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Predicting Lipophilicity of Drug‐Discovery Molecules using Gaussian Process Models
Author(s) -
Schroeter Timon S.,
Schwaighofer Anton,
Mika Sebastian,
Ter Laak Antonius,
Suelzle Detlev,
Ganzer Ursula,
Heinrich Nikolaus,
Müller KlausRobert
Publication year - 2007
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.200700041
Subject(s) - lipophilicity , drug discovery , gaussian process , computer science , process (computing) , ideal (ethics) , gaussian , algorithm , biological system , chemistry , computational chemistry , stereochemistry , biology , biochemistry , philosophy , epistemology , operating system
The lipophilicity of 14 556 library compounds at Bayer Schering was modeled using Gaussian process methodology. In a blind test with 7013 new drug‐discovery molecules from the last few months, 81 % were predicted correctly within one log unit, compared with only 44 % achieved by commercial software. Predicted error bars exhibit close to ideal statistical properties, thereby allowing assessment of the model's domain of applicability.