title: Physicochemical property profile for brain permeability: comparative study by different approaches.
abstract: A comparative study of classification models of brain penetration by different approaches was carried out on a training set of 1000 chemicals and drugs, and an external test set of 100 drugs. Ten approaches were applied in this work: seven medicinal chemistry approaches (including "rule of 5" and multiparameter optimization) and also three SAR techniques: logistic regression (LR), random forest (RF) and support vector machine (SVM). Forty-one different medicinal chemistry descriptors representing diverse physicochemical properties were used in this work. Medicinal chemistry approaches based on the intuitive estimation of preference zones of CNS or non-CNS chemicals, with different rules and scoring functions, yield unbalanced models with poor classification accuracy. RF and SVM methods yielded 82% and 84% classification accuracy respectively for the external test set. LR was also successful in CNS/non-CNS (denoted in this study as CNS+/CNS-) classification and yielded an overall accuracy equivalent to that of SVM and RF. At the same time, LR is especially valuable for medicinal chemists because of its simplicity and the possibility of clear mechanistic interpretation.
authors.affiliation: a Department of Computer-Aided Molecular Design , Institute of Physiologically Active Compounds, Russian Academy of Science , Chernogolovka , Russia ;
authors.forename: Oleg A
authors.initials: OA
authors.lastname: Raevsky
authors.affiliation: a Department of Computer-Aided Molecular Design , Institute of Physiologically Active Compounds, Russian Academy of Science , Chernogolovka , Russia ;
authors.forename: Veniamin Y
authors.initials: VY
authors.lastname: Grigorev
authors.affiliation: a Department of Computer-Aided Molecular Design , Institute of Physiologically Active Compounds, Russian Academy of Science , Chernogolovka , Russia ;
authors.forename: Daniel E
authors.initials: DE
authors.lastname: Polianczyk
authors.affiliation: a Department of Computer-Aided Molecular Design , Institute of Physiologically Active Compounds, Russian Academy of Science , Chernogolovka , Russia ;
authors.forename: German I
authors.initials: GI
authors.lastname: Sandakov
authors.affiliation: a Department of Computer-Aided Molecular Design , Institute of Physiologically Active Compounds, Russian Academy of Science , Chernogolovka , Russia ;
authors.forename: Svetlana L
authors.initials: SL
authors.lastname: Solodova
authors.affiliation: a Department of Computer-Aided Molecular Design , Institute of Physiologically Active Compounds, Russian Academy of Science , Chernogolovka , Russia ;
authors.forename: Alexander V
authors.initials: AV
authors.lastname: Yarkov
authors.affiliation: b Department of Biomedicinal Chemistry , Institute of Physiologically Active Compounds, Russian Academy of Science , Chernogolovka , Russia ;
authors.forename: Sergey O
authors.initials: SO
authors.lastname: Bachurin
authors.affiliation: c School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK.
authors.forename: John C
authors.initials: JC
authors.lastname: Dearden
mesh_terms: D001921:Brain / Q000378:metabolism*; D002491:Central Nervous System Agents / Q000737:chemistry* / Q000145:classification / Q000493:pharmacokinetics; D003198:Computer Simulation; D055808:Drug Discovery; D000069550:Machine Learning; D008962:Models, Theoretical; D010539:Permeability; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000145:classification / Q000378:metabolism; D013329:Structure-Activity Relationship
chemical_list: D002491:Central Nervous System Agents; D004364:Pharmaceutical Preparations
keywords: Binary classification; SAR methods; brain penetration; descriptors; physicochemical properties; “rules” of medicinal chemists