Classifying 'Drug-likeness' with Kernel-Based Learning Methods. - Klaus-Robert Müller; Gunnar Rätsch; Sören Sonnenburg; Sebastian Mika; Michael Grimm; Nikolaus Heinrich (J. Chem. Inf. Model., 2005)

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules. - Timon Schroeter; Anton Schwaighofer; Sebastian Mika; Antonius ter Laak; Detlev Sülzle; Ursula Ganzer; Nikolaus Heinrich; Klaus-Robert Müller (J. Comput. Aided Mol. Des., 2007)

Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach. - Anton Schwaighofer; Timon Schroeter; Sebastian Mika; Julian Laub; Antonius ter Laak; Detlev Sülzle; Ursula Ganzer; Nikolaus Heinrich; Klaus-Robert Müller (J. Chem. Inf. Model., 2007)

A Probabilistic Approach to Classifying Metabolic Stability. - Anton Schwaighofer; Timon Schroeter; Sebastian Mika; Katja Hansen; Antonius ter Laak; Philip Lienau; Andreas Reichel; Nikolaus Heinrich; Klaus-Robert Müller (J. Chem. Inf. Model., 2008)

Benchmark Data Set for in Silico Prediction of Ames Mutagenicity. - Katja Hansen; Sebastian Mika; Timon Schroeter; Andreas Sutter; Antonius ter Laak; Thomas Steger-Hartmann; Nikolaus Heinrich; Klaus-Robert Müller (J. Chem. Inf. Model., 2009)

A Maximum Common Subgraph Kernel Method for Predicting the Chromosome Aberration Test. - Johannes Mohr; Brijnesh J. Jain; Andreas Sutter; Antonius ter Laak; Thomas Steger-Hartmann; Nikolaus Heinrich; Klaus Obermayer (J. Chem. Inf. Model., 2010)

Efficiency of different measures for defining the applicability domain of classification models. - Waldemar Klingspohn; Miriam Mathea; Antonius ter Laak; Nikolaus Heinrich; Knut Baumann (J. Cheminformatics, 2017)