Prediction of blood-brain barrier permeability of organic compounds.
Using fragmental descriptors and artificial neural networks, a predictive model of the relationship between the structure of organic compounds and their blood-brain barrier permeability was constructed and the structural factors affecting the readiness of this penetration were analyzed. This model (N = 529, Q2= 0.82, RMSEcv= 0.32) surpasses the previously published models in terms of the prediction accuracy and the applicability domain and can be used for the optimization of the pharmacokinetic parameters during drug development.
Department of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia.
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Dyabina
Department of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia. genie@qsar.chem.msu.ru.|Institute of Physiologically Active Compounds, Russian Academy of Sciences, Severnyi proezd 1, Chernogolovka, Moscow oblast, 142432, Russia. genie@qsar.chem.msu.ru.
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Radchenko
Department of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia.|Institute of Physiologically Active Compounds, Russian Academy of Sciences, Severnyi proezd 1, Chernogolovka, Moscow oblast, 142432, Russia.
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Palyulin
Department of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia.|Institute of Physiologically Active Compounds, Russian Academy of Sciences, Severnyi proezd 1, Chernogolovka, Moscow oblast, 142432, Russia.
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Zefirov
D000818:Animals; D001812:Blood-Brain Barrier / Q000378:metabolism*; D002199:Capillary Permeability; D055808:Drug Discovery / Q000379:methods; D008955:Models, Cardiovascular; D008956:Models, Chemical; D008959:Models, Neurological; D015394:Molecular Structure; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000737:chemistry / Q000493:pharmacokinetics*
D009930:Organic Chemicals