Publications by authors named "Timon Schroeter"

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Visual Interpretation of Kernel-Based Prediction Models.
Katja Hansen, David Baehrens, Timon Schroeter, Matthias Rupp, Klaus-Robert Müller

Mol Inform· September 2011

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Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set.
Iurii Sushko, Sergii Novotarskyi, Robert Körner, Anil Kumar Pandey, Artem Cherkasov, Timon Schroeter

J Chem Inf Model· December 2010

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Truxillic acid derivatives act as peroxisome proliferator-activated receptor gamma activators.
Ramona Steri, Matthias Rupp, Ewgenij Proschak, Timon Schroeter, Heiko Zettl

Bioorg Med Chem Lett· May 2010

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From machine learning to natural product derivatives that selectively activate transcription factor PPARgamma.
Matthias Rupp, Timon Schroeter, Ramona Steri, Heiko Zettl, Ewgenij Proschak

ChemMedChem· February 2010

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Benchmark data set for in silico prediction of Ames mutagenicity.
Katja Hansen, Sebastian Mika, Timon Schroeter, Andreas Sutter, Antonius ter Laak

J Chem Inf Model· September 2009

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Bias-correction of regression models: a case study on hERG inhibition.
Katja Hansen, Fabian Rathke, Timon Schroeter, Georg Rast, Thomas Fox

J Chem Inf Model· June 2009

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How wrong can we get? A review of machine learning approaches and error bars.
Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Gilles Blanchard

Comb Chem High Throughput Screen· June 2009

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A probabilistic approach to classifying metabolic stability.
Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Katja Hansen, Antonius Ter Laak

J Chem Inf Model· April 2008

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Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.
Timon Sebastian Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius Ter Laak, Detlev Suelzle

J Comput Aided Mol Des· December 2007

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Predicting lipophilicity of drug-discovery molecules using Gaussian process models.
Timon S Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius Ter Laak, Detlev Suelzle

ChemMedChem· September 2007

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Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.
Timon Sebastian Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius Ter Laak, Detlev Suelzle

J Comput Aided Mol Des· September 2007

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Machine learning models for lipophilicity and their domain of applicability.
Timon Schroeter, Anton Schwaighofer, Sebastian Mika, Antonius Ter Laak, Detlev Suelzle

Mol Pharm· September 2007

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Accurate solubility prediction with error bars for electrolytes: a machine learning approach.
Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Julian Laub, Antonius ter Laak

J Chem Inf Model· May 2007

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