Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.
We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.
Schroeter
Timon Sebastian
TS
Fraunhofer FIRST, Kekuléstrasse 7, 12489, Berlin, Germany. timon.schroeter@first.fraunhofer.de
Schwaighofer
Anton
A
Mika
Sebastian
S
Ter Laak
Antonius
A
Suelzle
Detlev
D
Ganzer
Ursula
U
Heinrich
Nikolaus
N
Müller
Klaus-Robert
KR
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D008956:Models, Chemical; D015233:Models, Statistical; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship; D012995:Solubility
D004364:Pharmaceutical Preparations