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.
Fraunhofer FIRST, Berlin, Germany. timon.schroeter@first.fraunhofer.de
Timon Sebastian
TS
Schroeter
Anton
A
Schwaighofer
Sebastian
S
Mika
Antonius
A
Ter Laak
Detlev
D
Suelzle
Ursula
U
Ganzer
Nikolaus
N
Heinrich
Klaus-Robert
KR
Müller
D000465:Algorithms; D001185:Artificial Intelligence; D015195:Drug Design; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship; D012995:Solubility; D014867:Water / Q000737:chemistry*
D004364:Pharmaceutical Preparations; D014867:Water