title: Development of machine learning models of β-cyclodextrin and sulfobutylether-β-cyclodextrin complexation free energies.
abstract: A new set of 142 experimentally determined complexation constants between sulfobutylether-β-cyclodextrin and diverse organic guest molecules, and 78 observations reported in literature, were used for the development of the QSPR models by the two machine learning regression methods - Cubist and Random Forest. Similar models were built for β-cyclodextrin using the 233-compound dataset available in the literature. These results demonstrate that the machine learning regression methods can successfully describe the complex formation between organic molecules and β-cyclodextrin or sulfobutylether-β-cyclodextrin. In particular, the root mean square errors for the test sets predictions by the best models are low, 1.9 and 2.7kJ/mol, respectively. The developed QSPR models can be used to predict the solubilizing effect of cyclodextrins and to help prioritizing experimental work in drug discovery.
authors.affiliation: Department of Pharmaceutical Sciences, Pfizer Inc., Groton, CT, USA. alexei.merzlikine@pfizer.com
authors.forename: Alexei
authors.initials: A
authors.lastname: Merzlikine
authors.forename: Yuriy A
authors.initials: YA
authors.lastname: Abramov
authors.forename: Stacy J
authors.initials: SJ
authors.lastname: Kowsz
authors.forename: V Hayden
authors.initials: VH
authors.lastname: Thomas
authors.forename: Takashi
authors.initials: T
authors.lastname: Mano
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D016208:Databases, Factual; D055808:Drug Discovery; D019277:Entropy; D005079:Excipients / Q000737:chemistry*; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D012995:Solubility; D013816:Thermodynamics; D047392:beta-Cyclodextrins / Q000737:chemistry*
chemical_list: D005079:Excipients; D047392:beta-Cyclodextrins; C093196:SBE4-beta-cyclodextrin; C031215:betadex