":"J. Chem. Inf. Comput. Sci"},{"key":"2023051612053591800_btaa918-B28","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.1021\/ci300124c","article-title":"A Bayesian approach to in silico blood\u2013brain barrier penetration modeling","volume":"52","author":"Martins","year":"2012","journal-title":"J. Chem. Inf. Model"},{"key":"2023051612053591800_btaa918-B29","first-page":"237","article-title":"Dragon software: an easy approach to molecular descriptor calculations","volume":"56","author":"Mauri","year":"2006","journal-title":"Match"},{"key":"2023051612053591800_btaa918-B30","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1016\/j.drudis.2009.07.009","article-title":"Brain drug targeting: a computational approach for overcoming blood\u2013brain barrier","volume":"14","author":"Mehdipour","year":"2009","journal-title":"Drug Discov. Today"},{"key":"2023051612053591800_btaa918-B31","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/S0925-4439(02)00074-1","article-title":"A simple model to predict blood\u2013brain barrier permeation from 3D molecular fields","volume":"1587","author":"Ooms","year":"2002","journal-title":"Biochim. Biophys. Acta"},{"key":"2023051612053591800_btaa918-B32","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1602\/neurorx.2.4.541","article-title":"Medicinal chemical properties of successful central nervous system drugs","volume":"2","author":"Pajouhesh","year":"2005","journal-title":"NeuroRx"},{"key":"2023051612053591800_btaa918-B33","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1602\/neurorx.2.1.3","article-title":"The blood\u2013brain barrier: bottleneck in brain drug development","volume":"2","author":"Pardridge","year":"2005","journal-title":"NeuroRx"},{"key":"2023051612053591800_btaa918-B34","doi-asserted-by":"crossref","first-page":"81","DOI":"10.3390\/md17020081","article-title":"Predicting blood\u2013brain barrier permeability of marine-derived kinase inhibitors using ensemble classifiers reveal potential hits for neurodegenerative disorders","volume":"17","author":"Plisson","year":"2019","journal-title":"Mar. Drugs"},{"key":"2023051612053591800_btaa918-B35","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1002\/qsar.200710129","article-title":"In silico prediction of blood\u2013brain partitioning using a chemometric method called genetic algorithm based variable selection","volume":"27","author":"Shen","year":"2008","journal-title":"QSAR Comb. Sci"},{"key":"2023051612053591800_btaa918-B36","first-page":"05640","article-title":"Gradient boosting with piece-wise linear regression trees","volume":"1802","author":"Shi","year":"2018","journal-title":"ArXiv"},{"key":"2023051612053591800_btaa918-B37","doi-asserted-by":"crossref","first-page":"107516","DOI":"10.1016\/j.jmgm.2019.107516","article-title":"A classification model for blood brain barrier penetration","volume":"96","author":"Singh","year":"2020","journal-title":"J. Mol. Graph. Model"},{"key":"2023051612053591800_btaa918-B38","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1177\/2211068214561025","article-title":"TEER measurement techniques for in vitro barrier model systems","volume":"20","author":"Srinivasan","year":"2015","journal-title":"J. Lab. Autom"},{"key":"2023051612053591800_btaa918-B39","doi-asserted-by":"crossref","first-page":"10429","DOI":"10.3390\/molecules170910429","article-title":"Computational prediction of blood\u2013brain barrier permeability using decision tree induction","volume":"17","author":"Suenderhauf","year":"2012","journal-title":"Molecules"},{"key":"2023051612053591800_btaa918-B40","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vector machine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process. Lett"},{"key":"2023051612053591800_btaa918-B41","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1016\/j.ecolmodel.2011.02.007","article-title":"Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy","volume":"222","author":"Vincenzi","year":"2011","journal-title":"Eco. Model"},{"key":"2023051612053591800_btaa918-B42","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1002\/cmdc.201800533","article-title":"In silico prediction of blood\u2013brain barrier permeability of compounds by machine learning and resampling methods","volume":"13","author":"Wang","year":"2018","journal-title":"ChemMedChem"},{"key":"2023051612053591800_btaa918-B43","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J. Chem. Inf. Comput. Sci"},{"key":"2023051612053591800_btaa918-B44","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"MoleculeNet: a benchmark for molecular machine learning","volume":"9","author":"Wu","year":"2018","journal-title":"Chem. Sci"},{"key":"2023051612053591800_btaa918-B45","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1021\/jm00398a028","article-title":"Development of a new physicochemical model for brain penetration and its application to the design of centrally acting H2 receptor histamine antagonists","volume":"31","author":"Young","year":"1988","journal-title":"J. Med. Chem"},{"key":"2023051612053591800_btaa918-B46","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1208\/s12248-018-0215-8","article-title":"Improved prediction of blood\u2013brain barrier permeability through machine learning with combined use of molecular property-based descriptors and fingerprints","volume":"20","author":"Yuan","year":"2018","journal-title":"AAPS J"},{"key":"2023051612053591800_btaa918-B47","doi-asserted-by":"crossref","first-page":"1902","DOI":"10.1007\/s11095-008-9609-0","article-title":"QSAR modeling of the blood\u2013brain barrier permeability for diverse organic compounds","volume":"25","author":"Zhang","year":"2008","journal-title":"Pharm. Res"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaa918\/34561663\/btaa918.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/8\/1135\/50340578\/btaa918.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/8\/1135\/50340578\/btaa918.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T12:06:39Z","timestamp":1684238799000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/8\/1135\/5942084"}},"subtitle":[],"editor":[{"given":"Martelli","family":"Pier Luigi","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2020,10,23]]},"references-count":47,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,5,23]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaa918","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,4,15]]},"published":{"date-parts":[[2020,10,23]]}}}