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Antonius Ter Laak

Showing results (1-10 of 24) with videos related to

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Journal of Chemical Information and Modeling|January 30, 2019
Mechanistic Reactivity Descriptors for the Prediction of Ames Mutagenicity of Primary Aromatic AminesLara Kuhnke, Antonius Ter Laak, Andreas H Göller
Molecules (Basel, Switzerland)|December 28, 2019
Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional NetworksFloriane Montanari, Lara Kuhnke, Antonius Ter Laak, et al.
Journal of Cheminformatics|April 28, 2023
Large-scale evaluation of k-fold cross-validation ensembles for uncertainty estimationThomas-Martin Dutschmann, Lennart Kinzel, Antonius Ter Laak, et al.
Journal of Cheminformatics|November 1, 2017
Efficiency of different measures for defining the applicability domain of classification modelsWaldemar Klingspohn, Miriam Mathea, Antonius Ter Laak, et al.
Methods in Molecular Biology (Clifton, N.J.)|November 3, 2021
Machine Learning Applied to the Modeling of Pharmacological and ADMET EndpointsAndreas H Göller, Lara Kuhnke, Antonius Ter Laak, et al.
Plos One|February 18, 2011
Chemogenomic analysis of G-protein coupled receptors and their ligands deciphers locks and keys governing diverse aspects of signallingJörg D Wichard, Antonius Ter Laak, Gerd Krause, et al.
Journal of Chemical Information and Modeling|October 2, 2010
A maximum common subgraph kernel method for predicting the chromosome aberration testJohannes Mohr, Brijnesh Jain, Andreas Sutter, 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 Computer-Aided Molecular Design|July 17, 2007
Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery moleculesTimon Sebastian Schroeter, Anton Schwaighofer, Sebastian Mika, et al.
Pageof 3

Showing results (1-10 of 24) with videos related to

Sort By:
Pageof 3
Journal of Chemical Information and Modeling|January 30, 2019
Mechanistic Reactivity Descriptors for the Prediction of Ames Mutagenicity of Primary Aromatic AminesLara Kuhnke, Antonius Ter Laak, Andreas H Göller
Molecules (Basel, Switzerland)|December 28, 2019
Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional NetworksFloriane Montanari, Lara Kuhnke, Antonius Ter Laak, et al.
Journal of Cheminformatics|April 28, 2023
Large-scale evaluation of k-fold cross-validation ensembles for uncertainty estimationThomas-Martin Dutschmann, Lennart Kinzel, Antonius Ter Laak, et al.
Journal of Cheminformatics|November 1, 2017
Efficiency of different measures for defining the applicability domain of classification modelsWaldemar Klingspohn, Miriam Mathea, Antonius Ter Laak, et al.
Methods in Molecular Biology (Clifton, N.J.)|November 3, 2021
Machine Learning Applied to the Modeling of Pharmacological and ADMET EndpointsAndreas H Göller, Lara Kuhnke, Antonius Ter Laak, et al.
Plos One|February 18, 2011
Chemogenomic analysis of G-protein coupled receptors and their ligands deciphers locks and keys governing diverse aspects of signallingJörg D Wichard, Antonius Ter Laak, Gerd Krause, et al.
Journal of Chemical Information and Modeling|October 2, 2010
A maximum common subgraph kernel method for predicting the chromosome aberration testJohannes Mohr, Brijnesh Jain, Andreas Sutter, 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 Computer-Aided Molecular Design|July 17, 2007
Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery moleculesTimon Sebastian Schroeter, Anton Schwaighofer, Sebastian Mika, et al.
Pageof 3