Machine learning models for lipophilicity and their domain of applicability.
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 14,556 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.
Schroeter
Timon
T
Fraunhofer FIRST, Kekuléstrasse 7, 12489 Berlin, Germany. timon.schroeter@first.fraunhofer.de
Schwaighofer
Anton
A
Mika
Sebastian
S
Laak
Antonius Ter
AT
Suelzle
Detlev
D
Ganzer
Ursula
U
Heinrich
Nikolaus
N
Müller
Klaus-Robert
KR
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003663:Decision Trees; D008055:Lipids / Q000737:chemistry*; D008956:Models, Chemical; D015233:Models, Statistical; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D015203:Reproducibility of Results
D008055:Lipids; D004364:Pharmaceutical Preparations