T
Timon Schroeter
Researcher at Technical University of Berlin
Publications - 19
Citations - 1792
Timon Schroeter is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Support vector machine & Quantitative structure–activity relationship. The author has an hindex of 12, co-authored 19 publications receiving 1503 citations. Previous affiliations of Timon Schroeter include University of British Columbia & Fraunhofer Institute for Open Communication Systems.
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Journal Article
How to Explain Individual Classification Decisions
David Baehrens,Timon Schroeter,Stefan Harmeling,Motoaki Kawanabe,Katja Hansen,Klaus-Robert Müller +5 more
TL;DR: This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.
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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,Thomas Steger-Hartmann,Nikolaus Heinrich,Klaus-Robert Müller +7 more
TL;DR: A new unique public Ames mutagenicity data set comprising about 6500 nonconfidential compounds together with their biological activity is described and three commercial tools and an off-the-shelf Bayesian machine learner in Pipeline Pilot are compared.
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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,Jiazhong Li,Paola Gramatica,Katja Hansen,Timon Schroeter,Klaus-Robert Müller,Lili Xi,Huanxiang Liu,Xiaojun Yao,Tomas Öberg,Farhad Hormozdiari,Phuong Dao,Cenk Sahinalp,Roberto Todeschini,Pavel G. Polishchuk,A. Artemenko,Victor E. Kuz’min,Todd M. Martin,Douglas M. Young,Denis Fourches,Eugene N. Muratov,Alexander Tropsha,Igor I. Baskin,Dragos Horvath,Gilles Marcou,Christophe Muller,A. Varnek,Volodymyr V. Prokopenko,Igor V. Tetko +32 more
TL;DR: This work demonstrates that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs and can be used to halve the cost of experimental measurements by providing a similar prediction accuracy.
Journal ArticleDOI
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 Suelzle,Ursula Ganzer,Nikolaus Heinrich,Klaus-Robert Müller +7 more
TL;DR: This work investigates the use of different Machine Learning methods to construct models for aqueous solubility, evaluating all approaches in terms of their prediction accuracy and in how far the individual error bars can faithfully represent the actual prediction error.
Journal ArticleDOI
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 +8 more
TL;DR: This work presents a statistical modeling of aqueous solubility based on measured data, using a Gaussian Process nonlinear regression model (GPsol), and shows that the developed model achieves much higher accuracy than available commercial tools for the prediction ofsolubility of electrolytes.