Predicting lipophilicity of drug-discovery molecules using Gaussian process models

Timon S. Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius Ter Laak, Detlev Suelzle, Ursula Ganzer, Nikolaus Heinrich, Klaus Robert Müller

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

(Figure Presented) The lipophilicity of 14556 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.

Original languageEnglish
Pages (from-to)1265-1267
Number of pages3
JournalChemMedChem
Volume2
Issue number9
DOIs
Publication statusPublished - 2007 Sep 10

Keywords

  • Domain of applicability
  • Drug design
  • Gaussian process
  • Lipophilicity
  • Machine learning

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine
  • Pharmacology
  • Drug Discovery
  • Pharmacology, Toxicology and Pharmaceutics(all)
  • Organic Chemistry

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  • Cite this

    Schroeter, T. S., Schwaighofer, A., Mika, S., Ter Laak, A., Suelzle, D., Ganzer, U., Heinrich, N., & Müller, K. R. (2007). Predicting lipophilicity of drug-discovery molecules using Gaussian process models. ChemMedChem, 2(9), 1265-1267. https://doi.org/10.1002/cmdc.200700041