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Knut Baumann

Showing results (11-20 of 60) with videos related to

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Journal of Chemical Information and Modeling|May 13, 2009
Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity dataSebastian G Rohrer, Knut Baumann
Molecules (Basel, Switzerland)|November 13, 2021
Evaluating High-Variance Leaves as Uncertainty Measure for Random Forest RegressionThomas-Martin Dutschmann, Knut Baumann
Molecular Informatics|August 6, 2016
Chemoinformatic Classification Methods and their Applicability DomainMiriam Mathea, Waldemar Klingspohn, Knut Baumann
Molecular Informatics|May 28, 2024
Chemoinformatic regression methods and their applicability domainThomas-Martin Dutschmann, Valerie Schlenker, Knut Baumann
Expert Opinion on Drug Discovery|February 25, 2014
Recent progress in tight junction modulation for improving bioavailabilityDaniel Saaber, Sabrina Wollenhaupt, Knut Baumann, et al.
Journal of Chemical Information and Modeling|November 28, 2017
xMaP-An Interpretable Alignment-Free Four-Dimensional Quantitative Structure-Activity Relationship Technique Based on Molecular Surface Properties and Conformer EnsemblesJan Dreher, Josef Scheiber, Nikolaus Stiefl, et al.
Current Topics in Medicinal Chemistry|April 14, 2006
Inhibitors of cysteine proteasesRadim Vicik, Matthias Busemann, Knut Baumann, et al.
Journal of Computer-Aided Molecular Design|November 26, 2003
Evaluation of extended parameter sets for the 3D-QSAR technique MaP: implications for interpretability and model quality exemplified by antimalarially active naphthylisoquinoline alkaloidsNikolaus Stiefl, Gerhard Bringmann, Christian Rummey, et al.
Angewandte Chemie (International Ed. in English)|October 19, 2005
Charge interactions do the job: a combined statistical and combinatorial approach to finding artificial receptors for binding tetrapeptides in waterCarsten Schmuck, Martin Heil, Josef Scheiber, et al.
Mabs|March 18, 2026
AbDist: a lightweight, distance-based model for antibody affinity prediction as an interpretable benchmark for machine learning modelsMarc Hoffstedt, Jannis Wowra, Hermann Wätzig, et al.
Pageof 6

Showing results (11-20 of 60) with videos related to

Sort By:
Pageof 6
Journal of Chemical Information and Modeling|May 13, 2009
Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity dataSebastian G Rohrer, Knut Baumann
Molecules (Basel, Switzerland)|November 13, 2021
Evaluating High-Variance Leaves as Uncertainty Measure for Random Forest RegressionThomas-Martin Dutschmann, Knut Baumann
Molecular Informatics|August 6, 2016
Chemoinformatic Classification Methods and their Applicability DomainMiriam Mathea, Waldemar Klingspohn, Knut Baumann
Molecular Informatics|May 28, 2024
Chemoinformatic regression methods and their applicability domainThomas-Martin Dutschmann, Valerie Schlenker, Knut Baumann
Expert Opinion on Drug Discovery|February 25, 2014
Recent progress in tight junction modulation for improving bioavailabilityDaniel Saaber, Sabrina Wollenhaupt, Knut Baumann, et al.
Journal of Chemical Information and Modeling|November 28, 2017
xMaP-An Interpretable Alignment-Free Four-Dimensional Quantitative Structure-Activity Relationship Technique Based on Molecular Surface Properties and Conformer EnsemblesJan Dreher, Josef Scheiber, Nikolaus Stiefl, et al.
Current Topics in Medicinal Chemistry|April 14, 2006
Inhibitors of cysteine proteasesRadim Vicik, Matthias Busemann, Knut Baumann, et al.
Journal of Computer-Aided Molecular Design|November 26, 2003
Evaluation of extended parameter sets for the 3D-QSAR technique MaP: implications for interpretability and model quality exemplified by antimalarially active naphthylisoquinoline alkaloidsNikolaus Stiefl, Gerhard Bringmann, Christian Rummey, et al.
Angewandte Chemie (International Ed. in English)|October 19, 2005
Charge interactions do the job: a combined statistical and combinatorial approach to finding artificial receptors for binding tetrapeptides in waterCarsten Schmuck, Martin Heil, Josef Scheiber, et al.
Mabs|March 18, 2026
AbDist: a lightweight, distance-based model for antibody affinity prediction as an interpretable benchmark for machine learning modelsMarc Hoffstedt, Jannis Wowra, Hermann Wätzig, et al.
Pageof 6