Computational aqueous solubility prediction for drug-like compounds in congeneric series.
It was the aim of the present work to develop a quantitative structure-property relationship (QSPR) model for predicting the aqueous solubility of drug-like compounds in congeneric series. Lipophilicity combined with structural fragment information, fragmental based correction factors and congeneric series indices were used as descriptors for a principal component analysis (PCA) followed by multivariate partial least squares regression statistics (PLS). The derived PLS regression model for the prediction of solubility parameters was based on an in-house data set of 2473 drug-like compounds. The generated PLS model had a coefficient of determination (R(2))=0.844 and a root-mean-square (rms) error of 0.51 log units. It predicted the solubility of the test data set with a high degree of accuracy (R(2)=0.81). In addition, the PLS model was successful in predicting the solubility of new congeneric test sets when solubility values of corresponding scaffolds were accessible.
Du-Cuny
Lei
L
F. Hoffmann-La Roche Ltd., Pharmaceuticals Division, Grenzacherstrasse, CH-4070 Basel, Switzerland.
Huwyler
Jörg
J
Wiese
Michael
M
Kansy
Manfred
M
D001569:Benzodiazepines / Q000737:chemistry; D003198:Computer Simulation; D018360:Crystallography, X-Ray; D016208:Databases, Factual; D016018:Least-Squares Analysis; D008956:Models, Chemical; D009930:Organic Chemicals / Q000737:chemistry*; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D025341:Principal Component Analysis; D012995:Solubility; D013449:Sulfonamides / Q000737:chemistry; D014867:Water / Q000737:chemistry*
D009930:Organic Chemicals; D004364:Pharmaceutical Preparations; D013449:Sulfonamides; D014867:Water; D001569:Benzodiazepines