Binary Classification of Blood-Brain Barrier Penetration by the Logistic Regression Method

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Published: Sep 10, 2018
DOI: https://doi.org/10.18097/BMCRM00065
Keywords:
QSAR CNS blood-brain barrier binary classification descriptors

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O.A. Raevsky
Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severny proezd, Moscow region, Chernogolovka, 142432 Russia
D.E. Polianczyk
Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severny proezd, Moscow region, Chernogolovka, 142432 Russia
O.E. Raevskaja
Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 1 Severny proezd, Moscow region, Chernogolovka, 142432 Russia

Abstract

Stable classification predictive models of 83 drugs with different blood-brain barrier penetration capacity have been constructed by the logistic regression method using physicochemical descriptors characterizing steric, electrostatic interactions and hydrogen bond energy. The models are balanced, with the prediction level of 75-80%.

Article Details

How to Cite
Raevsky, O., Polianczyk, D., & Raevskaja, O. (2018). Binary Classification of Blood-Brain Barrier Penetration by the Logistic Regression Method. Biomedical Chemistry: Research and Methods, 1(3), e00065. https://doi.org/10.18097/BMCRM00065
Issue
Vol. 1 No. 3 (2018)
Section
EXPERIMENTAL RESEARCH

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