Predicting error bars for QSAR models

Timon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius Ter Laak, Detlev Suelzle, Ursula Ganzer, Nikolaus Heinrich, Klaus Muller

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Unfavorable physicochemical properties often cause drug failures. It is therefore important to take lipophilicity and water solubility into account early on in lead discovery. This study presents log D7 models built using Gaussian Process regression, Support Vector Machines, decision trees and ridge regression algorithms based on 14556 drug discovery compounds of Bayer Schering Pharma. A blind test was conducted using 7013 new measurements from the last months. We also present independent evaluations using public data. Apart from accuracy, we discuss the quality of error bars that can be computed by Gaussian Process models, and ensemble and distance based techniques for the other modelling approaches.

Original languageEnglish
Title of host publicationAIP Conference Proceedings
Number of pages10
Publication statusPublished - 2007 Dec 1
Externally publishedYes
Event3rd International Symposium on Computational Life Science, CompLife 2007 - Utrecht, Netherlands
Duration: 2007 Oct 42007 Oct 5


Other3rd International Symposium on Computational Life Science, CompLife 2007

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Fingerprint Dive into the research topics of 'Predicting error bars for QSAR models'. Together they form a unique fingerprint.

  • Cite this

    Schroeter, T., Schwaighofer, A., Mika, S., Ter Laak, A., Suelzle, D., Ganzer, U., Heinrich, N., & Muller, K. (2007). Predicting error bars for QSAR models. In AIP Conference Proceedings (Vol. 940, pp. 158-167)