title: Assessment of the classification abilities of the CNS multi-parametric optimization approach by the method of logistic regression.
abstract: Assessment of "CNS drugs/CNS candidates" classification abilities of the multi-parametric optimization (CNS MPO) approach was performed by logistic regression. It was found that the five out of the six separately used physical-chemical properties (topological polar surface area, number of hydrogen-bonded donor atoms, basicity, lipophilicity of compound in neutral form and at pH = 7.4) provided accuracy of recognition below 60%. Only the descriptor of molecular weight (MW) could correctly classify two-thirds of the studied compounds. Aggregation of all six properties in the MPOscore did not improve the classification, which was worse than the classification using only MW. The results of our study demonstrate the imperfection of the CNS MPO approach; in its current form it is not very useful for computer design of new, effective CNS drugs.
authors.affiliation: a Department of Computer-aided Molecular Design , Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka , Russian Federation.
authors.forename: O A
authors.initials: OA
authors.lastname: Raevsky
authors.affiliation: a Department of Computer-aided Molecular Design , Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka , Russian Federation.
authors.forename: D E
authors.initials: DE
authors.lastname: Polianczyk
authors.affiliation: a Department of Computer-aided Molecular Design , Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka , Russian Federation.
authors.forename: A
authors.initials: A
authors.lastname: Mukhametov
authors.affiliation: a Department of Computer-aided Molecular Design , Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka , Russian Federation.
authors.forename: V Y
authors.initials: VY
authors.lastname: Grigorev
mesh_terms: D001812:Blood-Brain Barrier / Q000737:chemistry; D002491:Central Nervous System Agents / Q000737:chemistry*; D015195:Drug Design; D016015:Logistic Models; D008970:Molecular Weight; D021281:Quantitative Structure-Activity Relationship
chemical_list: D002491:Central Nervous System Agents
keywords: CNS MPO; blood–brain barrier; logistic regression; multi-parametric optimization