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Predicting error bars for QSAR models

: Schroeter, T.; Schwaighofer, A.; Mika, S.; Ter Laak, A.; Suelzle, D.; Ganzer, U.; Heinrich, N.; Müller, K.-R.


Siebes, A.; Berthold, M.R.:
CompLife 2007: 3rd International Symposium on Computational Life Science : Utrecht, The Netherlands, 4 - 5 October 2007
2007 (AIP Conference Proceedings 940)
ISBN: 978-0-7354-0452-6
ISSN: 0094-243X
International Symposium on Computational Life Science (CompLife) <3, 2007, Utrecht>
Conference Paper
Fraunhofer FIRST ()

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.