Binary Classification of CNS and PNS Drugs


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Stable classification predictive models of 626 drugs acting on the central (CNS) and peripheral (PNS) nervous systems were constructed based on linear discriminant analysis, logistic regression, random forest, and support vector machine methods with physicochemical descriptors characterizing the steric factors, electrostatic interactions, and H-bonding features. Internal cross-validations demonstrated that these models possessed satisfactory statistical properties.

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D. Polianchik

Department of Computer-Aided Molecular Design, Institute of Physiologically Active Substances, Russian Academy of Sciences

Autor responsável pela correspondência
Email: danielpolian@yahoo.com
Rússia, Chernogolovka, Moscow Region, 142432

V. Grigor’ev

Department of Computer-Aided Molecular Design, Institute of Physiologically Active Substances, Russian Academy of Sciences

Email: danielpolian@yahoo.com
Rússia, Chernogolovka, Moscow Region, 142432

G. Sandakov

Department of Computer-Aided Molecular Design, Institute of Physiologically Active Substances, Russian Academy of Sciences

Email: danielpolian@yahoo.com
Rússia, Chernogolovka, Moscow Region, 142432

A. Yarkov

Department of Computer-Aided Molecular Design, Institute of Physiologically Active Substances, Russian Academy of Sciences

Email: danielpolian@yahoo.com
Rússia, Chernogolovka, Moscow Region, 142432

S. Bachurin

Department of Biomedicinal Chemistry, Institute of Physiologically Active Substances, Russian Academy of Sciences

Email: danielpolian@yahoo.com
Rússia, Chernogolovka, Moscow Region, 142432

O. Raevskii

Department of Computer-Aided Molecular Design, Institute of Physiologically Active Substances, Russian Academy of Sciences

Email: danielpolian@yahoo.com
Rússia, Chernogolovka, Moscow Region, 142432

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