PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Machine learning models for lipophilicity and their domain of applicability
Timon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius ter Laak, Detlev Suelzle, Ursula Ganzer, Nikolaus Heinrich and Klaus-Robert Müller
Molecular Pharmaceutics Volume 4, Number 4, pp. 524-538, 2007.


Unfavorable lipophilicity and water solubility cause many drug failures; therefore these properties have to be taken into account early on in lead discovery. Commercial tools for predicting lipophilicity usually have been trained on small and neutral molecules, and are thus often unable to accurately predict in-house data. Using a modern Bayesian machine learning algorithm-a Gaussian process model-this study constructs a log D7 model based on 14556 drug discovery compounds of Bayer Schering Pharma. Performance is compared with support vector machines, decision trees, ridge regression, and four commercial tools. In a blind test on 7013 new measurements from the last months (including compounds from new projects) 81% were predicted correctly within 1 log unit, compared to only 44% achieved by commercial software. Additional evaluations using public data are presented. We consider error bars for each method (model based error bars, ensemble based, and distance based approaches), and investigate how well they quantify the domain of applicability of each model.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3354
Deposited By:Anton Schwaighofer
Deposited On:08 February 2008