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Timon Schroeter

Showing results (1-10 of 10) with videos related to

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Combinatorial Chemistry & High Throughput Screening|June 13, 2009
How wrong can we get? A review of machine learning approaches and error barsAnton Schwaighofer, Timon Schroeter, Sebastian Mika, et al.
Molecular Informatics|July 29, 2016
Visual Interpretation of Kernel-Based Prediction ModelsKatja Hansen, David Baehrens, Timon Schroeter, et al.
Journal of Chemical Information and Modeling|May 14, 2009
Bias-correction of regression models: a case study on hERG inhibitionKatja Hansen, Fabian Rathke, Timon Schroeter, et al.
Molecular Pharmaceutics|July 20, 2007
Machine learning models for lipophilicity and their domain of applicabilityTimon Schroeter, Anton Schwaighofer, Sebastian Mika, et al.
Journal of Chemical Information and Modeling|August 26, 2009
Benchmark data set for in silico prediction of Ames mutagenicityKatja Hansen, Sebastian Mika, Timon Schroeter, et al.
Journal of Chemical Information and Modeling|January 25, 2007
Accurate solubility prediction with error bars for electrolytes: a machine learning approachAnton Schwaighofer, Timon Schroeter, Sebastian Mika, et al.
Journal of Chemical Information and Modeling|March 11, 2008
A probabilistic approach to classifying metabolic stabilityAnton Schwaighofer, Timon Schroeter, Sebastian Mika, et al.
Bioorganic & Medicinal Chemistry Letters|March 30, 2010
Truxillic acid derivatives act as peroxisome proliferator-activated receptor gamma activatorsRamona Steri, Matthias Rupp, Ewgenij Proschak, et al.
Chemmedchem|January 1, 2010
From machine learning to natural product derivatives that selectively activate transcription factor PPARgammaMatthias Rupp, Timon Schroeter, Ramona Steri, et al.
Journal of Chemical Information and Modeling|November 2, 2010
Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity setIurii Sushko, Sergii Novotarskyi, Robert Körner, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Combinatorial Chemistry & High Throughput Screening|June 13, 2009
How wrong can we get? A review of machine learning approaches and error barsAnton Schwaighofer, Timon Schroeter, Sebastian Mika, et al.
Molecular Informatics|July 29, 2016
Visual Interpretation of Kernel-Based Prediction ModelsKatja Hansen, David Baehrens, Timon Schroeter, et al.
Journal of Chemical Information and Modeling|May 14, 2009
Bias-correction of regression models: a case study on hERG inhibitionKatja Hansen, Fabian Rathke, Timon Schroeter, et al.
Molecular Pharmaceutics|July 20, 2007
Machine learning models for lipophilicity and their domain of applicabilityTimon Schroeter, Anton Schwaighofer, Sebastian Mika, et al.
Journal of Chemical Information and Modeling|August 26, 2009
Benchmark data set for in silico prediction of Ames mutagenicityKatja Hansen, Sebastian Mika, Timon Schroeter, et al.
Journal of Chemical Information and Modeling|January 25, 2007
Accurate solubility prediction with error bars for electrolytes: a machine learning approachAnton Schwaighofer, Timon Schroeter, Sebastian Mika, et al.
Journal of Chemical Information and Modeling|March 11, 2008
A probabilistic approach to classifying metabolic stabilityAnton Schwaighofer, Timon Schroeter, Sebastian Mika, et al.
Bioorganic & Medicinal Chemistry Letters|March 30, 2010
Truxillic acid derivatives act as peroxisome proliferator-activated receptor gamma activatorsRamona Steri, Matthias Rupp, Ewgenij Proschak, et al.
Chemmedchem|January 1, 2010
From machine learning to natural product derivatives that selectively activate transcription factor PPARgammaMatthias Rupp, Timon Schroeter, Ramona Steri, et al.
Journal of Chemical Information and Modeling|November 2, 2010
Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity setIurii Sushko, Sergii Novotarskyi, Robert Körner, et al.
Pageof 1