Predicting Error Bars for QSAR Models
- Fraunhofer FIRST, Kekulestrasse 7, 12489 Berlin (Germany)
- idalab GmbH, Sophienstrasse 24, 10178 Berlin (Germany)
- Research Laboratories of Bayer Schering Pharma AG, Muellerstrasse 178, 13342 Berlin (Germany)
- Technische Universitaet Berlin, Department of Computer Science, Franklinstrasse 28/29, 10587 Berlin (Germany)
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 D{sub 7} 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.
- OSTI ID:
- 21036171
- Journal Information:
- AIP Conference Proceedings, Vol. 940, Issue 1; Conference: COMPLIFE 2007: 3. international symposium on computational life science, Utrecht (Netherlands), 4-5 Oct 2007; Other Information: DOI: 10.1063/1.2793398; (c) 2007 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
- Country of Publication:
- United States
- Language:
- English
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